diff --git a/Week 1/Libraries Assignment/.ipynb_checkpoints/Assignment1-checkpoint.ipynb b/Week 1/Libraries Assignment/.ipynb_checkpoints/Assignment1-checkpoint.ipynb
new file mode 100644
index 0000000..0244acf
--- /dev/null
+++ b/Week 1/Libraries Assignment/.ipynb_checkpoints/Assignment1-checkpoint.ipynb	
@@ -0,0 +1,488 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "M7IlzQhajs71"
+   },
+   "source": [
+    "###Question 1:\n",
+    "Generate a dataset for linear regression with 1000 samples, 5 features and single target.\n",
+    "\n",
+    "Visualize the data by plotting the target column against each feature column. Also plot the best fit line in each case.\n",
+    "\n",
+    "Hint : search for obtaining regression line using numpy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "id": "X4-07o0-eHZU"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from sklearn.datasets import make_regression as mr\n",
+    "x,y = mr(n_samples=1000, n_features=5, noise=0)\n",
+    "\n",
+    "fig,ax= plt.subplots(2,3,figsize=(10,10))\n",
+    "for i in range(5):\n",
+    "  plt.subplot(231+i)\n",
+    "  plt.scatter(x[:,i],y)\n",
+    "  m, b = np.polyfit(x[:,i], y, 1)\n",
+    "  plt.plot(x, y, 'yo', x, m*x+b, '--k')\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "GOGDTvDVd57W"
+   },
+   "source": [
+    "### Question 2:\n",
+    "Make a classification dataset of 1000 samples with 2 features, 2 classes and 2 clusters per class.\n",
+    "Plot the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "id": "DspQLHVeeH01"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.datasets import make_classification\n",
+    "X,y= make_classification(n_samples=1000,n_features=2, n_informative=2, n_redundant=0,n_classes=2,n_clusters_per_class=2)\n",
+    "plt.scatter(X[:, 0],X[:,1], c=y)\n",
+    "plt.xlabel('Feature 1')\n",
+    "plt.ylabel('Feature 2')\n",
+    "plt.show() "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "7ghM2NebJXtR"
+   },
+   "source": [
+    "### Question 3:\n",
+    "Make a clustering dataset with 2 features and 4 clusters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "id": "sjjsnbxieIZN"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.datasets import make_blobs\n",
+    "X,y = make_blobs(n_samples=1000, centers=4, n_features=2)\n",
+    "\n",
+    "plt.scatter(X[:, 0],X[:,1], c=y)\n",
+    "plt.xlabel('Feature 1')\n",
+    "plt.ylabel('Feature 2')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "eskxgE9T1jh2"
+   },
+   "source": [
+    "## Question 4\n",
+    "Go to the website https://www.worldometers.info/coronavirus/ and scrape the table containing covid-19 infection and deaths data using requests and BeautifulSoup.  Convert the table to a Pandas dataframe with the following columns : Country, Continent, Population, TotalCases, NewCases, TotalDeaths, NewDeaths,TotalRecovered, NewRecovered,  ActiveCases.\n",
+    "\n",
+    "*(<b>Optional Challenge :</b> Change the data type of the Columns (Population ... till ActiveCases) to integer. For that you need to remove the commas and plus signs. You may need to use df.apply() and pd.to_numeric() . Take care of the values which are empty strings.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "id": "V7fs4Th9eI6W"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "200\n"
+     ]
+    }
+   ],
+   "source": [
+    "import requests as req\n",
+    "url =\"https://www.worldometers.info/coronavirus/\"\n",
+    "page= req.get(url)\n",
+    "print(page.status_code)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from bs4 import BeautifulSoup\n",
+    "soup = BeautifulSoup(page.text, 'lxml')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "            Country          Continent     Population   TotalCases NewCases  \\\n",
+      "0     North America      North America                 131,889,132            \n",
+      "1              Asia               Asia                 221,500,265            \n",
+      "2            Europe             Europe                 253,406,198            \n",
+      "3     South America      South America                  70,200,879            \n",
+      "4           Oceania  Australia/Oceania                  14,895,771            \n",
+      "..              ...                ...            ...          ...      ...   \n",
+      "234         Tokelau  Australia/Oceania          1,378           80            \n",
+      "235    Vatican City             Europe            799           29            \n",
+      "236  Western Sahara             Africa        626,161           10            \n",
+      "237      MS Zaandam                                              9            \n",
+      "238           China               Asia  1,448,471,400      503,302            \n",
+      "\n",
+      "    TotalDeaths NewDeaths TotalRecovered NewRecovered ActiveCases  \n",
+      "0     1,695,941              127,665,129         +350   2,528,062  \n",
+      "1     1,553,662              205,673,091               14,273,512  \n",
+      "2     2,101,824              248,754,104         +474   2,550,270  \n",
+      "3     1,367,332               66,683,585                2,149,962  \n",
+      "4        33,015               14,752,388                  110,368  \n",
+      "..          ...       ...            ...          ...         ...  \n",
+      "234                                                            80  \n",
+      "235                                   29                        0  \n",
+      "236           1                        9                        0  \n",
+      "237           2                        7                        0  \n",
+      "238       5,272                  379,053                  118,977  \n",
+      "\n",
+      "[239 rows x 10 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "table = soup.find('table', id='main_table_countries_today')\n",
+    "import pandas as pd\n",
+    "headers = []\n",
+    "for th in table.find('thead').find_all('th'):\n",
+    "    headers.append(th.text.strip())\n",
+    "rows = []\n",
+    "for tr in table.find('tbody').find_all('tr'):\n",
+    "    cells = tr.find_all('td')\n",
+    "    row = [cell.text.strip() for cell in cells]\n",
+    "    rows.append(row)\n",
+    "df = pd.DataFrame(rows, columns=headers)\n",
+    "\n",
+    "df = df[['Country,Other', 'Continent', 'Population', 'TotalCases', 'NewCases', 'TotalDeaths', 'NewDeaths', 'TotalRecovered', 'NewRecovered', 'ActiveCases']]\n",
+    "df.columns = ['Country', 'Continent', 'Population', 'TotalCases', 'NewCases', 'TotalDeaths', 'NewDeaths', 'TotalRecovered', 'NewRecovered', 'ActiveCases']\n",
+    "\n",
+    "print(df)\n",
+    "df.to_csv('covid_data.csv', index=False)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "QhHpN4yCxn-H"
+   },
+   "source": [
+    "# Question 5\n",
+    "\n",
+    "Generate an imbalanced classification dataset using sklearn of 1000 samples with 2 features, 2 classes and 1 cluster per class. Plot the data. One of the class should contain only 5% of the total samples. Confirm this either using numpy or Counter. Plot the data.\n",
+    "\n",
+    "Now oversample the minority class to 5 times its initial size using SMOTE. Verify the number. Plot the data.\n",
+    "\n",
+    "Now undersample the majority class to 3 times the size of minority class using RandomUnderSampler. Verify the number. Plot the data.\n",
+    "\n",
+    "Reference : Last markdown cell of the examples."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "id": "hLKcLL42lCa2"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Initial class distribution: Counter({0: 943, 1: 57})\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.datasets import make_classification\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "X,y= make_classification(n_samples=1000,n_features=2, n_informative=2, n_redundant=0,n_classes=2,n_clusters_per_class=1,weights=[0.95, 0.05],random_state=1)\n",
+    "from collections import Counter\n",
+    "counter = Counter(y)\n",
+    "print(\"Initial class distribution:\", counter)\n",
+    "plt.scatter(X[:, 0],X[:,1], c=y)\n",
+    "plt.xlabel('Feature 1')\n",
+    "plt.ylabel('Feature 2')\n",
+    "plt.show() "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Class distribution after SMOTE: Counter({0: 943, 1: 235})\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from imblearn.over_sampling import SMOTE\n",
+    "smote = SMOTE(sampling_strategy=0.25, random_state=1) \n",
+    "X_res, y_res = smote.fit_resample(X, y)\n",
+    "\n",
+    "counter_res = Counter(y_res)\n",
+    "print(\"Class distribution after SMOTE:\", counter_res)\n",
+    "\n",
+    "\n",
+    "plt.scatter(X_res[:, 0],X_res[:,1], c=y_res)\n",
+    "plt.title('Data after SMOTE')\n",
+    "plt.xlabel('Feature 1')\n",
+    "plt.ylabel('Feature 2')\n",
+    "plt.show() \n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Class distribution after RandomUnderSampler: Counter({0: 705, 1: 235})\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from imblearn.under_sampling import RandomUnderSampler\n",
+    "rus = RandomUnderSampler(sampling_strategy=0.333, random_state=1)  # Majority class will be 3 times the minority class\n",
+    "X_resampled, y_resampled = rus.fit_resample(X_res, y_res)\n",
+    "\n",
+    "counter_resampled = Counter(y_resampled)\n",
+    "print(\"Class distribution after RandomUnderSampler:\", counter_resampled)\n",
+    "\n",
+    "\n",
+    "plt.title('Data after RandomUnderSampler')\n",
+    "plt.scatter(X_resampled[:, 0],X_resampled[:,1], c=y_resampled)\n",
+    "plt.xlabel('Feature 1')\n",
+    "plt.ylabel('Feature 2')\n",
+    "plt.show() \n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "6_j0Smzgk6mZ"
+   },
+   "source": [
+    "##Question 6\n",
+    "\n",
+    "Write a Python code to perform data preprocessing on a dataset using the scikit-learn library. Follow the instructions below:\n",
+    "\n",
+    " * Load the dataset using the scikit-learn `load_iris` function.\n",
+    " * Assign the feature data to a variable named `X` and the target data to a variable named `y`.\n",
+    " * Create a pandas DataFrame called `df` using `X` as the data and the feature names obtained from the dataset.\n",
+    " * Display the first 5 rows of the DataFrame `df`.\n",
+    " *  Check if there are any missing values in the DataFrame and handle them accordingly.\n",
+    " * Split the data into training and testing sets using the `train_test_split` function from scikit-learn. Assign 70% of the data to the training set and the remaining 30% to the testing set.\n",
+    " * Print the dimensions of the training set and testing set respectively.\n",
+    " *  Standardize the feature data in the training set using the `StandardScaler` from scikit-learn.\n",
+    " *  Apply the same scaling transformation on the testing set.\n",
+    " * Print the first 5 rows of the standardized training set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "id": "wCJg725i4xiY"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
+      "0                5.1               3.5                1.4               0.2\n",
+      "1                4.9               3.0                1.4               0.2\n",
+      "2                4.7               3.2                1.3               0.2\n",
+      "3                4.6               3.1                1.5               0.2\n",
+      "4                5.0               3.6                1.4               0.2\n",
+      "\n",
+      "sepal length (cm)    0\n",
+      "sepal width (cm)     0\n",
+      "petal length (cm)    0\n",
+      "petal width (cm)     0\n",
+      "dtype: int64\n",
+      "\n",
+      "Shape of X_train=(105, 4)\n",
+      "Shape of y_train=105\n",
+      "Shape of X_test=(45, 4)\n",
+      "Shape of y_test=45\n",
+      "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
+      "0          -0.413416         -1.462003          -0.099511         -0.323398\n",
+      "1           0.551222         -0.502563           0.717703          0.353032\n",
+      "2           0.671802          0.217016           0.951192          0.758890\n",
+      "3           0.912961         -0.022844           0.309096          0.217746\n",
+      "4           1.636440          1.416315           1.301427          1.705891\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.datasets import load_iris\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "iris = load_iris()\n",
+    "X = iris.data\n",
+    "y = iris.target\n",
+    "df = pd.DataFrame(X, columns=iris.feature_names)\n",
+    "print(df.head(5))\n",
+    "print()\n",
+    "print(df.isnull().sum())\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
+    "print()\n",
+    "print(f\"Shape of X_train={X_train.shape}\")\n",
+    "print(f\"Shape of y_train={y_train.shape[0]}\")\n",
+    "print(f\"Shape of X_test={X_test.shape}\")\n",
+    "print(f\"Shape of y_test={y_test.shape[0]}\")\n",
+    "\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "scaler = StandardScaler()\n",
+    "X_train_new = scaler.fit_transform(X_train)\n",
+    "X_test_new = scaler.transform(X_test)\n",
+    "print(pd.DataFrame(X_train_new, columns=iris.feature_names).head())\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Week 1/Libraries Assignment/Assignment1.ipynb b/Week 1/Libraries Assignment/Assignment1.ipynb
index dee3cd8..0244acf 100644
--- a/Week 1/Libraries Assignment/Assignment1.ipynb	
+++ b/Week 1/Libraries Assignment/Assignment1.ipynb	
@@ -1,158 +1,488 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "provenance": []
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    },
-    "language_info": {
-      "name": "python"
-    }
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "M7IlzQhajs71"
+   },
+   "source": [
+    "###Question 1:\n",
+    "Generate a dataset for linear regression with 1000 samples, 5 features and single target.\n",
+    "\n",
+    "Visualize the data by plotting the target column against each feature column. Also plot the best fit line in each case.\n",
+    "\n",
+    "Hint : search for obtaining regression line using numpy."
+   ]
   },
-  "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "id": "X4-07o0-eHZU"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "source": [
-        "###Question 1:\n",
-        "Generate a dataset for linear regression with 1000 samples, 5 features and single target.\n",
-        "\n",
-        "Visualize the data by plotting the target column against each feature column. Also plot the best fit line in each case.\n",
-        "\n",
-        "Hint : search for obtaining regression line using numpy."
-      ],
-      "metadata": {
-        "id": "M7IlzQhajs71"
-      }
-    },
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from sklearn.datasets import make_regression as mr\n",
+    "x,y = mr(n_samples=1000, n_features=5, noise=0)\n",
+    "\n",
+    "fig,ax= plt.subplots(2,3,figsize=(10,10))\n",
+    "for i in range(5):\n",
+    "  plt.subplot(231+i)\n",
+    "  plt.scatter(x[:,i],y)\n",
+    "  m, b = np.polyfit(x[:,i], y, 1)\n",
+    "  plt.plot(x, y, 'yo', x, m*x+b, '--k')\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "GOGDTvDVd57W"
+   },
+   "source": [
+    "### Question 2:\n",
+    "Make a classification dataset of 1000 samples with 2 features, 2 classes and 2 clusters per class.\n",
+    "Plot the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "id": "DspQLHVeeH01"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "source": [],
-      "metadata": {
-        "id": "X4-07o0-eHZU"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.datasets import make_classification\n",
+    "X,y= make_classification(n_samples=1000,n_features=2, n_informative=2, n_redundant=0,n_classes=2,n_clusters_per_class=2)\n",
+    "plt.scatter(X[:, 0],X[:,1], c=y)\n",
+    "plt.xlabel('Feature 1')\n",
+    "plt.ylabel('Feature 2')\n",
+    "plt.show() "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "7ghM2NebJXtR"
+   },
+   "source": [
+    "### Question 3:\n",
+    "Make a clustering dataset with 2 features and 4 clusters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "id": "sjjsnbxieIZN"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "source": [
-        "### Question 2:\n",
-        "Make a classification dataset of 1000 samples with 2 features, 2 classes and 2 clusters per class.\n",
-        "Plot the data."
-      ],
-      "metadata": {
-        "id": "GOGDTvDVd57W"
-      }
-    },
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.datasets import make_blobs\n",
+    "X,y = make_blobs(n_samples=1000, centers=4, n_features=2)\n",
+    "\n",
+    "plt.scatter(X[:, 0],X[:,1], c=y)\n",
+    "plt.xlabel('Feature 1')\n",
+    "plt.ylabel('Feature 2')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "eskxgE9T1jh2"
+   },
+   "source": [
+    "## Question 4\n",
+    "Go to the website https://www.worldometers.info/coronavirus/ and scrape the table containing covid-19 infection and deaths data using requests and BeautifulSoup.  Convert the table to a Pandas dataframe with the following columns : Country, Continent, Population, TotalCases, NewCases, TotalDeaths, NewDeaths,TotalRecovered, NewRecovered,  ActiveCases.\n",
+    "\n",
+    "*(<b>Optional Challenge :</b> Change the data type of the Columns (Population ... till ActiveCases) to integer. For that you need to remove the commas and plus signs. You may need to use df.apply() and pd.to_numeric() . Take care of the values which are empty strings.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "id": "V7fs4Th9eI6W"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "source": [],
-      "metadata": {
-        "id": "DspQLHVeeH01"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "200\n"
+     ]
+    }
+   ],
+   "source": [
+    "import requests as req\n",
+    "url =\"https://www.worldometers.info/coronavirus/\"\n",
+    "page= req.get(url)\n",
+    "print(page.status_code)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from bs4 import BeautifulSoup\n",
+    "soup = BeautifulSoup(page.text, 'lxml')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "source": [
-        "### Question 3:\n",
-        "Make a clustering dataset with 2 features and 4 clusters."
-      ],
-      "metadata": {
-        "id": "7ghM2NebJXtR"
-      }
-    },
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "            Country          Continent     Population   TotalCases NewCases  \\\n",
+      "0     North America      North America                 131,889,132            \n",
+      "1              Asia               Asia                 221,500,265            \n",
+      "2            Europe             Europe                 253,406,198            \n",
+      "3     South America      South America                  70,200,879            \n",
+      "4           Oceania  Australia/Oceania                  14,895,771            \n",
+      "..              ...                ...            ...          ...      ...   \n",
+      "234         Tokelau  Australia/Oceania          1,378           80            \n",
+      "235    Vatican City             Europe            799           29            \n",
+      "236  Western Sahara             Africa        626,161           10            \n",
+      "237      MS Zaandam                                              9            \n",
+      "238           China               Asia  1,448,471,400      503,302            \n",
+      "\n",
+      "    TotalDeaths NewDeaths TotalRecovered NewRecovered ActiveCases  \n",
+      "0     1,695,941              127,665,129         +350   2,528,062  \n",
+      "1     1,553,662              205,673,091               14,273,512  \n",
+      "2     2,101,824              248,754,104         +474   2,550,270  \n",
+      "3     1,367,332               66,683,585                2,149,962  \n",
+      "4        33,015               14,752,388                  110,368  \n",
+      "..          ...       ...            ...          ...         ...  \n",
+      "234                                                            80  \n",
+      "235                                   29                        0  \n",
+      "236           1                        9                        0  \n",
+      "237           2                        7                        0  \n",
+      "238       5,272                  379,053                  118,977  \n",
+      "\n",
+      "[239 rows x 10 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "table = soup.find('table', id='main_table_countries_today')\n",
+    "import pandas as pd\n",
+    "headers = []\n",
+    "for th in table.find('thead').find_all('th'):\n",
+    "    headers.append(th.text.strip())\n",
+    "rows = []\n",
+    "for tr in table.find('tbody').find_all('tr'):\n",
+    "    cells = tr.find_all('td')\n",
+    "    row = [cell.text.strip() for cell in cells]\n",
+    "    rows.append(row)\n",
+    "df = pd.DataFrame(rows, columns=headers)\n",
+    "\n",
+    "df = df[['Country,Other', 'Continent', 'Population', 'TotalCases', 'NewCases', 'TotalDeaths', 'NewDeaths', 'TotalRecovered', 'NewRecovered', 'ActiveCases']]\n",
+    "df.columns = ['Country', 'Continent', 'Population', 'TotalCases', 'NewCases', 'TotalDeaths', 'NewDeaths', 'TotalRecovered', 'NewRecovered', 'ActiveCases']\n",
+    "\n",
+    "print(df)\n",
+    "df.to_csv('covid_data.csv', index=False)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "QhHpN4yCxn-H"
+   },
+   "source": [
+    "# Question 5\n",
+    "\n",
+    "Generate an imbalanced classification dataset using sklearn of 1000 samples with 2 features, 2 classes and 1 cluster per class. Plot the data. One of the class should contain only 5% of the total samples. Confirm this either using numpy or Counter. Plot the data.\n",
+    "\n",
+    "Now oversample the minority class to 5 times its initial size using SMOTE. Verify the number. Plot the data.\n",
+    "\n",
+    "Now undersample the majority class to 3 times the size of minority class using RandomUnderSampler. Verify the number. Plot the data.\n",
+    "\n",
+    "Reference : Last markdown cell of the examples."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "id": "hLKcLL42lCa2"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "source": [],
-      "metadata": {
-        "id": "sjjsnbxieIZN"
-      },
-      "execution_count": null,
-      "outputs": []
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Initial class distribution: Counter({0: 943, 1: 57})\n"
+     ]
     },
     {
-      "cell_type": "markdown",
-      "source": [
-        "## Question 4\n",
-        "Go to the website https://www.worldometers.info/coronavirus/ and scrape the table containing covid-19 infection and deaths data using requests and BeautifulSoup.  Convert the table to a Pandas dataframe with the following columns : Country, Continent, Population, TotalCases, NewCases, TotalDeaths, NewDeaths,TotalRecovered, NewRecovered,  ActiveCases.\n",
-        "\n",
-        "*(<b>Optional Challenge :</b> Change the data type of the Columns (Population ... till ActiveCases) to integer. For that you need to remove the commas and plus signs. You may need to use df.apply() and pd.to_numeric() . Take care of the values which are empty strings.)"
-      ],
-      "metadata": {
-        "id": "eskxgE9T1jh2"
-      }
-    },
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.datasets import make_classification\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "X,y= make_classification(n_samples=1000,n_features=2, n_informative=2, n_redundant=0,n_classes=2,n_clusters_per_class=1,weights=[0.95, 0.05],random_state=1)\n",
+    "from collections import Counter\n",
+    "counter = Counter(y)\n",
+    "print(\"Initial class distribution:\", counter)\n",
+    "plt.scatter(X[:, 0],X[:,1], c=y)\n",
+    "plt.xlabel('Feature 1')\n",
+    "plt.ylabel('Feature 2')\n",
+    "plt.show() "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "source": [],
-      "metadata": {
-        "id": "V7fs4Th9eI6W"
-      },
-      "execution_count": null,
-      "outputs": []
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Class distribution after SMOTE: Counter({0: 943, 1: 235})\n"
+     ]
     },
     {
-      "cell_type": "markdown",
-      "source": [
-        "# Question 5\n",
-        "\n",
-        "Generate an imbalanced classification dataset using sklearn of 1000 samples with 2 features, 2 classes and 1 cluster per class. Plot the data. One of the class should contain only 5% of the total samples. Confirm this either using numpy or Counter. Plot the data.\n",
-        "\n",
-        "Now oversample the minority class to 5 times its initial size using SMOTE. Verify the number. Plot the data.\n",
-        "\n",
-        "Now undersample the majority class to 3 times the size of minority class using RandomUnderSampler. Verify the number. Plot the data.\n",
-        "\n",
-        "Reference : Last markdown cell of the examples."
-      ],
-      "metadata": {
-        "id": "QhHpN4yCxn-H"
-      }
-    },
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from imblearn.over_sampling import SMOTE\n",
+    "smote = SMOTE(sampling_strategy=0.25, random_state=1) \n",
+    "X_res, y_res = smote.fit_resample(X, y)\n",
+    "\n",
+    "counter_res = Counter(y_res)\n",
+    "print(\"Class distribution after SMOTE:\", counter_res)\n",
+    "\n",
+    "\n",
+    "plt.scatter(X_res[:, 0],X_res[:,1], c=y_res)\n",
+    "plt.title('Data after SMOTE')\n",
+    "plt.xlabel('Feature 1')\n",
+    "plt.ylabel('Feature 2')\n",
+    "plt.show() \n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "source": [],
-      "metadata": {
-        "id": "hLKcLL42lCa2"
-      },
-      "execution_count": null,
-      "outputs": []
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Class distribution after RandomUnderSampler: Counter({0: 705, 1: 235})\n"
+     ]
     },
     {
-      "cell_type": "markdown",
-      "source": [
-        "##Question 6\n",
-        "\n",
-        "Write a Python code to perform data preprocessing on a dataset using the scikit-learn library. Follow the instructions below:\n",
-        "\n",
-        " * Load the dataset using the scikit-learn `load_iris` function.\n",
-        " * Assign the feature data to a variable named `X` and the target data to a variable named `y`.\n",
-        " * Create a pandas DataFrame called `df` using `X` as the data and the feature names obtained from the dataset.\n",
-        " * Display the first 5 rows of the DataFrame `df`.\n",
-        " *  Check if there are any missing values in the DataFrame and handle them accordingly.\n",
-        " * Split the data into training and testing sets using the `train_test_split` function from scikit-learn. Assign 70% of the data to the training set and the remaining 30% to the testing set.\n",
-        " * Print the dimensions of the training set and testing set respectively.\n",
-        " *  Standardize the feature data in the training set using the `StandardScaler` from scikit-learn.\n",
-        " *  Apply the same scaling transformation on the testing set.\n",
-        " * Print the first 5 rows of the standardized training set."
-      ],
-      "metadata": {
-        "id": "6_j0Smzgk6mZ"
-      }
-    },
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from imblearn.under_sampling import RandomUnderSampler\n",
+    "rus = RandomUnderSampler(sampling_strategy=0.333, random_state=1)  # Majority class will be 3 times the minority class\n",
+    "X_resampled, y_resampled = rus.fit_resample(X_res, y_res)\n",
+    "\n",
+    "counter_resampled = Counter(y_resampled)\n",
+    "print(\"Class distribution after RandomUnderSampler:\", counter_resampled)\n",
+    "\n",
+    "\n",
+    "plt.title('Data after RandomUnderSampler')\n",
+    "plt.scatter(X_resampled[:, 0],X_resampled[:,1], c=y_resampled)\n",
+    "plt.xlabel('Feature 1')\n",
+    "plt.ylabel('Feature 2')\n",
+    "plt.show() \n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "6_j0Smzgk6mZ"
+   },
+   "source": [
+    "##Question 6\n",
+    "\n",
+    "Write a Python code to perform data preprocessing on a dataset using the scikit-learn library. Follow the instructions below:\n",
+    "\n",
+    " * Load the dataset using the scikit-learn `load_iris` function.\n",
+    " * Assign the feature data to a variable named `X` and the target data to a variable named `y`.\n",
+    " * Create a pandas DataFrame called `df` using `X` as the data and the feature names obtained from the dataset.\n",
+    " * Display the first 5 rows of the DataFrame `df`.\n",
+    " *  Check if there are any missing values in the DataFrame and handle them accordingly.\n",
+    " * Split the data into training and testing sets using the `train_test_split` function from scikit-learn. Assign 70% of the data to the training set and the remaining 30% to the testing set.\n",
+    " * Print the dimensions of the training set and testing set respectively.\n",
+    " *  Standardize the feature data in the training set using the `StandardScaler` from scikit-learn.\n",
+    " *  Apply the same scaling transformation on the testing set.\n",
+    " * Print the first 5 rows of the standardized training set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "id": "wCJg725i4xiY"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "source": [],
-      "metadata": {
-        "id": "wCJg725i4xiY"
-      },
-      "execution_count": null,
-      "outputs": []
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
+      "0                5.1               3.5                1.4               0.2\n",
+      "1                4.9               3.0                1.4               0.2\n",
+      "2                4.7               3.2                1.3               0.2\n",
+      "3                4.6               3.1                1.5               0.2\n",
+      "4                5.0               3.6                1.4               0.2\n",
+      "\n",
+      "sepal length (cm)    0\n",
+      "sepal width (cm)     0\n",
+      "petal length (cm)    0\n",
+      "petal width (cm)     0\n",
+      "dtype: int64\n",
+      "\n",
+      "Shape of X_train=(105, 4)\n",
+      "Shape of y_train=105\n",
+      "Shape of X_test=(45, 4)\n",
+      "Shape of y_test=45\n",
+      "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
+      "0          -0.413416         -1.462003          -0.099511         -0.323398\n",
+      "1           0.551222         -0.502563           0.717703          0.353032\n",
+      "2           0.671802          0.217016           0.951192          0.758890\n",
+      "3           0.912961         -0.022844           0.309096          0.217746\n",
+      "4           1.636440          1.416315           1.301427          1.705891\n"
+     ]
     }
-  ]
-}
\ No newline at end of file
+   ],
+   "source": [
+    "from sklearn.datasets import load_iris\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "iris = load_iris()\n",
+    "X = iris.data\n",
+    "y = iris.target\n",
+    "df = pd.DataFrame(X, columns=iris.feature_names)\n",
+    "print(df.head(5))\n",
+    "print()\n",
+    "print(df.isnull().sum())\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
+    "print()\n",
+    "print(f\"Shape of X_train={X_train.shape}\")\n",
+    "print(f\"Shape of y_train={y_train.shape[0]}\")\n",
+    "print(f\"Shape of X_test={X_test.shape}\")\n",
+    "print(f\"Shape of y_test={y_test.shape[0]}\")\n",
+    "\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "scaler = StandardScaler()\n",
+    "X_train_new = scaler.fit_transform(X_train)\n",
+    "X_test_new = scaler.transform(X_test)\n",
+    "print(pd.DataFrame(X_train_new, columns=iris.feature_names).head())\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Week 1/Libraries Assignment/covid_data.csv b/Week 1/Libraries Assignment/covid_data.csv
new file mode 100644
index 0000000..4d87970
--- /dev/null
+++ b/Week 1/Libraries Assignment/covid_data.csv	
@@ -0,0 +1,240 @@
+Country,Continent,Population,TotalCases,NewCases,TotalDeaths,NewDeaths,TotalRecovered,NewRecovered,ActiveCases
+North America,North America,,"131,889,132",,"1,695,941",,"127,665,129",+350,"2,528,062"
+Asia,Asia,,"221,500,265",,"1,553,662",,"205,673,091",,"14,273,512"
+Europe,Europe,,"253,406,198",,"2,101,824",,"248,754,104",+474,"2,550,270"
+South America,South America,,"70,200,879",,"1,367,332",,"66,683,585",,"2,149,962"
+Oceania,Australia/Oceania,,"14,895,771",,"33,015",,"14,752,388",,"110,368"
+Africa,Africa,,"12,860,924",,"258,892",,"12,090,808",,"511,224"
+,,,721,,15,,706,,0
+World,All,,"704,753,890",0,"7,010,681",0,"675,619,811",+790,"22,123,398"
+USA,North America,"334,805,269","111,820,082",,"1,219,487",,"109,814,428",,"786,167"
+India,Asia,"1,406,631,776","45,035,393",,"533,570",,N/A,N/A,N/A
+France,Europe,"65,584,518","40,138,560",,"167,642",,"39,970,918",,0
+Germany,Europe,"83,883,596","38,828,995",,"183,027",,"38,240,600",,"405,368"
+Brazil,South America,"215,353,593","38,743,918",,"711,380",,"36,249,161",,"1,783,377"
+S. Korea,Asia,"51,329,899","34,571,873",,"35,934",,"34,535,939",,0
+Japan,Asia,"125,584,838","33,803,572",,"74,694",,N/A,N/A,N/A
+Italy,Europe,"60,262,770","26,723,249",,"196,487",,"26,361,218",,"165,544"
+UK,Europe,"68,497,907","24,910,387",,"232,112",,"24,678,275",,0
+Russia,Europe,"145,805,947","24,124,215",,"402,756",,"23,545,818",,"175,641"
+Turkey,Asia,"85,561,976","17,232,066",,"102,174",,N/A,N/A,N/A
+Spain,Europe,"46,719,142","13,914,811",,"121,760",,"13,762,417",,"30,634"
+Australia,Australia/Oceania,"26,068,792","11,853,144",,"24,414",,"11,820,014",,"8,716"
+Vietnam,Asia,"98,953,541","11,625,195",,"43,206",,"10,640,971",,"941,018"
+Taiwan,Asia,"23,888,595","10,241,523",,"19,005",,"10,222,518",,0
+Argentina,South America,"46,010,234","10,128,845",,"130,841",,"9,997,258",,746
+Netherlands,Europe,"17,211,447","8,635,786",,"22,992",,"8,612,599",,195
+Mexico,North America,"131,562,772","7,702,809",,"334,958",,"6,899,865",,"467,986"
+Iran,Asia,"86,022,837","7,627,186",,"146,811",,N/A,N/A,N/A
+Indonesia,Asia,"279,134,505","6,829,221",,"162,063",,"6,647,104",,"20,054"
+Poland,Europe,"37,739,785","6,661,991",,"120,598",,N/A,N/A,N/A
+Colombia,South America,"51,512,762","6,400,173",,"143,200",,"6,212,152",,"44,821"
+Greece,Europe,"10,316,637","6,101,379",,"37,869",,N/A,N/A,N/A
+Austria,Europe,"9,066,710","6,081,287",,"22,542",,"6,054,934",,"3,811"
+Portugal,Europe,"10,140,570","5,643,062",,"28,126",,"5,614,809",,127
+Ukraine,Europe,"43,192,122","5,557,995",,"112,418",,"5,445,577",,0
+Chile,South America,"19,250,195","5,384,853",,"64,497",,"5,252,450",,"67,906"
+Malaysia,Asia,"33,181,072","5,278,406",,"37,348",,"5,233,268",,"7,790"
+Canada,North America,"38,388,419","4,946,090",,"59,034",,"4,881,312",+350,"5,744"
+Belgium,Europe,"11,668,278","4,861,695",,"34,376",,"4,826,798",+50,521
+Israel,Asia,"9,326,000","4,841,772",,"12,707",,"4,798,473",,"30,592"
+DPRK,Asia,"25,990,679","4,772,813",,74,,"4,772,739",,0
+Thailand,Asia,"70,078,203","4,770,149",,"34,586",,"4,692,636",,"42,927"
+Czechia,Europe,"10,736,784","4,759,041",,"43,517",,"4,715,206",,318
+Peru,South America,"33,684,208","4,572,667",,"222,161",,"4,350,506",,0
+Switzerland,Europe,"8,773,637","4,453,053",,"14,452",,"4,438,309",,292
+Philippines,Asia,"112,508,994","4,140,383",,"66,864",,"4,067,381",,"6,138"
+South Africa,Africa,"60,756,135","4,076,463",,"102,595",,"3,912,506",,"61,362"
+Romania,Europe,"19,031,335","3,529,735",,"68,929",,"3,460,149",+390,657
+Denmark,Europe,"5,834,950","3,183,756",,"8,814",,"3,174,942",,0
+Singapore,Asia,"5,943,546","3,006,155",,"2,024",,"3,004,131",,0
+Hong Kong,Asia,"7,604,299","2,937,609",,"14,924",,"2,916,005",,"6,680"
+Sweden,Europe,"10,218,971","2,754,129",,"27,407",,"2,726,492",+20,230
+New Zealand,Australia/Oceania,"4,898,203","2,621,111",,"5,697",,"2,613,791",,"1,623"
+Serbia,Europe,"8,653,016","2,615,054",,"18,057",,"2,596,608",,389
+Iraq,Asia,"42,164,965","2,465,545",,"25,375",,"2,439,497",,673
+Hungary,Europe,"9,606,259","2,230,232",,"49,048",,"2,152,155",,"29,029"
+Bangladesh,Asia,"167,885,689","2,049,377",,"29,493",,N/A,N/A,N/A
+Slovakia,Europe,"5,460,193","1,877,605",,"21,224",,"1,856,381",,0
+Georgia,Asia,"3,968,738","1,861,665",,"17,132",,N/A,N/A,N/A
+Jordan,Asia,"10,300,869","1,746,997",,"14,122",,"1,731,007",,"1,868"
+Ireland,Europe,"5,020,199","1,734,582",,"9,491",,"1,724,921",,170
+Pakistan,Asia,"229,488,994","1,581,936",,"30,664",,"1,538,689",,"12,583"
+Finland,Europe,"5,554,960","1,516,117",,"11,958",,"1,503,989",+14,170
+Norway,Europe,"5,511,370","1,509,732",,"6,638",,"1,503,094",,0
+Kazakhstan,Asia,"19,205,043","1,411,831",,"13,848",,"1,383,020",,"14,963"
+Lithuania,Europe,"2,661,708","1,397,806",,"9,897",,"1,387,478",,431
+Slovenia,Europe,"2,078,034","1,356,546",,"7,100",,"1,349,424",,22
+Bulgaria,Europe,"6,844,597","1,339,851",,"38,748",,"1,292,944",,"8,159"
+Croatia,Europe,"4,059,286","1,309,728",,"18,687",,"1,258,432",,"32,609"
+Guatemala,North America,"18,584,039","1,291,293",,"20,289",,"1,269,891",,"1,113"
+Morocco,Africa,"37,772,756","1,278,992",,"16,303",,N/A,N/A,N/A
+Lebanon,Asia,"6,684,849","1,243,838",,"10,952",,"1,087,587",,"145,299"
+Costa Rica,North America,"5,182,354","1,238,883",,"9,428",,N/A,N/A,N/A
+Bolivia,South America,"11,992,656","1,212,131",,"22,407",,"1,177,145",,"12,579"
+Tunisia,Africa,"12,046,656","1,153,361",,"29,423",,N/A,N/A,N/A
+Cuba,North America,"11,305,652","1,115,251",,"8,530",,"1,106,660",,61
+Ecuador,South America,"18,113,361","1,070,188",,"36,043",,"1,034,145",,0
+UAE,Asia,"10,081,785","1,067,030",,"2,349",,N/A,N/A,N/A
+Panama,North America,"4,446,964","1,059,893",,"8,727",,"1,051,102",,64
+Uruguay,South America,"3,496,016","1,041,111",,"7,664",,"1,030,944",,"2,503"
+Mongolia,Asia,"3,378,078","1,011,496",,"2,284",,"1,009,212",,0
+Nepal,Asia,"30,225,582","1,003,450",,"12,031",,"991,322",,97
+Belarus,Europe,"9,432,800","994,037",,"7,118",,"985,592",,"1,327"
+Latvia,Europe,"1,848,837","982,505",,"6,715",,"971,406",,"4,384"
+Saudi Arabia,Asia,"35,844,909","841,469",,"9,646",,N/A,N/A,N/A
+Paraguay,South America,"7,305,843","837,602",,"20,155",,N/A,N/A,N/A
+Azerbaijan,Asia,"10,300,205","835,234",,"10,400",,"824,089",,745
+Bahrain,Asia,"1,783,983","729,549",,"1,574",,"727,915",,60
+Cyprus,Asia,"1,223,387","681,110",,"1,365",,"679,745",,0
+Dominican Republic,North America,"11,056,370","675,890",,"4,384",,"671,316",,190
+Sri Lanka,Asia,"21,575,842","672,754",,"16,897",,"655,852",,5
+Kuwait,Asia,"4,380,326","667,158",,"2,570",,N/A,N/A,N/A
+Myanmar,Asia,"55,227,143","641,873",,"19,495",,"620,159",,"2,219"
+Moldova,Europe,"4,013,171","635,145",,"12,218",,N/A,N/A,N/A
+Estonia,Europe,"1,321,910","628,070",,"3,001",,N/A,N/A,N/A
+Palestine,Asia,"5,345,541","621,008",,"5,404",,"615,445",,159
+Venezuela,South America,"29,266,991","552,695",,"5,856",,"546,537",,302
+Egypt,Africa,"106,156,692","516,023",,"24,613",,"442,182",,"49,228"
+Qatar,Asia,"2,979,915","514,524",,690,,"513,687",,147
+Libya,Africa,"7,040,745","507,274",,"6,437",,"500,835",,2
+Ethiopia,Africa,"120,812,698","501,157",,"7,574",,"488,171",,"5,412"
+Réunion,Africa,"908,061","494,595",,921,,N/A,N/A,N/A
+Honduras,North America,"10,221,247","474,590",,"11,165",,N/A,N/A,N/A
+Armenia,Asia,"2,971,966","451,831",,"8,777",,"435,162",,"7,892"
+Bosnia and Herzegovina,Europe,"3,249,317","403,615",,"16,388",,"379,084",,"8,143"
+Oman,Asia,"5,323,993","399,449",,"4,628",,N/A,N/A,N/A
+Luxembourg,Europe,"642,371","391,232",,"1,232",,N/A,N/A,N/A
+North Macedonia,Europe,"2,081,304","350,567",,"9,976",,"337,068",,"3,523"
+Zambia,Africa,"19,470,234","349,304",,"4,069",,"341,316",,"3,919"
+Kenya,Africa,"56,215,221","344,130",,"5,689",,"337,309",,"1,132"
+Brunei,Asia,"445,431","343,719",,225,,"243,601",,"99,893"
+Albania,Europe,"2,866,374","334,863",,"3,605",,"330,233",,"1,025"
+Botswana,Africa,"2,441,162","330,638",,"2,801",,"327,049",,788
+Montenegro,Europe,"627,950","296,542",,"2,846",,"291,794",,"1,902"
+Algeria,Africa,"45,350,148","272,010",,"6,881",,"183,061",,"82,068"
+Nigeria,Africa,"216,746,934","267,188",,"3,155",,"259,953",,"4,080"
+Zimbabwe,Africa,"15,331,428","266,359",,"5,740",,"258,888",,"1,731"
+Uzbekistan,Asia,"34,382,084","253,662",,"1,637",,"241,486",,"10,539"
+Afghanistan,Asia,"40,754,388","234,174",,"7,996",,"211,080",,"15,098"
+Mozambique,Africa,"33,089,461","233,731",,"2,250",,"228,805",,"2,676"
+Martinique,North America,"374,087","230,354",,"1,102",,N/A,N/A,N/A
+Laos,Asia,"7,481,023","218,970",,758,,N/A,N/A,N/A
+Iceland,Europe,"345,393","209,906",,229,,N/A,N/A,N/A
+Kyrgyzstan,Asia,"6,728,271","206,897",,"2,991",,"196,406",,"7,500"
+Guadeloupe,North America,"399,794","203,235",,"1,021",,N/A,N/A,N/A
+El Salvador,North America,"6,550,389","201,855",,"4,230",,"179,410",,"18,215"
+Trinidad and Tobago,North America,"1,406,585","191,496",,"4,390",,"187,078",,28
+Maldives,Asia,"540,985","186,694",,316,,"163,687",,"22,691"
+Namibia,Africa,"2,633,874","172,389",,"4,106",,"167,099",,"1,184"
+Uganda,Africa,"48,432,863","172,149",,"3,632",,"100,431",,"68,086"
+Ghana,Africa,"32,395,450","171,889",,"1,462",,"170,425",,2
+Jamaica,North America,"2,985,094","156,869",,"3,756",,N/A,N/A,N/A
+Cambodia,Asia,"17,168,639","139,103",,"3,056",,"136,044",,3
+Rwanda,Africa,"13,600,464","133,518",,"1,468",,"132,039",,11
+Cameroon,Africa,"27,911,548","125,379",,"1,974",,"123,280",,125
+Malta,Europe,"444,033","121,420",,885,,"120,149",,386
+Barbados,North America,"288,023","110,578",,648,,"108,647",,"1,283"
+Angola,Africa,"35,027,343","107,327",,"1,937",,"103,419",,"1,971"
+Channel Islands,Europe,"176,463","101,717",,228,,"101,321",,168
+DRC,Africa,"95,240,792","99,338",,"1,468",,"84,489",,"13,381"
+French Guiana,South America,"314,169","98,041",,420,,"11,254",,"86,367"
+Malawi,Africa,"20,180,839","89,535",,"2,686",,N/A,N/A,N/A
+Senegal,Africa,"17,653,671","89,053",,"1,971",,"87,024",,58
+Ivory Coast,Africa,"27,742,298","88,384",,835,,"87,497",,52
+Suriname,South America,"596,831","82,588",,"1,408",,N/A,N/A,N/A
+New Caledonia,Australia/Oceania,"290,915","80,064",,314,,N/A,N/A,N/A
+French Polynesia,Australia/Oceania,"284,164","79,254",,650,,N/A,N/A,N/A
+Eswatini,Africa,"1,184,817","75,191",,"1,427",,"73,116",,648
+Guyana,South America,"794,045","74,137",,"1,300",,"72,013",,824
+Belize,North America,"412,190","71,409",,688,,N/A,N/A,N/A
+Fiji,Australia/Oceania,"909,466","69,117",,885,,"67,226",,"1,006"
+Madagascar,Africa,"29,178,077","68,486",,"1,426",,"66,862",,198
+Cabo Verde,Africa,"567,678","64,477",,417,,"63,755",,305
+Sudan,Africa,"45,992,020","63,993",,"5,046",,"58,947",,0
+Mauritania,Africa,"4,901,981","63,848",,997,,"62,471",,380
+Bhutan,Asia,"787,941","62,697",,21,,"61,564",,"1,112"
+Syria,Asia,"19,364,809","57,743",,"3,165",,"54,578",,0
+Burundi,Africa,"12,624,840","54,721",,38,,"53,569",,"1,114"
+Seychelles,Africa,"99,426","51,220",,172,,"51,048",,0
+Gabon,Africa,"2,331,533","49,051",,307,,"48,674",,70
+Andorra,Europe,"77,463","48,015",,165,,N/A,N/A,N/A
+Papua New Guinea,Australia/Oceania,"9,292,169","46,864",,670,,"46,168",,26
+Curaçao,North America,"165,529","45,986",,295,,"44,720",,971
+Aruba,North America,"107,609","44,224",,292,,"42,438",,"1,494"
+Tanzania,Africa,"63,298,550","43,223",,846,,N/A,N/A,N/A
+Mauritius,Africa,"1,274,727","43,025",,"1,051",,"41,278",,696
+Mayotte,Africa,"286,259","42,027",,188,,N/A,N/A,N/A
+Togo,Africa,"8,680,837","39,572",,290,,"39,281",,1
+Guinea,Africa,"13,865,691","38,572",,468,,"37,757",,347
+Bahamas,North America,"400,516","38,084",,844,,"36,366",,874
+Isle of Man,Europe,"85,732","38,008",,116,,N/A,N/A,N/A
+Lesotho,Africa,"2,175,699","36,138",,723,,"25,980",,"9,435"
+Haiti,North America,"11,680,283","34,667",,860,,"33,734",,73
+Faeroe Islands,Europe,"49,233","34,658",,28,,N/A,N/A,N/A
+Mali,Africa,"21,473,764","33,164",,743,,"32,332",,89
+Cayman Islands,North America,"67,277","31,472",,37,,"8,553",,"22,882"
+Saint Lucia,North America,"185,113","30,215",,410,,"29,805",,0
+Benin,Africa,"12,784,726","28,036",,163,,"27,847",,26
+Macao,Asia,"667,490","27,673",,123,,"3,487",,"24,063"
+Somalia,Africa,"16,841,795","27,334",,"1,361",,"13,182",,"12,791"
+Micronesia,Australia/Oceania,"117,489","26,547",,65,,N/A,N/A,N/A
+San Marino,Europe,"34,085","26,185",,128,,"26,011",,46
+Solomon Islands,Australia/Oceania,"721,159","25,954",,199,,N/A,N/A,N/A
+Congo,Africa,"5,797,805","25,375",,386,,"24,006",,983
+Timor-Leste,Asia,"1,369,429","23,460",,138,,"23,102",,220
+Burkina Faso,Africa,"22,102,838","22,114",,400,,"21,596",,118
+Liechtenstein,Europe,"38,387","21,574",,94,,N/A,N/A,N/A
+Gibraltar,Europe,"33,704","20,550",,113,,N/A,N/A,N/A
+Grenada,North America,"113,475","19,693",,238,,"19,358",,97
+Bermuda,North America,"61,939","18,860",,165,,"18,685",,10
+South Sudan,Africa,"11,618,511","18,819",,147,,"18,115",,557
+Nicaragua,North America,"6,779,100","18,491",,225,,"4,225",,"14,041"
+Tajikistan,Asia,"9,957,464","17,786",,125,,"17,264",,397
+Equatorial Guinea,Africa,"1,496,662","17,229",,183,,"16,907",,139
+Monaco,Europe,"39,783","17,181",,67,,N/A,N/A,N/A
+Samoa,Australia/Oceania,"202,239","17,006",,31,,"1,605",,"15,370"
+Tonga,Australia/Oceania,"107,749","16,950",,13,,"15,638",,"1,299"
+Marshall Islands,Australia/Oceania,"60,057","16,138",,17,,"16,121",,0
+Dominica,North America,"72,344","16,038",,74,,"15,964",,0
+Djibouti,Africa,"1,016,097","15,690",,189,,"15,427",,74
+CAR,Africa,"5,016,678","15,440",,113,,"15,200",,127
+Gambia,Africa,"2,558,482","12,626",,372,,"12,189",,65
+Saint Martin,North America,"39,730","12,324",,63,,N/A,N/A,N/A
+Vanuatu,Australia/Oceania,"321,832","12,019",,14,,"11,976",,29
+Greenland,North America,"56,973","11,971",,21,,"2,761",,"9,189"
+Yemen,Asia,"31,154,867","11,945",,"2,159",,"9,124",,662
+Caribbean Netherlands,North America,"26,647","11,682",,38,,"10,476",,"1,168"
+Sint Maarten,North America,"43,966","11,051",,92,,"10,905",,54
+Eritrea,Africa,"3,662,244","10,189",,103,,"10,086",,0
+Niger,Africa,"26,083,660","9,931",,312,,"8,890",,729
+St. Vincent Grenadines,North America,"111,551","9,674",,124,,"9,493",,57
+Guinea-Bissau,Africa,"2,063,367","9,614",,177,,"8,929",,508
+Comoros,Africa,"907,419","9,109",,161,,"8,939",,9
+Antigua and Barbuda,North America,"99,509","9,106",,146,,"8,954",,6
+Liberia,Africa,"5,305,117","8,090",,295,,"7,783",,12
+Sierra Leone,Africa,"8,306,436","7,779",,126,,N/A,N/A,N/A
+Chad,Africa,"17,413,580","7,701",,194,,"4,874",,"2,633"
+British Virgin Islands,North America,"30,596","7,392",,64,,N/A,N/A,N/A
+Cook Islands,Australia/Oceania,"17,571","7,203",,2,,"7,150",,51
+Sao Tome and Principe,Africa,"227,679","6,778",,80,,"6,685",,13
+Turks and Caicos,North America,"39,741","6,752",,40,,"6,709",,3
+Saint Kitts and Nevis,North America,"53,871","6,607",,48,,"6,559",,0
+Palau,Australia/Oceania,"18,233","6,290",,10,,"6,276",,4
+St. Barth,North America,"9,945","5,507",,6,,N/A,N/A,N/A
+Nauru,Australia/Oceania,"10,903","5,393",,1,,"5,347",,45
+Kiribati,Australia/Oceania,"123,419","5,085",,24,,"2,703",,"2,358"
+Anguilla,North America,"15,230","3,904",,12,,N/A,N/A,N/A
+Wallis and Futuna,Australia/Oceania,"10,982","3,550",,8,,438,,"3,104"
+Saint Pierre Miquelon,North America,"5,759","3,452",,2,,"2,449",,"1,001"
+Tuvalu,Australia/Oceania,"12,066","2,943",,1,,N/A,N/A,N/A
+Saint Helena,Africa,"6,115","2,166",,,,2,,"2,164"
+Falkland Islands,South America,"3,539","1,930",,,,"1,930",,0
+Montserrat,North America,"4,965","1,403",,8,,"1,376",,19
+Niue,Australia/Oceania,"1,622","1,059",,,,"1,056",,3
+Diamond Princess,,,712,,13,,699,,0
+Tokelau,Australia/Oceania,"1,378",80,,,,,,80
+Vatican City,Europe,799,29,,,,29,,0
+Western Sahara,Africa,"626,161",10,,1,,9,,0
+MS Zaandam,,,9,,2,,7,,0
+China,Asia,"1,448,471,400","503,302",,"5,272",,"379,053",,"118,977"
diff --git a/Week 1/Libraries Examples and Resources/.ipynb_checkpoints/Examples-checkpoint.ipynb b/Week 1/Libraries Examples and Resources/.ipynb_checkpoints/Examples-checkpoint.ipynb
new file mode 100644
index 0000000..e8c672b
--- /dev/null
+++ b/Week 1/Libraries Examples and Resources/.ipynb_checkpoints/Examples-checkpoint.ipynb	
@@ -0,0 +1,2965 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "8WquFTv1VsII"
+      },
+      "source": [
+        "## Data Generation\n",
+        "### Using Scikit-Learn\n",
+        "\n",
+        "\n",
+        "#### 1. Data For Regression"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 865
+        },
+        "id": "qFxrI8pe0sBi",
+        "outputId": "50e0ff67-6700-4589-9436-94e7835f5964"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "(1000, 6)\n",
+            "(1000,)\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1000x1000 with 6 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "from sklearn.datasets import make_regression as mr\n",
+        "X,y = mr(n_samples=1000, n_features=6, noise=0)\n",
+        "print(X.shape)\n",
+        "print(y.shape)\n",
+        "\n",
+        "fig2,ax= plt.subplots(2,3,figsize=(10,10))\n",
+        "for i in range(6):\n",
+        "  plt.subplot(231+i)\n",
+        "  plt.scatter(X[:,i],y, color='blue')\n",
+        "\n",
+        "\n",
+        "\"\"\"\n",
+        "# house price dataset\n",
+        "n_features =6 :\n",
+        "area of the plot\n",
+        "number of roo ms\n",
+        "etc etc...\n",
+        "\n",
+        "\"\"\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "bTd795ffiTy6"
+      },
+      "source": [
+        "### 2. Data For Classification\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 484
+        },
+        "id": "QQG3Ho0k0nzo",
+        "outputId": "8814cfa9-eae9-4092-916a-cacc3350f0ed"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "(1000, 2)\n",
+            "(1000,)\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "from sklearn.datasets import make_classification\n",
+        "X,y= make_classification(n_samples=1000,n_features=2, n_informative=2, n_redundant=0,n_classes=2,n_clusters_per_class=2)\n",
+        "print(X.shape)\n",
+        "print(y.shape)\n",
+        "\n",
+        "plt.scatter(X[:, 0],X[:,1], c=y)\n",
+        "plt.xlabel('Feature 1')\n",
+        "plt.ylabel('Feature 2')\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ncXBbbxCigi7"
+      },
+      "source": [
+        "#### 3. Data For Clustering (Unsupervised Learning)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 484
+        },
+        "id": "AyGEnfO61DbO",
+        "outputId": "9ca241bc-d24c-4a9f-8364-7c49abee8f82"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "(1000, 2)\n",
+            "(1000,)\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "from sklearn.datasets import make_blobs\n",
+        "X,y = make_blobs(n_samples=1000, centers=4, n_features=2)\n",
+        "print(X.shape)\n",
+        "print(y.shape)\n",
+        "\n",
+        "plt.scatter(X[:, 0],X[:,1], c=y)\n",
+        "plt.xlabel('Feature 1')\n",
+        "plt.ylabel('Feature 2')\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "aiNTXG2GzMeY"
+      },
+      "source": [
+        "## Web Scraping Example\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "oMKlbtk5ipkx"
+      },
+      "source": [
+        "### Step 1 : Send a \"GET\" request to the URL.\n",
+        "\n",
+        "Status_code 200 indicates that the GET request has been successful. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "inSdmA5Jy1zT",
+        "outputId": "fe6e9967-7939-49fd-d894-86e02545e0e1"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "<Response [200]>\n"
+          ]
+        }
+      ],
+      "source": [
+        "import requests as req\n",
+        "url = 'https://www.mykhel.com/football/indian-super-league-table-l750/'\n",
+        "page= req.get(url)\n",
+        "print(page.status_code)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "35p0kiErjOOq"
+      },
+      "source": [
+        "### Step 2 : Parse the HTML using BeautifulSoup"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "OdBVdAYQzSao"
+      },
+      "outputs": [],
+      "source": [
+        "from bs4 import BeautifulSoup\n",
+        "soup = BeautifulSoup(page.text, 'lxml')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "52zGR4Lfjd4v"
+      },
+      "source": [
+        "### Step 3 : Find the tag,class name,id of the required item/container/object in the html file.\n",
+        "\n",
+        "In this case, it is a table.\n",
+        "Inspect the webpage to navigate in the HTML document.\n",
+        "As an identification feature, we used the CSS class name of the table. *The word class is followed by a _ .\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Xme8LfCAzUuY",
+        "outputId": "79dd254e-a093-4532-c70d-3330f6040a91"
+      },
+      "outputs": [],
+      "source": [
+        "table = soup.find('table', class_='os-football-table')\n",
+        "print(table)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ES4AqVRHkOGA"
+      },
+      "source": [
+        "### Step 4 : Extract the headers/ column names of the table.\n",
+        "\n",
+        "They are present inside the first tag \"tr\" .\n",
+        "\n",
+        "So use table.find('tr') or store all the rows using find_all('tr') and use the first one.\n",
+        "\n",
+        "all_rows= table.find_all('tr') and all_rows[0] .\n",
+        "\n",
+        "Inside the tr container, there are several th containers that contain the text data. Extract data from each of them by iterating over a loop. Store their text data in a list named headers."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "tjWCvUCgzYpj",
+        "outputId": "96f3f33b-07cb-4854-acc8-54ee5f936d99"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "['Position', 'Teams', 'Played', 'Won', 'Drawn', 'Lost', 'GF', 'GA', 'GD', 'Points', 'Form']\n"
+          ]
+        }
+      ],
+      "source": [
+        "headers = []\n",
+        "first_row=table.find('tr');\n",
+        "\n",
+        "for i in first_row.find_all('th'):\n",
+        " title = i.text\n",
+        " headers.append(title)\n",
+        "\n",
+        "print(headers)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "mYqe8JZhljUM"
+      },
+      "source": [
+        "### Step 5 : Store the data inside a Pandas DataFrame.\n",
+        "\n",
+        "1. Create a blank DataFrame with the header list as the column names.\n",
+        "2. Iterate over all the rows starting from second.\n",
+        "3. Inside the row containers (enclosed by tr and /tr), the data is present in td containers. Iterate over all of them and store their data in a list.\n",
+        "4. Append the list to the dataframe. This is done by adding the list to that row of the dataframe which has index==current_length of the dataframe. The 'loc' function of pandas come in handy."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "NhSn8aF_zdwI",
+        "outputId": "0b4606b0-68a4-4d15-d411-5f0474d1b78a"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "   Position             Teams Played Won Drawn Lost  GF  GA   GD Points Form\n",
+            "0         1       Mumbai City     20  14     4    2  54  21   33    46      \n",
+            "1         2         Hyderabad     20  13     3    4  36  16   20    42      \n",
+            "2         3   ATK Mohun Bagan     20  10     4    6  24  17    7    34      \n",
+            "3         4         Bengaluru     20  11     1    8  27  23    4    34      \n",
+            "4         5   Kerala Blasters     20  10     1    9  28  28    0    31      \n",
+            "5         6            Odisha     20   9     3    8  30  32   -2    30      \n",
+            "6         7               Goa     20   8     3    9  36  35    1    27      \n",
+            "7         8     Chennaiyin FC     20   7     6    7  36  37   -1    27      \n",
+            "8         9    SC East Bengal     20   6     1   13  22  38  -16    19      \n",
+            "9        10     Jamshedpur FC     20   5     4   11  21  32  -11    19      \n",
+            "10       11  NorthEast United     20   1     2   17  20  55  -35     5      \n"
+          ]
+        }
+      ],
+      "source": [
+        "import pandas as pd\n",
+        "mydata = pd.DataFrame(columns = headers)\n",
+        "for j in table.find_all('tr')[1:]:\n",
+        "  row_data = j.find_all('td')\n",
+        "  row = [i.text for i in row_data]\n",
+        "  length = len(mydata)\n",
+        "  mydata.loc[length] = row\n",
+        "print(mydata)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "P30hCJrCmvAG"
+      },
+      "source": [
+        "### This is an additional step to include the data of the column \"Form\".\n",
+        "\n",
+        "On inspecting the website, we realized that each row of the form column consists of 5 containers with class names of os-win, os-draw and os-loss , each of which renders a circle of specific color in the webpage.\n",
+        "\n",
+        "Here, for each row, we went to the last column (the last column had tag td, so we found out all the td and extracted the last one using -1) and then counted the number of containers with class name os-win (find_all os-win and then find the length of that list formed) and put that value in the specific row of the last column."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "N5EJHrWizgXs",
+        "outputId": "6362cc3f-80b6-46a5-e653-0a19a20d198a"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "   Position             Teams Played Won Drawn Lost  GF  GA   GD Points Form\n",
+            "0         1       Mumbai City     20  14     4    2  54  21   33    46     1\n",
+            "1         2         Hyderabad     20  13     3    4  36  16   20    42     2\n",
+            "2         3   ATK Mohun Bagan     20  10     4    6  24  17    7    34     4\n",
+            "3         4         Bengaluru     20  11     1    8  27  23    4    34     4\n",
+            "4         5   Kerala Blasters     20  10     1    9  28  28    0    31     1\n",
+            "5         6            Odisha     20   9     3    8  30  32   -2    30     2\n",
+            "6         7               Goa     20   8     3    9  36  35    1    27     1\n",
+            "7         8     Chennaiyin FC     20   7     6    7  36  37   -1    27     3\n",
+            "8         9    SC East Bengal     20   6     1   13  22  38  -16    19     2\n",
+            "9        10     Jamshedpur FC     20   5     4   11  21  32  -11    19     3\n",
+            "10       11  NorthEast United     20   1     2   17  20  55  -35     5     0\n"
+          ]
+        }
+      ],
+      "source": [
+        "x=0\n",
+        "for j in table.find_all('tr')[1:]:\n",
+        "  data=j.find_all('td')[-1]\n",
+        "  di = data.find('div',class_=\"os-form\")\n",
+        "  spa1 = di.find_all('span',class_='os-win')\n",
+        "  l=len(spa1)\n",
+        "  mydata.Form[x]=l\n",
+        "  x+=1\n",
+        "\n",
+        "print(mydata)"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# End of Part 1"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4cMGeud_slkT"
+      },
+      "source": [
+        "# Data PreProcessing\n",
+        "## Importing data from Sklearn/Kaggle etc.\n",
+        "Many libraries and websites provide pre-made datasets. Visit [Kaggle](https://www.kaggle.com/datasets) to explore some of the datsets it hosts.\n",
+        "\n",
+        "For now, we will be use the Sklearn Library to get the titanic dataset."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "biTuX-N1bpcf",
+        "outputId": "c7475f33-6705-42a8-f833-0d9917ec3601"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/sklearn/datasets/_openml.py:968: FutureWarning: The default value of `parser` will change from `'liac-arff'` to `'auto'` in 1.4. You can set `parser='auto'` to silence this warning. Therefore, an `ImportError` will be raised from 1.4 if the dataset is dense and pandas is not installed. Note that the pandas parser may return different data types. See the Notes Section in fetch_openml's API doc for details.\n",
+            "  warn(\n"
+          ]
+        }
+      ],
+      "source": [
+        "from sklearn.datasets import fetch_openml\n",
+        "data = fetch_openml('titanic', version=1, as_frame=True)\n",
+        "df = data.frame"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "E09pcdfHWb1Q"
+      },
+      "source": [
+        "## Step 1: Understanding the data\n",
+        "\n",
+        "### 1.1 df.head() \n",
+        "is a method used in pandas, a popular data analysis library in Python, to display the first few rows of a DataFrame.\n",
+        "\n",
+        "The method returns a new DataFrame containing the first n rows of the original DataFrame, where n is the number specified in the parentheses. By default, n is set to 5."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 337
+        },
+        "id": "LXVYGuOhmD6t",
+        "outputId": "cd3ee45e-a754-450e-851b-1096fb84afc9"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "\n",
+              "  <div id=\"df-b354346e-5e88-4495-9195-3ab8fb9695b2\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>pclass</th>\n",
+              "      <th>survived</th>\n",
+              "      <th>name</th>\n",
+              "      <th>sex</th>\n",
+              "      <th>age</th>\n",
+              "      <th>sibsp</th>\n",
+              "      <th>parch</th>\n",
+              "      <th>ticket</th>\n",
+              "      <th>fare</th>\n",
+              "      <th>cabin</th>\n",
+              "      <th>embarked</th>\n",
+              "      <th>boat</th>\n",
+              "      <th>body</th>\n",
+              "      <th>home.dest</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Allen, Miss. Elisabeth Walton</td>\n",
+              "      <td>female</td>\n",
+              "      <td>29.0000</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>24160</td>\n",
+              "      <td>211.3375</td>\n",
+              "      <td>B5</td>\n",
+              "      <td>S</td>\n",
+              "      <td>2</td>\n",
+              "      <td>None</td>\n",
+              "      <td>St Louis, MO</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Allison, Master. Hudson Trevor</td>\n",
+              "      <td>male</td>\n",
+              "      <td>0.9167</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>113781</td>\n",
+              "      <td>151.5500</td>\n",
+              "      <td>C22 C26</td>\n",
+              "      <td>S</td>\n",
+              "      <td>11</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Allison, Miss. Helen Loraine</td>\n",
+              "      <td>female</td>\n",
+              "      <td>2.0000</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>113781</td>\n",
+              "      <td>151.5500</td>\n",
+              "      <td>C22 C26</td>\n",
+              "      <td>S</td>\n",
+              "      <td>None</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Allison, Mr. Hudson Joshua Creighton</td>\n",
+              "      <td>male</td>\n",
+              "      <td>30.0000</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>113781</td>\n",
+              "      <td>151.5500</td>\n",
+              "      <td>C22 C26</td>\n",
+              "      <td>S</td>\n",
+              "      <td>None</td>\n",
+              "      <td>135.0</td>\n",
+              "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n",
+              "      <td>female</td>\n",
+              "      <td>25.0000</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>113781</td>\n",
+              "      <td>151.5500</td>\n",
+              "      <td>C22 C26</td>\n",
+              "      <td>S</td>\n",
+              "      <td>None</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b354346e-5e88-4495-9195-3ab8fb9695b2')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "        \n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
+              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-b354346e-5e88-4495-9195-3ab8fb9695b2 button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-b354346e-5e88-4495-9195-3ab8fb9695b2');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ],
+            "text/plain": [
+              "   pclass survived                                             name     sex  \\\n",
+              "0     1.0        1                    Allen, Miss. Elisabeth Walton  female   \n",
+              "1     1.0        1                   Allison, Master. Hudson Trevor    male   \n",
+              "2     1.0        0                     Allison, Miss. Helen Loraine  female   \n",
+              "3     1.0        0             Allison, Mr. Hudson Joshua Creighton    male   \n",
+              "4     1.0        0  Allison, Mrs. Hudson J C (Bessie Waldo Daniels)  female   \n",
+              "\n",
+              "       age  sibsp  parch  ticket      fare    cabin embarked  boat   body  \\\n",
+              "0  29.0000    0.0    0.0   24160  211.3375       B5        S     2   None   \n",
+              "1   0.9167    1.0    2.0  113781  151.5500  C22 C26        S    11    NaN   \n",
+              "2   2.0000    1.0    2.0  113781  151.5500  C22 C26        S  None    NaN   \n",
+              "3  30.0000    1.0    2.0  113781  151.5500  C22 C26        S  None  135.0   \n",
+              "4  25.0000    1.0    2.0  113781  151.5500  C22 C26        S  None    NaN   \n",
+              "\n",
+              "                         home.dest  \n",
+              "0                     St Louis, MO  \n",
+              "1  Montreal, PQ / Chesterville, ON  \n",
+              "2  Montreal, PQ / Chesterville, ON  \n",
+              "3  Montreal, PQ / Chesterville, ON  \n",
+              "4  Montreal, PQ / Chesterville, ON  "
+            ]
+          },
+          "execution_count": 2,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "df.head()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "VAL_93jPs0fN"
+      },
+      "source": [
+        "### 1.2 df.info() \n",
+        "is a method in pandas that provides a summary of the DataFrame's metadata. It returns information about the DataFrame, such as:\n",
+        "\n",
+        "* The number of rows and columns in the DataFrame\n",
+        "* The data type of each column\n",
+        "* The number of non-null values in each column\n",
+        "* The memory usage of the DataFrame\n",
+        "\n",
+        "This method is particularly useful for gaining a quick understanding of the DataFrame's data types and whether there are any missing values. It can also help identify any potential issues with the data, such as columns with mixed data types. Additionally, it can be used to check the memory usage of the DataFrame, which can be important when working with large datasets."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "2QXcvvQysR26",
+        "outputId": "b3c7fca0-1627-402f-8c94-16c92a119459"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "RangeIndex: 1309 entries, 0 to 1308\n",
+            "Data columns (total 14 columns):\n",
+            " #   Column     Non-Null Count  Dtype   \n",
+            "---  ------     --------------  -----   \n",
+            " 0   pclass     1309 non-null   float64 \n",
+            " 1   survived   1309 non-null   category\n",
+            " 2   name       1309 non-null   object  \n",
+            " 3   sex        1309 non-null   category\n",
+            " 4   age        1046 non-null   float64 \n",
+            " 5   sibsp      1309 non-null   float64 \n",
+            " 6   parch      1309 non-null   float64 \n",
+            " 7   ticket     1309 non-null   object  \n",
+            " 8   fare       1308 non-null   float64 \n",
+            " 9   cabin      295 non-null    object  \n",
+            " 10  embarked   1307 non-null   category\n",
+            " 11  boat       486 non-null    object  \n",
+            " 12  body       121 non-null    object  \n",
+            " 13  home.dest  745 non-null    object  \n",
+            "dtypes: category(3), float64(5), object(6)\n",
+            "memory usage: 116.8+ KB\n"
+          ]
+        }
+      ],
+      "source": [
+        "df.info()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "uJCV-2Num4Tx"
+      },
+      "source": [
+        "### 1.3 df.describe()\n",
+        "is a method to generate descriptive statistics of the numerical columns of the data. It displays the central tendency, dispersion and shape of a dataset's distribution, excluding NaN values."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 300
+        },
+        "id": "Zk_C0941mIz9",
+        "outputId": "666a15a4-3bed-47f0-bab5-1d331a61034c"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "\n",
+              "  <div id=\"df-29747124-0830-4756-a92c-117908e87e59\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>pclass</th>\n",
+              "      <th>age</th>\n",
+              "      <th>sibsp</th>\n",
+              "      <th>parch</th>\n",
+              "      <th>fare</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>1309.000000</td>\n",
+              "      <td>1046.000000</td>\n",
+              "      <td>1309.000000</td>\n",
+              "      <td>1309.000000</td>\n",
+              "      <td>1308.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>2.294882</td>\n",
+              "      <td>29.881135</td>\n",
+              "      <td>0.498854</td>\n",
+              "      <td>0.385027</td>\n",
+              "      <td>33.295479</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>0.837836</td>\n",
+              "      <td>14.413500</td>\n",
+              "      <td>1.041658</td>\n",
+              "      <td>0.865560</td>\n",
+              "      <td>51.758668</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.166700</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>21.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>7.895800</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>3.000000</td>\n",
+              "      <td>28.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>14.454200</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>3.000000</td>\n",
+              "      <td>39.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>31.275000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>3.000000</td>\n",
+              "      <td>80.000000</td>\n",
+              "      <td>8.000000</td>\n",
+              "      <td>9.000000</td>\n",
+              "      <td>512.329200</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-29747124-0830-4756-a92c-117908e87e59')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "        \n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
+              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-29747124-0830-4756-a92c-117908e87e59 button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-29747124-0830-4756-a92c-117908e87e59');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ],
+            "text/plain": [
+              "            pclass          age        sibsp        parch         fare\n",
+              "count  1309.000000  1046.000000  1309.000000  1309.000000  1308.000000\n",
+              "mean      2.294882    29.881135     0.498854     0.385027    33.295479\n",
+              "std       0.837836    14.413500     1.041658     0.865560    51.758668\n",
+              "min       1.000000     0.166700     0.000000     0.000000     0.000000\n",
+              "25%       2.000000    21.000000     0.000000     0.000000     7.895800\n",
+              "50%       3.000000    28.000000     0.000000     0.000000    14.454200\n",
+              "75%       3.000000    39.000000     1.000000     0.000000    31.275000\n",
+              "max       3.000000    80.000000     8.000000     9.000000   512.329200"
+            ]
+          },
+          "execution_count": 4,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "df.describe()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "5GdOGiJL5Doh"
+      },
+      "source": [
+        "### Column Names\n",
+        "### 1.4 cols=df.columns\n",
+        "is a method in pandas that returns the column labels of the DataFrame as a pandas Index object. The column labels can be used to subset or manipulate the DataFrame. This method is useful for quickly obtaining the column labels of the DataFrame without having to manually type them out."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "KvgZi8BK4-Io",
+        "outputId": "80e1d7ec-45a7-4f1a-bb0e-f137031c9320"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Index(['pclass', 'survived', 'name', 'sex', 'age', 'sibsp', 'parch', 'ticket',\n",
+            "       'fare', 'cabin', 'embarked', 'boat', 'body', 'home.dest'],\n",
+            "      dtype='object')\n"
+          ]
+        }
+      ],
+      "source": [
+        "cols = df.columns\n",
+        "print(cols)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "sRHcTmcZqfXL"
+      },
+      "source": [
+        "### 1.5 df.unique() and df.nunique()\n",
+        "They are used to display all the unique values in a dataframe and the number of unique values in a dataframe respectively.  df.col_name.unique() and df.col_name.nunique() does the same for a colume.   Note : df.col_name and df[\"col_name\"] are identical."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "51ROI2S6CcTR",
+        "outputId": "cb773ad0-6b83-49af-b5d0-369988b52e7c"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "pclass has 3 unique values\n",
+            "survived has 2 unique values\n",
+            "name has 1307 unique values\n",
+            "sex has 2 unique values\n",
+            "age has 98 unique values\n",
+            "sibsp has 7 unique values\n",
+            "parch has 8 unique values\n",
+            "ticket has 929 unique values\n",
+            "fare has 281 unique values\n",
+            "cabin has 186 unique values\n",
+            "embarked has 3 unique values\n",
+            "boat has 27 unique values\n",
+            "body has 121 unique values\n",
+            "home.dest has 369 unique values\n"
+          ]
+        }
+      ],
+      "source": [
+        "for col in cols:\n",
+        "    print(\"{} has {} unique values\".format(col,df[col].nunique()))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "8Z_-k_GFtvAd"
+      },
+      "source": [
+        "# Step 2 : Null Values\n",
+        "\n",
+        "### 2.1 df.isna().sum()\n",
+        "is a chained method that is often used with df.isna(). It calculates the total number of missing values (null or NaN) in each column of the DataFrame. Specifically, it returns a Series object that shows the count of missing values for each column of the DataFrame."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "cUBUKu4qtH7d",
+        "outputId": "e33c27b3-8071-47d0-aa37-ab3b1bb95a32"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "pclass          0\n",
+              "survived        0\n",
+              "name            0\n",
+              "sex             0\n",
+              "age           263\n",
+              "sibsp           0\n",
+              "parch           0\n",
+              "ticket          0\n",
+              "fare            1\n",
+              "cabin        1014\n",
+              "embarked        2\n",
+              "boat          823\n",
+              "body         1188\n",
+              "home.dest     564\n",
+              "dtype: int64"
+            ]
+          },
+          "execution_count": 7,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "df.isna().sum()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "VGcZ2KFtd-51"
+      },
+      "source": [
+        "## Dropping out columns with excessive null values.\n",
+        "\n",
+        "### 2.2 df.drop()\n",
+        "Sometimes, too much of your data is missing for a particular feature. Eg, try running df.isna().sum() again and you will find that the feature Cabin has 1014 missing values. \n",
+        "\n",
+        "Likewise, the feature \"boat\" has 823 missing values.\n",
+        "It only makes sense to remove such features from our dataset.\n",
+        "\n",
+        "Again, if you find out that some feature name does not make sense in a dataset, that feature should be dropped. Here, name and ticket number has no relation to whether a person survived or not.\n",
+        "\n",
+        "df.drop() can also delete rows. To delete rows, pass in the row indexes as labels and axis=0.\n",
+        "\n",
+        "axis=1 refers to column and axis=0 refers to rows."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "TTCS5abfd-Js",
+        "outputId": "619aa0fa-5235-45b8-d056-f174daef17e1"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "\n",
+              "  <div id=\"df-8dd61d97-a5e3-4f45-a987-7e4701cd7f4a\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>pclass</th>\n",
+              "      <th>survived</th>\n",
+              "      <th>sex</th>\n",
+              "      <th>age</th>\n",
+              "      <th>sibsp</th>\n",
+              "      <th>parch</th>\n",
+              "      <th>fare</th>\n",
+              "      <th>embarked</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>female</td>\n",
+              "      <td>29.0000</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>211.3375</td>\n",
+              "      <td>S</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>male</td>\n",
+              "      <td>0.9167</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>151.5500</td>\n",
+              "      <td>S</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>female</td>\n",
+              "      <td>2.0000</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>151.5500</td>\n",
+              "      <td>S</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>male</td>\n",
+              "      <td>30.0000</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>151.5500</td>\n",
+              "      <td>S</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>female</td>\n",
+              "      <td>25.0000</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>151.5500</td>\n",
+              "      <td>S</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8dd61d97-a5e3-4f45-a987-7e4701cd7f4a')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "        \n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
+              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-8dd61d97-a5e3-4f45-a987-7e4701cd7f4a button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-8dd61d97-a5e3-4f45-a987-7e4701cd7f4a');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ],
+            "text/plain": [
+              "   pclass survived     sex      age  sibsp  parch      fare embarked\n",
+              "0     1.0        1  female  29.0000    0.0    0.0  211.3375        S\n",
+              "1     1.0        1    male   0.9167    1.0    2.0  151.5500        S\n",
+              "2     1.0        0  female   2.0000    1.0    2.0  151.5500        S\n",
+              "3     1.0        0    male  30.0000    1.0    2.0  151.5500        S\n",
+              "4     1.0        0  female  25.0000    1.0    2.0  151.5500        S"
+            ]
+          },
+          "execution_count": 8,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "df = df.drop(['cabin', 'boat', 'name','ticket','body',\"home.dest\"], axis=1)\n",
+        "df.head()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "lSatrB7ruJhU"
+      },
+      "source": [
+        "## Filling the Null Values\n",
+        "\n",
+        "### 2.3.1 For numerical columns.\n",
+        "### df.fillna()\n",
+        "Is a method in pandas that is used to fill missing or null values in a DataFrame. It can be used to fill in missing values with a specified value or with values computed from other data in the DataFrame, such as the mean, median or mode. The method can also be used to forward or backward fill missing values to carry forward the last known value or the next known value, respectively.\n",
+        "\n",
+        "Refer to <a href = \"https://scikit-learn.org/stable/modules/classes.html#module-sklearn.impute\">sklearn.impute</a> for more ways of filling in missing values. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "HLy713BcuCJ9",
+        "outputId": "72cf66c8-7794-4a02-c096-88c75672eda9"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "<ipython-input-9-dec89c434036>:2: FutureWarning: The default value of numeric_only in DataFrame.median is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
+            "  df.fillna(df.median(), inplace=True)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# df.fillna(df.mean(), inplace=True)\n",
+        "df.fillna(df.median(), inplace=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "lrg6gdPwdLA2",
+        "outputId": "202aaaf7-24c3-4c39-fae5-6464a3a12a46"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "pclass      0\n",
+              "survived    0\n",
+              "sex         0\n",
+              "age         0\n",
+              "sibsp       0\n",
+              "parch       0\n",
+              "fare        0\n",
+              "embarked    2\n",
+              "dtype: int64"
+            ]
+          },
+          "execution_count": 10,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "df.isna().sum()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7Qi4CQM__Oep"
+      },
+      "source": [
+        "### 2.3.2 For Categorical columns.\n",
+        "As we can see there are 2 values missing in the feature \"embarked\" and 564 in the feature \"home.dest\"\n",
+        "\n",
+        "One way is to replace the missing values with value with maximum frequence of occurance. This is obviously a method with obvious flaws, but is our best shot.\n",
+        "\n",
+        "The other option is to just drop this feature (as discussed earlier)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Nwtttd25_N5o",
+        "outputId": "734ca5c6-871b-47d1-afa4-53e044c77360"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "RangeIndex: 1309 entries, 0 to 1308\n",
+            "Data columns (total 8 columns):\n",
+            " #   Column    Non-Null Count  Dtype   \n",
+            "---  ------    --------------  -----   \n",
+            " 0   pclass    1309 non-null   float64 \n",
+            " 1   survived  1309 non-null   category\n",
+            " 2   sex       1309 non-null   category\n",
+            " 3   age       1309 non-null   float64 \n",
+            " 4   sibsp     1309 non-null   float64 \n",
+            " 5   parch     1309 non-null   float64 \n",
+            " 6   fare      1309 non-null   float64 \n",
+            " 7   embarked  1309 non-null   category\n",
+            "dtypes: category(3), float64(5)\n",
+            "memory usage: 55.5 KB\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Fill NaN values with mode for column\n",
+        "df['embarked'] = df['embarked'].fillna(df['embarked'].mode().iloc[0])\n",
+        "\n",
+        "df.info()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "oLn1BftYqQ_a"
+      },
+      "source": [
+        "# Step 3: Duplicate rows\n",
+        "### 3.1 df.duplicated().sum()\n",
+        "The df.duplicated() method returns a Series with True and False values that describe which rows in the DataFrame are duplicated and not. The df.duplicated().sum() returns the number of rows which have more than one occurance in the dataframe."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "HScSs5hRqiEq",
+        "outputId": "8ff87787-fb8e-44cf-c2dd-b31dfa0055b3"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "202"
+            ]
+          },
+          "execution_count": 12,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "df.duplicated().sum()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "1MbhUwDl4xl2"
+      },
+      "source": [
+        "### 3.2 df.drop_duplicates()\n",
+        "is a method in pandas that is used to remove duplicate rows from a DataFrame. By default, it removes all rows that are completely identical to a previous row. It can also be used to remove duplicate rows based on a subset of columns. This method is useful when ensuring that each row in the DataFrame is unique and that no data is duplicated."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {
+        "id": "_Lme4NFZx8OZ"
+      },
+      "outputs": [],
+      "source": [
+        "df.drop_duplicates(inplace=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "7sXlqzrOrwOt",
+        "outputId": "047bdda0-c6ba-475a-eeea-1a5f0d249c8b"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "Int64Index: 1107 entries, 0 to 1308\n",
+            "Data columns (total 8 columns):\n",
+            " #   Column    Non-Null Count  Dtype   \n",
+            "---  ------    --------------  -----   \n",
+            " 0   pclass    1107 non-null   float64 \n",
+            " 1   survived  1107 non-null   category\n",
+            " 2   sex       1107 non-null   category\n",
+            " 3   age       1107 non-null   float64 \n",
+            " 4   sibsp     1107 non-null   float64 \n",
+            " 5   parch     1107 non-null   float64 \n",
+            " 6   fare      1107 non-null   float64 \n",
+            " 7   embarked  1107 non-null   category\n",
+            "dtypes: category(3), float64(5)\n",
+            "memory usage: 55.5 KB\n"
+          ]
+        }
+      ],
+      "source": [
+        "df.info()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Qkr02gGI_1NE"
+      },
+      "source": [
+        "# Step 4: Normalization and Standardization of Numerical Data\n",
+        "\n",
+        "<a href=\"https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.Normalizer.html\">Normalization</a> is the process of converting a range of values into a standard range of values, typically in the interval [-1, 1] or [0, 1]. It's not a strict requirement but it improves the speed of learning (e.g. faster convergence in gradient descent) and prevents numerical overflow.\n",
+        "\n",
+        "<a href=\"https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html\">Standardization</a> is the process of rescaling the attributes so that they have mean as 0 and variance as 1. It brings down all the features to a common scale without distorting the differences in the range of the values.\n",
+        "\n",
+        "In this dataset, let's normalize the fare column and standardize the age column. We are doing this randomly just to show how its done!!"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "hSXpSHC6DA4x",
+        "outputId": "ad7ac292-a4e2-48fe-db0e-28496e8142b0"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "pclass has datatype float64 and has 3 unique values\n",
+            "survived has datatype category and has 2 unique values\n",
+            "sex has datatype category and has 2 unique values\n",
+            "age has datatype float64 and has 98 unique values\n",
+            "sibsp has datatype float64 and has 7 unique values\n",
+            "parch has datatype float64 and has 8 unique values\n",
+            "fare has datatype float64 and has 281 unique values\n",
+            "embarked has datatype category and has 3 unique values\n"
+          ]
+        }
+      ],
+      "source": [
+        "cols=df.columns\n",
+        "for col in cols:\n",
+        "    print(\"{} has datatype {} and has {} unique values\".format(col,df[col].dtype,df[col].nunique()))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "UK8pcQG9EBBg",
+        "outputId": "a898841d-0aeb-4807-fb2b-b3041fc8e4e0"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "\n",
+              "  <div id=\"df-30648161-7f74-4fa4-8355-b2925047d03f\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>pclass</th>\n",
+              "      <th>survived</th>\n",
+              "      <th>sex</th>\n",
+              "      <th>age</th>\n",
+              "      <th>sibsp</th>\n",
+              "      <th>parch</th>\n",
+              "      <th>fare</th>\n",
+              "      <th>embarked</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>female</td>\n",
+              "      <td>29.0000</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.412503</td>\n",
+              "      <td>S</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>male</td>\n",
+              "      <td>0.9167</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>0.295806</td>\n",
+              "      <td>S</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>female</td>\n",
+              "      <td>2.0000</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>0.295806</td>\n",
+              "      <td>S</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>male</td>\n",
+              "      <td>30.0000</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>0.295806</td>\n",
+              "      <td>S</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>female</td>\n",
+              "      <td>25.0000</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>0.295806</td>\n",
+              "      <td>S</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-30648161-7f74-4fa4-8355-b2925047d03f')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "        \n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-30648161-7f74-4fa4-8355-b2925047d03f button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-30648161-7f74-4fa4-8355-b2925047d03f');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ],
+            "text/plain": [
+              "   pclass survived     sex      age  sibsp  parch      fare embarked\n",
+              "0     1.0        1  female  29.0000    0.0    0.0  0.412503        S\n",
+              "1     1.0        1    male   0.9167    1.0    2.0  0.295806        S\n",
+              "2     1.0        0  female   2.0000    1.0    2.0  0.295806        S\n",
+              "3     1.0        0    male  30.0000    1.0    2.0  0.295806        S\n",
+              "4     1.0        0  female  25.0000    1.0    2.0  0.295806        S"
+            ]
+          },
+          "execution_count": 17,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "import numpy as np\n",
+        "from sklearn.preprocessing import MinMaxScaler\n",
+        "arr= np.array(df['fare'])\n",
+        "minmax= MinMaxScaler()\n",
+        "df['fare']=minmax.fit_transform(arr.reshape(-1,1))\n",
+        "df.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 18,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "iiPBsiGwIOPW",
+        "outputId": "ef6d4ef1-2cba-4f3d-a03e-5df78b29c2d4"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "\n",
+              "  <div id=\"df-b754a9fe-818a-49c0-837e-226d3b50973a\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>pclass</th>\n",
+              "      <th>survived</th>\n",
+              "      <th>sex</th>\n",
+              "      <th>age</th>\n",
+              "      <th>sibsp</th>\n",
+              "      <th>parch</th>\n",
+              "      <th>fare</th>\n",
+              "      <th>embarked</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>female</td>\n",
+              "      <td>-0.060648</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.412503</td>\n",
+              "      <td>S</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>male</td>\n",
+              "      <td>-2.092555</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>0.295806</td>\n",
+              "      <td>S</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>female</td>\n",
+              "      <td>-2.014175</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>0.295806</td>\n",
+              "      <td>S</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>male</td>\n",
+              "      <td>0.011705</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>0.295806</td>\n",
+              "      <td>S</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>female</td>\n",
+              "      <td>-0.350059</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>0.295806</td>\n",
+              "      <td>S</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b754a9fe-818a-49c0-837e-226d3b50973a')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "        \n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
+              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-b754a9fe-818a-49c0-837e-226d3b50973a button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-b754a9fe-818a-49c0-837e-226d3b50973a');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ],
+            "text/plain": [
+              "   pclass survived     sex       age  sibsp  parch      fare embarked\n",
+              "0     1.0        1  female -0.060648    0.0    0.0  0.412503        S\n",
+              "1     1.0        1    male -2.092555    1.0    2.0  0.295806        S\n",
+              "2     1.0        0  female -2.014175    1.0    2.0  0.295806        S\n",
+              "3     1.0        0    male  0.011705    1.0    2.0  0.295806        S\n",
+              "4     1.0        0  female -0.350059    1.0    2.0  0.295806        S"
+            ]
+          },
+          "execution_count": 18,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "from sklearn.preprocessing import StandardScaler\n",
+        "arr= np.array(df['age']) #This has to be done only if one column is standardized. For more than one column, it is simple.\n",
+        "scaler = StandardScaler()\n",
+        "df['age']=scaler.fit_transform(arr.reshape(-1,1))\n",
+        "df.head()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Kp4VwHfA6u7-"
+      },
+      "source": [
+        "# Step 5: Encoding Categorical Data\n",
+        "Categorical data is data that represents categories or labels, such as color, type, or sex. In order to use categorical data in machine learning models, it needs to be transformed into numerical data.\n",
+        "\n",
+        "\n",
+        "* Ordinal encoding is a method of encoding categorical data where each unique category is assigned an integer value. The assigned integers are based on the order of the categories, which may not be meaningful in all cases. For example, if we have a categorical variable \"size\" with three categories - small, medium, and large - we could assign the values 0, 1, and 2 respectively. However, this method assumes that there is an inherent order to the categories, which may not always be true.\n",
+        "\n",
+        "* One-hot encoding, on the other hand, is a method of encoding categorical data where each unique category is transformed into a binary vector. Each binary vector has a length equal to the number of unique categories, with a value of 1 in the corresponding index of the category and 0 in all other indices. For example, if we have a categorical variable \"color\" with three categories - red, blue, and green - we could transform it into three binary vectors [1,0,0], [0,1,0], and [0,0,1], respectively. This method avoids the assumption of an inherent order to the categories and works well with machine learning algorithms.\n",
+        "\n",
+        "In Python, you can use the `sklearn.preprocessing` module to perform both ordinal and one-hot encoding. The `OrdinalEncoder` class can be used for ordinal (or label) encoding, while the `OneHotEncoder` class can be used for one-hot encoding. Additionally, pandas provides a `get_dummies()` function which can be used for one-hot encoding of categorical variables.\n",
+        "\n",
+        "\n",
+        "After encoding the categorical data to numbers, we can retrieve the categorical values using the inverse_transform function."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 19,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "DVUTm4Bv5ISn",
+        "outputId": "2cd34bdb-c55b-494a-cd4e-daae0483045c"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "\n",
+              "  <div id=\"df-cb363cb2-0067-45c9-b3c3-2978246dd5eb\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>pclass</th>\n",
+              "      <th>survived</th>\n",
+              "      <th>sex</th>\n",
+              "      <th>age</th>\n",
+              "      <th>sibsp</th>\n",
+              "      <th>parch</th>\n",
+              "      <th>fare</th>\n",
+              "      <th>embarked</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>female</td>\n",
+              "      <td>-0.060648</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.412503</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>male</td>\n",
+              "      <td>-2.092555</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>0.295806</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>female</td>\n",
+              "      <td>-2.014175</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>0.295806</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>male</td>\n",
+              "      <td>0.011705</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>0.295806</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>female</td>\n",
+              "      <td>-0.350059</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>0.295806</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cb363cb2-0067-45c9-b3c3-2978246dd5eb')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "        \n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
+              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-cb363cb2-0067-45c9-b3c3-2978246dd5eb button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-cb363cb2-0067-45c9-b3c3-2978246dd5eb');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ],
+            "text/plain": [
+              "   pclass  survived     sex       age  sibsp  parch      fare  embarked\n",
+              "0     1.0         1  female -0.060648    0.0    0.0  0.412503         2\n",
+              "1     1.0         1    male -2.092555    1.0    2.0  0.295806         2\n",
+              "2     1.0         0  female -2.014175    1.0    2.0  0.295806         2\n",
+              "3     1.0         0    male  0.011705    1.0    2.0  0.295806         2\n",
+              "4     1.0         0  female -0.350059    1.0    2.0  0.295806         2"
+            ]
+          },
+          "execution_count": 19,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "from sklearn.preprocessing import LabelEncoder\n",
+        "le1 = LabelEncoder()\n",
+        "le2 = LabelEncoder()\n",
+        "df[\"survived\"]=le1.fit_transform(df[\"survived\"])\n",
+        "df[\"embarked\"]=le1.fit_transform(df[\"embarked\"])\n",
+        "\n",
+        "df.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 22,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 423
+        },
+        "id": "57qawhpH-boh",
+        "outputId": "1e05b6c9-03f9-4d30-eb9c-1d437c1a90aa"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "\n",
+              "  <div id=\"df-6d6a22cc-645d-439c-98db-1eec31da8352\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>female</th>\n",
+              "      <th>male</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1303</th>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1304</th>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1306</th>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1307</th>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1308</th>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>1107 rows × 2 columns</p>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6d6a22cc-645d-439c-98db-1eec31da8352')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "        \n",
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+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
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+              "      display: none;\n",
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+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-6d6a22cc-645d-439c-98db-1eec31da8352 button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-6d6a22cc-645d-439c-98db-1eec31da8352');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ],
+            "text/plain": [
+              "      female  male\n",
+              "0          1     0\n",
+              "1          0     1\n",
+              "2          1     0\n",
+              "3          0     1\n",
+              "4          1     0\n",
+              "...      ...   ...\n",
+              "1303       0     1\n",
+              "1304       1     0\n",
+              "1306       0     1\n",
+              "1307       0     1\n",
+              "1308       0     1\n",
+              "\n",
+              "[1107 rows x 2 columns]"
+            ]
+          },
+          "execution_count": 22,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# We prefer this method as it generates the column names itself.\n",
+        "import pandas as pd\n",
+        "df_encoded = pd.get_dummies(df['sex'])\n",
+        "df_encoded"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 23,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "qKe5uZx8rw4y",
+        "outputId": "2720cdd7-21e5-4645-e9e6-2327d37b95d8"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "\n",
+              "  <div id=\"df-d61f1b4f-2632-4ea4-870d-2c0914cde298\">\n",
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+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>pclass</th>\n",
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+              "      <td>0</td>\n",
+              "      <td>male</td>\n",
+              "      <td>0.011705</td>\n",
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+              "      <td>0.295806</td>\n",
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+              "      <td>1</td>\n",
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+              "    <tr>\n",
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+              "      <td>1.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>female</td>\n",
+              "      <td>-0.350059</td>\n",
+              "      <td>1.0</td>\n",
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+              "      <td>1</td>\n",
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+              "  </tbody>\n",
+              "</table>\n",
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+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d61f1b4f-2632-4ea4-870d-2c0914cde298')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "        \n",
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+              "  </svg>\n",
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+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
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+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-d61f1b4f-2632-4ea4-870d-2c0914cde298 button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-d61f1b4f-2632-4ea4-870d-2c0914cde298');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ],
+            "text/plain": [
+              "   pclass  survived     sex       age  sibsp  parch      fare  embarked  \\\n",
+              "0     1.0         1  female -0.060648    0.0    0.0  0.412503         2   \n",
+              "1     1.0         1    male -2.092555    1.0    2.0  0.295806         2   \n",
+              "2     1.0         0  female -2.014175    1.0    2.0  0.295806         2   \n",
+              "3     1.0         0    male  0.011705    1.0    2.0  0.295806         2   \n",
+              "4     1.0         0  female -0.350059    1.0    2.0  0.295806         2   \n",
+              "\n",
+              "   female  male  \n",
+              "0       1     0  \n",
+              "1       0     1  \n",
+              "2       1     0  \n",
+              "3       0     1  \n",
+              "4       1     0  "
+            ]
+          },
+          "execution_count": 23,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "df=pd.concat([df,df_encoded],axis=1)\n",
+        "df.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 24,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "HjzlByZkQO2d",
+        "outputId": "66ed6c58-3a4e-4778-b350-418fad944392"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "\n",
+              "  <div id=\"df-a190c573-4611-4de5-b951-e0bcbe1692e4\">\n",
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+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
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+              "\n",
+              "    .dataframe tbody tr th {\n",
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+              "</style>\n",
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+              "  <thead>\n",
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+              "      <th></th>\n",
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+              "      <th>2</th>\n",
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+              "      <td>female</td>\n",
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+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>1.0</td>\n",
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+              "      <td>male</td>\n",
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+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
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+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>1.0</td>\n",
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+              "      <td>female</td>\n",
+              "      <td>-0.350059</td>\n",
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+              "      <td>0.295806</td>\n",
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+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a190c573-4611-4de5-b951-e0bcbe1692e4')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
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+              "\n",
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+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-a190c573-4611-4de5-b951-e0bcbe1692e4 button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-a190c573-4611-4de5-b951-e0bcbe1692e4');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ],
+            "text/plain": [
+              "   pclass  survived     sex       age  sibsp  parch      fare  embarked  \\\n",
+              "0     1.0         1  female -0.060648    0.0    0.0  0.412503         2   \n",
+              "1     1.0         1    male -2.092555    1.0    2.0  0.295806         2   \n",
+              "2     1.0         0  female -2.014175    1.0    2.0  0.295806         2   \n",
+              "3     1.0         0    male  0.011705    1.0    2.0  0.295806         2   \n",
+              "4     1.0         0  female -0.350059    1.0    2.0  0.295806         2   \n",
+              "\n",
+              "   female  male  \n",
+              "0     1.0   0.0  \n",
+              "1     0.0   1.0  \n",
+              "2     1.0   0.0  \n",
+              "3     0.0   1.0  \n",
+              "4     1.0   0.0  "
+            ]
+          },
+          "execution_count": 24,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "from sklearn.preprocessing import OneHotEncoder\n",
+        "ohe = OneHotEncoder()\n",
+        "arr= np.array(df['sex'])\n",
+        "col=[\"female\",\"male\"] #Here, you need to specify column names.\n",
+        "df[col]=ohe.fit_transform(arr.reshape(-1,1)).toarray()\n",
+        "\n",
+        "df.head()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "K5lXCrsOTPt3"
+      },
+      "source": [
+        "# Looking for imbalance in dataset for classification tasks.\n",
+        "\n",
+        "Consider a classification dataset of 1000 samples and 2 classes such that 900 samples belong to the first class and 100 samples belong to the second. In such situation, the dataset is said to have imbalanced classes. Examples of such datasets are Cancer detection and bank fraudulent transactions.\n",
+        "\n",
+        "When the samples of a certain class are much more than the other class, our model may get biased towards the prediction. To fix it, we try to balance the classes by resampling our dataset. Resampling refers to 2 techniques : oversampling the minority class or undersampling the majority class. <a href=\"https://encord.com/blog/an-introduction-to-balanced-and-imbalanced-datasets-in-machine-learning/\">Read this</a>\n",
+        "\n",
+        "All these comes under Data Augmentation.\n",
+        "\n",
+        "Refer to this <a href=\"https://machinelearningmastery.com/smote-oversampling-for-imbalanced-classification/\">website</a> to learn how to implement SMOTE, an algorithm to oversample minority class. For other methods, refer to this <a href=\"https://imbalanced-learn.org/stable/references/index.html#api\">website</a> .\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/Week 1/Libraries Examples and Resources/.ipynb_checkpoints/Matplotlib_Examples-checkpoint.ipynb b/Week 1/Libraries Examples and Resources/.ipynb_checkpoints/Matplotlib_Examples-checkpoint.ipynb
new file mode 100644
index 0000000..6972db6
--- /dev/null
+++ b/Week 1/Libraries Examples and Resources/.ipynb_checkpoints/Matplotlib_Examples-checkpoint.ipynb	
@@ -0,0 +1,1080 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Introduction"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Matplotlib is an excellent 2D and 3D graphics library for generating scientific figures. \n",
+    "\n",
+    "Some of the major Pros of Matplotlib are:\n",
+    "\n",
+    "* Generally easy to get started for simple plots\n",
+    "* Support for custom labels and texts\n",
+    "* Great control of every element in a figure\n",
+    "* High-quality output in many formats\n",
+    "* Very customizable in general\n",
+    "\n",
+    "\n",
+    "## Installation \n",
+    "\n",
+    "To install matplotlib, type the following command in your terminal :\n",
+    "    <code> pip install matplotlib </code>\n",
+    "    \n",
+    "## Importing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Import the `matplotlib.pyplot` module under the name `plt` (the tidy way):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You'll also need to use this line to see plots in the notebook:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "That line is only for jupyter notebooks, if you are using another editor, you'll use: **plt.show()** at the end of all your plotting commands to have the figure pop up in another window."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "# Basic Example\n",
+    "\n",
+    "Let's walk through a very simple example using two numpy arrays:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Example\n",
+    "\n",
+    "Let's walk through a very simple example using two numpy arrays. You can also use lists, but most likely you'll be passing numpy arrays or pandas columns (which essentially also behave like arrays).\n",
+    "\n",
+    "** The data we want to plot:**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "x = np.linspace(0, 5, 11)\n",
+    "y = x ** 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0. ,  0.5,  1. ,  1.5,  2. ,  2.5,  3. ,  3.5,  4. ,  4.5,  5. ])"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([  0.  ,   0.25,   1.  ,   2.25,   4.  ,   6.25,   9.  ,  12.25,\n",
+       "        16.  ,  20.25,  25.  ])"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Basic Matplotlib Commands\n",
+    "\n",
+    "We can create a very simple line plot using the following ( I encourage you to pause and use Shift+Tab along the way to check out the document strings for the functions we are using)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10c2515f8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(x, y, 'r') # 'r' is the color red\n",
+    "plt.xlabel('X Axis Title Here')\n",
+    "plt.ylabel('Y Axis Title Here')\n",
+    "plt.title('String Title Here')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Creating Multiplots on Same Canvas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10b8ad6a0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plt.subplot(nrows, ncols, plot_number)\n",
+    "plt.subplot(1,2,1)\n",
+    "plt.plot(x, y, 'r--') # More on color options later\n",
+    "plt.subplot(1,2,2)\n",
+    "plt.plot(y, x, 'g*-');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "___\n",
+    "# Matplotlib Object Oriented Method\n",
+    "Now that we've seen the basics, let's break it all down with a more formal introduction of Matplotlib's Object Oriented API. This means we will instantiate figure objects and then call methods or attributes from that object."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Introduction to the Object Oriented Method"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The main idea in using the more formal Object Oriented method is to create figure objects and then just call methods or attributes off of that object. This approach is nicer when dealing with a canvas that has multiple plots on it. \n",
+    "\n",
+    "To begin we create a figure instance. Then we can add axes to that figure:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.text.Text at 0x111c85198>"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x111b3afd0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create Figure (empty canvas)\n",
+    "fig = plt.figure()\n",
+    "\n",
+    "# Add set of axes to figure\n",
+    "axes = fig.add_axes([0.1, 0.1, 0.8, 0.8]) # left, bottom, width, height (range 0 to 1)\n",
+    "\n",
+    "# Plot on that set of axes\n",
+    "axes.plot(x, y, 'b')\n",
+    "axes.set_xlabel('Set X Label') # Notice the use of set_ to begin methods\n",
+    "axes.set_ylabel('Set y Label')\n",
+    "axes.set_title('Set Title')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Code is a little more complicated, but the advantage is that we now have full control of where the plot axes are placed, and we can easily add more than one axis to the figure:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x111c73470>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Creates blank canvas\n",
+    "fig = plt.figure()\n",
+    "\n",
+    "axes1 = fig.add_axes([0.1, 0.1, 0.8, 0.8]) # main axes\n",
+    "axes2 = fig.add_axes([0.2, 0.5, 0.4, 0.3]) # inset axes\n",
+    "\n",
+    "# Larger Figure Axes 1\n",
+    "axes1.plot(x, y, 'b')\n",
+    "axes1.set_xlabel('X_label_axes2')\n",
+    "axes1.set_ylabel('Y_label_axes2')\n",
+    "axes1.set_title('Axes 2 Title')\n",
+    "\n",
+    "# Insert Figure Axes 2\n",
+    "axes2.plot(y, x, 'r')\n",
+    "axes2.set_xlabel('X_label_axes2')\n",
+    "axes2.set_ylabel('Y_label_axes2')\n",
+    "axes2.set_title('Axes 2 Title');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## subplots()\n",
+    "\n",
+    "The plt.subplots() object will act as a more automatic axis manager.\n",
+    "\n",
+    "Basic use cases:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10c243400>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Use similar to plt.figure() except use tuple unpacking to grab fig and axes\n",
+    "fig, axes = plt.subplots()\n",
+    "\n",
+    "# Now use the axes object to add stuff to plot\n",
+    "axes.plot(x, y, 'r')\n",
+    "axes.set_xlabel('x')\n",
+    "axes.set_ylabel('y')\n",
+    "axes.set_title('title');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Then you can specify the number of rows and columns when creating the subplots() object:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXkAAAEACAYAAABWLgY0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAADqpJREFUeJzt3E+MnHd9x/H3x3FTqShESiNFxSGpFEApiIAQuD7kMCRVs8nFiAtOJCoiofrQIC5VHQ5V9lAp5YZoWiJXLhUH5ErQg0uhCUIZobQJGDWJQ2vHDq2M/6CgpBAJpFTG+vaw0zAM9u6zu88zY//m/ZJGmmfmt/N9xv7Mx88+z65TVUiS2rRj0TsgSRqOJS9JDbPkJalhlrwkNcySl6SGWfKS1LANSz7JoSSvJDm2zprPJzmV5Pkk7+93F6VhmG0tgy5H8l8E7rnck0nuBW6rqncC+4HHe9o3aWhmW83bsOSr6mngJ+ss2Qt8abL2O8D1SW7qZ/ek4ZhtLYM+zsnvAs5MbZ+bPCZd7cy2rnpeeJWkhu3s4TXOAW+f2r558tivSeJ/lKNBVVV6fDmzrSvGVrPd9Ug+k9ulHAH+CCDJHuCnVfXK5V6oqhZye+SRR5Zq7jK+5y26qrNtvpZj9nZseCSf5MvACPjtJD8EHgGuXct0Hayqrye5L8nLwM+BB7e1R9KcmG0tgw1Lvqoe6LDmoX52R5ofs61lsDQXXkej0VLNXeTsRb7nZWO+lmf2VmW753s2NSypec7TcklC9XvhdTOzzbYGs51sL82RvCQtI0tekhpmyUtSwyx5SWqYJS9JDbPkJalhlrwkNcySl6SGWfKS1DBLXpIaZslLUsMseUlqmCUvSQ2z5CWpYZa8JDXMkpekhlnyktQwS16SGmbJS1LDLHlJapglL0kNs+QlqWGWvCQ1zJKXpIZZ8pLUMEtekhpmyUtSwyx5SWqYJS9JDbPkJalhlrwkNcySl6SGWfKS1DBLXpIa1qnkk6wkOZHkZJIDl3j+rUmOJHk+yYtJPtH7nko9M9daBqmq9RckO4CTwN3AeeAosK+qTkyt+Qzw1qr6TJIbgZeAm6rqFzOvVRvNk7YqCVWVjmt7y/VkrdnWYDaT7VldjuR3A6eq6nRVXQAOA3tn1hRw3eT+dcBrl/ogSFcQc62l0KXkdwFnprbPTh6b9hjw7iTngReAT/eze9JgzLWWws6eXuce4LmquivJbcA3k9xRVT+bXbi6uvrm/dFoxGg06mkXtGzG4zHj8XjIEZ1zDWZb/ekz213Oye8BVqtqZbL9MFBV9dmpNV8DHq2qf51sfws4UFXfm3ktz1tqMJs8J99brifPmW0NZuhz8keBdyS5Ncm1wD7gyMya08AfTHbmJuBdwH9tZYekOTHXWgobnq6pqotJHgKeZO0fhUNVdTzJ/rWn6yDwF8DfJzk2+bI/q6r/GWyvpW0y11oWG56u6XWY39JqQNv5lraH2WZbgxn6dI0k6SplyUtSwyx5SWqYJS9JDbPkJalhlrwkNcySl6SGWfKS1DBLXpIaZslLUsMseUlqmCUvSQ2z5CWpYZa8JDXMkpekhlnyktQwS16SGmbJS1LDLHlJapglL0kNs+QlqWGWvCQ1zJKXpIZZ8pLUMEtekhpmyUtSwyx5SWqYJS9JDbPkJalhlrwkNcySl6SGWfKS1DBLXpIaZslLUsMseUlqWKeST7KS5ESSk0kOXGbNKMlzSb6f5Kl+d1Pqn7nWMkhVrb8g2QGcBO4GzgNHgX1VdWJqzfXAvwF/WFXnktxYVa9e4rVqo3nSViWhqtJxbW+5nqw12xrMZrI9q8uR/G7gVFWdrqoLwGFg78yaB4CvVtU5gMt9EKQriLnWUuhS8ruAM1PbZyePTXsXcEOSp5IcTfLxvnZQGoi51lLY2ePrfAC4C3gL8EySZ6rq5Z5eX1oEc62rXpeSPwfcMrV98+SxaWeBV6vqDeCNJN8G3gf82odhdXX1zfuj0YjRaLS5PZYmxuMx4/F4q1/ea67BbKs/28z2r+hy4fUa4CXWLlD9CPgucH9VHZ9aczvwV8AK8JvAd4CPVdV/zryWF6c0mE1eeO0t15O1ZluD2c6F1w2P5KvqYpKHgCdZO4d/qKqOJ9m/9nQdrKoTSZ4AjgEXgYOX+iBIVwpzrWWx4ZF8r8M82tGAtnO008Nss63BDP0jlJKkq5QlL0kNs+QlqWGWvCQ1zJKXpIZZ8pLUMEtekhpmyUtSwyx5SWqYJS9JDbPkJalhlrwkNcySl6SGWfKS1DBLXpIaZslLUsMseUlqmCUvSQ2z5CWpYZa8JDXMkpekhlnyktQwS16SGmbJS1LDLHlJapglL0kNs+QlqWGWvCQ1zJKXpIZZ8pLUMEtekhpmyUtSwyx5SWqYJS9JDbPkJalhlrwkNaxTySdZSXIiyckkB9ZZ96EkF5J8tL9dlIZhrrUMNiz5JDuAx4B7gPcA9ye5/TLr/hJ4ou+dlPpmrrUsuhzJ7wZOVdXpqroAHAb2XmLdp4CvAD/ucf+koZhrLYUuJb8LODO1fXby2JuSvA34SFV9AUh/uycNxlxrKfR14fVzwPQ5TT8QaoG51lVvZ4c154BbprZvnjw27YPA4SQBbgTuTXKhqo7Mvtjq6uqb90ejEaPRaJO7LK0Zj8eMx+OtfnmvuQazrf5sM9u/IlW1/oLkGuAl4G7gR8B3gfur6vhl1n8R+Keq+sdLPFcbzZO2KglV1elou89cT5432xrMZrI9a8Mj+aq6mOQh4EnWTu8cqqrjSfavPV0HZ79kKzsizZO51rLY8Ei+12Ee7WhA2zna6WG22dZgtpNtf+NVkhpmyUtSwyx5SWqYJS9JDbPkJalhlrwkNcySl6SGWfKS1DBLXpIaZslLUsMseUlqmCUvSQ2z5CWpYZa8JDXMkpekhlnyktQwS16SGmbJS1LDLHlJapglL0kNs+QlqWGWvCQ1zJKXpIZZ8pLUMEtekhpmyUtSwyx5SWqYJS9JDbPkJalhlrwkNcySl6SGWfKS1DBLXpIaZslLUsMseUlqmCUvSQ3rVPJJVpKcSHIyyYFLPP9Akhcmt6eTvLf/XZX6Za61DFJV6y9IdgAngbuB88BRYF9VnZhaswc4XlWvJ1kBVqtqzyVeqzaaJ21VEqoqHdf2luvJWrOtwWwm27O6HMnvBk5V1emqugAcBvZOL6iqZ6vq9cnms8CureyMNEfmWkuhS8nvAs5MbZ9l/bB/EvjGdnZKmgNzraWws88XS/Jh4EHgzsutWV1dffP+aDRiNBr1uQtaIuPxmPF4PPicLrkGs63+9JntLufk97B2LnJlsv0wUFX12Zl1dwBfBVaq6geXeS3PW2owmzwn31uuJ+vMtgYz9Dn5o8A7ktya5FpgH3BkZgduYe2D8PH1PgjSFcRcaylseLqmqi4meQh4krV/FA5V1fEk+9eeroPAnwM3AH+TJMCFqto95I5L22GutSw2PF3T6zC/pdWAtvMtbQ+zzbYGM/TpGknSVcqSl6SGWfKS1DBLXpIaZslLUsMseUlqmCUvSQ2z5CWpYZa8JDXMkpekhlnyktQwS16SGmbJS1LDLHlJapglL0kNs+QlqWGWvCQ1zJKXpIZZ8pLUMEtekhpmyUtSwyx5SWqYJS9JDbPkJalhlrwkNcySl6SGWfKS1DBLXpIaZslLUsMseUlqmCUvSQ2z5CWpYZa8JDXMkpekhlnyktSwTiWfZCXJiSQnkxy4zJrPJzmV5Pkk7+93N6X+mWstgw1LPskO4DHgHuA9wP1Jbp9Zcy9wW1W9E9gPPD7Avm7LeDxeqrmLnL3I99yVub56Zy/je96OLkfyu4FTVXW6qi4Ah4G9M2v2Al8CqKrvANcnuanXPd0mA9n+3E0y11fp7GV8z9vRpeR3AWemts9OHltvzblLrJGuJOZaS8ELr5LUsqpa9wbsAf5lavth4MDMmseBj01tnwBuusRrlTdvQ942yvMQuTbb3uZx65rt2dtONnYUeEeSW4EfAfuA+2fWHAH+BPiHJHuAn1bVK7MvVFXpME+ah95yDWZbV64NS76qLiZ5CHiStdM7h6rqeJL9a0/Xwar6epL7krwM/Bx4cNjdlrbHXGtZZPKtpiSpQYNceF3UL5lsNDfJA0lemNyeTvLePuZ2mT217kNJLiT56LzmJhkleS7J95M81cfcLrOTvDXJkcnf8YtJPtHT3ENJXklybJ01c8/XUHO7zB4q24vKddfZQ2S7uVxv9WT+Ohe0dgAvA7cCvwE8D9w+s+Ze4J8n938feHZOc/cA10/ur/Qxt+vsqXXfAr4GfHRO7/l64D+AXZPtG+f49/wZ4NH/nwu8BuzsYfadwPuBY5d5flH56n3uIrO9qFwvMtst5nqII/lF/ZLJhnOr6tmqen2y+Sz9/cxzl/cM8CngK8CP5zj3AeCrVXUOoKpenePsAq6b3L8OeK2qfrHdwVX1NPCTdZYsJF8Dze00e6BsLyrXXWcPke3mcj1EyS/ql0y6zJ32SeAb25zZeXaStwEfqaovAH39JEaX9/wu4IYkTyU5muTjc5z9GPDuJOeBF4BP9zR7s/s2r3wN9ctTi8r2onLdaTbDZLu5XHf5EcrmJPkwaz8pceccx34OmD6/N68fudsJfAC4C3gL8EySZ6rq5TnMvgd4rqruSnIb8M0kd1TVz+YweyktINuLyjUsLttXVa6HKPlzwC1T2zdPHptd8/YN1gwxlyR3AAeBlapa71ujvmd/EDicJKydx7s3yYWqOjLw3LPAq1X1BvBGkm8D72PtvON2dJn9IPAoQFX9IMl/A7cD39vm7C77toh8DTG36+whsr2oXHedPUS228t1HxdJZi4OXMMvL1xcy9qFi9+bWXMfv7yAsId+LhJ1mXsLcArYM+/3PLP+i/Rz4bXLe74d+OZk7W8BLwLvntPsvwYemdy/ibVvNW/o6c/8d4EXL/PcovLV+9xFZntRuV5ktlvMdS9huMTOrAAvTUL38OSx/cAfT615bPKH+QLwgXnMBf6WtSvh/w48B3x3nu95au3f9fhh6PJn/aes/RTCMeBT83rPwO8AT0zmHgPu72nul4HzwP8CP2TtyGrh+Rpq7iKzvahcLzLbreXaX4aSpIb5v1BKUsMseUlqmCUvSQ2z5CWpYZa8JDXMkpekhlnyktQwS16SGvZ/GNp0aN6HtcEAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1121a05f8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Empty canvas of 1 by 2 subplots\n",
+    "fig, axes = plt.subplots(nrows=1, ncols=2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x111f0f8d0>,\n",
+       "       <matplotlib.axes._subplots.AxesSubplot object at 0x1121f5588>], dtype=object)"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Axes is an array of axes to plot on\n",
+    "axes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can iterate through this array:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1121a05f8>"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "for ax in axes:\n",
+    "    ax.plot(x, y, 'b')\n",
+    "    ax.set_xlabel('x')\n",
+    "    ax.set_ylabel('y')\n",
+    "    ax.set_title('title')\n",
+    "\n",
+    "# Display the figure object    \n",
+    "fig"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A common issue with matplolib is overlapping subplots or figures. We ca use **fig.tight_layout()** or **plt.tight_layout()** method, which automatically adjusts the positions of the axes on the figure canvas so that there is no overlapping content:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10c21ec18>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(nrows=1, ncols=2)\n",
+    "\n",
+    "for ax in axes:\n",
+    "    ax.plot(x, y, 'g')\n",
+    "    ax.set_xlabel('x')\n",
+    "    ax.set_ylabel('y')\n",
+    "    ax.set_title('title')\n",
+    "\n",
+    "fig    \n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Figure size, aspect ratio and DPI"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Matplotlib allows the aspect ratio, DPI and figure size to be specified when the Figure object is created. You can use the `figsize` and `dpi` keyword arguments. \n",
+    "* `figsize` is a tuple of the width and height of the figure in inches\n",
+    "* `dpi` is the dots-per-inch (pixel per inch). \n",
+    "\n",
+    "For example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11228ea58>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize=(8,4), dpi=100)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The same arguments can also be passed to layout managers, such as the `subplots` function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x112565748>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(figsize=(12,3))\n",
+    "\n",
+    "axes.plot(x, y, 'r')\n",
+    "axes.set_xlabel('x')\n",
+    "axes.set_ylabel('y')\n",
+    "axes.set_title('title');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Saving figures\n",
+    "Matplotlib can generate high-quality output in a number formats, including PNG, JPG, EPS, SVG, PGF and PDF. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To save a figure to a file we can use the `savefig` method in the `Figure` class:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig.savefig(\"filename.png\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here we can also optionally specify the DPI and choose between different output formats:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig.savefig(\"filename.png\", dpi=200)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "____\n",
+    "## Legends, labels and titles"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now that we have covered the basics of how to create a figure canvas and add axes instances to the canvas, let's look at how decorate a figure with titles, axis labels, and legends."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Figure titles**\n",
+    "\n",
+    "A title can be added to each axis instance in a figure. To set the title, use the `set_title` method in the axes instance:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ax.set_title(\"title\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Axis labels**\n",
+    "\n",
+    "Similarly, with the methods `set_xlabel` and `set_ylabel`, we can set the labels of the X and Y axes:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ax.set_xlabel(\"x\")\n",
+    "ax.set_ylabel(\"y\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Legends"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can use the **label=\"label text\"** keyword argument when plots or other objects are added to the figure, and then using the **legend** method without arguments to add the legend to the figure: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x113a3d8d0>"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11392f320>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure()\n",
+    "\n",
+    "ax = fig.add_axes([0,0,1,1])\n",
+    "\n",
+    "ax.plot(x, x**2, label=\"x**2\")\n",
+    "ax.plot(x, x**3, label=\"x**3\")\n",
+    "ax.legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice how are legend overlaps some of the actual plot!\n",
+    "\n",
+    "The **legend** function takes an optional keyword argument **loc** that can be used to specify where in the figure the legend is to be drawn. The allowed values of **loc** are numerical codes for the various places the legend can be drawn. See the [documentation page](http://matplotlib.org/users/legend_guide.html#legend-location) for details. Some of the most common **loc** values are:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11392f320>"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Lots of options....\n",
+    "\n",
+    "ax.legend(loc=1) # upper right corner\n",
+    "ax.legend(loc=2) # upper left corner\n",
+    "ax.legend(loc=3) # lower left corner\n",
+    "ax.legend(loc=4) # lower right corner\n",
+    "\n",
+    "# .. many more options are available\n",
+    "\n",
+    "# Most common to choose\n",
+    "ax.legend(loc=0) # let matplotlib decide the optimal location\n",
+    "fig"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setting colors, linewidths, linetypes\n",
+    "\n",
+    "With matplotlib, we can define the colors of lines and other graphical elements in a number of ways. Eg: `'b'` means blue, `'g'` means green, 'b.-' means a blue line with dots , etc."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x111fae048>]"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1122f5e80>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "ax.plot(x, x**2, 'b.-') # blue line with dots\n",
+    "ax.plot(x, x**3, 'g--') # green dashed line"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Colors with the color= parameter"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also define colors by their names or RGB hex codes and optionally provide an alpha value using the `color` and `alpha` keyword arguments. Alpha indicates opacity."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x112179390>]"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x112c79d30>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "\n",
+    "ax.plot(x, x+1, color=\"blue\", alpha=0.5) # half-transparant\n",
+    "ax.plot(x, x+2, color=\"#8B008B\")        # RGB hex code\n",
+    "ax.plot(x, x+3, color=\"#FF8C00\")        # RGB hex code "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Line and marker styles"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To change the line width, we can use the `linewidth` or `lw` keyword argument. The line style can be selected using the `linestyle` or `ls` keyword arguments:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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61Q9nS2jBU1Irmxe6WnYfb3jUcHz31++cPi+SX4IIQbwptYR7eDvhwBE8qeUtIqyq1GqF4r0CC57U8uaFu1KrFYr38j++llqt3D3obk3H0YI3gvAh3q78uiW1BvVe7t6U2mCrgvsCb0VauSu1ynnhi55aivfyPu5KrXJeUE9t8OOu1HLnhQek1hUo7YEgAgxXEg5shS+YpFYrJL9yHCUcfBkbi2bLlqmirYJRal2B4r2sKBMOjLOMqFGD/+EtSS2hpDpIrVZIfgkiwHBFfudvmh+UUqsVkl85kwcNwtQff7Tb5/qPJrfizq7PVQupJVxn8ujJyJ+Tj/oV9XEp7BIq+1bisTseI6kNcURRxE8LfsLC2QvR/67+6NS4U7WVWq1Q1BlB+AlepbYgpwCorf0+erzYIyilViuhILRKHFVqczcccphwEFVZgsZ3NCaprYZoqdRuObYFWZVZAICEygTs2LMD11tdh86gg769nqS2muFKpXZFyQr0reyLrw9/jdgBsaynNtU7PbXVBZJfgnABe1KrrLzYq9SiTPtjNb6jsfdOhPAo7rYfJN2ShDonG8C8lS/AZgC6tg2R+USmr0+NcIMqtR/YyIoktTqDDudnnUfep9aEgw6DO+DOWXf6+xQJF6ly+8HNeWErtdJY5ppMLHx/IcaPGa95wVeoQ20PhNcJho+33ZVare0HVe35JfyDL3tqq9LzS/gHd6VWuauYlkpteXk5pk2YhnN/nrMkHNjr+SV8j7tSy50XVKmtEtTzSwQE/pRfX0mtVoLhjYA/8HW8V6AuFKOEA/8ik1oH8uJJqSUCG5La4IPklwgIvCF8VZJaBxcjX/XUkvyq8aTwVUlqHcwLf/TUUsKB5yGpJZSQ1FZfSH6JgMCVj/rLXtQmL4EmtVoh+ZWj9aP+6iC1hOs4i/ciqSWUkNSGLocPA2FhQLNmJL9EAOCK/E5/fXpQSi1RNV6+9z5MW77U7iKvJ6LbIjPsEZLaEEOS2n9N+hcqfqpAQmUCLgmXcKXJFdxhuIOkNgRRSu0vP/2ClctXomvLrmgR3YKkthpSWAicOgWYTEBODvsymYBbbwWGDFEfP2IEYDYDX35JUWeEl9HSfoCHtd/fKyWv0ItUNUBrpfZ8wT6H8V7prXQYu3IsSW01QWulVqwUoTPocPjSYXSv7A4ASBATcLHmRfT+Z2+S2mqEq5XamJQYROujsfTPpbj10q3YIezAff+6j6Q2SBBF4OpVudCmpgK3364+dt48YPRo9XhREV9+s7KAxYu1PQ+SX4KLuz21CS0TLGP/+O4fmh+XXsgCG3fbD5JuSZKNvTo8G+afTtqt/IYbEhBVN8rXp0m4iNb0A0lqpflgifTqoJctIpSk9uLoi8ibY433atGnBTL6ZPj7dAkNqKTWwbywlVpZpFeXFPm8sJHahB8SsPD9hXhmzDO4ZdAtfjxTApBLbXg40KKF+pglS4AnngBKS+XjQ4bw5ddg4D9WTg5/vHVroE8f4OefnT9fanvwEoHa26l5m9xiM6KTPdN+QPFegQ9PannzwtPtBxTvFdiUFZY5rcY5klplMoarlVqK9wo8RFFEWYF6XmiVWu68oEptwCKKQEkJEMWpQWzdCrzwgrWKK0ntwIHATz+pj1+5EhgwQD3euzewYYN6fOdO4NFHAb2eibDBwH5u3Rq400HENe3wFkJYpNbBhciR1NZvUd8nPbW2ohvq+CLeyyK1TuZFRVkFV14Sb0mUvUh5uv2g2223IXvECHxpJ+2BxNc7uCu1ye2T3ZJardSoUQPT35/u8fsl1NiTWt68EMIE1bxwVqklAp9jx4D337f21kpS27MnsGaN+vjycibASkwm/v1LlVydTi60mXb27unUiS1i8wYkvwFOsEgt4RpSvNdUW+Fbvx7ZGuO9zEVmp1UXf0qtK4x75x1sv/9+TKN4L7exSK2TeeFvqSV8h0xqncwLntTqUnQwdDaQ1AYhV64Ay5ZZJVb6bjAA33yjPj4/H5g9Wz3uTGYloqPZWHo6//hWrYCCAnacv6G2By/hykf9+1vt5767LjDd7Kn1UPuBvwjUFhB/4eij/i9iY5E8ZwGap7axW3UpMBWgopQvtcqPE2mhWPDBi/eqLKl0S2rdbT8gAg+3pZbaD4IOsxk4ckQusjk5QM2awMyZ6uMPHmTCqSQ9HTh5Uj1uMvH7bBs0AM6cUY+XlQHbtrEqrl7PKrqBAOX8+gGpUhv/ZbxlzJn8Lt63OCilltCObaX2nQlP4p2d6+wu8nq89i24o/04ktoQQFmpLTQVYs6Xc6Dbo0MiEpGLXJyIOIFeEb1IakMEntRKIrtpxyZk789G86jmSClIgSCQ1AY7yvQDk4mlGTz7rPrY06f5VdXYWHYfv/7Kbkv9sPn5QFyc+viaNVkfr/JSUVEBzJoFpKRY2xICSWq1Qj2/HkTZfuCsUovh2u/74W9dyAIjAgpl+4GWSq359EWH8V7Nu9bHk+ue9OVpEB6GJ7W8eSFWqCu1hYWFaIzGAIBEJKLw1kK8tP4lktogx5HUKucFV2pTdDhw5QB65ffCweYH8e6ad0lqAxhbqc3LY4u6lFy9yuRSmX4QHc2X3+Rk/mPl5wPt2wMzZgB33GEdj4lh6QoJCVaRlfpseYSHA88/r+38gp2Ql19v9dSOnzbe36dGuEFVpFZrT+2JQctg/vGA3cpvRWysT8+V0I5Wqa0sr+SuctfSU7u5fLMs3qth24YkvgGMK1ILcNIPXOipHdloJBa+vxCPj3mcxNdPiCJw7RpQt676d2Yzy5qVKriS1IaHs5/Dw+XHx8UBlZXq+yksZL2xyoprrVpA164seUGZgPDnn0D//vLjBQGYP7/Kp1qt8UnbA4zqcW/3dmqWWtucWr0O0QbPtB9QvFdg4orUemtHMYr3CjxckVrLx8oO5oU77QcU7xUYuNJTa3dHMWo/CHomTADOnpX32ZaWstYEXvxXXByrxCoxmfiV24YNWQVYWZmdPJnfsmAPo5F9EdW07UFzTq0dqZUqtdLvfNFTS/FeVrwV71UlqbWZF4ltEmXzwps9tRTv5TuqJLU28yK5XbJsXviip5bivbyL21KbooOhk0H+ZoekNmhYtIhtmWsrsyYTy5SNj+cfz0s7MJmARo3U43q9VX5tI72UrQ0Shw/zJdpVsrLcv49QImAqv25JrV79jtvfC8Uo4UCNFO/FEz578V7uSq1t1cXbUusq2zdtwjKK96oS7kqtcl7QQrHgp8pS62hekNQGPIcPsyQCZQLCBx/wK61NmgDHj6vH9+0D2rRRj3fsCPzxh3xMpwPWr2e/U/K//wG1azPpDYRIr1Dh6affxJEjJRBFYOPGaYGS9nDzZ5sK6PfHvw9KqdUKya8cRx/1f15bh8oRb6FhZGOVvJSXlKtfpDjzIpCklnAdKd7r3L5zqN+gPp4d9iyKc4tdk1rOvCCpDX5IakMLXvpBTg4wbBhfZtu2ZeKqZPt2oEsX9fhttwGbNqnHV60C+vVTj//wA0tGkNoSgjH9oDogisDly+p5If28erURBQXGm0cHcNtDRp8MWV9UsEitVkJBaHnY21Fs4cLJ+A+vEQrAY0UF+L+fv0Dzp97yafsB4Tucbb7w3e7v0CC/ATKQgVzkYtLaSRjSa4hFav3RfkB4F61Su/vMbhwtP4q2SW3RuVlnmdRK7QcktYGPUmo7dADq1VMf17s3X067drXK78qV7Hv//kCEneicnBz++IMPMim2jfIyGIC0NP7xgwY5Pi/CPZxJre1uc2Vlnntcv8lvu2Ht/PXQRBXQuk1ueWm5vNJyU17ioisdxnsZ0iLRc1JPX54S4QG07ihWWVHJnRfJ7ZIRrY/G6smrkZidCIDFexW1KsKDix/089kRVcETlVpbqc0emY2+m/viaPOjGLZ+mL9Pj1AgSW1kJPu4X8mYMdZdxmz7XpcvB+6+W318/fr8x7Htu+3fn1VqO3QAWrRglVhlNm3nzvz7GTNG+7kRVUcptUqZlcYuXPCs1Gol6Ba8EZ7FXalNbJMoq+Dbq9Su3J4K86GdFO8VJHhKaqV54axSm/5jOvJ+t8Z7pbZK9eHZElrwtNRqrdQOnTAUC2ssxCNjHvHBWRLOmDuXiaet0JSWAkuWAA89pD7+yhW2wEyJvcqswSBfKCZ9b9bMeowgMAHeto0SDnyNP6U2JkadiiH9/MYbrN9aKyS/PsBbCQeO8JXUauWe8eOxaP16bs/vopuLvQjv42up1YpxlhHThGk49ecpS7wX4RtkUutkEaEnpVYrdw+6G3cP4pQICY+wcSOwdataYl59FRg6VH38zp3At9+qxx3JrER0tFVYeMkKAPDee2yxGuFbAkFqbWVWKbh6PVCnjv37mDPHtcf0ifyGctSXlHAw1TbhYP16ZDtIOHBEoEmtVijey7u4LLWKhWL+7qmleC/PE+hSS3iHc+eAgwfV8jJ0KL9/9YcfmHAqOX2af/96vXpMp7MvRGPGACNHal8o5kqkdajHe0kJB0qaNYvEJ59MAmCVWnsyK/3sDamNjVXLrKtSq5VmzSKBm9Fiv/3m/PiAiTqrjriymYG7UqsMVg/UhWIU72VFSjg4f/A8UlqmwDjLqNrMwF2ppfSD6ofbUsubFyS1AYvUU6uUl86dgdtvVx//yivA66/zx199VT3+1lvAxInq8VGjgNmz1eN79wIHDsgFhiK9fI8oAj17GrF1q1H1u3r1jGja1OhTqVUKrqektipUy00ugonls2Zhqp2EgyH5+Rjx13HYWf/JgKzUeouuvXqFrOzaUlZYhpf//jJKvi5BemU6Lq27hBGbR2Bgy4FBUaklPI+t1C5dvBRLFy9Fr0690KZ+G6rUVkOU6QeJiUBmpvq4N98EXn5ZPT5oEF9+bdsMbOFt1AAAt94KPPecWl5SUvjHt23Lvgjv4IlK7eXL7MtVbKXWUbWWt7Ax2PCJ/FbHKq+WSu25o3sdJhwkpdTEQwseqhZSSzAslVonFbnKikrsEfegVyV7I5AgJiCnMAdN72pKUlvNqEql9qfcn9ArvxfWFqxF97HdSWqDCElqy8uZ0CpZvBh46SXrNrkSzzwDfPyx+nheti0A1KzJH2/enMWFKQWmdWv+8T17si/Cu/i7/YAns9VRarVClV8FLrcfKCpytjm15178FeZfTthNOKiVkYzE1pyrIxFwuCK1Wiu1xWOKkTfHmnDQekBrtH2MSirBgjd7ahv90AgL31+If4z5B24ddKufz5RwxNatwLvvyoWmtBR4/HHgiy/UxwsCcPKketxeZdY2/UCvZwLVvz9/AwcA6NuXfRG+wZ9SGx4OVFSox1u2BP7zn9CUWq04lV9BED4FcA+Ai6Iotr05NhXAUwBybx72siiKK732LD2AJ6VWa6X2vonPY9GWjZRwEMB4Q2q1Vmop4SAwCYSFYpRw4D9OnGCxXUp5ufVWYMEC9fFXrgDffacet5d+YLtgzDbSy14rwR13ANevW28bjRTv5Qt4UssTXJMJMJs9+9j2KrXKsbvu4i/uSkwEqLvQMVoqv58DmA1A+R72XVEU39XyIJMHDfJavJe5yKxpQZAnpVYrlHDgP0oLSmXb4fpSarVCCQe+JRCklvA9+fnAjh1qgUlLA2bOVB9/6hRrS1DCSzmwNx4dbb8toWNH4PBh7ekHystOqCccuIu/pdaTPbW2CQfqccIRmtIeBEFIA7BMUfktFEXRaVaXIAhiGazCpzXeSym1vBeoAlMBykv4UqtczezPnlpKOPAcbkmt7YJC6qmtNnhCalXzgqQ2YBFF4No1tbTUrs3fvSs7G+jeXT2emQns2aMeP3SIfWysJC2Nv1nD9evAzz/LRUaL1BJVw168V9OmkXjjjUnVQmqJqnHy5GlMmTIPCxYYnaY9uCO/wwDkA9gJ4DlRFLmxBoIgWB7hy9hYNPrmB7TMyKwWUku4jr14L5JaQglJbWihjPQqKQHuvVd93L59/BaBJk2Ao0fV42fOMHFVkpAA5OaqxwsLWVuBbWQTSa3/UFZqx4wx4sgRI+fR0L3dAAAgAElEQVRII3hVUHcI9EgvwsrJk6fRo8dsmEzTAER7TX4TAOSJoigKgvAqAL0oiiPs/K3lEcwAHhFaolf6MyS1IYJSat/78D1EbY1CgpiAXOTiXOw5dKvoRlIbQoiiiNLr6jc7639bj007NqFN/TZIN6eT1FYTJKm9fBlo2lT9+/PnWdqAtFBMwmAAPv2U/dy/P/Drr+znTp2YuCqpU4eJq5KyMvb3SnlJSQG8vNEm4YCqtx8YwZdce+NqPLGjGOF9jh5lOxAq58df/6rOpn7ssWlYsOB5AHUAeCnnVxTFSzY35wJY5uh4o83PtdvWxNg9Y6vysEQAwavU8vqtlVJ76cwldBQ7AgASkYj8lvkYv3I8SW01wJ7U8uYFT2p3HNqBWy/din0p+/DSDy+R1AY4oqjuRwWYgA4fLheY0lJWRbt2TX183br8doILF1hqwdq1QIcOLO/2jjvY7+rVY19KYamoYCvgbalZE1i/3u3TJTTiz/QDktrAprAQOH5cPSc6dGC7ACpZv55FACpp3lx+e8OGDdi8eR0A7bG6WuVXuPnFbghCsiiKF27e/CuA/Y7+2HjzuxnAtIwMzU+O8D1VlVqpIqdvr3dYqd0zeo8s3iujcwYiY6k5P5BxV2pjUmOQ0jnFaaW2vGc5Fr6/EMPHDEf95vX9cKYEj4oK4O231QJz5Qqr6CoFOCoK+P57oLJSPp6fDxQVqfsea9dmYpyfL4/0MhhY60P//sC2bey7RF6ed86VsI8ktY6qtN6WWr2e7S7Ha1fp2RPYtMmzj0s4p7KS/XuU5oHJxNIm7rlHfeyPP7IIQCUPPMCXX3uLTJWxgFlZWejZsw9On5Yqv9OcPm8tUWdfA8gCUE8QhDMApgL4iyAI7QBUAjgFgOPmaijey394W2q1QvFegYOvpFYrFO/lO9asYe0GSpFZv16dUhAWBkyfzsRVyZUrrAJrS3g425jBNupLktpr1/iLfvbsAerXp21y/UGgSK29iq2yUpuVxZdfZcWfcA9bqQWAdu3Ux/zyC3DffWxDF1sGDODLr73dB+3FAjZvDjzxhHp+NGyoPnb69GHIzp6K48ediy+gQX5FURzKGf5c073fhOK9vEegSK1WKN7L+1RZam/OA1uplcao/SCwkRaKKYXl9df5Qjl0KHDpknr84kWgQQP5mCCwF51jx9TH5+So5RdgvbpRUdYqrrOFYunpjn9P8V6uo5Rae3JrMgWG1GqF4r3co7KStR/ExKh/t3s38Oyz1jc7ktTedhs/Tzg+Xi2+gH2ZbdAAaNVKPTd4awEAoFkzYP58beeVkZGG1atHY8qUmdw8biWaFry5gyAI4iv330/xXi7irtTSQrHqh7tSazsvSGoDH16k1/3381+00tOB06fV4wcPAi1aqMczM4G9e9Xj2dlA167q8Q8/ZC0Iyt5JSj/wDbbxXqLIhKO0FEhIiMTgwZOqhdQSVUOK9zp/vhIpKWGYPn0YMjJYvMnZs8Abb6jnRbt2wO+/q+9r7152bVBiL0nl9Gl27YmLk8+B5s2BV17x6Gm6hCA4X/DmE/n19mMEOrbxXsmNkzFu7DgU5xaT1BIASGpDDdtIr4wMvij0788qLbbpBwDwxx9A+/bq47t3Z+KqZN064C9/UY9PnMheGJXS0rEjCa2/EEXWRsIT2YULjbh82cj5KyM8Ee9FUhscXL8OfPONdW4cO3YaGzfORmnpNLBe1xto3HgqVq8ejYyMNBw7xq+qGgys7UlJXp48SaVuXXZskyasX1dJZSW7RkVFeeoMPYMW+a1S2gOhxlGl9uvfvkZyTjLSkY7ctbl4atFTGJQ5yK/tB4T30SK1O4/uxN7cvWga0RTtG7an9oMgRhTZV1iY+neTJrHFOJLMSFK7aRNbqGPLypWsp1EpvgD7e578Nm3KxEkpMPbWF8+Y4dq5EVXHkdTajlGlNvQoLwf271fPCbMZmDtXffyNG8qFYfPAFndJ/wPr4PjxaZgyZSa++mqq3QVjZjMTV+W1ql49dk1KSWF9+86kNiws8MRXKyS/TqhS+4GNxOrb61HzUE0k5iQCYPFeRZ2LMHzNcD+fGVFVPNlTu3LiSvS90BdHux/F6PWj/X1qhEa+/ppVWpUCs3SpNY7Llj//BLZuVY/zeuP692fpCgAQGcn65FJSmKTw+msB4Avl5vOE1/Gn1ErExrKFRSS1gYO0UMx2Ply5Ajz/vPrYGzf4b2Zr1gQ++USdpJKYyITTmqRSCav4StRBTg47oE4d1rKUkCBvV7InrIKgfjNeXQlZ+fWE1Moqcg4qtY23N0beXmu8V2qrVB+fLaGFKu8o5kal9olJT2Dh+wvxyJhHfHSWBI/du9nOYUp5MRqBfv3Uxy9dCixerB63t9BDuco5OpqN8arEggAsWQLMnMn69QjfEgiVWqXEzp3L+reVtGsHfPWVZ58DwcdWak0m9iZV+ZJvNrN/27x5MWaMOkklJoalnyiTVMrK7Cep/P3v7DH0euDbb8OwefMNyAX4BgwG64Xl//7P5VMNCaqd/PpSarVC8V7+xV2p1aXoYOhk8Er7AcV7eYf8fNbTqhSWhx4CeOtuZ88GPudk2Bw9ypdfe5E9vAgmABg1CnjkEe0LxerVA2pRh4tHCUSp1br5wk8/8eWXcB9JauvX578Z7dWLbcJim34AsGuMcvFpRAQb4+VQ20tS6d2bSbNyTtirzn7wgfXngQOHoV8/Kd7L2vM7fTp9iuiMoJFfntTy5MWXUqsVivfyDjKpdfBGR5LamJQYy3ywSG1ng2wRIfXUBi7SQjFbWcnM5OdPTp4sf5GQaNiQL7+u5k8OHAikpqoFxl5ObZs2/HFHhHq8l23CgS3NmkXik08mWW4Hs9RqheK95DhKOHDGiy8CR47Id5orL2e3k5PVx585A5w7px43mfjJKwYD2xhGOR+UVV+JX37R9LS52MZ75eRUwmAIw/TpozX/twhlfCK/k0dPhnGWETVqqB+urFBbRc6e1Ca3S/ab1BLegSe1vDlBUls9sI300un4AeZvvAFMm6ZeBGY08uXXVZnt3JlVZpXy0qwZ//isLO/LaajL7+HDJdi40agaP3rUiAceqB5SqxVb2Q91Tp48jX79ZsuqnStWTMWAAaNx40aa5Q3Pxo38BZ/Ll/Or6Pbk12BgAgxYI70MBvUOhhI7d7IKsK/IyEjDV1/Rp8kVFSy73N41XolP5PfaR9fw7O5n8XC3h0lqQxhJan+Y/wO+/fRb/CXrL2if2l4ttaYCACS1wY4oMinhfXz/zTfAe+9Zqy+S1L7wAtCnD/v5zjutx0dF2U8/4JGRwcRVKS/duvGPv+8+9kV4H62bL/ByiwH2+++/r9pjB5rUEmoOHmRtBsr58PbbLHJrypR5NuILAHVw5co0LFgwE2wDWsb583z51evV8lu3Lmtj4PHZZ+wa5mihmC2+FN9QwFZqHe1CePEiO1YrPpHfhMoE7DqzC3Xuq0NSWw3RXKk1FUAQBKyqWIWs4iwsNS1Fk8eakNRWA7ZvBxYsUF+UnnqK9dMquXaNn37w3/8Ct9+uTkyQKrnSNrmSrNiT2SFD2BfhOwJhRzGS2sDDdptc23kwdCjQqJH6+GeeYXFbSp59lsnv+fP8hAOWfGDF3hvjF19ki8ak+eEs0qtlS0dnR1QVb0mtVnwiv3nheci8PxM9Xujhi4cjPISrUmtbuXdUqW37Q1ssfH8hxowZg/6D+vv7NAkOZ8+yDRKUF6JevYA331Qff+wYX3LtvQAp8yel1cv16rFV1EoGDmQB77QBg+/xp9TyaNSIbdtMUutflJFemZn8dqN77gFWrFCPt27Nl197LUsmE/uekhIGQJ1w0LFjGCZOtM6LlBT+/fCuL4Tn8KfU1qvH/t/v3+/8WJ/Ib9z/xVHCQQDhLanVCiUc+J7iYuDECfVFKD0dGD9effzOncCwYepx3gIPQC2zElev8se7dwc2bFCnHxiN/OMjI9kX4Tn8KbWxsfYrtNL34cOBzZvVf9ugATB4sGefD2FFktqoKP6bzUmTWM61ySRPP/jqK+DRR9XH8/poAavMKunYkT2+ck5I225Pnz4M2dnqhINvvhltd1MXwn14Usu7ZnhTah19upOcbG2x09JM4BP5paQD3+BvqSV8i+02udKFp3ZtFuelZONGeQ+tRK9efPm1J7P2KrmtWgHvvCO/KDmK9KpXj0X8KAn1RV6eINClVmultmXLSISHG1XjoZpw4C3mzwd++EGdfvDJJ6xtSUlhIftkSIk9mTUYrNvk2s4D3uYOAOv7f+EF+8+XEg48SzBJrScJmqizUIakNrSwjfQqKWGVECW//87EVbkIrGNHvvw6+yhRSXo668lTXoxS7ezPkpwMTJhg95Q0E+ry6yje6z//mVQtpFYrlHAgR2u815YtwPr16rkxaRLwj3+o7/fAAZYjrETLhi1168rnA4/p04FXX3V+fq5ACQdqNmyQXz9DVWq1QvLrR0hqQwtRZFUTXjX05EngscesFyVJalu2ZFvjKqlXj59+4Kj6wks/SLNTLElOZgvYCO9jW6nNzi7Bvn1G1THZ2UbMn189pJZwjXPngNWrT2PixNm4dMn6Uf/q1VORna2ueK5Zw28fkuK6lPCkNS7OfpTXyJFsManW9ANaz+5ZJKnlba2ekmIdC1Wp1QrJrxfgSe3qX1dj7fq16JDWAU3Cm5DUVnOuXmUvQMp32cnJLMYHAFauZN/79we2beOnHzhbMCZtkytdfJQ7CEnUqwccPuzOGRGuomw/sFeBuXDBudTy3ug4gqQ2cKmsZPNCOScyM4F771Ufv3Ah8OKL8wDI471yc6dhypSZqgqoqxnXd93FpMl2XjiS2sRE9kV4FntSq7xeOJLaXbuq9tjVSWq1QvLrAq7sKCaEyaV27aa16HquK/bo9mDkRyNJaoOM0lJg0SL1xai0lC0OUxIeDrz/vno8J4dJkSAw6V21CujQAXjtNTYmiuw420gvs1mdHRkVRekH/sKTUusqJLWBizLSKz7eukjLlo8/5rcfPPkkX36ZzPLjvXJy1OXZzp0hSz2wnRc8mjWzv5kL4T6ekNqqEopSqxWSXyik1kkLQlV3FIv8IRIL31+IkWNGIj0r3T8nSlgQRdZOoLwY5eWxj/uVH9UJAj/9AGCCo9y6Uqdji8+KiuTjtWqxjNu6da0CvG0bq75s3gwkJDjeJlf5GITnCASpNZn4YftdurDoOZJa3yNJrdnMj89avpzlxkoLxSQGD2ZvmJW4upi0aVMgOTkMFy6o470MhjDV8e3a8Xc9JDxLoPXU7tkDjBhBUquVai2/vpBarVC8l/exXShme/F54QVWiVXSoQNfYubMYTJiS82aTEwvXVIff+GCekteQQD+/W8mK1rSDwDg1ludnyPhOoEgtc42YKhdmx2flQX89pv6fqKiSHx9xR9/sEVatvOivJxVZZcuVR8fEcH6cpU46r+33SZXmguZmfzju3QBtm4dhn791PFe06ePruppEnYINKnVWqk1GoEHHvDs86nOBKX8iqKI0uulsgVh9loQIEC9fXKKDoZOBvlOc9R+ELAopbZ3b3WlFWBVGd4Lzt/+pq62CAIb422hajKp5RdgH0uazeoLk70eu5EjnZ8bQAkHVSWYpFYrLMbLaGecqApnz7I4L+U8ad2av/lCURHw44/qcVfSD/R69uaaR9eu9vOv7UHxXu4TrFKrFXodcQ1BlJoMvfUAgiBqfYwqS61eh2hDtOVn215bktrARZLamBigBudt2KBB7KMc2/QDgO0m1rix+vhWrdR7tgOsJ5cXF/bII0yMlBejO+9kL2KEb1DGe4kiq7Tp9ZF46qlJ1UJqiarBi/eqXz8Nmzer50P9+mx7bCV//MH/928vSeXECfX1pW5d1kqwbp36+LIy4Px559vkEp5nwwYW+ehPqXV0naD2A/8gCAJEUXSYM+KTyq+7UhuTGoOUzikktUHOa68xmbW9KJWWAv/7H9C2rfr4s2etyQi25OTw5ddgYH+jvPjYE9mFC906HcJFpEqt8kXq559LkJNj5PyFEd9955nHJqkNXJTb5Erz4urV0/jpp9myj/qzs6fi449H46671BVPe7F99nps7bUlpKQAS5bIK3KOpLZmTdDOYl5AS6X20CG2eyVJLeEqPpHfEZEj0CSiiVpqU0hqg5nly9ke2soL0uLFbMWxklWr2E5jSkwmvvwaDNboFttIL16VGAB++YXfDkF4F3tSq6y8UKU2tFBGehUUAA8/rD7u3Dm+uNaqNQ+lpfJ4r+PHp2Hu3JkA1BscmEzWJBVbEhNZ+oFyTtiT4lq1+BvFEJ7B3+0HJLXBSWFhIXJycmAymZCTkyP7uVu3bhgzZoxL9+cT+S3tWoqXN77si4ci3ODECeD4cfVFadIk/seGH33E75k7d44vv7ze2Oho9qLIY+ZM9uVsoZgEia9n8afUAqza1r07SW2gIVVqc3OBNm3Uv79+nY2bTPL0A2nrbaWcJifzH6e0lB/vlZtbibvvtvZR2s4NnvyGhwNvvunqWRKu4k+pjYpi86hpU5LaYCUnJwe//fabTGptRbewsFD1N1FRUTAYDGjUqJHLj+cT+R06fqgvHoZQIPXUKi9G99zDf9F68UVwP2a+7z6+/Loa2fPss8DAgexC9O23wBtvOI70ouxJ7+DvSq1SZr//nr3pUtKlC7B2rWcfn7BPZSUQpk7OQkUFW0VuOy/Ky9mxZWXqJBWdjgmOrfgCbCHZ9ev8JJWmTVlqgq2wrFkThl271PFeKSlh+OorT5wxoQWe1PKuHf6o1C5eDMyYQVIbiEiV2oqKCrRs2dLp8bt27cLQocwVJanV6/Vo164dBgwYAIPBYBmTvsfGxkKo4haCPpFfivjyLMr0g6ZNgfR09XFPPgnMm6cej4/ny6+rvXHSwjDlBcneLmO9e1t/Xr9eW5YtoZ1Ak1peKwKvUrtjB19+Ce/x5ptsu1tlyP61a+r/R+HhLH7t2jX5eGUlq/46SlJRRnqZzfznc+SIeuzkSYr38iaBLLVaK7W//kriG0j8/vvveOyxx2SV2t69e2PDhg1O/7Z37974888/3ZZarQRl1Fl1RZLa8HB+1Nbbb7MM2pwcefrBrFnAuHHq4+1tQWlPZm+5hQmq8iLUqRP/+IceqnpvXKjHsigTDiSaNYvEJ59Mko0Fq9RqheK95PASDpxFWq1dy2RTOTdWrGBvdpXMns3/hMZk4i8m1evl8itJLeeTSABsq+66dd1LP6B4Lz4bNji+flYHqdVKqL+OeBJHPbXx8fH44IMPnN5HfHy8rFKr1+vRpEkTTY8fExODmJgYd09DMyS/fuSHH9huYrYXpdJSlorwMqdFurgYOHlSPe4of9J2m1zpAmRvM4Wnn2ZfviDUL1pHjpTgt9+MqvHTp40YNap6SK1WlLIfypw8eRr9+skTDlatmopHHx2N0tI0GI38N7XPPssiAJWcP8+XX72ef924eJEvv3PmsIWmUo+tM6m1l33tKhkZafjqK/XitlBEktqFC4EbN6q31Gol1F9HXKGwsBDLli2rUk9tB3uh1QoaN26MxYsXe/qpewWSXw+yezf7GEb5jrtnT+DRR9lWtr/+yo698072US+vx1ZLmLqt1PJaHgBg9Ghg7Fi3TomoArxKrfJF6o8/+H976hTw4YdVf+xAklpCDi/S69575TI7Zco8G/EFgDq4dGka3nuPJRz87W98+TUY+PKbk8M+0VEyejTbRlkZsm9Pam1blgjPUpVK7SefeOaxA1VqCedIldqrV6+ia9euTo+/fv26T3tqAx2SXwfk5rIMWuUF6bbb2IuHkm3bgJdeUo9nZbE+uA4dWK/dHXewcXvpB/b2BPnrX9lja00/4C1eIaqOFqmVvtvrbawqJLWBi63UpqfzW5buvx/4+Wf1IrC1a4E+fay3z5/nJxwAlQDsvzHu35/FhSnnBq+3H2C7HhLeJZTaDwjfcP36dXTu3FlWqY2OjkaBvcgkG5KSknzaUxvohJT82ks/yMgABg9WH//LL8Dw4erx8HC+/Nr7qM9kYi9O27ax7xI9ewJffy2/QDmS2vh4/keYhHv4U2qVpKYCzz1HUhsIVFayL16utNHI+mlNJnmk17JlLE1FSXi4WnwBdf99SkoYAHXCQatWYXj6aX4VF+C3SRHewZ9SGx/P5mO7diS1wYayp9a25SA3NxerV692KqQ6nQ4dOnSQ9dQaDAaIouj0b8PDwzWlLoQK1UJ+lVIbGcm2PFTy7bf8kPV77uHLrz2ZtVd9adNGLS7SzzwaNmRfhHcI5Ert88+zlAMljRvzFy8S3mXJEraQSNlrvWAB/5px4gT//5+9xaTStUSZjqJMRpk+fRiys9UJB8uXj6ZdxLxMsFRqjUb2RQQeS5Yswblz51QLxkwmE7c6GxUVZRHYGzduINpJBJIgCFhIW5N6hICWX0lqb9zgx2dt3cp6aaWFYhJ9+vDzQV2V2fR01rKgvBDxFoQAQJMmbFMGe1Bzvmfwt9R6YkexzMxIREUZVeOhmnDgafbsYbsDKufHCy8ADz6oPn7dOuA//1GP27s2KN/QSlJrr+L22mvAW285XyhGCQeeJ1ikViv0OuIbbCu13bp1Qy0N/5PGjRsHk8kkk1pl+oFtby21H/iPgJLfY8dYz6ztBam0FOjWjbUMKImKYguElDh6wbLdJle6CNnbTKFZM5ZH6ylC/aLlLN6rOkitVijhQI6zeK9r1/hRXvfeCwwYoL6/L75gEYBKeHmygP1PZy5f5o+PHMk2bNGafuBKgg8lHKjhxXv5W2qdXS+81X4Q6q8j3ub222/Hjh07ZOkHhw8fRjMNuy5t2bIFdevWJakNAnwiv2PGyC9ICQnA9u3q4yoqWGuCEi3VF53OevGxN0czMuxvpUt4D0lq//ijBLt2GVW//+MPI3791ftSa+/FSq8H6ijXGBFeR1oo9vvvp/HUU7NhMlk/6s/OnorVq60Vz5kzWfVUSd26fPl19VOeAQPkLQnOpLZpU/ZFeB6e1C5YALRoUb2llnCPwsJCbkat9PPHH3+M5s2bO72f7t2745ZbbpFValNSUjQ9hwzqTfI5tv3O+y7uQ3hYuJO/YPhEfmfPlt+2J6C86otOx6omvD3bExNZJUeq6DqD3oh5Fk9VagsKXH9TEhOjfoEiqQ0MJKnNyWH/LnkZ57NnAxMmSIvA5gGQx3sdPz4NU6bMtFRAHS0m5dGhA/DEE2p5sbcFfJcu7IvwHu5Wan/7reqPbSu19q4XyclsvQgRuKxatQr79+/nyi2vpzYyMtLSZlBSov7Ukcerr77q6adNVJGrxVdRUl4CvY7J4fqT65Ffmo/7W9wPAJi1bRYuFV3C67e/DgBYeWwlisuLNd23X9oe8vJYO4Py3bNOxwK8JXFxln4QFkbVF2/gzx3FeFKrfLEiqfUPlZVASQm/9WP5cmD6dOu8kJINnn6a30ur09mmH/DjvXJyKi23MjKAVq3U86F9e/5z7dNHHiFGeA9/px84u16Q1AYmtj21LVq0QFJSktO/mT17NpYvXy6T2szMTFVPrfSd2g8CD9tK7dHLR3Gp6BJubXArAOCnQz/hyOUjeKHHCwCAr/d9jf25+zHnnjns+CtHseP8Dov81o2qiz0X91ju26AzYPOZzZqeh0/kd+ZM9cWJ97GRIABDhvjiGYUmktTyXqB8IbVmM9ulTklmJmt3IakNHHbvBubOlQuMycT+fX75pfr4oiJ++oG9NgOpkhsXB1RWhuH6dXW8l8FgDaoeMIDf3kB4D57U8q4XvpDa48dZ6gZJbfAzatQozJ8/X9ZTu2DBAssGDI6YO3cuIiMjSWoDlOul13G1+CrS4li72q6cXdiXuw/D2g0DACzctxCrTqzC5/d9DgDYcX4Hfj76s0V+SytKsSPH+kJi0Bmw6sQq2e2cAuuLij5aL7vdJrENrpXY7MPuAJ/I73PP+eJRQhdHUms75s1KraM+OUlqs7L4H13GxfE/Gic8x/nzwMqV6nnRvj3w8cfq43Ny2Ja2vHEeyrYEqX/WXpRfVhYT5qgo4OTJYejXTx3vNX06J0ybcBtJap1dLwKpUms08rPVCd/hKKfWZDJhzJgxGDRokNP76dixI2rUqCGr0LZt21bTc0hOTnb3NAgXsa3Uns0/i0N5h9CvcT8AwKbTm7DsyDK81e8tAMDaE2sx73/z8NOQnwAApkITlhxYYpHf+Kh4nLt+znLfep1cXpVymx6XDl1N68f/7ZPb48n2T1pu397odvTJsH7El5mciczkTIzCKKfnFVBpD4QcpdTae7EymfwrtVphMV5GO+OEKxQXs3535XxITgb++U/18UeOsIQCJfZ2AbSXfsDZ/h0Aq95v3mxdEOQs/aBmTevPFO/lGYJRarVCCQe+YceOHdi8eXOVcmq1Mpy3cxThF4rMRcgpyEGTeFZ9OnL5CFYcXYGx3cYCANadXIcZW2bg18d+BcDaDl7b9JpFfgFgy9ktlp+dyaxBZ4CpwLpQo0l8E/Rs2NNyu11yO3x010eW2+317fHVX7+y3E6JScGDraxZlTXCqq6wgmhvL10PIQiC6O3HCGR48V6iCKSlReKFFyb5TWqpp9b/KOO9pk0bBp0uTTYHwsP5W9Hu2AHwtnNv25Ztya3k8GG2Wl5Jw4YsQkxJfj4wf7729APC80jxXkqptXe9uHAh+KSW8B62ldqkpCS04F0AFBiNRkybNk0mtbxeWsqpDVwqxUqECayqkVeUh81nNlt6ZA/kHsDMbTMtbQfbz23H6BWjseMp1mrwh+kPjFg6Aruf2Q2ApScM+W4IDvzfAQDAobxDGLhwII6MZpmRx68cR98v++Lk2JMAgHPXz+GpZU9hxaMrAAD5JflYengpHs983PLcKsVKt6RVC4IgQBRFhxOTKr8eRBSBK1fkL0yrV5fg1Ckj52atypUAACAASURBVGgjt3eyqpDUBi5S+oHJxCqnPXow8e3Xb7bso/4FC6YCGA3AWvFs3Jgvv/Yqs/baElJSgMcfV88Pewk+sbEsopDwHs6k9vffWWWepJZwhTlz5mDixImySu2ECRPwzjvvOP3bcePGYdy4cSS1AUppeSmOXTmG1omtAQAXCi/g450fw5hlBAD8eelPDPl2CPb+fS8AJr8vrn7RIr+RNSKx4dQGy/1pqdReLLxouZ2iS8GdTe603G4Y2xDfPmTNp02NSbWILwDERsZaxBcAwoQwi5j7G5JfDfCklvdiRZXa0KKykm2+EB+v/l1uLtuAQZl+oNezsSlT5tmIL25+nwZgJgDrBgcmEz/mLzkZaN2aPyd4REezjR8I70OVWqIq2Muplb7ffvvtmDx5stP7admyJZ588klZpVbLBg0AEBcX5+5pEC5SUVlhyaa9UXYDPx76EY+2fRQAk9sRS0fg56E/AwByb+Si/1f9cW6CtW92zs45FvlNqpMk66k16AwwFVrbDPQ6PUwFJksfb3J0Mgw6g+V2Qu0E/CvrX5bj46PiYXrO+ve6Wjq8P+B9y+2I8Ah0NHT04H8N3xHS8utPqQ0LY5txkNQGB0VFwPPPq1MxatdmLQJKdDp++oHUb3n+PD/eKzy8Es2by+dDeTkQESE/MiIC2L/fU2dHaMGfUqvTAWlpJLXBzrFjx7B06dIq5dRGaew5ysrKQhY1SQcEFZUV2HtxL9rrWSbjjbIbeGXdK3jvzvcAAFeKr6Dp7Ka4/CLbSrJCrMAzy5+xyG9MrRisPbHWIqdJ0UnIvZFraW1IqJ2AK8VXYK4wIyI8AvFR8SgpL0FJeQkia0RCV1OHx255zPL72hG1sfnJzRAhQoCAmuE1LS0PABAeFo5nOj1juS0IAiLCFS8+1YRqKb+BVKn97Tfg3Dn1cb16sZ4+wj9UVgLz5qnnSF4e22ZbWWmtVQv45BO11Fy/Dty4oX6TEhXFUiyu3UxdiYuzzonCQiAlJQyAOt5ryJAwfPUVCB/iT6l1tPnCL78AU6aQ1AYqUk9tTk4OIiIi0KNHD6d/c/DgQTz33HOUUxvElFeWW3pWK8VKzN011yKM5gozenzWA9tHbocgCKgQK9D1v11R/EoxwsPCEVkjEh/+/iHe7vc2IsIjUDeyLgrLClFsLkZURBR0NXUQIaKgtAC6WjrUjqiNyBqRuFpyFfFR8agZXhM9G/bE9dLriIuMQ3hYOObeOxcVYgUiEAFBEHD9peuW5ycIgiUjV6KToZNv/4MFKEElv4EktVortVlZfPklPM/evSzSSzk3Fi+WpwsArPI+ZgwTVyWXLwP168vHwsOZhJw/Lx+vW5cdz6vQr1ljjfxSFm2mTx+G7GyK9/ImgSq1Wiu1+/YB6emefV6E+2zatAl33323rFJ722234TcNW9D169cPV69eJakNYHbm7EQHfQdLb+qzy5/F7AGzEREeAVEUEfNGDC6/eBlREVEQIGDCqgkYestQ6GrpEBEegaNXjspkNS4yDnlFeUiKTkJ4WDgSaifg4o2LSI1JhSAISNGl4OKNi0iPS4cgCBjbdSzKKqwCs3H4Rlnc17q/rZM9XylGTMLbi8mqCz75r5R1sx+lWbNIfPLJJNXvg1FqtULxXnKUCQfTpw+zG2llu02u7ZwYN46/nXXfvkx2lFy4wM+bNRiAo0fV4zk5avkFgLffBmrUsM4NZ5FeHR20QlG8Fx8p4cARwS61WqFPrj2Ls57apKQkLFq0yOn9NGzYUNVTm5am7d9tZGQkIqmM71PMFWaEh4VbZPbz3Z9jSJshiIpgF++seVn4fvD3iI9iizfuWnAX9v19H5Ki2Y5zy44sw+TbJltkNbFOIkyFJjSq2wiCIFgWiTWv1RyAdeMF6f76ZPRBYVkhksDu7/0B7yO6pvUF7Ojoo5aeXwCWrXol2iZpy0AmXMMn8vvbb0YAwIULRrz+um+l1pM5tVWBJ/uhCi/hYN26qVi1ajTatFG/eDRvzloQlDz4ID+2y2Dgy29ODl9+hw1jbQvKOdGgAf/5P/KIo7NznYyMNHz11VTnB4YIFRVsm+TY2OottVoh+XWdq1ev4vPPP69ST2193jteDmlpaXjvvfc8/dSJKrLnwh40r9fcIrMvrn4Rz9/6PBLrJAIAmn/QHGufWIuMuhkAgNc3v44eDXugWT22CDD3Rq5MVqUEBEl+JZlNjUkFwBINcm/kolHdRgCApzo8hVo1rFvW/jD4BzSMtb7gLHpQ/obKNqcWgEx8CffRGq3r0/r44cPAK6+4dx+BIrWEGqlSGxvL3776L3+Zh9On5QkHJtM0vPDCTKxYoZbAhAS+/Obk8OW3e3dWsVXOC3u7x738suZTI9zA1c0XNCQyaSYQpZbQhm1ObX5+PgYOHOj0b0pKSqinNsgxV5ghCILl4/tF+xehT0Yfi8w+uORBvNrnVbSoz14E/vbj3zDvvnmWRWXrTq7Dg60etByfHJ2MnIIci/xKlVpJfqWNF9oktgEA9GrYC+ZKs+X5TP/LdDSIsVZENg7fKIvrerHHi7Ln37x+c8/9xyC4HMg9gOLyYlX/8ltb3tLc9hEwzSEktcHHW28BW7fK0w/Ky4GNG9mCPiX5+fyEA5Z8oEavZz2zvLnAg7cdL+E9/LmjGElt9eXatWto2LChrFJbp04dFBQUOBXVpKQk6qkNcPbn7kdqTCriIlms2msbX8MDrR6wyGzW/Cy81fct9GjIFhDO2TkHSXWSkJjBZPZayTWcyT9jOV6SWUl+lVm1KTEpuFRk/Ujw0VsetVR5AWDuvXORUCfBcvuDuz6QPd8BTQfIbgdKTm11wXb7ZImNpzdi3p55yCnIwR2N78CE7hNkv193ch0O5R1SyW/dyLo4lHdI0+P6VH4TE1lgP0ltYLJiBbBrl1pkPvsM6NdPffyWLcDSpepxexst1K0bhmvX1AkHiYn8i8mSJWyhGeFb/Cm1UVFsYw+S2uDDtlKr7KvNycnBpUuXsH//fqdSGhsbixEjRkCv11u+UuztxqIgLCyMsmp9jLnCDBEiaoazVcVLDy9Fm8Q2lraAZ5ax6K7b0m4DALyw+gWM6jwKdze7GwCw/fx2tE5sbZFZqVIrYS+rVqJ7andZ68Dztz4vq9QueXCJbM493fFp2fOXKsKE58kvycfBvIMwFZhQr3Y9yxyQmL9nPjaf2Yy5A+fKxk9dO4XP97Bd6OpG1VXdr16nx7pT61TjBp2BO87Dp/LbsiWrFhK+4fhx4MgRtcSMHcvvJ/ziC4C33sNeWgVvl7G4OKCkRD3O7n8YHn10Ks6ckScczJ3LTzgg8fUsgVKptbe4NDkZeOMNwGj07GMTnuXf//43zp4963JPbevWrVFcXIzatWs7vH9BEDBr1ixvPX3CRQ7nHUZcZJylB/aDHR+go74jujfoDgB45LtHMLj1YDzU+iEAwML9C3Fvs3st8ltUXoSTV09axMcQLZdZqe1AIlWXiivFVyy3B7UYhBSd9c3PG7e/IUs/+Gfvf8qer1Kw6BMAz2K7fbLEngt7sOP8DtUbi1+P/4rB3w4GwP4/Kv/f1KtdD+cLFBFKYHNCwnZuSLRKaIVuKd1U4/2b9Ef/Jv2xCM4XrvpEfnv3NgII3YQDT2Ev/eCOO4AuXdTHv/46q9oq6duXL7/22gnsVXKHDwduv12eiuEo/aBnzzRs2EAJB54mGKSW1wPOgxZ5+Q5lpfb+++/XtJHCjBkzcPXqVVVPrdRHSz21gY25wowKsQKRNdjr8ZoTa5BQOwGZyZkAgMnrJqNtUls83PphAKyPsnuD7hjZYSQA1rYQLoRb5FcfrZdXZqMVW+ZGy9sQOhk6oU6E9dO/kR1Gym6/d+d7sjkjPQ8JaeEZ4XkKywphKjChab2msvHsc9l4ZvkzyCnIQZeULpYd5yTyivKwaP8ilfzaSqztHLD9ve3ckbgl8Rb8557/QB+t51bmWyW0QquEVqpxV2LefCK/GzYYffEwAYuzeC+l1GZk8Bd0jR8PvP++erxmTb78uiqzffuyncN4IsOja1f25QqUcKDGXrxXdZJarZD8+oY2bdrgwIEDsrFDhw6heXPni3WOHDmCOnXqkNQGKCeunkC4EI60OPYa8+X/vkR8VLylzWDCrxPQJL4JxnYbCwD45egvMOgMFvk1V5hx/Mpxy/1J6QeW2wq5TY1JxbWSa5bb/Rv3hwjrivvnbn1OJiV/7/x32fNV9m3SvPIeuTdy8c2Bb2AqNCGmVoxqsd6eC3swcc1EbHlyi2y8RlgN7L24FwBw/jq/UsuT2AYxDdBB3wEGnQG3JN6i+n275HbY+dRO1XhSdJJKpD1NwCx4q45UVgK7dp3Gww/PxqlT1o/6s7OnYvXq0fj55zTMmGFdKCYxdSr/o9/kZP7jmNRzDgDQpg0TWqXEZGbyjx8wgH0R3kWSWkliP/6YLRKszlJLuI/tjmL28mq///57tG7d2ul9jRgxAmazWValzcjQ1vsYzQvZJryGucKMsooy1KnJqqNbz26FKIqWBWHvZb+HGmE1MKrLKADAZ7s/Q83wmpZ2gBNXT+DI5SMW+dXrnFRqdQYcu2KN2clMysTFGxcttx9q/RBKy0sttyf2nCh7vv2b9JfdllIXCM9SVlGGC4UXUFBagNaJ8n/zRy4fwdQNU7HwgYWy8YuFFzFqBZsnzes1V8mvcrGghD7aWgGznQsSabFpmNRDHeuaFpeGXU/vsnsOYUIY4Kf3OiS/HuSXX4C5c63ywqR2HgB5vNfx49MwZcpMdOkyldtPa68yazBYt8m1FZbevfnHDx7MvgjfoJRae1m1PKldvty9xyaprT4sWLAAu3fvVkluYWGh6tioqCiLwLZr1w7hGhvlx48f7+mnTVSRs/lnUWQuskRkLT28FPkl+Xg883EAwNtb38b10ut4s++bAJj8mgpMFvkVIODI5SOW+zPoDJYqnXQ7+1y25XZqTCq2n9tuud0rrRfO5p+13H4i8wmUV1qrMVIvrwTv42bCOxSUFmDp4aV4tO2jsvHjV46jyWyW4Zkel46TY0/Kfl8nog42nNqguj9ZLy2nUquP1suOkUiOTsbOp3bCoDNw38zUqVkHf2v3N03nFCiQ/Dpg3z7gxx/VIvPggwBvPUZODjteDj/eKyenUtZOYBvp1bIl//k88QRLyyB8iztS6y4ktcGLsqe2R48eaMjbbUXBokWLsGbNGpnUSjm1tpVag8GAmJgY+pg4wDBXmFFSXgJdLbYoa8+FPbhQeAF3NrkTALBg7wIcyjuE6X2mA2A7iO27uA9z7mFZjRcLLyL7XLZFfg06gyy+SR+txy6TtZpm0Bmw6cwmy+1WCa1wueiy5Xa/xv0sGbYAk9snMp+w3O6W2g3dUq2Lh3ir6wn3Ka8sV/WkFpQWYOzKscgpyIG50oy1T6yV/b6sogyjVoxSya+0+BBgvbTKuLCk6CTkFeWpHjM+Kh7PdnwWSdFJMOgMqr+LiohStTwAbCOOjgYHW5YGISElv5cuATt3quWlY0dgyhT18fv2Af/8p3r89Gn+/fN6Y2vWDENZmTrey2AIQ//+wIkTzrfJlaDXOM8SCFKr1wO5ucBdd5HUVicee+wxLFiwQDa2YMECDB061OnffvPNN6hVqxZJbYBysfAiLhResPTIbjy9EbtNuy09tAv2LcC6k+vwxaAvALBA/uVHl1vkt2Z4TRzMO2i5P4POgF+P/yq7bVuVS41JRZG5yHK7o6EjyiqsW6IOaDpAtoo+Kz0LWelZltvpcelIj0v3wJkT9qgUK7Hj/A6YCky4VHRJ1a9aZC5CwtsJKHypUPbvulaNWpZIrzAhjCurReYiFJuLLTvYAUB0zWjERcYhqkYU9Do9bphvyLZMrhFWA78/9TsERU+BIAiWN1mhjlP5FQThUwD3ALgoimLbm2N1ASwGkAbgFICHRVHM9+Lz5CItFFMKS3IyMHKk+vgtW4BBg9TjxcX8+7e30MteW0KnTiyb1jb94MKFYejXb6psS9/Gjadi+vTRiIlhm3sQniUQpNaVSq3RSPFegUBBQYGsf5bXUzt16lRNAjto0CC0bdtWVqVNS9OWahJJIcY+pbyyHIVlhZZNF45ePordF3ZbUgZWHV+Fbw58Y8ki3XZuGz7b/RmWPsJCzgtKC7Dy+EqL/Cr7JpW3lQvImsQ3QUactd+6S0oXS38vAPRt1Bd9G/W13G5Rv4UlExdgImQrPoRnKKsoQ0RYhExWRVHEU8uewn/u+Y9qW+Jen/eytIs83vZxmazWjqiNcCEc+aX5lnkGsDdC9WvXR15RHirFSuTeyJW1HQiCgPHdxqO0olR2fwBw+cXLDjfcaJfcrmonHiJoqfx+DmA2gC9sxiYBWCOK4luCIEwE8NLNMY9QWQlcvmyVlRo1+JssrFkD9O+vHu/Zky+/9tIP7C0Ya9YMmDhRLTP2pDgpCXhI3h6FjIw0rF5N8V6eINikViuUcOBbVq9ejZUrV6pEV0tPbUJCAuce1TzwwAOeftpEFblSfAVHLx9F11QWTbPv4j58++e3mPaXaQDYblFvbXkLa55YAwA4d/0cPvz9Q4v81o6ojQOXrMkYqmgvxaYLBp1BVpltXr85HmhpnQ+dDJ3w3cPfWW63SWyD9+58z3I7oU4CsupkeeLUCQf8fv53tEtuh4jwCNl4j8964MjlI8grysPViVdlsioIApYeXopX+7yK5GjrCvQwIQz6aD3OXme906ZCkyXnWKJBbANcLLwouz8A+OiujxBZIxIGnQH1a9dXPU+p11sJ7TTnHk7lVxTFzYIgKE3tPgDSMqv5ADbAgfw+9tg0TJ8+DGlpacjLAwoK2C5OSvbuBe65h0mMbfpBhw58+XVVZhs2lKcfSN/tFWRSUoA3+fPOJSjeS41tvFd1lVqtkPxWDWVPbdOmTdGpUyenf7d161Z89NFHqpxaZT+tXq+nnNoApKKyAldLrlpEIacgByuOrsCIDiMAALtNuzF1w1RLZfbo5aMYtWIUfn/qdwCAudKMnw7/ZJFfZZuBctMFpew2jG2I1gnW1fXN6jXDjL4zLLfbJLbBxuEbZfc3puv/t3fn8U3Uif/H35+kd5uWtvRIaDkKcpRLQBCXU0GQWxBFv56Iuh64yrIsrq7Hrifo1wNh+SkqrqyKgn491gsRUEABBVFAROQqNKG00Jb0vub3R5hpJpOk0zZpk+b9fDz2ATOTphPJtq9OP/P5/EnZjgqLgtnk4QoK+czKH1fi9zO/w1Ziw9OXPo3kmGTV8SvevQKbZ29WpoSTnSo9hYKyAgCO95ZrrMrvD+f4BYBLulyC0+WnYYmzINygDmoA2HPHHrfB6npDIbWMpo75TZUkKQ8AJEk6KYTwOpfJm2/+BatXPwxJuht1dZ3Qsyewf7/2cXFxwPHj2v3eZj9ITNTGS+fO7h+fng58+aXX10U+5C1qN250DPnwZ9R6iln57xxTG7yefvpp/PWv6ml65s2bpyt+H3jgATz00EOM2gBVWlWK763fK+NWjxcfx6Kti7B04lIAwP6C/Zi1dhb23em4GmuvtGPR1kVK/CZEJWDPqT3K87kbhuAau86zG2TEZ+Cm829Stju164Qts7co22aTGa9f/rqyHRcRp5nei3xHntLLZrehd2pvzRCPqW9PxbPjn0W3pG6q/c9uexZ7T+0FANw95G5N/MrDT1zjV57qzSAMSgQ7e2LME25nRHB+T7jDK7W+VVlTCVuJDVa7FTb7uT/PbevhqxveJO+HY1Fb+w8AzwB42GPMOg8ncJ7SKyMDkCTtDV9JScCZM6AW1tpXahm1wcndmFrXsbXTpk3DYh1roI8aNQqLFi1SXanNyNC38lNYWEjd59vq6qQ65JfmK3eoF1cU47UfX8O8ixzTreWezcXl71yuXJk9U34G175/LXL/7JhMP9wYjjW/rFHi13VeWjli5DvX5ePydnpcOgaZBynbKTEpeGXKK8rHJ0Un4cDcA8p2bEQs7h9xv7IdZghDh/j65XXJP97Z+w5GdBqhCcthrw3DD1bHQgjf3vytsrKcrLiyGMeLj2vi12KyKPFrtVsxwDxAdXxkx5GqxThkr097HZFhkUiNTXW7YtjE8yY2/sWRbt6i1vnvzktgN0VTvwvkCSHSJEnKE0KkAzjl/eGPnPtzI2JjRyEjYzQqK7WBEh3duNkPyLcYteQLP//8M9544w3NDWMNjant378/erpb2tCNIUOGYIi7ZQ2pxVXXVuOrI18psxnYK+247b+3KRPsF5YXoueynihcWAjAMW7ywY0P4t6h90IIgaToJPyc97MqVk+VnkJtXS2MBiNSYlJQWF6I6tpqhBvDkRSdhPjIeFTWVCIyLBKmCBMW/GEBaqVahIkwRIdH4/CfDivnF24MV4ZAAI5pm6b0mNKC/4VCizzeOcIYodr/1Jan8PWxr2G1W7F80nL8IfMPquOv7X4NCVEJmvjVMzetu/3X9LkGF2VcBIvJoprqTfb0uKfdnr+75XSp+fwatUfgmHqhEfTGr4B6HY6PANwEYBGAGwF86P3DHwFQilmzBFavHu31kToXGaJGcBe17gK3JaP2m28cyzUzagOX65Vak8mEyZMnN/hxOTk5ypha+UaxiRMnKldpOU9tYJMkCVa7VbnaWVtXi8e+eQwPjXIMFamqrUK3Jd1w7N5jyr/d1LenovyBchgNRsSEx2DtL2vx78v/jQhjBJKik1BeXa5M12SKcMx/a6+yIz4yHtHh0YgJj8GZ8jNIjklGuDEcM7Nnoqy6DKZIE4wGoypehRA4du8x1fbDo9X3VPBKrf/sz9+PXwt+hdVuxaVdL0X35O6q41euuRKzz5+Ny3tertq/y7YLn//+OQDgaNFRTfy6XtGXZZgyYI4zw2wyux1L+8y4ZxAfqZ02yXnoCvmPu6h1jllfXal1ZRRGZb5iSw+LskCHOc6M2y5oeGlkPVOdvQVgNIBkIUQOgIcBPAVgjRDiZgDHAFzl/Vkc03s9+eTdDZ4Q6ReIUav3Sm1BAXDRRe6PUetbv349LnW5y3TkyJG64nfixIkoLS1l1Aawjw98jInnTYTRYIQkSbj8ncux5so1ytW6rku6onBhIaLDo2EQBjz97dOYd9E8xEfGI8IYAXuVXRWr7aLaIb8sH+lx6TAajEiLTUNeSR4yEzIhhEC/tH44XX4aGeEZEELg0YsfhSTV/8r517t+VS2u4Losq3xVmXyvqrYKdVIdosLUU+wt/345slOyMaqzegnRJ7c8iVU/rwIAvDr1VU38eopY5yVy3R2f1mMakqKTNPuXTlyKZZOWeTz/jHh9w52ocQIiak3qqFX2mcxIiUnRTDcnuw0+iF9JkjxNajnWw36Na699htN7NUJrR21LzH7AGQ58T579wNuY2szMTKxfv77B5+rVq5dmTK3F0/QqLgwG3tjR0nLP5iItLk0Zo/jk5idx94V3KzcH9VzaE1tv3qrc9HPLx7fgp9t/QnpcOoQQ2GndqYpVs8msTNckhFBuGpOvsMk3jcnPd1Xvq1BdW62cz5sz3lSFzI5bd6jOVx7vK3NesYp86/czvyPCGIGOCerVBR/Z9AiWfb8MBWUFWDZxGe4cfKfq+IHTB1BRU6GJ34YiNjM+E8UV2mn/Zw+YjbFZY2ExWdA1STvd0/RebibhB/hDtI9V1FTgZMnJ+pj1MAQhEKPWl1rkzg9O8+Xw1VdA796tH7We4jY9HWip+fUZv02Xn5+PRYsWNXpM7fnnn48+fbRj39zp0KGDZjYFaj1f/P4FhnUcpsTsTR/chCfHPKlMmTV85XCsv369EhUrd6/E9F7TlcUQjAajKlbNcWbVdE3yTWOZCZkAgL6pfVFUUaR8/oXDFqp+tfz1TV8jMar+Sq18M5rMNZjIf7459g2++P0L2EpsmNJ9iiYiV+xcgXZR7fC3EX9T7ZckSTWllyvXWTJkA8wDMLn7ZFjiLBiQPkBz/IGRD7g9z/PTz+fCC37EqG0c3vbsA3qv1NpsjlkrfCnQopb0cx1TW1pailvcrc7ioqamhmNqg5zNbkNidKLyq+Yl25dgZvZM5eaekStH4l+T/qXcqLNw/UK8Nu01DDQPBADsy9+HnOIcJX7lm37k+JUXXpDjV/5VtPx803pMU30jWjphKc5LPk/Zdh5jC0CZRkzmbjJ+ah7nKb0SoxM1wwmW7ViGsuoyLBi2QLV/a85WPLHlCQBAcnSyJn7lqbtcye81gzCgpEr7g/P0ntNhr7Jr9l/d52pc3efqxr04ajLn4QeMWt9h/HrRmsMPkpK8Dz1g1Aa3M2fOIDlZPe9kTEwM5syZ02Cspqenc0xtgNt0dBOyU7KRGuuYAn3e5/Nw84Cb0TetLwDHBPuLL12M4R2HAwDe2/8e+qb2VYIkMixSFavKKmLnfuPseod775TeKK0qVbbvGnyXaizk2qvWquZHlRd4kMmrn5H/7bTuREFZgWZu4KU7lmL+uvkAgLmD5+LFiS+qjocZwvDb6d80z+c8G4K1RHultndqb5RVl2n2X93nakzpMcXjlF7OPwyR7wXzmNq2ICTjtzWjNioKMJkcq8r17s2oDSauK4q5G1tbUFCA/Pz8Bse9JiYmYvHixUhPT1etKKYHo7fl5ZXkISY8BqZIx0wFK39ciaEZQ9ErpRcAxx3utw28DZd2ddwkuGjrIswdPBeTuk8CABwuOoxDhYeU+HVdRcx14QXXm4Yu63qZ8rkB4NGLH1WtMLVi6grV+cpL88pcV6mi5quurUZZdRkSohJU+zce2YjF3y52zIaQdSmeGfeM6vjeU3vx1ZGvNPHb4JReJvdTel1guQAPj3oYFpMFfVP7ao6PzRqLsVnaW3QSohI0507Nx6gNDm0qfgPhSq23m8VeeQV47DHffl7yj/vuuw85OTmq0NUzptZsNqOqqgpRDfz0IoTAggULvD6GWs53x7+DBO9wAAAAIABJREFU2WRG53adAQCPfv0ohnccjou7XAwAuOvTuzCr9yxlKdJ1h9chwhihxG90WDRy7bnK81niXFYVc9nu1b6XMh8qANzY/0ZVzC6buEx19/09Q+9RnW//9P7NfcnUgFOlp7A1ZyusdivS4tIwM3um6vh7+9/D+/vfx7tXvqvaX1xZrEzplRmfqXleeXy1qw6mDsqUXl3aaef8HNNlDIZmDNXs753aG71Te2v2k2+11OILrhi1/hEU8RsIUeuL4QdcWKplubtSe/vttyM2NrbBj33vvfcgSZIStRMmTFBdoZX/TEhI4JXYAJRfmo8wQ5gyfdaafWuQmZCpxMNdn9yFIR2G4MbzbwQAvLLrFQzNGIpbB90KAMi152J/wX4lfjVL5MZpl8h1Pn5xl4tVN4jdO/RexITHKNuPXvKo6nxdp/JyvspLvlFVWwV7pV2zzO2Pth/x0YGPNHMF77Ltwox3ZwBwhKdr/Hq6Icx5NgR3V2qzU7Ixo9cMzf4RnUbAOt/z0qyxEbGIjWj4axc1DqM2NLVqjgXKlF4tdaMYZzjwDXklqIb07dsXR48eVe2bOHEievXq1eDHHjx4sKmnRy3gR9uPiAyLRHZKNgDHeMm02DTlyuyj3zyKroldlSum205sw9Gio0r8miJN6iu1rnHrst09uTtq6mqU7Rm9Zqjegw+OfFC1otV1/a5TnW+P9j2a/ZrJu6KKIuw7tQ/DOg5T7d+RuwOT3pqEgrICDMschi03b1Edr66rxn8P/lcTvw1FbAdTB9V7QtYrpRc+vuZjmOPMbhfbyIjP0EwrRr4nR623oQeM2tDVIvG7YkXbjlq9GL/e6RlTa7VasWXLFvTv3/CvfR991HF1zfVKLQWeM+VnUF1brcz3+vnvn0NAKOMi//n1PxFhjMB9w+8DALy//32EGcKUYDlTfgYnS04q8esar66/ajbHmXHwTP0POEMzhqq+Cd7Y/0bUSvVfnOYOmas6X9fA4hU5/8srycOLO16E1W5FXEQclkxYojp+rOgYbv/kduy5Y49qf0JkgjKll6flcd2uLBafoUzp1S2pm+Z416Su2HbLNs3++Mh4TO7e8GIw1DSuUevuKq3NbsPp8tM+/bxGYUR6XDrMJjcxKwcuozZotEj83tbwYhsNaqnFF6hlLFmyBNu2bWv0mNoJEyYgPl67lKU71113XcMPIr9xvkK/P38/SqtLcYHlAgDAW3veQnFFMe4YfAcA4OWdL6OwvBCLLl0EwHFTkNVuVeI3ITJBNV2TxWTBT3k/qba/O/6dst0tqRt2n9ytbI/rOg55JXnK9h8v+COMov4blHxjmqxTOy7I4y+1dbXItefCareipKpEczPW0aKjuHrt1ZqwrKipwOObHwfg+Pd2jV9PwxCcp/SS3Mw1aTaZsWLKCs3+5JhkfHzNx417cdRkjFrSraLC+3ABHVp9FCqjNni5Xqm12WyYMmUKzjuv4Slyvv32W/zwww8cUxukiiuKYa+yK9Npbc3ZihNnT2BWn1kAHDG779Q+vDDhBQCOqb9+yvtJid+y6jL8YP1BeT5znBn78vcp2xaTRXXcYrJgc85mZXuAeYDqyuyU7lMwouMIZXtm9kzVGM1+af0Ap0XEXJdyJd8rrSrF0h1LsXD4QtX+/LJ8dHre8cNFcnQyCv5aoDqeFJ2EPaf2aIY3Od8QmFeSh9q6WlWMJMckY0THEaiT6mAQ9bOtmCJNsP7ZipTYFLdTeoUZwjDxvInNe7HkEaOWdGsoauW/FxY2+1O1SPzedBOjNpjoHVM7e/ZsrF27VrUvPT1dV/yuXr26yedH/uH8736k8AhyinOUlbq++P0LbDuxTRlm8MGvH2D9kfVYNX0VAOBY8TF8dOAjJX4ToxJxwn5CeW6zyYzPfv+sfjvOrJqTtEtiF8Qcr78hbGjGUMSG1w8luLzn5arJ+4dmDFXd+Z4Wl8Ylcv1EkiTk2nNV8wYDjiux09+ZDqvdiqKKIhy956jq60aYIQwPbnwQC4YtUMVoSkwKjMKIWqkWp8tPo7KmEpFh9d8ITBEmCAjYq+yqmwYjwyLxxCVPIDkmGRaTBRLUV3ENwoAPrv7A7WuQFwMh32HUkm6+jtrwcEdMerpyOmFCg0/RIvG7cmVLfBZqiN4xtUuWLMHs2bMbfL7bb78d06ZN45jaIFBSVYJTpaeQlZgFAPg572dsO7ENtw1yjEn68NcP8Z89/8GaK9cAAH7K+wmv/fiaEr81dTXYllv/a2hl0QV522XRBdd5bHu174ULO9QvpDCs4zDV0IKRnUZiZKeRynZWYpZyrgAQbgxv3n8A8kiSJHzw6wdKtPzz4n+qYrVOqkOXF7qg9P5S1U19kcZIfH30a5TXlAOA21iNj4xHQVmBstgH4FhqWZ6qzWKyoLS6VBW/QggcvuewalEOmesSveR7rXmjGKM2yLR01Mr7kpKABubSb0irD3sg/1i7di3ef/99XWNq5XiVhx/06KHvzvQxY8b4+rSpEZyv1NrsNuw+uRsTznP8xPt97vf490//xtKJSwEA3x7/Fou3Lsb6G9YDcNwg9uaeN5X4bR/THrln62c/cL0JyHU8ZceEjqoJ8nun9sYdF9yhbA/NGIqvb/pa2e7RvgceGPmAst0uqh0XXvATeZncDqYOmkiY8c4MvDH9DVVYCiEw56M5KKxwfIP604V/0sRqWmwa8krykJmQqfo4s8mMw4WHATjeg87xCwD/GP0Pt0MNdt620+trcP785BuMWtLN31HrKW6Tk5sdtXoxfgNYWVkZTpw4obo6O3DgQIzWMW3EoUOHsH37dlXUOo+n5ZjawFZeXY5jxcfQs31PAI6bgN7d9y7+OuyvAIDtJ7Zj/rr5yrRNx88ex0ObHlLiN8wQphoj29BUXq4rjHVJ7IIxXep/uOnZviden/a6avu9q95TtlNjU3F1n6uVbaPBCCP4jcxfPj34KUZ1GqWZZaLv8r7Ye2ovAODYvcfQMaGj6vjPeT/DZrdplq41m8xK/NrsNk189k/vj6KKIlX8AsAbl7+BqLAomE1mpMVqh53cNeSupr1A0o1RS7r5I2rT07Uh24pRqxfjtxXU1tbCaGz4i8GSJUvwt7+pf803f/58XfG7cOFCLFy4sMHHUctxvhGnqKIIXx76Upma63DhYfztq7/hnZnvAAAOFR7CVWuuwi93/QLA8Q1uxa4VSvymxqbixNn6MbUNxW0HUwdV0GTGZ+Jvw+vfW53adcJvc39TtlNjU5WZFwAgOjwaA8wDmv8fgXR57rvnsOfUHljtVrw0+SXN7BMLvlyA1VesVpZLljkPS7DZbZr4lZfIdY3fGT1nYHjmcJhNZiRFJ2nO55P/+cTtebpO+Ua+w6gl3Voqal3jNgCjVi/Grw+5m/3A3dja66+/HsuWLWvw+SZPnoyMjAyOqQ0C1bXV2F+w3zGrABzDCp7e+jSeHPskAMccpBf/+2IcvsfxK+KSqhLc8/k9SvzGhsdiw5ENyvO5XomVY1Ye6mA2mZFXmqdsp8WmYUr3Kcp2SmwKPru2/gazxOhEbLppk7IdHR6NmwfcrGwbhAEGY3B+EQtkVrsVOcU5sNltGNFpBNrHtFcdH/vGWDw19illFgzZ2v1r8e3xbwE4rvq7xq88xto1fi0mC3af3I202DSUVpdqzuf58c8rSzg7c11xjvyHUUu6lZc7otVTzDYnar2Nqw3iqNWL8dtE27dvxwsvvKCKWrvdrnmcPKbWeUqvUaNG6focffr0QZ8+fXx96qST81RKFTUVWL13NW46/yYAjqm+pr8zHRtudASrvcqOkStHoui+IgBAuCEcS3YswRNjnoAQAqmxqci156piNb8sX/kcKbEpKK4oRlVtFSKMEUiMSkT35O6orq1GuDEcpkgTnhv/HOqkOhiFEVFhUbD/za4MWQk3huP/Tf5/yrkbhEETVOQ/K3auwKjOo9A9ubtq/3XvX4eNRzcCAL647guM6zpOdTwqLKpJS+ROOm+S2xvC3pzxJmLDYz3GyyDLoIZfDDUJo5Z083XUhoU1PJ42RKJWr5CO38rKShw7dkwVsKmpqboWRygqKuKY2iBWJ9Vhl22XEog1dTWY++lcLJ+0HEIIVNRUIHFRIsruL4MQAkZhxK0f34rr+10Po8EIU6QJW3K2qGK1oqYCZdVliAmPgSlSPV1TdHg04iLiUFhRiKToJIQbw3HX4LtQUVOB2IhYGIQB22/Zriy8IITA9lu2q85ZvjlN5u5GImqekyUnER0WrbqZDwAe2vgQPjzwIWx2G16d+iqm9JiiOv7FoS+QEJWgiV95gQUAHhdgcLf/5gE3Y1zXcbCYLG5/iJl30Ty35+96wxk1X6BErSXOoglcRm2AaYmodRe3jNpGa5PfPauqqhAREdHg47755huMG6e+EnPJJZfoit/x48fj0KFDTT5H8r3q2mqEGcKUHzZe3vky5gyYA6PBCEmScOErF2LLzVsQYYyAgMDw14bjzMIziAmPQZghDG/ueROLL12M+Mh4RIVFITosGqfLT6N9THuEG8ORFJ2E/LJ8pMelwyAMSI1NVe6AF0JgbNZYnK08i5hwx3y1L01+STVl1Km/nFJ9k3r+sudV588xtf637cQ27LTuhK3Ehqk9pmJIhyGq439Z9xeM7zoe1/e/XrXfarfi57yfAQC59ly4cp3aTdarfS8MMg+C2WR2O4PBc+Ofc7vgBhdd8L9AjlqLyYL2Me0ZtYGCUdvmBFX86h1Tm52djW3btGuuu+rfvz9WrVrFMbVBYqd1J/ql9VPmfL3ns3vw2CWPwRRpAgCY/9eMX+f+qoyrfHDjg5jaYyrS49IhhIDVbsXJkpPomNBRGTdrs9vQNamr4+PPTe8lXz3LiM9AXkme8nx3XnCnannUddevUwXNf//nv6rzvbbftaptfiPzLXlKr5jwGM1Y2ue3PY+OCR0xo9cM1f5VP63Cv374FwDH9G6u8evpSqzzMAR3x6/qfZXbK/EPjHxANcWbK9fZGqj5GLWkG6M2ZAVs/FqtVvzlL3/RPabWefhBr169dH0OvUMcyD+qa6thEAblG8Gqn1Zhao+pyq+cJ7w5ASumrFBWlpq5ZibWX79eidXPfv8Mdwy+Az0jHdOBpcWlwWa3KSEkx6y8LKp8dU6+A35059HKBP0AsPjSxUiOTla2d9++W3XlVl7dTJadku27/xjk1u6Tjn8D+UZC2d83/B2Pb34cAPDUmKc0S+ieLjuN4opiTfw6D0Nwd6W2R3IPt1E0Z+AcXN7zco9XcId3HK7/RVGTMGpJN94oRg1okfj9+uuvlYCtqKjA/fff3+DHGI1GjqkNcj/n/YysxCzlxpwHNzyIWwfdqsTngJcG4O0r3lbuWH9227PondobA80DAQAFZQXIPVu/rKp8VU6OX3lbngs3Iz4Dp0pPKZ9/9vmzVTcFvTnjTVX8rJymXnrw8p6Xq7adw5eaz92y2V8e+hJv730bVrsV03tOxx8v+KPq+Ce/fYKSqhJN/CZGJSp/9zSW9qe8nzT7L8y4ELcNvA1mkxmjOmlvPJ0zcI7bc+/crrPbWRKo+eSolZfD9bRULqOWGLXkKy0Sv87z0iYnJ+uK37S0NI6pDTDVtdUQQii/3l37y1pclHEROsR3AOC4s/3PF/1Zidc7PrkDT415CiM6jQAAbM7ZjIu7XKzEr9nkuDIrx68cs/LHu/4KeljmMEioH3bw95F/x3lJ9fOVfn7t56q4umfoParzd53blHynsLwQB04fgM1ug9lkxtCMoarjy3Ysw+HCw/jf8f+r2n/g9AGs3O34IcRdXJpNZnxz7Bu3+w3CgLTYNLdjZqf3mo6xWWM1+8dmjXW7n3yPUUu6uUatp7lqi4r0PR+jtu0qK2v4vaJDi8Tv+vXrlSu2HFMbuH7J/wWpsanKsIFnvn0Gl2Zdiv7p/QEAk96ahPkXzcf4buMBACt3r0RUWJQSv/YqO44VHVPFq+tctc6/as6Iz8Dp8tPK9lXZVylDFABgyWVLkBhdf4Vv8aWLVefrGjH8DYBvubtSu+3ENhw8fVBzQ9h7+9/DrR/fCgC4sf+NmvhNjE5Ebo72RrGGxtL2T+sPe6V2uNOV2VdiVu9ZHuMlPS5d9V4i32HUkm6MWtJLT9RarY4FPdyNp87Ort/Xt2+Dn65F4nfMmDENP4h8rrq2GnVSHSLDIgE4lkTNSsxShgnM+3wexnUdpyyJ+/CmhzGz10zM6jMLALDLtgvmOLMSv/LqUDJ5TK3MEqe+UjvEMkS14tTcIXOREpOibL829TVVXN14/o2q83ed2J9852zlWRSUFSArMUu1f8ORDZj3xTxY7VaMzRqLt694W3Xcarfivf3vaeK3oYh1/UFINrjDYLw0+SWY48zKcBZngyyD3M5NK9/0SL7T2lErT93lHLXOYcuoDSCMWtLLl1FrsQCJiYAPLnQF7A1v1LCDpw8iJjxGufK6YucKdEvqhou7XAwAuOXjWzC602jMHjAbAPD+/vcxpMMQJX4raipwuPCw8nzyqlHO265L5Dp/45vcfTISIuuv5D806iHVr6AXDFugOt8/ZP5Btc0rtf6TezYXH/z6AWwlNqTFpuHuC+9WHd+SswUv7nhRtQoc4Ljaq0zpddbDlF5uIrZTu04YaB4Ii8mCwZbBmuMjOo7Ahhs2aPZ3TOiomb+YfItRS7oxakmvAI1avRi/AaS6tho1dTWIDo8GAGw6uglxEXHKJPePf/M4MuIzlCukL+54EVmJWbh36L0AHOMniyqKlPh1F7POww5cr+QONA9EbV2tsn1D/xtUN309fsnjqmB1vUHMbDKDfE+e0qusukz5wUW2++RuLN2xFK9MfUW1/2jRUcz9bC4AYLBlsCZ+XX+wkTnfEHiy5KTmeI/kHrj3wns1+/uk9sHO23Z6fA2MG99j1JJujFrSqzlRaza3etTqxfhtQUeLjqJOqlN+1bxm3xoYhAFXZF8BwDEvbXxkPO4f4bghcMORDTAKoxK/dVIdDp45qDyf6xhac5wZJ86eULYz4jNwvPi4sj0mawzOVp5Vtu8cfCcE6t+U8tK9Mnm4g4xXav3nTPkZbDiyATOzZ6r27z65GwNecix+0S+tH366XT2DQaQxEptzNmueTzWll5srtRnxGUiLTdPsz0rMwg+3/gCLyYKU2BTN8cToRFzT9xp9L4qahFFLuvkzar3NVcuoDT4hErV6MX6bobq2GpW1lcp0Wt/nfo+SqhLlyuvLO19GYXmhMgfp6r2rUVheiEWXLgIA5BTn4MTZE0r8WkwW/Hb6N+X5zXFm7D65W9m2mCzYenyrst0ntQ9+LfhV2Z7aYyqKK4uV7blD5qrOd3Tn0apt14UByDdq6mo0Cx7kl+Zj4fqFsNqtiDBG4KNrPlIdL64oxvx18zXx6xyo7uallRfqcLdfntIrMz5TczwlNgXrrl+n2R8ZFul2jC01n7uodTdXLaOWGLWkW2Oj1vXfPDtbvS/Io1Yvxq8XVrsVRRVFymIG6w6tw7GiY7h1kOOu9uU/LMdvp3/D0olLAQA/WH/A7pO7lfg1CAMOnD6gPJ/FZMG+/H2q7e+t3yvbGfEZ2GXbpWwPzRiqrDYGADOzZ2JKjynK9uTukzG5+2Rlm1N5+V9NXQ12WnfCareiuLJYc7X8ZMlJDHhpAGzz1UFqNBiVKb1MESbN85pNZpwsOamZYSE1NhXhhnC0j2kPi8mC2rpaVaAkRCZg440bNR8XFRaFl6a85IuXTA1g1JJujFrSqzFR6/weCPGo1Suk4remrgalVaXKCmL7Tu3DocJDmNpjKgDHDWFbcrbg2fHPAnBMwL/+yHqsmr4KgGPRhfVH1ivxazFZsOnoJuX5LSYLPvv9M9W287jKHsk9VFdqR3YaicyE+qtyM3rNUK1INcA8AAPMA5Rt+bzJt6prqzWzB1TWVGLeF/Pwr0n/0uwf+qpjGq9wQzhu7H+jKjpTYlJQUFagufqbGJWISGMkKmsrYa+yw15pV5ZlBhyxevug21FZW6m6adBoMKL8gXKP8SKE4JVaP2HUkm56Fl9g1BLAK7UBok3Fb35pPo6fPa7MM7sjdwc2HtmoDDv44NcP8Pbet/HeVe8BAH4/8zte+fEVJX6jwqLwS/4vyvO5xqvrGNsOpg6oqKlQtvum9cWk8yYp26M7j1bOBXCsLnVhxoX1Hx/fQZmpgfxDkiTsyN2BIR2GqCK1TqrDoJcH4cTZEygsL0TF3ytUsRphjMBrP76GZ8Y9g5jwGGV/bEQs4iPjcbbyLKrrqnG6/LRq+IjR4AiX/NJ81Q2AQgi8OvVVxEfGw2KyKDc1OnthwgtuXwMDx7cCKWqVvzNqA5M/VxRj1LYtjNqgEtDxW1NXg7OVZ5EUnQTAccPYlpwtuK7fdQCAzcc2Y/kPy/HWFW8BcNwc9NTWp/DVDV8BAMqqy/DJwU+U+G0oZl2nceqS2AXdk7or2/3T+uPhUQ8r2xdlXoRPr/1U2c5KzFItzxoTHqMKJ/KPl3e+jJziHFjtViyduFT131wIgfH/GY8j9xxRLZhhEAZY7VYUlBUAAPJK8lQ/iAghlPG0rvPPjs0ai6raKljiLKiT6jTnc+zeY26XRr6237XNfq3kGaOWdKuoaDhSGLUEMGrbqFaN3+KKYuw9tRfDOg4DAPx2+je8uutV5YawHbk7MH/dfHw35zsAjiu7z297XolfU6RJM4ZWE7cuK4xV11Yr21mJWbimT/2d69kp2ap5T7sldcPyycuV7cToRIzJ4oId/iJP6WW1W3F++vmaZWvHvDEGb854U7Ny1+ObH0dOcQ4A4IERD2hiVV5G2Tl+Acf74VTpKRiEAadKT2muwj83/jnNxwBQfnPgibvwpaZj1JJu/oha56DxNK0Xozb4MGpDWovG74GCA7jjkzuw4UbHZPcnzp7ALR/fgv137Vce8/6v7yvx6zoXqRwxyrbrIgzxHdAvrZ+y3TGhI164rP5XyZ3bdcYPt/2gbCfHJGP+H+Yr2xHGCNUUUeQf//n5P5jQbQKSY5JV+/su76vMdrH3jr3ondpbdfxM+Rnkns3VxK/FZFHi12q3auJ3dKfRqK6rhqs1V65BbHgsUmJTNLMzANp5jMm3KmoqlB923E3rxaglhXPUeosVRi0xakmHFo3fdlHtsOfUHmXbdZomOWblO9fNJjMkSVK202LT8MdB9cMKUmJT8OMff1Q9/zsz31G2o8KiMPG8iX5+VaGrqrYKBmHQhOMjmx7B9tztsNqtWDV9leoHEgBY9v0yZCVm4Q8x6hXfzHFmJX5tJTZN/Lou2iG7od8NGN91PMxxZs1yvQBUV++ddUvq1vCLpEZrKGrlvzNqiVFLujFqyZvaWiA/H7DbdT28ReJXnp4pJTYFxRXFqKqtQoQxAolRiRjVeZRyZ7wp0oTVV6yGBAkCAlFhUciZl6M8T7gxHA+Prh9zaxAGZMRntMRLCEl7T+3FwdMHYbVbMan7JHRu11l1fPx/xuPBkQ/iki6XqPZvO7ENXxz6AoBjLmPX+PW0ulhmQibMcWaYTWbV4huy5ZOWa64WA8Adg+9o7EujJmDUkm6MWtKrqYsvyH9n1LZtctQ6vx9OngSqz/02V/63NhiAlBSgRw9dT9si8VtVW4VoQzQMwoBj9x5DuMExrZQQAh9e/aHqsc7z2JJvVdVWQZIkRIZFqvY/991zGN5xOAZ3GKza//cNf8eHBxz/PmlxaZr4dV0uWdnvNMuBu8i9MvtKtwsvvHH5G15XkevUrpPHY9R0gRK15jgzOpg6MGoDGaOW9OKKYuSNp6itqVE/zmAAUlPr3wf9+gFpaUBERLM+fYvEr/O0Ts5hRL51oOAATJEmzbjlBesW4PWfXkdBWQFWTV+l3DAo23NqD+Ij4zXxa46r/7dyF7mZ8ZkorNB+k7vzgjsxo+cMWEwWt0MLPC2Py+WTfStQopZXaoMAo5b0kqPW2w2FjNrQ1cpRq1dAT3VGal8d/gobj26E1W7FrN6zML7beNXx57c9jz6pfXDXkLtU+2vqapQpvdxeqfUwDOECywWYbJ8MS5wFvVJ6aY7LNya6co1o8i1GLenGqCW9GLXkjRy1zu8JPVHbv78jasPD3T9vK2H8tgLnKb1SY1M1N2kt3roY8ZHxuP2C21X71x9ej6e2PgUA6NKuiyZ+Xad6c94POMZIn608qzn+P33/B7VSrWb/nIFzMGfgnMa9OGqyQIparigW4PwdtZ7mqk1KYtQGG0YtedPGolYvxq8fbTuxDZU1lRjVeZRq/xObn8A/vv4HAMe8tI9d8pjmYw+ePqjZ5zxkxN2sB/3S+uFo0VHN/jkD5+C6ftchNTbVbby4zqpAvtXaUauEbJz2Ki2jNsAwakkvRi154y1qhQAkyfE4OWrl90KQR61ejF8dqmurUV5TjvjIeNX+Tw9+iiXbl8BWYsOMnjNUM1EAjkU6fjv9myZ+ncfkerpSu/vkbs3+YZnD8PCoh2GOM2OAeYDm+LSe09yev7xCHvmW8+ILrRm1ziHLqA1QjFrSi1FL3rhGrc1WP/uB87+zPPtBiEWtXoxfOMbBbjuxDVa7FR0TOmpmnFi5eyV25O7AK1NfUe0vKCtQpvTqnaK9emoxWbDp6CbN/oz4DGVKL3ezHkzrMQ3ju47X7B9kGYRBlkGNeWnUBK29opi3qDWbzEiJSWHUBgpGLenVnKjllF5tn7uotdnqr9TKGLU+0Sbjt6q2CqVVpZqlab89/i225mzFgmELVPs352zGrLWzAADTe07XxK/rMsky1WwIbo6fn34+iiuKNfsnnjcR1vnaK74yU6QJpkiTx+PUNO6i1vUqrb+iNi0uTRuy534Akqf3YtQGEEYt6eWLqOWV2raLURuQgjJ+C8oKcLjwMIZ0GKLav+HIBsxaOwsFZQWY0G0CPr32U9Xx0qpSfH7oc038NhSxHUwdUFlTqdlUyi9tAAANUElEQVQ/0DwQH1/zMcxxZreLbXRL6sZVxFpAIEatso9RG1ico9ZbrDBqyV3UunvPMGpDk94xtUYjozYABWT8Hi8+jpd2vgSr3Yq02DQ8OfZJ1fH9+ftx31f3YevNW1X7TRGm+im93ESsxWRxO9VX53adMbm7Y0qv7JRszfEB5gFYf8N6zf7kmGRM7j65Ua+N9JOj1tvQA0YtAWDUkn7NjdrevRm1bRlvFAsJLRK/nmY92HtqL+Z9MQ9fXv+lan9RRREe3/w4AKBX+16a+DWbvK8sZhAG1El1muNZiVl4/rLnNfszEzLx8TUfN+5FUZO5Rq27q7QcU0sAGLWkH6OWvGHUkpMWid+LXr0IXdp1weF7Dqv2t4tqh32n9mke39DyuBaTxe2NXxaTBbl/zkVqbCrCDNqXFh0ejXFdxzXlJZAOeqLWZrfhdPlpn35eRm0QYtSSXoxa8oZRS03QYsMerHYrJElSLWGbFpuG/LJ81NbVqqIkOToZj4x6BGaTGeY4s+bjYsJjsObKNZrPYRAGzdK+1HyMWtKNUUt6MWrJG0YteSJJwJkzjvfEaZfuaN9e11MISX4D+YkQQhr00iCYTWa8O/NdRIdHq46fLDmJtNg0VdxSy2jNqHU3ppZRG8AYtaRXU6LW07+/2cyobWuaGrUWC6O2rfMWtc6EcHxvkJdSd/n6IISAJElev2i0SPz6+3OQWmveKMYrtUGmvLx+6h1vscKoJUYtecOoJU/0Ri3giFkPUasX47eNYdSSbv6OWndxw6gNTs5R6+2qPqM2NMlR6/yekKPWmRy1zu8HRm3b5hq1cus5//9fkhzbPohavRi/QYJRS7q1RtTKX7AYtcGFUUveMGrJkwCNWr0Yv60sUKLWEmdxG7jtY9ozagOFP6I2Pb3hoGHUBh9GLXnT1Kg1mx1RGxHROudN/hfkUasX49dPGLWkG6OW9GLUkjeeora62nFc/rd2XiaXURsaPEWtTB5zLd8o1saHqDF+G4lRS7r5M2q9hQ2jNvg0JWo9jalm1LY9jFrypKGolYVI1OrF+D0nUKLW3ZhaRm2AYdSSXpz9gLxh1JInjFq/8nv8CiGOAigGUAegWpKkIW4e47f4DZSo5ZXaIOAatZ6uwBUV6Xs+Rm3b5S5q3b1vGLWhiVFLnjBqA0JLxO9hAIMkSfJ4Gawp8cuoJd0YtaRXY6LWW8wyatsmPVErSYDRyKgNNYzaoNIS8XsEwAWSJHmctdg5fgMlajmlVxBg1JJectQ29F5h1IYmRi15wqhtk1rqym8RgFoAL0uStMLNY6Q+/+rDqCUHRi3p5Rq1nt4z5eWM2lDEqCVPGLUhrSXi1yxJkk0IkQLgSwBzJUna4vIYCY807nkZtUGIUUt6MWrJG0YteSJJjpi12Ri15JGe+A1rzieQJMl27s98IcT/ARgCYIvmgRvrTyi5VzI6D+jMqA0WjFrSq7lRm53NG8XaMj2LL8hR67z4Qr9+jNq2rjFRKy++0KcPvz4QAGDTpk3YtGlToz6myVd+hRAxAAySJJUIIWIBrAPwD0mS1rk8Ttpl3cWoDTT+jFouvtC28EotecMVxciTpkRtEK4oRoHFr8MehBBdAPwfAAmOK8hvSpL0lJvHtfo8vyFFjtqG7mhn1FJjopZTeoUeOWqd3xeMWgIYtRTQuMhFW8KoJb2aErXe4pZR27YwaskTRi21AYzfYMCoJb0YteSNa9TK7xU9UZue7vjaQW0To5ZCCOO3NTFqSa/mRK27uGXUti11dfVjauX3gnyl1vlrK6M29DBqiTQYv/7AqCW9GrP4AqM29DBqyRNGLVGTMX4bwx+zH5jN6m9YzkHDqA1ejY1ab0MPzGbHPJT8ptV26IlaIRz/v09JUb8/0tIYtW0Zo5bI7xi/QMtErbuwYdQGH19GLa/Utj2MWvLE04pi8oIcMkYtkd+17fh1jVpPwVJYqO/5GLVtF6OWvGHUkieMWqKgE5zx6++o9RQ0XAIx+HBMLXnDqCVPXKPWG0YtUVAJrPitqGg4Uhi1BDBqyTtGLXmiN2qFcHxvYNQStTmBE7+JiYxaqo9adzHLqCVGLXnCqCUinQInfgFGbVvmLWqd/+5tTC2jtu3Ss/iCEI7/OU/pxaht++SotdmAggLPj2PUEpFOgRO/+fmM2mDU2KjlldrQojdqeaU29DBqiaiVBE78BsM8v6GEUUveMGrJE0YtEQU4xm+oYdSSN96i1vnfWY5a5/cCo7Zta+yYWnmIGr8+EFGAYfy2FYxa8oZRS54waokoxDB+Ax2jlrxh1JInjFoiIrcYv62FUUveMGrJE0YtEVGzMH59jVFL3rhGrdVaP0+t83KoBoNjSi9GbejgPLVERC2C8atXc6LWXeAyatsWRi15wqglIgoojF9GLXnDqCVPGLVEREGp7cavHLXuQrahZXIZtW2fu6jNy6ufp1Z+PxqNHFMbalyj1nnZZOfHCOGIWUYtEVFQCb74ZdSSN4xa8oRRS0RECKT4LS1l1JJnjFryxFPUyuThKa6zH3ApdSKikBQ48RsZyagNRXLUOv+Qk5cHVFerx9QyakNPQ1ErY9QSEVEjBE781tUxatsSb1HrjFEbehi1RETUigInfgN9qjNycBe18uwHzjj7Qehh1BIRURBg/JIDo5Y8cY7aggLPj2PUEhFREGD8tnWNjVrn8daM2raNUUtERCGI8RusGLXkCaOWiIjII8ZvoGlq1JrNjqiNiGid8yb/Y9QSERE1G+O3pXiKWnn2A3laL+fZDxi1oYE3ihEREbUYxm9z8UotecKoJSIiCjiMX0/0RK18pZZRG1oYtUREREEr9OKXUUueMGqJiIjavLYTv5z9gDxh1BIREdE5gR+/jFryhFFLREREjRQ48fvyy4xacpAkR8zabIxaIiIi8qnAid/jxxm1bR2jloiIiFpZ4MRvoM32QPo1JmqTkx1X8ZOTHdtERERELYjxS54xaomIiKiNYfyGIkYtERERhSjGb1vCqCUiIiLyivEbDBi1RERERD7B+G1NjFoiIiKiFsX49QfXqJX3OZMDllFLRERE1GIYv43BqCUiIiIKaoxfgFFLREREFCLadvxKEnDmDGC11ket8zHHJ3f8yaglIiIiavOCM34ZtURERETUBIEVv4xaIiIiIvKjwInfjRsdG4xaIiIiIvKTwInfYJjtgYiIiIiCmp74NbTUyRARERERtTbGLxERERGFDMYvEREREYUMxi8RERERhQzGLxERERGFDMYvEREREYUMxi8RERERhYxmxa8Q4jIhxK9CiN+EEAt9dVJERERERP7Q5PgVQhgALAUwHkBvANcIIXr66sSobdu0aVNrnwIFIL4vyB2+L8gdvi+oqZpz5XcIgIOSJB2TJKkawGoA03xzWtTW8YsWucP3BbnD9wW5w/cFNVVz4rcDgONO2yfO7SMiIiIiCki84Y2IiIiIQoaQJKlpHyjEUACPSJJ02bnt+wBIkiQtcnlc0z4BEREREVEjSZIkvB1vTvwaARwAMAaADcAOANdIkrS/SU9IRERERORnYU39QEmSaoUQcwGsg2P4xKsMXyIiIiIKZE2+8ktEREREFGz8dsMbF8Agd4QQrwoh8oQQP7f2uVBgEEJkCCE2CCH2CSH2CCH+1NrnRK1PCBEphNguhPjx3Pvi4dY+JwocQgiDEGKXEOKj1j4XCgxCiKNCiJ/Ofc3Y4fWx/rjye24BjN/gGA9sBfA9gKslSfrV55+MgooQYjiAEgBvSJLUr7XPh1qfECIdQLokSbuFEHEAdgKYxq8XJISIkSSp7Nw9JlsB/EmSJK/f1Cg0CCHmARgEIF6SpKmtfT7U+oQQhwEMkiSpsKHH+uvKLxfAILckSdoCoME3JoUOSZJOSpK0+9zfSwDsB+cMJwCSJJWd+2skHPeocJweQQiRAWAigFda+1wooAjo7Fp/xS8XwCCiRhNCdAZwPoDtrXsmFAjO/Wr7RwAnAXwpSdL3rX1OFBCeA7AA/GGI1CQAXwohvhdC3OrtgVzkgogCwrkhD2sB3HPuCjCFOEmS6iRJGgAgA8CFQojs1j4nal1CiEkA8s79tkic+x8RAAyTJGkgHL8VuOvcMEu3/BW/uQA6Om1nnNtHRKQhhAiDI3xXSZL0YWufDwUWSZLOAtgI4LLWPhdqdcMATD03vvNtABcLId5o5XOiACBJku3cn/kA/g+OIbhu+St+vwfQTQjRSQgRAeBqALwjk2T8aZ1cvQbgF0mSXmjtE6HAIIRoL4RIOPf3aACXAuBNkCFOkqT7JUnqKElSFhxtsUGSpBta+7yodQkhYs799hBCiFgA4wDs9fR4v8SvJEm1AOQFMPYBWM0FMAgAhBBvAfgWQHchRI4QYnZrnxO1LiHEMADXArjk3BQ1u4QQvMJHZgAbhRC74RgD/oUkSZ+28jkRUWBKA7Dl3D0C2wB8LEnSOk8P5iIXRERERBQyeMMbEREREYUMxi8RERERhQzGLxERERGFDMYvEREREYUMxi8RERERhQzGLxERERGFDMYvEREREYUMxi8RERERhYz/DwEb5ZxgSvJdAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1124aaa90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(12,6))\n",
+    "\n",
+    "ax.plot(x, x+1, color=\"red\", linewidth=0.25)\n",
+    "ax.plot(x, x+2, color=\"red\", linewidth=0.50)\n",
+    "ax.plot(x, x+3, color=\"red\", linewidth=1.00)\n",
+    "ax.plot(x, x+4, color=\"red\", linewidth=2.00)\n",
+    "\n",
+    "# possible linestype options ‘-‘, ‘–’, ‘-.’, ‘:’, ‘steps’\n",
+    "ax.plot(x, x+5, color=\"green\", lw=3, linestyle='-')\n",
+    "ax.plot(x, x+6, color=\"green\", lw=3, ls='-.')\n",
+    "ax.plot(x, x+7, color=\"green\", lw=3, ls=':')\n",
+    "\n",
+    "# custom dash\n",
+    "line, = ax.plot(x, x+8, color=\"black\", lw=1.50)\n",
+    "line.set_dashes([5, 10, 15, 10]) # format: line length, space length, ...\n",
+    "\n",
+    "# possible marker symbols: marker = '+', 'o', '*', 's', ',', '.', '1', '2', '3', '4', ...\n",
+    "ax.plot(x, x+ 9, color=\"blue\", lw=3, ls='-', marker='+')\n",
+    "ax.plot(x, x+10, color=\"blue\", lw=3, ls='--', marker='o')\n",
+    "ax.plot(x, x+11, color=\"blue\", lw=3, ls='-', marker='s')\n",
+    "ax.plot(x, x+12, color=\"blue\", lw=3, ls='--', marker='1')\n",
+    "\n",
+    "# marker size and color\n",
+    "ax.plot(x, x+13, color=\"purple\", lw=1, ls='-', marker='o', markersize=2)\n",
+    "ax.plot(x, x+14, color=\"purple\", lw=1, ls='-', marker='o', markersize=4)\n",
+    "ax.plot(x, x+15, color=\"purple\", lw=1, ls='-', marker='o', markersize=8, markerfacecolor=\"red\")\n",
+    "ax.plot(x, x+16, color=\"purple\", lw=1, ls='-', marker='s', markersize=8, \n",
+    "        markerfacecolor=\"yellow\", markeredgewidth=3, markeredgecolor=\"green\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Control over axis appearance"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this section we will look at controlling axis sizing properties in a matplotlib figure."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plot range"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can configure the ranges of the axes using the `set_ylim` and `set_xlim` methods in the axis object, or `axis('tight')` for automatically getting \"tightly fitted\" axes ranges:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11253afd0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(1, 3, figsize=(12, 4))\n",
+    "\n",
+    "axes[0].plot(x, x**2, x, x**3)\n",
+    "axes[0].set_title(\"default axes ranges\")\n",
+    "\n",
+    "axes[1].plot(x, x**2, x, x**3)\n",
+    "axes[1].axis('tight')\n",
+    "axes[1].set_title(\"tight axes\")\n",
+    "\n",
+    "axes[2].plot(x, x**2, x, x**3)\n",
+    "axes[2].set_ylim([0, 60])\n",
+    "axes[2].set_xlim([2, 5])\n",
+    "axes[2].set_title(\"custom axes range\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Special Plot Types\n",
+    "\n",
+    "There are many specialized plots we can create, such as barplots, histograms, scatter plots, and much more. Most of these type of plots we will actually create using seaborn, a statistical plotting library for Python. But here are a few examples of these type of plots:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x1122be438>"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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WXU9RtmdtL9terLqWomzvsv2w7SfzE+/XVV1TEbbPtv0D24/l9e+ruqaibJ+VXxB4T9W1bIbtru3/zP8f/LDqeoqwfZ7tb9o+nP8beN9G6w/6cv5Dkv5U0r8N+LibYvssSV+R9BFJF0u6yvZEtVUVdpvW6q+jX0q6ISIulvQBSZ+p099/RPyfpKmIeI+kSyT9oe1LKy6rqOslPVV1EVtwTFI7It4TEXX7u79F0v0R8S5J75Z0eKOVBxrmEfF0RDyr+lxodKmkZyNiKSJWJX1d0uUV11RIRDwi6YWq69iMiDgaEY/n71/R2jfzRdVWVUxEvJq/PVtr56hq09e0vUvSRyXdWnUtW2DV8B5UtndI+t2IuE2SIuKXEfHSRtvU7j9ywC6S9NxJn59XzcIkFbZbWhvd/qDaSorJ2xSPae06jIciYqHqmgr4R0l/pxr9AFpHSHrI9oLtT1VdTAG/Jelntm/L21z/bHt8ow1KD/MzXGQEFGb7XEl3Sbo+H6HXRkQcy9ssuyS9z/ZvV11TL2z/kaTl/Dcjqz6/TZ/qsojYrbXfMD5j+4NVF9SjMUm7Jf1TXv+rkm460waliojpsvdZof+S9NaTPu/Kl2FAbI9pLcj/JSLurrqezYqIl2wflLRH9ehBXybpT2x/VNK4pN+0fUdE/FXFdRUSET/N/8xsf0trrdNHqq2qJ89Lei4ifpR/vkvShhMwqmyz1OEn/YKkt9tu2t4u6ROS6nhWv84jq69Keioibqm6kKJsv+n4/YryX5GnJR2ptqreRMQXIuKtEfE2rX3fP1y3ILd9Tv5bnWy/QdKHtcGFjcMkIpYlPWf7HfmiD+kMg4BBT038mO3nJL1f0n22vzPI4xcVEb+S9FlJD0p6UtLXI2LDM8rDxvadkv5D0jts/8T2X1ddU69sXybpLyX9fj61rG73zH+zpIO2H9dar/+BiLi/4ppGyYWSHsnPWXxfa7cbebDimoq4TtLX8u+fd0v6+41W5qIhAEgAs1kAIAGEOQAkgDAHgAQQ5gCQAMIcABJAmANAAghzAEgAYQ4ACfh/ShjBriS2ojYAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x112497828>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(x,y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([ 14.,  11.,   9.,  12.,   6.,   7.,  13.,  13.,   6.,   9.]),\n",
+       " array([  28. ,  123.5,  219. ,  314.5,  410. ,  505.5,  601. ,  696.5,\n",
+       "         792. ,  887.5,  983. ]),\n",
+       " <a list of 10 Patch objects>)"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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bgCu65XcCN69Yf3l31cG5wPmMv3T2tFdVH6iqF1bVeYyf+9ur6u3AF1i8vlgGHkpyQbfqEuBuFvB1wXjI5hVJnpkkjPviHharL8L//Sv3pI69G955LMnLuj58x4p9jm+GnzBfxvjKkwPA1fP+xHvWN8ZnsYcZX2F0F3Bn1wfPA27r+uJW4Dkr9rmG8afp+4HXzfsYZtQvv8OTV90sZF8Av8H45Gcv8HnGV90sal9c2x3XPsYfPp62KH0B3Ag8Avyc8ZvelcBzT/bYgZcCoy5br1tP235hSpIa54exktQ4g16SGmfQS1LjDHpJapxBL0mNM+glqXEGvSQ1zqCXpMb9L+T+/wUw8NjiAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1131285f8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from random import sample\n",
+    "data = sample(range(1, 1000), 100)\n",
+    "plt.hist(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x113dd10b8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data = [np.random.normal(0, std, 100) for std in range(1, 4)]\n",
+    "\n",
+    "# rectangular box plot\n",
+    "plt.boxplot(data,vert=True,patch_artist=True);   "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Further reading"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* http://www.matplotlib.org - The project web page for matplotlib.\n",
+    "* https://github.com/matplotlib/matplotlib - The source code for matplotlib.\n",
+    "* http://matplotlib.org/gallery.html - A large gallery showcaseing various types of plots matplotlib can create. Highly recommended! \n",
+    "* http://www.loria.fr/~rougier/teaching/matplotlib - A good matplotlib tutorial.\n",
+    "* http://scipy-lectures.github.io/matplotlib/matplotlib.html - Another good matplotlib reference.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/Week 1/Libraries Examples and Resources/.ipynb_checkpoints/Readme-checkpoint.md b/Week 1/Libraries Examples and Resources/.ipynb_checkpoints/Readme-checkpoint.md
new file mode 100644
index 0000000..73997ce
--- /dev/null
+++ b/Week 1/Libraries Examples and Resources/.ipynb_checkpoints/Readme-checkpoint.md	
@@ -0,0 +1,14 @@
+# Introduction to NumPy, Pandas, and Matplotlib
+This document provides a collection of resources to get you started with NumPy, Pandas, and Matplotlib, the fundamental Python libraries for data science.
+
+## NumPy
+- Official Documentation: NumPy Tutorial [Link text] https://numpy.org/doc/stable/user/
+- Medium Article: "A Beginner's Guide to NumPy in Python" by Krish Naik Medium NumPy Beginner's Guide
+## Pandas
+- Official Documentation: Pandas 10 Minutes to Pandas [Link text] https://pandas.pydata.org/docs/dev/user_guide/10min.html
+- Medium Article: "Pandas Tutorial for Beginners" by Lenin Setiawan Medium Pandas Tutorial
+## Matplotlib
+- Official Tutorial: Matplotlib Gallery [Link text] https://matplotlib.org/stable/gallery/index.html 
+- Medium Article: "A Beginner's Guide to Matplotlib for Data Visualization in Python" by Yamac Gellert Medium Matplotlib Beginner's Guide
+
+These resources provide a strong foundation for understanding and using these powerful libraries. Remember, practice is key! Explore the provided tutorials and experiment with the code examples to solidify your knowledge.
\ No newline at end of file
diff --git a/Week 1/Libraries Examples and Resources/Examples.ipynb b/Week 1/Libraries Examples and Resources/Examples.ipynb
index e8c672b..1d54090 100644
--- a/Week 1/Libraries Examples and Resources/Examples.ipynb	
+++ b/Week 1/Libraries Examples and Resources/Examples.ipynb	
@@ -1,2965 +1,2885 @@
 {
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "8WquFTv1VsII"
-      },
-      "source": [
-        "## Data Generation\n",
-        "### Using Scikit-Learn\n",
-        "\n",
-        "\n",
-        "#### 1. Data For Regression"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 865
-        },
-        "id": "qFxrI8pe0sBi",
-        "outputId": "50e0ff67-6700-4589-9436-94e7835f5964"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "(1000, 6)\n",
-            "(1000,)\n"
-          ]
-        },
-        {
-          "data": {
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",
-            "text/plain": [
-              "<Figure size 1000x1000 with 6 Axes>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "import matplotlib.pyplot as plt\n",
-        "from sklearn.datasets import make_regression as mr\n",
-        "X,y = mr(n_samples=1000, n_features=6, noise=0)\n",
-        "print(X.shape)\n",
-        "print(y.shape)\n",
-        "\n",
-        "fig2,ax= plt.subplots(2,3,figsize=(10,10))\n",
-        "for i in range(6):\n",
-        "  plt.subplot(231+i)\n",
-        "  plt.scatter(X[:,i],y, color='blue')\n",
-        "\n",
-        "\n",
-        "\"\"\"\n",
-        "# house price dataset\n",
-        "n_features =6 :\n",
-        "area of the plot\n",
-        "number of roo ms\n",
-        "etc etc...\n",
-        "\n",
-        "\"\"\""
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "bTd795ffiTy6"
-      },
-      "source": [
-        "### 2. Data For Classification\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 484
-        },
-        "id": "QQG3Ho0k0nzo",
-        "outputId": "8814cfa9-eae9-4092-916a-cacc3350f0ed"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "(1000, 2)\n",
-            "(1000,)\n"
-          ]
-        },
-        {
-          "data": {
-            "image/png": 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",
-            "text/plain": [
-              "<Figure size 640x480 with 1 Axes>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "from sklearn.datasets import make_classification\n",
-        "X,y= make_classification(n_samples=1000,n_features=2, n_informative=2, n_redundant=0,n_classes=2,n_clusters_per_class=2)\n",
-        "print(X.shape)\n",
-        "print(y.shape)\n",
-        "\n",
-        "plt.scatter(X[:, 0],X[:,1], c=y)\n",
-        "plt.xlabel('Feature 1')\n",
-        "plt.ylabel('Feature 2')\n",
-        "plt.show()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "ncXBbbxCigi7"
-      },
-      "source": [
-        "#### 3. Data For Clustering (Unsupervised Learning)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 484
-        },
-        "id": "AyGEnfO61DbO",
-        "outputId": "9ca241bc-d24c-4a9f-8364-7c49abee8f82"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "(1000, 2)\n",
-            "(1000,)\n"
-          ]
-        },
-        {
-          "data": {
-            "image/png": 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",
-            "text/plain": [
-              "<Figure size 640x480 with 1 Axes>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "from sklearn.datasets import make_blobs\n",
-        "X,y = make_blobs(n_samples=1000, centers=4, n_features=2)\n",
-        "print(X.shape)\n",
-        "print(y.shape)\n",
-        "\n",
-        "plt.scatter(X[:, 0],X[:,1], c=y)\n",
-        "plt.xlabel('Feature 1')\n",
-        "plt.ylabel('Feature 2')\n",
-        "plt.show()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "aiNTXG2GzMeY"
-      },
-      "source": [
-        "## Web Scraping Example\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "oMKlbtk5ipkx"
-      },
-      "source": [
-        "### Step 1 : Send a \"GET\" request to the URL.\n",
-        "\n",
-        "Status_code 200 indicates that the GET request has been successful. "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "inSdmA5Jy1zT",
-        "outputId": "fe6e9967-7939-49fd-d894-86e02545e0e1"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "<Response [200]>\n"
-          ]
-        }
-      ],
-      "source": [
-        "import requests as req\n",
-        "url = 'https://www.mykhel.com/football/indian-super-league-table-l750/'\n",
-        "page= req.get(url)\n",
-        "print(page.status_code)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "35p0kiErjOOq"
-      },
-      "source": [
-        "### Step 2 : Parse the HTML using BeautifulSoup"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "OdBVdAYQzSao"
-      },
-      "outputs": [],
-      "source": [
-        "from bs4 import BeautifulSoup\n",
-        "soup = BeautifulSoup(page.text, 'lxml')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "52zGR4Lfjd4v"
-      },
-      "source": [
-        "### Step 3 : Find the tag,class name,id of the required item/container/object in the html file.\n",
-        "\n",
-        "In this case, it is a table.\n",
-        "Inspect the webpage to navigate in the HTML document.\n",
-        "As an identification feature, we used the CSS class name of the table. *The word class is followed by a _ .\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "Xme8LfCAzUuY",
-        "outputId": "79dd254e-a093-4532-c70d-3330f6040a91"
-      },
-      "outputs": [],
-      "source": [
-        "table = soup.find('table', class_='os-football-table')\n",
-        "print(table)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "ES4AqVRHkOGA"
-      },
-      "source": [
-        "### Step 4 : Extract the headers/ column names of the table.\n",
-        "\n",
-        "They are present inside the first tag \"tr\" .\n",
-        "\n",
-        "So use table.find('tr') or store all the rows using find_all('tr') and use the first one.\n",
-        "\n",
-        "all_rows= table.find_all('tr') and all_rows[0] .\n",
-        "\n",
-        "Inside the tr container, there are several th containers that contain the text data. Extract data from each of them by iterating over a loop. Store their text data in a list named headers."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "tjWCvUCgzYpj",
-        "outputId": "96f3f33b-07cb-4854-acc8-54ee5f936d99"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "['Position', 'Teams', 'Played', 'Won', 'Drawn', 'Lost', 'GF', 'GA', 'GD', 'Points', 'Form']\n"
-          ]
-        }
-      ],
-      "source": [
-        "headers = []\n",
-        "first_row=table.find('tr');\n",
-        "\n",
-        "for i in first_row.find_all('th'):\n",
-        " title = i.text\n",
-        " headers.append(title)\n",
-        "\n",
-        "print(headers)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "mYqe8JZhljUM"
-      },
-      "source": [
-        "### Step 5 : Store the data inside a Pandas DataFrame.\n",
-        "\n",
-        "1. Create a blank DataFrame with the header list as the column names.\n",
-        "2. Iterate over all the rows starting from second.\n",
-        "3. Inside the row containers (enclosed by tr and /tr), the data is present in td containers. Iterate over all of them and store their data in a list.\n",
-        "4. Append the list to the dataframe. This is done by adding the list to that row of the dataframe which has index==current_length of the dataframe. The 'loc' function of pandas come in handy."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "NhSn8aF_zdwI",
-        "outputId": "0b4606b0-68a4-4d15-d411-5f0474d1b78a"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "   Position             Teams Played Won Drawn Lost  GF  GA   GD Points Form\n",
-            "0         1       Mumbai City     20  14     4    2  54  21   33    46      \n",
-            "1         2         Hyderabad     20  13     3    4  36  16   20    42      \n",
-            "2         3   ATK Mohun Bagan     20  10     4    6  24  17    7    34      \n",
-            "3         4         Bengaluru     20  11     1    8  27  23    4    34      \n",
-            "4         5   Kerala Blasters     20  10     1    9  28  28    0    31      \n",
-            "5         6            Odisha     20   9     3    8  30  32   -2    30      \n",
-            "6         7               Goa     20   8     3    9  36  35    1    27      \n",
-            "7         8     Chennaiyin FC     20   7     6    7  36  37   -1    27      \n",
-            "8         9    SC East Bengal     20   6     1   13  22  38  -16    19      \n",
-            "9        10     Jamshedpur FC     20   5     4   11  21  32  -11    19      \n",
-            "10       11  NorthEast United     20   1     2   17  20  55  -35     5      \n"
-          ]
-        }
-      ],
-      "source": [
-        "import pandas as pd\n",
-        "mydata = pd.DataFrame(columns = headers)\n",
-        "for j in table.find_all('tr')[1:]:\n",
-        "  row_data = j.find_all('td')\n",
-        "  row = [i.text for i in row_data]\n",
-        "  length = len(mydata)\n",
-        "  mydata.loc[length] = row\n",
-        "print(mydata)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "P30hCJrCmvAG"
-      },
-      "source": [
-        "### This is an additional step to include the data of the column \"Form\".\n",
-        "\n",
-        "On inspecting the website, we realized that each row of the form column consists of 5 containers with class names of os-win, os-draw and os-loss , each of which renders a circle of specific color in the webpage.\n",
-        "\n",
-        "Here, for each row, we went to the last column (the last column had tag td, so we found out all the td and extracted the last one using -1) and then counted the number of containers with class name os-win (find_all os-win and then find the length of that list formed) and put that value in the specific row of the last column."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "N5EJHrWizgXs",
-        "outputId": "6362cc3f-80b6-46a5-e653-0a19a20d198a"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "   Position             Teams Played Won Drawn Lost  GF  GA   GD Points Form\n",
-            "0         1       Mumbai City     20  14     4    2  54  21   33    46     1\n",
-            "1         2         Hyderabad     20  13     3    4  36  16   20    42     2\n",
-            "2         3   ATK Mohun Bagan     20  10     4    6  24  17    7    34     4\n",
-            "3         4         Bengaluru     20  11     1    8  27  23    4    34     4\n",
-            "4         5   Kerala Blasters     20  10     1    9  28  28    0    31     1\n",
-            "5         6            Odisha     20   9     3    8  30  32   -2    30     2\n",
-            "6         7               Goa     20   8     3    9  36  35    1    27     1\n",
-            "7         8     Chennaiyin FC     20   7     6    7  36  37   -1    27     3\n",
-            "8         9    SC East Bengal     20   6     1   13  22  38  -16    19     2\n",
-            "9        10     Jamshedpur FC     20   5     4   11  21  32  -11    19     3\n",
-            "10       11  NorthEast United     20   1     2   17  20  55  -35     5     0\n"
-          ]
-        }
-      ],
-      "source": [
-        "x=0\n",
-        "for j in table.find_all('tr')[1:]:\n",
-        "  data=j.find_all('td')[-1]\n",
-        "  di = data.find('div',class_=\"os-form\")\n",
-        "  spa1 = di.find_all('span',class_='os-win')\n",
-        "  l=len(spa1)\n",
-        "  mydata.Form[x]=l\n",
-        "  x+=1\n",
-        "\n",
-        "print(mydata)"
-      ]
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "8WquFTv1VsII"
+   },
+   "source": [
+    "## Data Generation\n",
+    "### Using Scikit-Learn\n",
+    "\n",
+    "\n",
+    "#### 1. Data For Regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 865
     },
+    "id": "qFxrI8pe0sBi",
+    "outputId": "50e0ff67-6700-4589-9436-94e7835f5964"
+   },
+   "outputs": [
     {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# End of Part 1"
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1000, 6)\n",
+      "(1000,)\n"
+     ]
     },
     {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "4cMGeud_slkT"
-      },
-      "source": [
-        "# Data PreProcessing\n",
-        "## Importing data from Sklearn/Kaggle etc.\n",
-        "Many libraries and websites provide pre-made datasets. Visit [Kaggle](https://www.kaggle.com/datasets) to explore some of the datsets it hosts.\n",
-        "\n",
-        "For now, we will be use the Sklearn Library to get the titanic dataset."
+     "data": {
+      "text/plain": [
+       "'\\n# house price dataset\\nn_features =6 :\\narea of the plot\\nnumber of roo ms\\netc etc...\\n\\n'"
       ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
     },
     {
-      "cell_type": "code",
-      "execution_count": 1,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "biTuX-N1bpcf",
-        "outputId": "c7475f33-6705-42a8-f833-0d9917ec3601"
-      },
-      "outputs": [
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "/usr/local/lib/python3.10/dist-packages/sklearn/datasets/_openml.py:968: FutureWarning: The default value of `parser` will change from `'liac-arff'` to `'auto'` in 1.4. You can set `parser='auto'` to silence this warning. Therefore, an `ImportError` will be raised from 1.4 if the dataset is dense and pandas is not installed. Note that the pandas parser may return different data types. See the Notes Section in fetch_openml's API doc for details.\n",
-            "  warn(\n"
-          ]
-        }
-      ],
-      "source": [
-        "from sklearn.datasets import fetch_openml\n",
-        "data = fetch_openml('titanic', version=1, as_frame=True)\n",
-        "df = data.frame"
+     "data": {
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IkubOdV97FEWRZ5Xgy0mv2tuJmpqIFi3yv1aa1pmnEQRKQAqR9SktqC7aVkGImYnJLACoV8QUmtHRcLqQRPAh9HVoiGjdOvn2Q3sA0INbh/CWLf4JoqLwFXQEXwsXemtiLoelAzJg6h0wDq8haTdU1gAIMdMxdxkAEA8yU2hkSCr4UNE1aA8A+rGXASCSm/42f75+X0FH8GXaOvO0gkAJGEeQRdulElF/v5qYQWQAMAuvNZWq9h83qroG7QEgWlSmv91wg/uoDZG6verqqE37OnMTwNQ7YByyQ9JdXUQf/nD1mqp8ng+XO8EY0Wc+Uy1mbnOXW1u5ME5M8IWdaVizBQDwXlMp7D+Xq3Zq0tDxIatrhQLRo486Ozgq9VYAAN6oFG89fNj5PbHOackS7ivI2qboqNWhV2ldZ24KCJRALOh8gMsOSV90ER8+t1977Vqir33N+Zj164nOP7/aCXFabP3oo0Td3ZV9dBSVAwCo46YtbgucvRZtJ2m/r74qt9/TT3NtsxO22CWCLACqUSne6kRPD9Gdd/J1Tl7JINzQqVdpW2duEjnGolgOny7Gx8epqamJxsbGqLGxMenm1B26q1WXy1x0/BYovv46Fyj7tfN59xTA4tjf/pZox45ap8EtiYTo4cFQtj8m2qOJba4HgmpL0kGB/frz5xOddZb/1LtSieiNN2rbGlaXdGt01Jhqj6a2WzdJ258s5TLRnDlEhw6pHyt8ia99jWj58nA+gynfl2lI22Ms5W8TBtWwkyPKatXiHG7ndbu2zNbSUlsJu7+/tkI2qlyrY6I9mtjmrBOVtkTN4GCtjtj1xm3r6ak93+RkOF0y8Xs01R5NbbdOnO7/Uimd99ngYDD/Qda24TMki6w9IplDnWMt2Govoqbj3FFUqybyX6C4ZIl6ZjwrBw9W/390lM9D1l1UDgCgTpTa4nfdMHopRn7sOmLXGzecaraFKXaZ1PcI6hO3+1+k2h4aSqZdTgjbCIuXbafVZ4jSLzQRBEp1zNAQn8J24YVEV17J/86dq0+soqhWbaW9nU9DGRkh6uvjf19/nb8eJDOeFyoBF4pAAhAtUWuLE2H1MkhZAztOaybC1FtJ4nt0A85ZtjEtKNftQ3iRJp8har/QRBAo1Slx9OxEUa3ajr3ugZi3m6TwoAgkANESh7ZY0aGXYRwvr1TAYeqtxP09ugHnLPukKSiXIew9n8sRyS5DS4vPYNKIX5wgUKpD4urZ0VWtOkhPYxLC4+XMoLcUAH3I2vcpp4S/li69DOp4+aUC9qu3QhRNkKULOGf1QVqCcj/Es/rll8OdhzGi8XH//YpFnswlaUwb8YsTBEp1SFw9OzoKprn1NH7lK0RPPsmdhyefrA0+ZJwHnXg5M+gtBUAvsva9YkWtnal2WujSy6DBhl9hSGthbDfs9eEEuopaBgXOWf2QhqDcD+uz+u67w51L1vc4cIBnvBQ6lVSnqmkjfrESU3KJREGmmWr6+uSytfT1hb+WTHY6v2Nls8vYM+e4XTuKra3N+bOYmFEqaky0RxPbnHVk7NtuZ0EyboXRy8lJxkZG+HvDw4y1tqrr0cCA3PfR2en9Pbh9xqDH6WBkRO47GBmpPs5UezS13ToQ2Rnd7v+kM8CFyZIbdhM61dmZXEbAOP3CtCBrjxRTexKlnsXJiaAPp6A4OSdugYXAL+Wt7IPd6dq6t2KRsYmJYJ/B7dgsY6I9mtjmemBwkAcffprQ1sYDDpVOCxHkdHUF00sn7SkUKteU1TMZ5zFoinA/57CzM+wv5E1Q58xUezS13boI03EaJar+RkNDtD5FEt9N3H5hGkCgZKHexclOEj071p7VkRH/c8sardPW3Mx7b8U1VB2eIJuTeMh+hpaW+hpZMtEeTWxzvTA8LGdnxaK3M2LVPNUOFrteeo0kE1UCpjD6wli4YE7GOYy6hx8jSvVHkI7TqJG9D7u6GOvujs6PkNWnKEj7iF8UoI4ScMU6p90+j9Zv4XCYazplp3MjzILOw4eJFi2qrAMS1163Lrp1S07tlf0MBw9i0TIAQdm/X26/Awfc32OsMv/eLbmAF9Y1QH7rbnI5opkziYaHiVatkju/k5YEWU9hPY9MFr6o1yQkvUYKxI9XWY+kkH1WHzkSfu1SEKz6FBVJ+IWmgECpTvEr2JqkaBHpWdBpz5rkJQRhcWqv6mfAomVQ7ySd4XJ0NFito/XrKzojsyh6926uR0uXyp3f/hmDBHP286QhCxmcs/pEteM0amQ15MEHk31GR50RMO1+YVIgUKpj0tizIxA9jWEQzo41AHETgqB49XiqZN6Lo8cIgDQTNDukzKhEsSjXhgMHgtc6EjqjEoAEGVEJUrjW6TxpyUIG5wwkzfz5yQdrMsSRETDNfmFSIFCqc9LWsyPI53mbwuIUgAgh6O1VO5dqj6dM2l47SdeQACAJwtTSkRmV+OY35QIS2YDKjlVnVAKQICMqqoVr3c6TpmlvcM5AkuzYke7ZHHFPQU2rX5gUCJRAKimX+fQbXdgDkHyeaM4cuWNnzybavLm2x7O11b/HU/SWyjpgaanQDUBc6Kil4zcqcdllcgFJ2JHmIKNEbm1vaeG609xcPRVxdFStTW4jM2mb9gbnDKigMk3Xb984OyhnzlTbH1NQU0BMySUSBZlmkkM1250gTNY7p214ONw1CgWeTc/6WmurfKaeiQme3S7JDFNpwUR7NLHNpqAzLa2f3jhl3CoWK7WK/DI/ybYxSBrk/v5ajbCnIW5uZuyEE+QzdMlobhqzkPlhqj2a2u60oVIPTWZf3f6G3+bnC1i3fD76NP31CrLeASnsPS1Hj+qrCh10zQGRfA/PFVcQnXyy/37XXltb+Xp0lI/0yKwhOnSIZ9Oz8uab8tnqpk0juu46732smbMAyDJW3dm6Ve4YGU1wG5UQ15uYILr+ej5aIzhwgOhznyP6ylf4/x98kLsoqhSLXFO2bSNaskRt3c3QENHy5TwDppVjx6r/f/gw0bvverdDjFitWyc3MoNpb8BKkIQqcaIyTVd2X5X1xF6ceKLcfldfza8lc71yuTpZTNQE+f3Tfs+EJqbALVHQi+OMU09LPi/XSyNz7iDFHUVPsGxtlJYW3tvi1Htrvy4RY8uX144MhdlUCkL61U4pFDCilFZMbHNaCVoEOmihQ5XrFQpcT5xsdcYM+bYK3XQa4bK/NjGhtyh2koU748JUe0x7u1VGapJApbCyahHmwUF9NiijZSq6FFcNI9nf36phPT21Rb/TdM94gYKzFtIuTkngV5HdaqCqD94gAmXfv7WVOyuybezs1OtsBBE+L2QDP6cpglnDRHs0sc1pRFZ3ZJ0Emal2QafRhdncdNNt6p+u6xaLZjgoYTHVHtPWbrvDq9K5mQQq03RVp/TKdGbqstGJico1gxaMdiPocgfZzm2ZAC9N94wXCJQspE2ckkamIrv9plfpzVCpcu0lziri09zM2P33Ry9ybltfn/d3smyZ/HeSdUy0RxPbnDZUdce6BVl7EOZ6sltjo7xuxhG0bdoU3++ZJKbaY5rancYRDT/6+uSfxyr7MhbvOiX7iItqW90IOiIo27k9MCCvYfbRvSDBW9TI2uNxcU/1A8mjml6WsUrq24UL/feXzcrkVeGaMblzCA4fJvriF9WO0YlXtrqhIb4uAYB6RlV3BIUCX/NjRaw9sOuEWHuwbh3RH/4QvCaSLOPj7u9ZdXPBgmCFbFXRVR/OSrnMP8OePVznFizAWkrTcbMfN1R9gKiQzQr78stcN1TOGWfmO6FTTz/N1za+/LLccX5+hpcmemXolSmSvWsX0c03q98z99xD9O1vV5+/VOJrQU1ZB4lAqQ4JKggyxw0NEa1ZE+z8puJV30CkPpZFPITgnICsEVR3Dh3iD9u77uL/l0kn3t0d7FpRsGdP8CBRhWJRf52VoSH+XZvs5IBqghQsFiRd508kXRgd9W6/VyesIJfj5xI2E2dpDtH2K66QS3xgb6sdP03M5XiJhSVLqhPcCB9DNlA7cEBuPytOWiwTvKUJZL2rQ4IKgt9xokcjiDGZjFd9AxUHadYs/ndgIHi2QADSShhHpLu7cv/HEXTo5LTT4nEwH35Yb2dKmCLAIL2EsZ+k6/x51f4KgvXZrSvznQqyQRJROD/DOiJIVJuRWCaw1IkI6Pzq46UFBEp1SBBB8OrNIArXS2UyS5d694ioOEjvvEO0aBHR5ZfDOQHZI6wjcuONXGeS7tVWIZfjJRdOOSXa63R2cn3QhV8PNWNEN91E9OSTGU0HnGGC2I+9SHKSuBVoViGf5yPUExOV+zef5yM8afNhmpv9R15kf9M9e9w7QLzI5YiamuT3l8EevKUZBEp1SD5PtGGDmiDccIN3b6Vpvby6+Pznvd/X1QNnWg8MAHbC9gYfOsSdmqR7tVVgjGjxYl7HrVDQ31vd1ETU3090//16zyuj5wcO8HowGPE2C1X7kRnRiBtr7a+uLvXjy2Winp7qGRtf/CKvV5Q2Zs6sXaNpR/Y3PeWUYB3ajBGNjcnvr6JzJnR8IVCqQ4aGiNauVTvmrbeq/28vMCabwCFLzJrlv7BV53C+ST0wADgRtjd427aKTZnEm2/yQE+sF7ASVBuKRaL9+4kuuyx8++yoOi8Y8TYH1WeSW5HkKJEpYCqKS3/4w+GvNzpK9MAD6RtNIuIdFn7PfL/fVIwIivNFTanEA1EZTOj4QjKHOqJc5ouigyx0fuwxonPP5Q7OwYM8YYPV4IpFfe2Mkw9/WH4ho53p072TLoj3li3jvXG5nB4hNqEHBgA32tt5D6nVbh56SN7Jzuf5CHeaEjb4IQKk5mbeQ2zVzpYW9XWduRzR3/wN0bRp3vsFTQqj6ry4LRgH6UOM7C5bVvtMEv/v6SE66yx+XxaL/L4V09OiximBSLHI1+A5TS/V4WiHfS4vXMgDuqjYssW5U9Zq3zfcwLN9Ov2mRNwH2b9f7npdXURnn83t+eBB//1LJX79efMqOkPEs925Jd7wS1CRKmJKV54oaapdkBSDg7XVk3Vufrn1c7lKAdkkCkC6bcPDvH5A0Da1tFT/X9QscKpnkM/rabNM0bk0Y6I9mthmk1AtyCxbdySN2/PPV9cU2bRJ7fi2NrlCjkFrqjBWqasSRBfj0CdT7TFN7Xa6P8S9FebeCdsmr3uus7P2mDD3qo6tUGDsiSfUjlFta7HoXEzb/hsVCrVFc616EUXB3t5e97pI4ve0f960FKRFwVkLaRKnJEiqOr2TUfT31wYXSW2iGJqbMUe1dXRwcQnyPaah6F9YTLRHE9tsEpOTtQ94p21ggO8fZ3FI3dvs2VwHBbKfZdkyvu/EhH/xRjfNV3FQguqiX1FMHZhqj2lrt1MhUB33TtC2yBTAtdqOIO5nuHW79VZ5n+bP/5wXlb/2WvXrWDsgvH4jIsZ6engHTG8v/yt+W7+g0upjRFkEV7azJ2oQKFlImzjFSRzV6e2bXTS8eqmSEDYnwVepUh722m1t3NlR6QVLSw+MDky0RxPbnFbcqrT39/vbgbXSe9y6pnsTveMyQWKpVHFi/Xr6/b4blQ6XILqIESV30t5unfeOKrIdBk6jK4zxe7WxMXm7lt0aGtT2FwGJzG9UKNTOIGpu5gGUn84GGX3yw03zk0bWHrFGKeMkkY1u40a+lsk6L/6733VedMxYvG0j4m2zFkwsl/kc7Pvuq8zJPvVUohUr/AvbqcIYT8iwY4f7PHEnSiX+vZpQnA0AN7wKmMqscxTJTBYu5McsXRpZU6UJuvbwgQeIzjuPqKGBJ3rw4sEH+TqFZctqr2Uv3qhSU8UvGY11PdnoKF+bevCg8+dVXXOAotrpQ+e9o4rs2tsDB6qvL+6j7duJxsf1tilKjh1T21+sxZL5jZz05PBhvq5z5ky56/kV91Wxd5F4w1QQKGWcJBb+t7ZWG8XAAK9PkBYef5zooov4v50ct9ZWXrNFJGGIgj17+HfyzDO1129r4+nbW1rgRIDsIOp3uDn6X/iC3HlEhs32dp4aW7a6fRT09PAFy0E7o26+2T8hw/TpPMPm9dc7OyyMVSdSUKmpIoPVyZk50z0JAJF8CmmvgBmdQcmh+95RQSUpg7i+030UlFyO6LbbeKa9ODuX83lv/RIByfz5PGHE4GC46733nve1rAlZvDqjGEtXyvhIiWmEK1HSPtwdJapz+WXWCfgdL6bGjIzw9ThJD3Hbt02bePt6eqIZIlcdrk7rsHRUmGiPJrY5TchMFznpJDnb6e2tPvfAgLr9NTeHn/YrpgDZ7be/n69DSkLbVBZhB50iF3bNgY41MKbaY9rbHfW948XkJLcp2evrXHtdLFbuO2HPmzbx1+NYHrB4sfPrwiY6O+Odaix+38FB7/1MXwqANUoW0i5OUTI5yZ0CGePo6KgWiSBJFwoF7rikef3Addcl176sJGQIg4n2aGKb04TO5AubNvFzWgOUnh61rJ6iQyiMEyQSSzjxpS8loy99fWoLtoMStHNH1xoYU+0x7e2O497xQqbTw7rGV5fdfOlLzveyV9a2OOy5rY0HSXGv5bbqSFhbTTMIlCykXZyiZHKSsRUr5IzD3kuUZCaZLG5ZSsgQBhPt0cQ2pwmd6bxFb7JTUoPly+XP09MT3NmyJmJwChhk053r3qw9wWlMy6trxMJUezSh3UnfO52d3vfG4GC0WS/tyVHcRlC7u6O15euu42nHk8gSHMfIdBqQtceGuKf6gfgYGiKaO5evyfFCVG22L8prb+draJqbo2qhHr78ZaK+PqL165NuSYVCgc8rtpJEhXMA0oDs+oMGnydSPk+0bx9fJ2NfRzA6SvT00/JtmjeP6I03iEZGiDZtIjrhBLnjli4lWryYF3c85RSiCy8kuvJK/nfuXK67b71VWbcTB3YNF9rd2lq9X9IalOQaGCBH0vfOeef577NlS3TXF2smRQHs9vaKTvT18b+vv070oQ9F1wYiosceI/rsZ+UKvgoKhXD+mlVHYKsVkMwho7gtnLYjswj38GGtTdPO//v/EnV28kWYSSO+z0cfrWSLQkIGUO/IZFBqaeEZrbwol4lWrXI+h5/W2TnttEqigm3biN59V+64wUH3BdWjo8lk4mOsVsOtGetkNSjqTHSyAbPKwn6gnyD3jg78Ej/lcjzRkl+WyDAwVpvUIJ/nn198H9u3804SGcRxcXD4MNHll6t1GAnsviBs1UJMI1yJYsJwt05UaoxYF+Hap5HongdcD1taCqmlGRPt0cQ2pw2/KT1xJn6xz63XOTUwiU0k0ZHFrdCoX42msOhaA2OqPZrabid0JyHySxyQxGadyupkG4VCupYmiBpKQY61+y5pXuuoC9RRqmNkayf19hLdcgvvPXBKs9nSIj/sG7SWSBbo7ubD8Bg1AsAdMaXHKS30xo18ykhU6fjt/I//wVOLC5s1vVf00CH52jZOWl8oOPfS797NR8gGBvgMhbCIlMM6UoyD5BgY4Kntrf6BSO8eZCSqXOb3ZNRcdx3R//1/E738MtHdd/vvv2ePd1kD8Vpa/B/G1Ebbenr4FGQxOrZ/Px9dF79ZlLaqWiIg0bprMQVuiZKlXhwZZHtHRaVnHWk2SyXG5s+X3z+JBYpht1mzqv9fKPAF4SZnfUkCE+3RxDanFbdexIkJ+fTAQbeGhtosoKUST+nt1Xtqwib03IugWp/P8+9IF2FTjJtqj6a224pfsgX7iIbMqGSUyRnsW0+PfLKV4WH/WTWNjYydfHIwm4pKb/zKH1jt2ckWW1r4CL8odxDGVp1QLREQ1Wh3KrLeffWrX2XnnnsumzVrFisWi2zJkiXslVdeqdrnvffeYzfffDNrbm5mJ554Imtvb2d79+6t2ud3v/sd+/SnP81mzpzJisUiu+2229gf/vAH6XZkQZxUkBUd4eSHnV533XXyKcgXLOCpOO+/Pz5h1LktX+7saGG6nTwq9ggNqQ+cHoRxbdZaJWkIlGbPZuyCC9RrMflln9Kh9bqn4QWddqNqj9ARPQSpWSaTLS/uqa+nn+49bU5MK1PNXhmkfloUmtPT452xWJQ2kOk4KZX4/rqmyKmmHddRd82NVARKixcvZo899hh76aWX2C9/+Uv26U9/mp1xxhnsnXfemdrnpptuYm1tbWzr1q3spz/9KTv//PPZ/Pnzp96fnJxk55xzDlu0aBH7xS9+wZ577jnW0tLC7rjjDul2mC5OqvjNLbXeaLJFV922xsZ0OBdJbkmn3DUNFXuEhmQfnYUj3bZZs7zn7ouH81/+ZfJ6EkR/ZNYK6Oi1T0vdFFV7hI6ER6UgrOo9qnJvxrEuSDzPow7gOjr0dxCJ79lv1Fa240S3f6OSdjzqWk6pCJTs7N+/nxER++EPf8gYY+ztt99mxx9/PBuwVO779a9/zYiI7dy5kzHG2HPPPccaGhqqena+9a1vscbGRjYxMSF1XZPFKSgyCyPDLPzDptdgVUl6EWQYwtgjNCRb6Bjl0Lk1NCTfhiCbqOvkhS6nLw11U8LaI3REHR2Bttu9I9O5m8/zkQ2dHStu9l4oRF+vSXwf1mf5unXhz2mdIuvlJ6h8Np3+jcrSkKhrOaWyjtLY2BgRETX/70TvP/vZz+gPf/gDLVq0aGqfs88+m8444wzauXMnERHt3LmTPvKRj9CcOXOm9lm8eDGNj4/Tr371K8frTExM0Pj4eNVWb7S384V6XjAWbZrNeoIxol274kkDKupjOdVvyTrQkGwhm3gmLo4dS7oFwXjgAW/7L5d5/SkdZKFuCnREHR2/u9s5ROIAIvf6Y089xRMLiKQwhUL49rjZ+6FD/FoHD/IEA1HURCsWKwkJFi7kadG7u3mpk7DnFVjPvXBhdfIDld/Tzb8pl3nyhyef5IkdnnyS/79cdj+XStpx2XpZUWtSbIHSsWPHqKOjgz7+8Y/TOeecQ0REe/fupWnTptFJJ51Ute+cOXNo7969U/tYhUm8L95z4t5776Wmpqapra2tTfOnMYN58+T2O/HEaNtRT0RtsCIDj1OxTWuRvCwCDckeWXC600JHh7ODIjpW1qzRcx3TMwRCR4Kh43f3OodbodvmZt7pa8+EFkcn79q1RBs2RHPuK65wztp2//08YAqKrKYG+T2t57Z22F59NdeXq6/277gVNfXcgk9R9Hb+fB54yRC1JsUWKK1cuZJeeukl2rx5c+TXuuOOO2hsbGxq27VrV+TXTCOyN89ll0XbjiS4/36e3jxuojRYkUKVsdr3xGtuzlIWgIaYi+h5fOqp6h7HtDjduRxRU5P+886eXTl/1Dj1+Lp1rNgR7Wvw8AiEA7NgQbh2Jk296IibzQVFOLhBkL132tuJ3niDB0b/e7CPDh/mgYNwvuNKJS5GUYpFXsBVdyrqt94i2rqVb/bf6EMfCn5eWU31C1i8zu2nK7t3u3fceo0eWtOO79jhX4CcqDIyFyWxBEqrVq2iZ599lkZGRqhksbRTTz2Vjh49Sm+//XbV/vv27aNTTz11ap99tjkD4v9iHzvTp0+nxsbGqq0ekY3cH3lEzzB2mrjrLvkaUDqIw4nwm6YU5/S/uIGGmIvXVFGZh3WpRPT880SXXhpdGxnjOqjbGXr8caLBwdpe8qiw9vh6dazYKZV4O91ih6zUOKoXHYlierZwcP0cay/nV6ae0j338MDo8OHq98SsiXvukZuuq6ujdM8e7ozr7oD8+78nWrSIb/bfKGgHkooPYg1Y/LD6Nyq64tZx6zZ6WCrx19vb5UfGrroqBk0KtgRKjmPHjrGVK1ey008/nf37v/97zftiAeUzzzwz9dorr7zCyGEB5b59+6b2eeSRR1hjYyN7//33pdph8gLKsIiFj/bFj/ZMJnFknjJ5a2urpA/2+y6jQrU+VlpRsUdoiNn097svDhY246c9S5ZEn/Chp4e3169GjOwmajOJhdTDw4x1d0evU9ZFzbILoXt7qxdph61xFAeq9lhPOhJlOmVxfvv9USxWEi343TuTk9weurr4NjxcydLW2up9r6okoNq0KXxWX6ENcaYvz+UYe/rpYLUmg/y2fqUZ7PeNaoILr0QLOpJNhEkuk4qsd5///OdZU1MT27ZtG9uzZ8/U9u67707tc9NNN7EzzjiD/eAHP2A//elP2QUXXMAuuOCCqfdFSs5PfvKT7Je//CX73ve+x4rFYt2k5NSB7IMvyVomad+sdQeicCJkstjFIRxxoGKP0BBzGRjgmaq87tXmZp7pyV7MOe6tq4vbnI5Aaf16HhTZ6635fRdht1yOseefr2hHmI6VtGfVVLXHetGRqNMpW6/jdn94vTc46BzoRGH/4jkY1q/J5XityDj1KEj2TdHZE+S3E691dNSmgLf7N6pBY9COW5lMiGHv5VQESkTkuD322GNT+4gibyeffDI74YQT2F/91V+xPXv2VJ3njTfeYH/xF3/BZs6cyVpaWtitt95aV0XedCD74BP7dXXFKwxp3uwPF+t3OTzMtzAOhWzVaT/hiDtFeVBU7BEaYiYy5QnStskWzfba/Oo1xbEJ7Qg6omQCqvZYLzqS1G8u41/o1ITmZrXnoGjfzTcnrzNRbC0tcr+njK/h91vqHFGSaa9XoCRTFsGLVARKaQFOjjNeBhF3pWwTNrvBywY4XqhOk5CdSplmTLRHE9ucFGmrjVRvU4qFFgwMyBUeD6JbSWOqPUbdbpXntq7fXNb51qkJPT3qz8HBwWDT2UzYLOW/PH8nHVMyVQrV6ui49RrlD+vzIFCyYKqoRomfuEVdaM3EzTqErEN0gk6TSNsaAtVpOibao4ltjhPrPdDbm7ytEiU/nS/IdsIJes4jtKO/39mhDKNbacBUe0zLiJKu31z2OajTnyiVKmuaZJ+DJo5wq2x+gZKuKZnWKXoy95eOJQlRTiVFoGTBVFGNChlxk5kfWm+bGFHSZbyyD4+uLrV54HESZFTNRHs0sc1xgbWN4bdcrnZtgC7Nkv19TJm6y5i59hjXGiXZ53aY31zlOahzhsp111WeexMT/s/Bycnkp8PGsXk9c8P4GgInHXFbd6mr4zbqddmy9hhbHSWQDmRr8RD5V8quJ/J5XgCNSF+abtn0l3ffXZve1avidlzUc/FbwJGt0wPcEfp61VX6z71nT6U2TW+v976yugXSi1eNGifC/OYqz0Gd9dIee6ySTvuDHyT60Y+897/nnniK0ybN5z7HC7Q61cwK42sQuev8sWP87+rVXF82bSIaGSF6/fXaAsFBkG131IXLEShlHHvRuW3b5MXNLdd9PVIu8wJoRPqMV/XhkaYApN6L3wK1ehrAHVE75JJL5PYvFuXPLTQmnyeaM0fumKidDhAtQZ7bQX5zledgmGK1XoyO8ppLInA65RSir3yl8twpl4keeED/ddPIwYNEV19dG+iUy0S28l++WH0Nv2d9Lsf3u+UW3tmjs+NW1keKunA5AqUM41R07vLL5Y4VIih6I0dGiLq6omqpGYjvRJfxqlbGTlMAUs/FbwHH7x4A3syezZ2Rxx7jPbMrVnjvL4o+7t5NNDxM1Nzsv6+1+GRanA4QPbKjiIIgv7nsMS+/zLVCpi0NIT3Sw4d54DRnDvd/7rmH6J13wp3TRESg88Uvch9wzRq1462+hkrnum78fCQnnYsCBEoZxW2o1F7t2g2rCIppXuvWqTn2qqS94rv4TmQCnOZmHsx4BTSq0ySI0hOApGVIHCRHkN+2UNDfDlM5coT3/C9aRHTZZf5BJ2NEGzcSTZtGdNFFRN/+NtcNu3aI/2/cWK2paXE6QDzk87yXP6rfXLajT0znWrOGqLPTWQMKBaLBQaLNm9Xb4cShQ0RLl9bPaJIdsXrngQeCd2YJX2PbNrn9o3jWe/lIbjoXBQiUMkiYKTG5HBe/crkyXU84+0EcexWSHiVxw/4wkfkeDh/mDpB9rq+doNMbkw5A0DsNZH/b3l6ivj4+Kr1vH9Gtt4bvOa5H7A6mm3aIqXz2NQJpcTpAfET5m6v6A7t3c8f9m9/kI6JdXXwbHiZ6803eubhjB1Fjo3pb3KjH0aSkiOpZr6pzkRAsV4RZmJohJyhhU3HaM8SUSjz9pMgus3x5sMrRpm5uaS77+/3rMsimYFUt9BumiJsOwhS/NdEeTWxz1AS5B7xqYmALpiUTEzwl+6pV/O/EhPfvlrbyAkEw1R6TaneUv7lq1st8vjqdNbJmpnsbHk5HoXuZTL9RlSqhgG02ClNFNSgoFqt3c6r+rCLuKkISJgCJm6DFb020RxPbHAcq98DAgF67FNe0d+w0NiavGVFtdvsPWvQ6LeUFgmKqPSbZ7ih/cxGsX3qp/L08OOheqgRbPFtzs/f3XywytmkTL/Ar9MeuR2mpvxZlqRLS3NZUYqqoBgXFYvVtToFJUHGXHQUKGoAkQZCeShPt0cQ2x4XMPTA5qb9OkLiG1QEcHmastTV53Yh6E/WRwha9NhVT7dHUdnsRdESoVMJIUtgt7Myenh65gtREvEPK3imVlpHooFooa485xhiLYYZfooyPj1NTUxONjY1Ro84JsCmlXOZrY0ZH+e0CwjMywhNaiO82yALJvj5e90iGoSG+zsx6nWKRp99csoSvl0rLWoJymSeX2LOHz1P2a5uJ9mhim+PE7x7Yto0v6NZFTw/RnXfW3me6r5NWnniC6Etfctchsdb09dfToxM6MdUeTW23GyJpVBr9jFyOr3sSCazS2EZd5HJqn8+qD1u21PoabscQ8aRe8+bJPeuDoOpP+PlkXlooa49YUptBok66UI+I5AlhUiLv21ebIMMNa1r2jg6ilhaiAwf4wlungnD2ellxJsZIQ/FbkCx+94Du5CPf/rbz60knOYmLVauQnh8kSxrqqM2a5f4eY0TXXMMd+9NPj61JWlDx2y65pDbRQVsb71D1QiTxsPoamza512kTv/Pf/i0vMxPFs96ppI1fQqw4SpUcF/xQkDbskfjTTxOtXVvftU6OO45ocjL8eV5+mWjrVqIf/CDY8fl8dS2DUokHs14ZW/J53hv24IO1DyNRJ+GZZ/j/7T1CMucHIC50Z0TavZtr3cKF0V4nrYyPy+2nGjiKDheREnjhQnR+AGfSUEftH/6B6Mc/5s86a+mThgZem2zjRv7/WbOITjiB6N13k2ilOqUS0fz53Ifz49lnifr7eYAjfL8DB4iWL3c/Zu1aPtr21FOVUZuFC7ndHzjgfpw16LBrb1jcRietvo6TPxNLqRLtkwVTSBbnBdtxW8hmzVb3/PP+Wdqi2KZPZ2z27OTn86Zpk1lHIBI7eJ2jUDBvnYKJ9mhim9PErbfqt6GOjuprTE5yjYPWVDaV7JiDg7VrEIj4a2nTEVPt0dR2O6EjaZRfMgGvzZo9T6xTvOSS5G0uyFYq8fWVItHGxITaWstSqbKO2s9vEN+d/fjBQfnftK9P770k4+u4JbGSXZPvpIWy9oipdxnArbjs7t28kOHhw0TTpxNdfz3RwYPO54hyit7EBC+uWI+49cQyxv92dLhPk5MZUj50qHIu1fMDECVidOILXyD62tf0n//JJyv39tAQ0Zw5RIsX16/WWFEtJDo0xAt0HjpU+54o3uk1/QXUHzpGb/2mh3lRLvMpYEND/Dl74AAfXQnLrFl6aznJMD5O9NZblanLO3bwkRRZdu8muuce/m+ZkT67TyBGbX7zG7nrnXKKfNtkCDN9Lo5C2giUDEdmnvC11zoHUlaam50rZoNgNDcTrVjhHaR4GT9R+PUWfucHICqsc80feiiaaxw4wJ0DLye/HlEtJFou82DWj9Wr0eliOjrXsvo5qDI89hjRySeHK0Dd0UF09CjRzTcHPwdR5XO88w4PXFpa+FSv2bPDnVeG8XHeqf3//D/8d1EJkgTd3VwLg/gNYtzlm99UP1YHYabPxVFIG4GS4cj0Hrzzjncg1djInQw4Gvp46y2ixx+X23fLFufXda23qJcF7kCeKJN/uI1wR0F3N9FnPxv9ddKMffG1U8V6r997+3Y5x0ysCwNmEmShvBcyDqoMhw/z9URBEJ2BDz/sPltG5VxWDh7k302cI9TPPst/F+t6ZhVuuilch7fsd7h/f/BrOCHr67jt197ONc+e1MJJC4OAQMlwdDjBsguDgTxegamdjRudH1Y6euyI6meBO5BDp8Nkd8CPHo0/E9bvfx/ftdJGqcQDmJERXn5gZISnwbU6Bn6/t8ozBJ0uZuLWeSGmXAUNlrwc1MFBvpVKwc6twvPPR3+NOAka9B04wEuIFArRLqfQ7VPomD5nzd7npoVBQR0lw9m6lWjRoqRbAcLS1uac51884IiqnU9RN6FQ4D1yTlac1loqJtqjiW12wi2zkHhAqfS+udX68sqaBPTS00N0113Vr1mzn776Kh91s2P9vZub5WtPiXpySWOqPSbR7jB1ZlSu4Vb7plzm02+DjpKA4KjWVyIiamrineeyPoVq3SM3vHwdIj0jQ3ZQR6kOGBri62CA+bitJfLrsXv0Uf7/qObmguzgtZ5RNfmHWw81gqR4mTev+v/20SOnIImo+veeP79WX5xobub3BtYpmUUcdWa86qjl8zzRCoiXxkaik05SP074lDI+hc7ZCVFPnwsDAiVDiXMdQJYoFHgvbJxGt3Sp3H5u01q8hpTTLC4gXehymHQXmlyyhD+EURxbnVdfrfxb9Zkgfu/77iN6/33//Q8f5rMXwqxrAfETS50ZHzD9O35EJj1VLr1UzqcQSXScsi0HzZIZ5fS5MKDgrIGkoSK2iVx+OTc+Iv6wjxoxTP35z/PRHz+8Hiaix86J9nbubOoY/gbZRZfDpKvQZC7HU3xfcYXzND7gz7p1ROecw+0/6DPBbdTJDb8CkCBdhF0orwOxBmV0FH5LmhHrgPJ5b5+iXCa68Ubvc914Iz+Hqh/i5eskBQIlA0lDRWwTGRzkDlmxGP33Zx2mXrjQ+yEhAqowef7TKC4gXehymHT1PDNGdMstvMabNdj/h3+oZNMC3jDGM13Nnh3fM4ExrlkdHcEcIRAvfkGK6vMnyJoUkSFv2bJg62ZA9ORy1dPqvHyKbdv8syQfOsT3u+gifW1MCky9SzlOaV11OCqLF4c/h2mIAnVu6biD0NhI1NlZm9nHOkwdR55/APzQVZhPNuBqafHfx1rMNJ/n1xajvkCOAwfkp/fqAjXazEHn8yfMmhS3aeIgedra1EaIt23Tu1/aQaCUYtxEyTovPSjPPx99Csm0smmTvnMddxzRvff6z6vFWiKQNLocJtmAa3SUaHiYF5T0QxQz3b4dCSGCEGetFytIF24GOp4/OlKMizUoXV3STZ9i+XL1Y0AtXV3cB+rt5X/Tsg4ozWDqXUpxS+M7OsrnlDc2hq9/JKox1xsHDxLNnEn03nvhz3X4MHfuFi70n/rmtpaIiPe8YH0RiBrhMNnXA5VKPEiSeVh6TaOxBlzTpvF9ZRYUi2KmcLzNAov0zSHMWla/jJmqUzGDFEX9X/+LaNUqom9+sz79Fl1cdJH6NH37dEvZDvas+DEIlFKIjCgFrWRt5fDh8OcwFR1BkkDFubPP+3VaxF4qcUcUPTwgCnQk/5ANuFSLmcLxNgeZaZogXQRdy6qSMdPr/GGStoyPE33jG+rHAU7QtdBhfrO/+zuiL39Zf82luEGglEJkROmdd+JrD/AmqHPnNWqIrFIgSmQdJq8Hm0zApWIb4vhSCclqwjBrVjzPB6yrrB90ZMx0e96B6Am6FjrsbyZmCixcaHanMNYopRBZUWpujrYdwBvZxe9O6Cz+CUAUyCzc9io0ScRtQ2bxtujpFNP66nHtpC5mzJDfV3zPDQqeQD5P1N+ffucG6CNsxkyUNEmWIGuhdf1me/boWd+WJAiUUoisKK1ejUKNSRE2W922bdFXSwcgKLoebPk80de/7r/fgw9Wj1Q980xtJkngTS7HSx8cPCh/TKnE17yqTOXevJnossvU2wfMxS+BCxG/9+bPd34PJU3iZ/HicEVbdf1mp5xifqcwAqUUIptV6vbbecFBe1aptjaeshpER5hsdUNDPE25DFjcDuJG92hnezsfgWhsrH2vsZGfq7m5+nwiO9bwMM/S1NXFHXoET86IZ8VVV8ntv2oVd6C+9jWihx6SO6a5mdeiW7YsWBtBvDiVFgmKV8ZMwYEDRGed5dyJgudY/Jx1lvtIvwxhfzPhpxKZ3ymMNUopxK84G2O85+aDH+Q9vILmZu7g3HknP8f55/PqyH6FwYAaxSLRb3/Ls3qpojrnF4vbs0/aFrjqWrgtGBoiWru2OkunyIg3Ps5HZTdu5HZ11VV83dOCBbze2Re+UK1xp59O1NNDNG8e0be/zZ19UEmi0dzM//qxdClP5rN8ubwWYeaCOehYD2LXpSVLnBO4WHFbX4vnWPyEtVcdv9nGjUT798vtm+pgmtUBY2NjjIjY2NhY0k1RYnCQsVJJJPH233I5vg0OVs4xOcnY8DBjXV18u+Ya+fNhc99GRtR/z8lJ+d8zl2OsrY0fkzVMtMeo2uxk46VStQ3HTV+f3D3a0eF/rsFBfi+r2leh4P3+rbcmrwFp2Xp6KjohNMbtOxe6MjGh9myxHp/kvSkwUUMYi6fdbjbn5B94ncNNlyYmGGtpUXt2+d2X2PRvTzwR7j4K85u1tVXus5ERuWOC+FRhkbVHiqk9iWKqqDLGb9aeHvkb1MvBHhxM3nizsvX1qf+WsoIhfsc0OCRRYKI9RtFmHQ5NFMjep7Nn804Yt2BepWNAdUubwzV7djLXddJ7v+C0s1NNi/yuZ/29R0a4No6MRNvJY6KGMBZ9u/1sTqYDzk+XZP0Ru+Mrzps2283qpiPwUPnNcjnG2ttrnwmynTdJdAojULJgqqgyFtzZsBvJ5KR/Ly22aEVItqe+UMhukMSYmfaou806HJqomJxkrFiUtwW3EbCgzriJ2/BwsqP1dj3q7PS+tzo69F4v7pFREzWEsejbHbb3XkaXZP0Ip85Ep/skqU6GLG8qzw6/Do7BQbXnQWtrrd27BVxJdwrK2iOSOaScoJlH9uypXsx5/fVYq6SLIEXbiOTn/D79NFLvZh2VdUBR4LXQO5+XTwpAVJsJT5x7cFBjg1PMrFn8r1i4LItKSm4/rPP7y2X+u3rx5JP6rmd66t8soVLvyEkDZHRJ1o9wet4tWcJ9kdmzK68dOSJ3PiBHLldZp7h1Ky/4+uUv83/bE3o4lYA47TSiNWsq90R7O0/6IsvoKF8D+ZWvVK4nMpnaS0WESYoVKzEFboliau8TY/KjEPatpye6aS/1vnV3B5tikuYh6Dgx0R51t1nWroNM8fRDpvdfdTRI3Lv9/fWrO7Nmye87fbrea1tHCGR/O691JrLXS2pk1EQNYSw9I0pO/kGpJD/S2Nwstw7O+pzs78eslqi3Uon/th0djDU21r5vna0is360VOKj0yojStbNProU5/RcGTD1zoKpospYMIelUMA8YL9t2rTgxzY314qJ7NBxWoeg48REe9Td5qQWuMqui8JU3fRsM2b465F1XcCmTXLnDTL9zh74JHUfm6ghjMW3RskriHHzD1R8hp4e7+dYZ2f9dpjEuQnb7+vjv0lrq9xxAwPx/T5p9msw9S4jyBR6E1hTiYu/wJmjR4Mfe/hw9f9VppgYPwQNtCBbKy3IFE83dNdHAvHw/vve7x8+TLRoEZ9C88Uv8mkzMixZwlOty+JUZFtlqheIHq96R37+gXgtn/fXpTvvdH+OrV1L9MADKDAbB4cPE42NEU2fzuvMWUspeHHzzfH+PqY/VxAopRyZQm+CUok/+LAWKV5UnUxRTHNkJFzlbGAufg4NUbVDqgOVdVHbt0NHTGP3bu6gHjjgvZ81CL/zTvkivk6dObLrLlFHJz68OuNk/INymWuBny45PcfWr5er4wX0kMvxzq8bb1Q7zk8jdGJ9rpgKAiUD8BM+q7N91lnJtLHeEWKwbp1cFfR8nhfrDFM5G5hN3KOLKr3/GAHINsLZFQF7LufuGHd0uHfmJDEyCvxx64ybN0/u+I4OOV2yPsdEAWOTRw5MgzHeQWJCp5bJz5Tjkm4AkKO9nU+VsFbKXrCg4mCLjDXf+16y7ax37r6bb6pV0EF94mfXOpHt1T/lFKIXX9R/fZA8TU1E111H1NzMnxliZOCZZ3jPtHXEsVTiAZWXholAa9my6qldRNGNjAI5RBBjRVYDlizho0OyuiSm9QJzaGoiGh+Pb5mGyaPKOcayv5plfHycmpqaaGxsjBobG5NujnaGhmofciBZhNPQ0cEfOlE5vyZioj2a0mbRYeLWmTJ3rrtO5HLcgZ4xQ36uOzAXe2eO173jh9MzqK3NP9AKiin2aCfpdgsNGB11dpBzOX5fvP662vNq2zaeWhqYwxe+QPTQQ9EHSkHvqTiQtUdMvTMctxoWQD+NjXJJNYgq4rNxI3+AnHpqdW0CAHTjVBNj7txKkpEtW4jee8/5WBHYHzqEIKlesCehCTMdGOsuzSCqtZEmT6uqV/7qr/hIcrGofqyYqnvrrUQtLd77EZk/qoxAyUBEobgnnyT63OeQ4S4u/vt/539lgyUrBw9Wgiar8wqADvyKfn7xi/yv21z25maiQiH6doJgBNEcP3RnOsS6SzOIYm2kydOq6hGxbrC9nT8zvIIdolpbLpWIbruN6OmnuW8jsBfRzko2XwRKhmHtNb766uqbFETLkiXODxhVULEe6MQv7TdjRBs2eHeo5HJyC4KvuSZ4O4E6bW1E/f3hNceNLGSkAuroHgFcsMDf2RY0NXG9CjKSAcKTy1WP8EybRvTII+4JXXI5oqeeqr5XNmxwTgF/7Bj/65UAxkQQKBkEptklh7UHRjxgurqCnQs1a4BO/NJ+E/nfZ7IdLmeeKbdfkhx/PJ9WkgU2biS67LJap3ZgQN4xlQFTp+oPnSOA+TzR+efL7Xvddfy+fvjh4NcDwXAb4fEbZbzsssq9smAB0U03eV/n7/8+W+uykfXOEI4e5TdnkGl2s2cTHTmiv031hLUHRjxgFiwgevxx94WxXlh7cu2ZiQBQIU4n9z//k0+vED2HaeQPfyD67neTbkV4OjoqDo1TBrOJCT6rQAeYOgVkcEv4MTRE9OyzcudYsoSfR7YwMtDH3/0d0Sc/6fyebAbWbdv8Zx8cOsT3u+giHa1OHowoGcDQEI/0VYuE5XJ8eBtBUjh6eriIiLVhTz3F/xLJFwN2Az25ICy6nFyZNUpPPJHuIClLLFni/b7sdLzZs93fQ50jIItTsphTT+XrVGRSg1vvNZlRcKAfrym2slkvhe/jh+x+JoBAyQW7U5zUFCkx3U51LZJw3K+6Sn+b6olZs4jOO48Xkj3llNqMYkTh1i2hJxeExa/oJxF/4PkVBV21Kpr2ATWsDqXXc0jmdydy7yjLSkYqED1u0/4PHiT6zGfkgh7GKvcaOgjTgdCXNWt40Gv1b848E+uoBQiUHPBLsxsXXou0/RBzS/16JYE377xD9Bd/wUeVDh+ufk8kZXjpJaL77iPq7eW1CWRATy5Qwe4wHz1a+f/27XxxLZG70/wXf+FeN4WIOzAf+pD+doNgbNzI07l7PYe8Uj3LkJWMVCBajh7Vk1131qyKP6LaQRhF1sd6xDp91+rnbtxY2xk/Okq0dGmt3yu7VODkk5MfaNAGqwPGxsYYEbGxsTHffQcHGcvlRK6oypbL8W1wMNq2Tk4yNjLCWF8fY729te1w23I5xopFxjZt4sdPTvLzDQwwls/LnwdbuK1QkP+9or6X0oqKPaaFJNs8OMhYqVR9/9htulRirLOzdj+7ltmPa2ur3IcjI8nbDzb+O6o8h5zuD6+tUGBseLjyjDAREzWEMfPaPTjI/Qpd9/bwMN/uuCN5O6u3rVCo2Lybvvgdxxj/t5+f09BQ/f9SKZ3+jqw9UkztSRTZL2Ny0vuBk8txxyKqB4zqA8/eNvuNqGIM2OLbGhq4M1SvmOYsMJZcm2VtWDjQAwOM9fQwNmuW+35EjHV0VHeoMFbRP2hGclsux3+D1lbvfezPIdHB9qUvyV1nZCTW21g7JmoIY2a1Owr/wU2XsEW/Cf/Qz8912oaHa+8NlePjGmhQRdYejZl6981vfpPmzp1LM2bMoI997GP0L//yL9qv4bfAkLHoak6ESf1dLNZOoQgzbQ9Ey7FjROvXY/5vEsShI7pQsWGxz003EXV38ymjbvvlckSDg7WLda1TuUAyMMafAaOj3vvs2sWntIjC4w89xDP9PfSQ3HWwRiQ4JmmIKtZi9kGz7HrhpksgOkolrvfCPwySSMOemKG9nZ/TvjbbXnBWIO4jY0uixBS4hWLz5s1s2rRp7H/+z//JfvWrX7EbbriBnXTSSWzfvn1Sx8tGjX19ctFxX5+OT1UhSIQvtmKRsYmJ2nNiGk36N/uQdr2QVK9qGB1Jos1R27DbqEKYkW1s8W1heucxohSMuHwRFazT9e2jxCr7we6zt11zTe3vLOvnWrcLLvC/p2SXivT2yvs9svd2UDI19e68885jK1eunPp/uVxmp59+Orv33nsd93///ffZ2NjY1LZr1y6pL0PWMdH9kAniEHkNZU5OMrZ0afJGis1/6+nRey+ZQFJOjoqOBNUQnQR5oKlsXh0+k5P83pQ914wZydsSNrmtVDK/g8YEDWEseh1xCm6c1oP47Ydp+vFvs2dHPxXxS1+qvWeCdsA9/bT3vajyvJJZsyR7b4chM4HSxMQEy+fz7Lvf/W7V65/97GfZX/7lXzoe093dzYioZpNdo+QmGFGtUQriELndMIOD8gkFsCW/NTeb77SokoSTo6ojQTVEJ0mNKFnx62WGc6V/E2uUovpus9A5Y4KGMBatjsgm/PDbr78/2pGk5ubkbSqtWy7H2Gc/G935C4VaPzHoWtRcjrEvfMF9ZEfleeW3ZimupGqZWaN08OBBKpfLNGfOnKrX58yZQ3v37nU85o477qCxsbGpbdeuXVLX8kq3GrTmhEw9piC1dB5/vDatq1jn5Fc1GaSHw4ejWfMGqlHVkaAaohPZOjlBkE1N395O9NprPPX9pz5F1NhY/X5rq1yh2izQ0hLPdW64geiyy7hrEAXz5kVz3qwTpy/ih9f6RfFaRwdP7e2338qV0RV/7e0l6u+P5txZgLFo1yofOlSb4jtoWQHGiL7+9UqNpeuuI7rlFu4THz2q9ryy3qN2n1j23o5zrVPqA6UgTJ8+nRobG6s2WdrbnQuIBqk5IVuPKYhDtH9/9f+RvEEfbgsSowILq9NHGA3RhcoDTbxfKMjtK9vhMzREdNZZvCDh975HND7Ok8d0dBCNjPAOm3rpmJGtHxKG2bN5Mo6NG6O7Bopcx0dUOiKbeOrhh/33O3BAS5NqaGkhuvlmbjdBC7LXA3EkuLj2WqKtWyvBhZufK8voKNf+b3yDPxtOOIHojjvUAjBxj9o7ipNMquZG6gOllpYWyufztG/fvqrX9+3bR6eeemok12xvJ3rjDe4I9PXxv6+/rh4kOWWxE0VK3SJ8WewPuyCZTIAzx47Fez04LtGThI7owO2BZg9yRGajRx/l/3d7UBUK8h0+bhp28CDXq8OHaztssswzz0R/jSNHojs3ilyHI00aItu59tpr+q9dLMrtd/Ag72TZsoWPRIDkeOcdokWLqjvqhZ/7pS+FP3+5TPTAA0Q/+Yl6AGa/l2Xv7Tg7mFMfKE2bNo3+9E//lLZu3Tr12rFjx2jr1q10wQUXRHbdfJ73hFxxBf+rOt1OdehQOESlkve53R52GJUwDzgu8ZGUjujAqePm3XedO3LcAqvmZqKeHqJ9++SCJFkN+81vwn46EMXUSjdUp46DCmnSENnOtbPOktuvWPS+D4tFok2buNZceaXcOYl4J8uyZbzzsaeHaMYM+WOBfuwd9fk80fTp+s6/YQPRJZfw51Vvr9wx9ntZ9t6OtYNZz5KoaNm8eTObPn06e/zxx9nLL7/MbrzxRnbSSSexvXv3Sh0f98LPMNnzvLJNeS1kQzpws7a0FmCLgyRT+wbVEZMKRTIWPq2qrJ60tCRvS6Zt+Xz1/4vF6K/Z1pYtrTFRQxjT127ZxFMTE3L7DQxUnkn2963PqcnJYDZvv+exJbdZk5JFke2wt1ftHrU/m+JMqpaZrHeChx56iJ1xxhls2rRp7LzzzmM/+clPpI+NW1R11GNyyjbl9bALmskEW/TbsmW1mX+y5riokGTQEVRHTAuUgmANrrq6krebLG69vdx5tQaxmzbpv057O7/Wpk3R1B9JGhM1hDG97RZOrl9wo7Kfn8+BDtnsbMPD0WQ7XLVK/R4Nem+HJXOBUhhMGlGyotor7HZzYUvP1tzMRwzFbxl1QbU0YmLQYWKbVUCxyXi2jo7a7z4K53PTpthvoVgx1R51t1u2Q1V2P7/nUdS13XRs8H/ktqg6w8SIkuq9Z78Pe3qi72CWtcccY4zFONMvEcbHx6mpqYnGxsZiyV5VLvNFc6Oj/Oe1k8vxtUivv65/vvjQEF9bgMQO6UTMAxcLw+2/VanEF8qrJA4xjbjtUQcmtlkWkbQh+0+CdDA4WG3ffs+LIPT28jVkWcVUe4yi3eUyT+a0Zw9ft7FggbNfIbufF9u28ey9aaOpieib3+QZ/NasSbo1ZtDVRXT33XrPmc/z9bPTplW/rnLvOfmwzc38tTvv1Oszy9rjcfouCQQii92yZdwxtj78VOsxqYpbezvRkiWVY15+Wb8x1BstLTyDjw4Y4/fAjTc6p1UWiy1VU9EDEASUFYiXXI4HMEuWVOv4DTfwtOC6kM1MBsxHJJ7StZ+XzyFKmegM6nUwNsYT2MRd2sNEREf9woX6fcO1a6uDJPu9dPnl3v6rW6fdW28RrVtHdM45CflF+gax0ktSw/RBhhz9ji+V1IYeh4eTH+I1fROVy+Ma0te5WDGNmDhtxsQ2y4A1B8lsYtp1VFMe/aZ1m46p9hh3u4NM3/fzOdI6xV98xqTbkYZt2TLn161rfCYm1H/DE09kbOHC2uPyecY6O9XvJfu96qWFUfhFWKNkIUlRDboGZXDQ+4a1rnPxOgfWHQTfCgX/BbFRbll1dkx0ckxsswwdHcnbmYlbY2O44/v6osk4RZTtThaBqfYYZ7tVHVW3+9FpAb3TuY8/PlmbFP6VfV1LvW2lUiWjnVdHfZhO9NZWxq69lrFLL2Xs6qsZe/75as1RuZcEutb2q4BAyYJpojo5yZ10mZtVVfSwyW1Ll9Y6G3EHnl5ZEU3GNHtkzMw2O2HtuHErQ4At+u3556PTknrIpmmqPcbVblVHNUhvvlVL/vmfGWtoSM6eSiXehq4uxj7xieTtO4pN1p+zdvB6ddTrTuYggvCgI0M6skWrImuPWKOUQrZtc16/YsdtPQvWHYRn1arqubTlMl9QeN99fMFosUh06qlE115L9Oab0XzXr76q/5ygfkGil/Rw1VX61j0Kcjmi/n7+LNCxcB+YiV+xaKd1ctu3e+sCY0S7dvH9xDon65qnjRt5UdmkePttok9+Mrnrx4Gsj3H4cLVfKLMuTQfCH123Tv1eIkppodn/DZa+pZBt29T27+jg4kjE/z70EJyhMBSLRPPnV/4/NMSzUl14IdHVV/OsOrffzheQfv3rfB+vquZ2cjmiQsH/mHXrKhW0AQiDWCQLXUgHuoMkIqK77uK/8dAQ0Zlncr268kr+98wzoSX1gkrQI9izR+7cbvu99pp8+6LgnXeSvX6aEAGV8AvLZe5TPvUU/yt8Rd0BlLjugw/K7W+/l0SiEDe/KJcjamvj+8UNAiXDsYqecOiRHjMcBw4QnXUW/z7dHEzRe0LEe25aW+XOnc/zXt9HH5XrIbIGwQAEASPM2adQIPryl7leLV3K9cnK6Ch/HcFS9gkS9ITtzT/rLLnjQTwIv/CeeyqdvKLTZO5crgMLF3Ld0H3dw4fl9rXfSyJbNFFtsKSaLVo3CJRSSJBIf8sW9BirUCjw3gs3RCB0443uUxiIKlMY3niD1y7xo1zm6cbb24l6erz3der5A0AVvx5mYDa5HO94IeJ65cWNN6LjJesECXrC9uYnMR0K+NPd7d7Ju2VLRTd009wc7F5qb3fueC6Vki2ZgkAphQSJ9B95BD3GqjzwAJFbjTGxdNBrrZg1kMnniebMkbuu6MmbN09tfwCCEPb+UZlWCuKlrY3o6ae5Y3LXXf5rWw8dUp/aDcwiSNATpje/XCa67bbQzQYxYe/kHRzk+qGT1av53yAjQ+3tvON5ZISor4//ff31ZOtKIlCKCbd5om584Qtq53/vvaAtq08OHSK64gqi8fHw5xKOqGpPXpoXL4LsEPb+KZX8Rz9BvKxezUew29uJVq7kU2q++lW5YxEoZZugQU/Q3nyMWJuHfbbKW2/pOa8Iwu+8M9zIkEgUcsUV/G/SiWiQ9S4GnLJNlUpczOw3DDJTmYdwRP2qlouK2KInT3V/kE2izlDmd5/ZKZWIbriBj3iK9hARffvb8ucA0dDWRvSZz/AONzwjgBsi6HHyOzZudHdU29v5KIOKHm3ZorXpIEZGR3liKh2abg/Cg9xLaQWBUsSIZAD2G9EptbfbvoLZs4mOHIm2vcUij+KPHCF67LFor2U69kBG9OQtW8bfs/6OTj15qvuD7KHSiRIUv/uMMT5idNZZPJFJocBHXK3pfq3nAPFSLPJ04kuW8N9n+fJwjk1c6YJBsgR1VK1pv/0ol4mefDJ0U0FCHDig3uFSLPLMyrfd5h+Eq9xLqUZf6ab0klRxOpXCWzL7lkq8mrLuQmFEvJp1dzc//xNPhK88n5VNFP61F3uzFu6zF3Xr7/euiG3Hr4J21jCxWGQUbQ5SvTzs9dzuM69iytbC1ps3J1tYMu2briLfPT21RSL9nhGyemYv9GgiJmoIY+a2242RkeRsrbmZ69Ly5cnbvYlbsch9PdXj+vv5b+9VzNYUUHA2BajWM/Dbd/duHqGffbbedhLxlI5Yh1CLyArjNoWBiKfbtL/X28uz28n05GVpiBrIEaQopOr57feT230mMmY6tYWoklq6s5OPMidZWDLtuH2HKuRyRH/7t3wBs/W317EW5NFHoStAH0kmGrrpJp6EYMkSntAEVDN9OtHEhPv7Bw4QrV2rft5ikf/NzGiRBAiUIkR27u6uXUTf/77cvnv28Bs8KA0NcHRk6empDCOrOJijo0SXX86nVV5xhdy16kl0gFoniup94Tedz3o+lRpLDzyg1g4QDLffPoxTqns6JwBEySYa+upX5ROY1CNeQZIgSOHreszCi0ApIlTm7q5YIR+8vPQS0e9+F7hZCJIkaW3lmVsE9kAm6hEBkG2CFIWUQWVNJBEyVqUZa9HYcplo3z75Y3M5PqLd28u1DCPUQAbVxDKqiWJkuPhiohNOIBoY0HM+oJd6zMKLQCkitm+XH/lRCV7QgxIPX/+69wMiyhEBkH2iSA0fJHivx95BN3I5oltuIRobI/rOd5JuDdGaNUQzZ/J/q2RCFYlg/uZvMIIE/BHB0ZYtvHPX6rf4jURGkeTln/5J37mAPC0t3iNM9ZyFF3WUIgIOSPaw1sLaulXuGNwHwIkgRSH9UF0TSVSfvYNuMMY7SJqakm4J5+BBvjZs6VK1Ub+kq9iDcKjWXAzD0BBfY3vhhXzNrb1zV4xEDw25n0OkItddtBTEy8aN7uvU6z0LLwKliIADYjY33lj9gLI+UK68kujuu+XOg/sAOBG0KKQXQabziYANVHjooaRbwFGZylQs8tHCNFSxB8GxP2cuvJD/3ytQCXOtZcv8O1eI+L3lFbC1txP192ttHoiZAwd47byentpnQksLH9Vubo42cE8rCJQiwq/HGKSbQ4cqFexlHih2gowIgPpC9MQGrV5uJ8h0PhGwQacq6FprERe9vTz47e1NRxV7EAy354zMqI4qKklcnEainVi4UG6UfGCAqLFRuckgQvJ5PtX3yiuJurv5b97TwwPklhYeRG3c6B+4xzkaGicIlCJCpscYpBth6LIPFEG9D1MDedrbid54g48E9PWFGxHwGx2yBu/WB1pzM+8NLhSCfgqQJHPmQGdMx299IZH/qI4KQZK4+I1YC5/H6TNYn4nLlhFdfbXatUG02O+r0VEeMG3cWLtuyS1wj3M0NG4QKEVEucwdkNWreURupVTief/xcEs3x44Fe6BgjUB9ErQ3TWRUvOKKcCMCW7YQvfee83tWR2XLltoH2po1RN/6Fu9FnDEj2PVBMmB6r/kEWV8YhiBrZ2XvM6cOl+bm6mfivHnq1wfpwClwj3M0NAkQKEWA0wLJlpbqOeSXXy5X7AvBVHIUCvIPlK6u8CMCwFyS7k0TD6pDh5zfF44KkfsDbflyonPOCVaEENQS9eJ2TO/NDlGVC3BDJbj2u89EB9GaNTzxiJMGHT5cvf+HP4yZNSZjDdzjHg1NAgRKmnGLrA8d4sPShw9Xgp/77+fV7u3BUEMD0SWXcKd782YIip24vo85c+QfKMcfH35EAJhJkr1p5TLPwHjDDd7TQ2fO5Jri9UBjjL+PdPbhaGsjGhzkTq2oYq8bTO/NFlGUC/BCdg21331m7xT2oqODd9bMnUu0eLF5awFBLXv2xD8amgQIlDQSJLK+/36id9/lC3FXreJ/33uP6B//kTssDQ1Iu2knLoEVhRrti+2d+Pa3ze4xAcFIsjdNOCmLFlX32DqxezfRww/7TyPdvZvoxz/GeiVVTjqJ6OabuX7/9rd8RHnHDvlaeh//ONHs2fLXw/TebBFFuQAvvNZQW2ltdb/PVJIcCWf5sstQ4DpLnHZa/KOhSYBASSNBI+tp07gz9dBD/O+0afx1v+k0dhrwa2pDPJTyeZ4q3I/du83uMQHBSKo3LUgmxtdek9uvp4fov//3YO2qV95+mweia9ZUghhZx2DWLB6cHjkit39XF6b3Zo0oygX44ZZ104pbp2SQJEcgOlRn2eRyfCsUgs3QsQbucY+GJgFca40EjaydFoGrCFGhwJ0bMU0PU/XCkctVP5RkF55u3Zq9tJjAmyR604I6KSr7b97M0/hiZEmdAwd4z/mWLXL7v/OO2vkxvTeb6C4XIHvNN95wLzT65pvO04eDJDkC0XH55Wp+n7inHn1U/Vr2wD3u0dBEYHXA2NgYIyI2NjYW6XVGRsRMf+9tZKRyzOAgY6VS9fulEmM9PXLn+trXGJuc9D4fNvmtrY1/h0F+V/tvaD8P4MRljzpxa3MQmw9LkPsxn1c/ZmSEa8vwMGOf+ETytmni1tzMWC6n95zDw/ruJVMxUUMYk2v35CS3vb6+ig1GyeSkt8+Qy/HnorUdfX3J2xY2vs2axX+bwUHGCgXvfTs6au8pVZ/RyUcaHOT3iV3rxGtp9YVkdYRiak+ixCWqQnDcHox2wRE3l9N+sjdtc3PtTSiEtqsreSM2aevt5d+d/UE1MeH9u7r91mkWiCQx0clxa7OqzesgLielq6vSbjhGwbbGRv3nXLUqHgc6zZioIYyls92yHS/WAF2ls0Z3RwG26q25ueK3tLZ6/w5uz6LJSf/Oeacgy4pTwOUUVKUJBEoW4hQn2cjarxdHdXO6iXVfI8tboVDplbGLTWsrY52dzr+r1xaFk5wF0ugs+OHV5rh701SclIaGcHYhRkaDjGJh49uKFdGct55HrU3UEMbi7bSVHZXq6JC736ydsn4dRNatrY2x/n71zkZs8tvwsJ7ZDWGDnbhHQ8OCQMlC3KIqc7NF5XhYH54yQ7HY+HbddYx94Qve+3R2Bgs8dU67ygImOjl+bY6zN03FSQm7iWAPjk7w7eabo/1t6jFYMlFDGIun3W7T+Z3uk8lJxlpa1O45q3/h13nY0sK1Q2bEAlvwrblZPuDt6/O+f0wLdsIga485xhiLb0VUMoyPj1NTUxONjY1RY2NjLNcsl/mCxz17eLYPkUFN8NRTvDClbsSCuttuI1q/npsG8CaXk/ueCgW+uHXHDv67vvwy0d13+x/X18drLAFOEvYYFpk2+9m8TkTWO6LobTyX44t1N2zgSQqAGk1NRGNj0Zxb/Davv87vtTjvwSQxUUOIom+30AW7Jgi/wJ4UYts2XgNJllyOJ5t4/HGi/fuJXn2V6Otf98/MWyjIZ+8F0TIyglp5All7PC7GNtUV+bz3zSibKrGnp1KoVgbGuJht2IAgSRbZ7+nQIe6EXHQR//+2bXKBkslpMYE8fjavE5Eha/Xq6LNPMcZTnL/8crTXMQnZzhWi6IIkospvs307f0bY74dSiT8/kEo8+/jVdMvlePmRJUsqwfPoqNo1GOP316JFaschSIqeXI6XiHHLuCs6VebP575L1jtTdIL04Akhm1LxzjuJ+vvVzs0Y0lNHxbZtlX/XRVpMkFpEat/e3niu98AD8VzHBEolosFBos5OteNkUvi2tfHU7CMjvAi5DFu2ONfVGh11Tu8MsoMoL7JunVpNt6EhXvcLZAMvv0/ozmc+Q3TWWXwU8cor+d+5c+PVB6dyOGkHI0oJIQrMLVtW2ztpz1O/cCF/MI+OYpQoTaj8hgBEQT5PNGdOPNdSrfeTRUSPvOiFXbKE6IQTiO6/n+i99/yPnz2baHy88v+2NqKvfY2oWHTv4f3GN/zPu2mT2kgCyAZDQ+qjynv2uE/RA9mkVOJBktNyDNGZElWtLitO96sJo94YUYoJpyhatsCcV9VuEC/H2boWkigSCIAVHVM7W1qgLV40NBAtX0503nmV14aGeG9sT49ckERENDFBdNVVRF/6EtHwMF9bdNllvDPsiitqi8nKjFoXi0QHD7pf0z6SALKBCHZUp96eckqwgtX1RKGQrWLb69dz39OtM4WId6ZEObrjdr+aMOqNZA4x4BdFyy7Ale098purSkR08sn8GocOeQvmeecR/fKXREePel+znhgcrA2A6mURtQ6StscgpLnN5TJ32IOMOIt561/7Gg8ETH8aiBH4n/yE6Pe/j+46OhanFwpEjz7q35nilrhDBE+rV/ORaz+ylFQmzfboha52C5tXCZKErT/2mPoaI1OZOVO+E6Onh2jePB5IEhHt3Ut04AC307VrvTsj0kwuxzvCDhzw31cketDtz/jdr/akNHEhbY8xZOBLnCRTiXoVlVVN7To5ydjzzzN29dWMnXeec+pHcV63uj/W67q1DZv7htpI4TExtW+SbZZJ1+pVy8np304a1NmZvH3V4ybzDPBKP6+jfoppmKghjOlrt2p5EautyxaPbm6O3xamT4//mrNm8dTloo6iU2r1IHUUo9yiakdfn1p6ed33a9wahTpKFpISVb+CrypOt1tNJHtBSWvtFpnaLgMDjOXzyRu+aVuWnI64MdHJSarNXg8tewA1MOBu735aMDmJmmtJbaWS3DPALWD2q6uVxc4dEzWEMX3tlg12nGxd1mkVRUz7+vi/W1vTEyiE3WbO5AGS9TU3/bN2PgepoxjF1tQUzXl7evR17Ae5X/1qPOlG1h6RzCFCtm+Xz0KzYIH7UOfQENHSpc7nOHaM/+3oILrkEv7v/fv5OqglS/i2bVslW9vChdUpjFtazMg6kja2bEEtAhAtbguuR0e5Htinf4laR26JAZYscdeYe+4JP5Vs1iyiL3yh0sbBQSSAkGH3bv67+OmJW/p5JJWpP2TXJXZ18XIWVlsX697cpuqKaVD29XJf/7rzPWYiTtPx3PSPMf6ZN28meu01bquPP84TqOhm1iw5zRwb477b1VcTvf02b09YSiWib3/b+bcV30HQpDCy92tqS6nEFLglSlK9T7JR9Kc+VVsd29prLNOL0dzMe3zs53DqBbEOo8pWczZxs4+26dyKxWz10MaJib3BcbdZ1u6tW9Bev8nJZKbZYKtsOnpSZWYQZAUTNYQxfe0OO4roNVXXS0Oc7rF62np6ovn8N9/Mz61yjPitdGm37PWDzKZJ66i3rD0i612EyEbH3/te7UJBkQnknnvkFmwePlxbPG73bl77xH787t2VdJBPPinXRhMRo21RcOAAskiB6PAbjXaCMf5XNXuRKFYKkuPVV8PXFxF1tUZGeOKGkRG+OBqZN7OHVyZcmVHEoNlaxT3W1RW05UQG5d6oobs7mgLfTzzBE+qoIMIMHdrd0cETWciwZ4/6+cPer0mDQClC/FK7eiGcHnFz6YYxouuvl8uEYjoNEd3lQQQDABmC3luMqaeCxn0cDZ//PNGJJ8rtu349z7YVthCkmJ7nlGocZIuwpSmCBtb5PJ/OF5T584Mfm1Xeeae6vlrcLFkS/fQ4k0upYI1ShHjNHZdBV2+BG0kaZpwcO0bU1MQD15/8pHr0rq2tUoiNSO03Su18WmA8Ye8tleAH97F+GhqIvvUt+f2PHKl9Lc5CkMBM2tu91x764bbuzQ+/dU5efO976tcD0SDWoy1YwP8vs3ZN7BuEsPdrUiBQihgRRatWz7aShcWTSTM2RvRP/0TU388XQdqN9Pzz5X8jHYIBgBdhHBEiPjqxbZvcwyjstUAtOqb9MhZuATWoD4IGO2GvKTqBs06W/S/Gqqe8xZEUJon7NSyYehcD1iHuVavUj8+qkcYNY7xw3IIFtVNT7NMQenr46ybOpwXm4zWn24tcjmfDu/Za+Wlc4lrQmfQRZColAHEgOoFbWpJuSbSUShV/IAjLl3Of4sor9bUpKkyeHhclCJRiQkTRl14a/BxRrbWpJ7ycDuv8/rvu4umNIRggKdweWoUC/+sUxDPG09zaE7uIaVxuwdKSJZXzgvSBdWQgLViTjjQ3E/3nf2YrWMrleImFTZsq67buvDP4evP+fqLp04k++EH9bQ2LGLG2Jo5BUpha4HrHyNAQ7+kNyrFjfC3N1Vfra1M9smuXXHYpCAaIA69sZ0734L59zkF8a6t7sOOXEW/79vB1lLLKxRdzxymIk6QLrCMDaWBoiI9OW0er/+iPwiV3SBuMEf1f/1dtx/QNNwQbdWeM6+7HP66leVKsX080POyfndBtxBpJYarBGqWYcCseqcru3bz396mnkikUWyyanynvllv4miVBqcSnHjkFQCbOpwXmMDRUuzbOfj863YNOi2LLZaJFi9yvJR6K69bVFqHUMWKR1bn8//ZvRN/8Jp9Ck8RnbGvDekiQPF4FsJ9+Opk2RcXTT1c+k+h8CtORtGsX0Ysvyu8fVGfE+umODq7t+/fLHYcRa28wohQD5TJ3hnQ8YDduJLrssviDpFyOP7B37+a92k88EW4aYZJYgyQi/2lJAESBcDzsCURk70d7r5/sQ/Huu2vXLekYsZg1y/v9jg4ebJjGrl28g8hpGmQcYD0kSBovHyaLnSNWDh3SM9quss6pv5/v39wsf4zT+umoU37XCwiUYkC2eGSa5/mK7CjTpnGn7JpriC6/POlW6SFooU4AgiLjeHR0EB09Kl+EVPVhNzpKtHQp0Zo1RD/8odqxThw5wh/upVL1621tfKrg+vVEP/5x+OskwZ491dMgP/Wp6K9ZKPDvDVN9QdIEKYANqvn97+X26+nhHWV33cU7v0ZG+LPA7h/aO0+c1k/71fIUHeAYsfYmkkDpjTfeoOuvv54+8IEP0MyZM+mss86i7u5uOnr0aNV+//Zv/0YLFiygGTNmUFtbG91///015xoYGKCzzz6bZsyYQR/5yEfoueeei6LJkSI7rBnn2qNcTq1CttPahyz1QiC7VPrIso74OR7ifiyV5LPXqRa4FgHZxo18Op4O5s1zX9dnsrMltC6f57XtdNaCsTs8hQJ3lvbtQ5CkgyzrSFxEOTVr5szozm0axSLR7bdX/i9mDfT2Eu3dW62r777rv37aK3sqMvgqwCLgf/2v/8VWrFjBnn/+efbaa6+xLVu2sFNOOYXdeuutU/uMjY2xOXPmsKuuuoq99NJL7KmnnmIzZ85kjzzyyNQ+P/7xj1k+n2f3338/e/nll1lXVxc7/vjj2YsvvqjUnrGxMUZEbGxsTNtnVGFkhDHulnhvIyOMdXTI7Rtmy+X4FuRa/f2Vz9XfH31b4966uhjr6+O/xeRkIrdL5pG1xzTpiG4N6esLZ7uDg87nHRys7JOE/XzpS4z19jK2aVOtDQX9zEluuRxjbW2VzzE5yVippPca69YxNjwM3VFBxR6zrCNxIevD9PQw1tiYvN2avJVK7vruxeQk/52cdGRwsFa32tqCXSdLyNojxdQedv/997MPfOADU/9/+OGH2cknn8wmJiamXvvrv/5r9qEPfWjq/5dffjm7+OKLq87zsY99jH3uc59TunbS4iQerm7Oi/VhLCtIsgb38Y/XXjefZ6yzkz+cVc+ZzzM2MBCNw5C2LahgAW/C2GNSOqJbQ8LYud15t+P0UEyDDenUNiLGisXo228PSnV/BmhNMMLaowk64uX4xo2sDzMxwdjppyevOyZvfp1hTjhpvl1T0nQ/pQVZe4xtjdLY2Bg1W1am7dy5kz7xiU/QtGnTpl5bvHgx/eY3v6G33nprap9FtjROixcvpp07d3pea2JigsbHx6u2JFEZ/lSdPuPEzTfzodjeXqIdO7jZWCmX+XqBINPMymWeTOKee8ydRiMLkjykj7h0JGoNCWPnjHlPE7WupfFLDxs1u3dXbEiHtln5xjdq10PpZt266uksUU1BgtbES9p1xCkNt9e026iR9WF27CB6881Ym5Y5hL8mu2ZaNikQUn4HJ5ZA6be//S099NBD9LnPfW7qtb1799KcOXOq9hP/37t3r+c+4n037r33Xmpqapra2tradHyMUMhWPPYSJJVrlctEn/tcbZBk5RvfCHZ+IqKvfz34sV4UCtE7P7KoChaIljh1JGoN0WHnXk67eCiuW6c3OAkCY9yGiCqfOSy5HNGttxJdf738MUHWQsybV/3/qNZlQmviI+06EjYbZlTI+DBIM60H0Rm2bZv3frJJgfw0xauWH1AMlG6//XbK5XKe2yuvvFJ1zOjoKH3qU5+iyy67jG644QatjXfjjjvuoLGxsalt165dsVzXD7cCpkuWVN+kS5a4C5JbQUlBoUC0YgWvp3L4sPt+jIVLeRlVccqJCd7jkRb8eu+BOt3d3URE1NTUlFodiUND3ByPYlHueBmnXUdApgNhQ+IzqySScYIx7kjKptxtbSWaMUP9Oi+/XO04yGSRKpV4scfVq2uLVnoBrVHHS0NM1BFdjm9U+BVhjyvBU7HIg8asc/nl3oGxbFIgL01J2+hlGlEqOHvrrbfSihUrPPf54Ac/OPXvN998ky688EKaP38+Pfroo1X7nXrqqbRv376q18T/Tz31VM99xPtuTJ8+naZPn+65j51yubp4o7UYo07sxSO9Ck6+8UZtm7Zs8S5cqxrANDd7B1R+x771lveolSrvvMOnBS5fnq4idugp08ctt9xCGzdupBdeeIFmuRTfSVpHgmhIEJwKx86fT3TWWbwH2cm2hDMum9JVBCd2nYmbrVsrbT5yJJ5rioDmz/4smJ7cfTffrEWAH3yQa7C9KKS41oMP8oK+1iBVBWiNPF4aQmSejqg4vkkVQvcqwi46Ety0SwddXZUsnXPnRnutpDl8mGuNPe23YHRU7jxumuJVRNjrunVHVIukdu/ezebNm8c+85nPsEmHVWNi8eTRo0enXrvjjjtqFk9ecsklVcddcMEF2pM5yCyEiwKRoUp1MZ9bewsF9YWDn/1s8EWHPT3RZNfK5fjn0bEoNJ/3vo7sgvCRkWjvhXpCZUFzWnQkioQwflmKnLLXBVnoa7/e6tX6bVZ2a22NNytWWxtPPtPcHF6TrN+7TBapoFn+oDX+qNqjKToie8/09UlfMnLsOtbfH23mTat9DA4mp2VxbW7JewYHw/kvfgm5/JIGZYFEs97t3r2b/Zf/8l/YRRddxHbv3s327NkztQnefvttNmfOHHbNNdewl156iW3evJmdcMIJNek4jzvuOLZ+/Xr261//mnV3d2tPDx40WAlL2JvULk5BMtiJbeZM9WMKBd6G/n7vYCTMFjQQa2pi7IkneJriL3yh8n06/b4DA/IZCYEeZMUpTTqiO1CS6ZyJMqXrX/6lt/YUCsmlGNexdXVVgk9dmeqcUoV7ZZFSvS60Rh7VzhZTdESllEgacNOxzk79mTed7GNyMlgHsYmbPUCU1efmZu4fBtWntNxrUZBooPTYY48xInLcrPzrv/4r+7M/+zM2ffp01trayu67776ac/X397M/+qM/YtOmTWN//Md/zP7pn/5JuT1uX0aSEbXumzSJGiU9Pdwhier8fX08kFENxG69tfZ3tZ/D6nBG0XsP3JEVpzTpiM5ASaVzJoqUrp2d3vbT2Wl+T621x123Nspqsl9KZZ1aU2+pf1Xs0SQdUSklkjR+OjYwwO/FVavkbWvWLDX7iCpdfxq3vj7+uw8PBxsht3fEmTh6qZvU1VFKErcvI8mIWvUm9XsQ9vQkb8i6t+FhdSFcssTbMVm6lAd39h4WFGSLj6TrmgVBV5t1d86oOsgDA/42JK6/fLm83c2enbxeWDerZut2plQcB9kCwGG0Jqmp40liooYwJtduEzruVHRMxf7EZ7aPEhUK3Mex61uSRawbG50Du1mzohnl6ukJN0pnv38wooRAqQq3LyPJiFql0rXbg1D02HR0JCcWUW6trfKfrbGRsc2b1YQEBdmSwUQnR1ebdT6cVB3kyUn5Oe0qHS+FAmPPPx+9HjQ3M/bP/8x1QaXHXWVkR9dv4/c7WT+TkwOocu4kpo4njYkawph8u9PecaeiY6r2J9Yod3fXjpzY9S3JESUxrd+p/USMrVih71qzZunRL6s+mjR6GRUIlCykcURJ98M7i5vKdxNk9CnrzkRaMdHJ0dVmXZ0zQRxkFftwmwLjtAlHv6Ulek1YudI/GUV/v/v3FUZvwzgObiN5YZNz1OtibBM1hDG1dqe5405Wx7q6GJuY0D/jpaODfydPP+2/b5A12DKbl97lcoydfHL0ehh0Ez6tCaOXUYJAyYLfGqWkImrT1wHEtTU0eL9fLDK2aVOw9VJZdibSiolOTppGlFQcZKuzFcV6QpHUhTHeAxynLritXXQbVRscDD4lJo0BTT1PnTFRQxgzt912VDpdokr2JHvusBkvnexVdmRe5Zx+fo7OzdoRl/bRyyiRtUelgrNZw6sYo/j/xo3R1FMi4vnpZQsm1jPHjnm/f+AA0dVX83onqjCGIo8gPmQKlra1eddIkq21cs891YUEg9iHH48+WtHHD31I//m9cCu6KWqAOBVMDFozrlQKXlNER1FIJ2TrLaEuE9CNn45ZibI4rsy5g9q8E+LzXnWVvnMScQ3w83N0Yi0M7FdEGBDVdaBEVCnG2Npa/XqYB6MK8+ZFe/6009ycdAs4cCZAHOjonJG9V7u7oy0we8EF1fpoffgmCWP8b0dHxZEql3nBXfGeF21tRAMD+hyHqAIa2e87Lb8LyA5eOpZlhF+4ZEnSLQmGW0ecKCJ8xRX8b1SDA6ZS94ESUbIRdb0/xG6/PekWcOr9dwDxEbZzJi336s6dfNSmXCbato2P5DQ2Jt0qjn2Uxm9UR9Dby7V/2TJ9jkNUAY2O0UkAguKmY1mkuZloeLjiFwrbM4k4ZkllFQRK/5ukImqVIewsct99yV4fzgRIgjCdM2nRjFyO6MYbic48k0/vu/pqovHxZNtkR4zSyI7WzJmjX/ujCmiSnjoOgNCxrq6kW1JLLsftzk8rGyS84MOHuR0JW7LaXlrwex7ENUsqiyBQShiZh53Te1lB5/xhO11d3Ant6eHfH5wJkCaCds7IaoYfXV1Eq1bJ72+HMaJDh/hIUloRozRJTlOLMqBJeuo4APk80UUXhTvHiSfqaYtA2NWDD7oHNMInuOUWuXPaO1va2/n03iRpbCQaHOSbkwb09GDdkQ4QKKUAr4edmxHoJuiUmeZmolmz9LZFFxddxJ3Qu+7ydybE9KGnnuJ/o1yACkBYvDRDNkHMRRcRLV2qv21pwD5Kk/Q0tSgDGizGBklSLvNNxQ/I5YiKRaJNm/j9umVLuDbYOxnsduW0Frq5me8ju07aqRMl6bVKs2fzNjhpwBtvcN8H647Ck2NMZnmr2YyPj1NTUxONjY1RY1om0TtQLvO59Hv2cKNcsKByc9vfO3CA6POf5z26uigU1M6Xy3GheeklvnA8LYgh99dfr/7+tm3jGxEXDiEeQ0N8obd1DUOpxHui4GzoxxR7tJLWNh89SvTww0SvvUZ01llEN9/M7+m5c/lIj5O6W+2DyHtfExHBkD0A+eIXiR54wP2YOEZgvDQeyJNWe/TD1Ha74fTs9MPJPstlPoXXb3Q6l6vWKXGup57iNmXVwWnTePuWLXPXts5Od02wXsPuTwjKZa6fUSTN+cQniH70I//9Rka4LwPUkbbHWJKVJ0xWahcwVqmL0tGhlqOfyLuIZC6nVmOkUKjk2Z+cVDtWZ10Dr0Jp1u/KXhxO1Fqp18r2SWKiPaaxzYODjLW2Vt+3ra3V97VMIcHOzvjqd3jZrq7NXkdpctK/4GVnZ/y/nxtpLjSaFtJojzIk0e6o7ie3Z6ff5lSjx6vGmdCszk7nej9Or5dKvPC0X/0y2fZ7+QEq9TCXLuU1H4tF72sXi4w98YTcObu6/H9baIozKDhrwVRRteNUGExWmGQrY69YIbff8HBt22Tbs369Hoeoo8O9UJrfdyVEyivAQzHaaDDRHtPWZj97c7MBu5MS1NkJu3V3qxdtXLJEXZtkNTMtdu7UXrcCuvVM2uxRlrjbHdX95FdE2W3r6qq1Mz8NsnfKWh3+/n73jk5dWvXlL/t/H7L+lSj+7NaRZd2CFLV1+m2hKe4gULJgqqhaCeLQdHVVeg/6+uSO2bQpeBX51avlrvHEE7U94UE28dnsPSW6nb8sVrZPEhPtMU1tlhnBLRT4fl49iUGdHR3b8DDXGlU9k9lPVJ1X1YGk7Ryj2/KkyR5ViLPdUd5PIyN6nqUyGlQqOfsbcenX1Vf7fx9+bbH6TdaZLl7BUBAfxv7bQlO8kbVHJHMwAJViiVbOPruyDkc2m1NrK1+bo5IlTqz/kV3f9IMfEL3/vty+buRyRPPn12YOIwr2XXmBYrQgTcjY2qFDfD+vzHqytYWiYP9+tQQ1bW3y8/BPOy2YZiZp517tFa9ZC+gC4EXU95OqrbglS5HRoN27K/XQVI/VwZEj/omeRGZLP79pyxa+punCC/n/DxxwT08exIex/rZHj0JTdIFAyQCCCsLOnRXjnj9fPuuTSoamoaGK4W/aJNeuxx93d/Rk17cyRrRjR+3rUYhnWgp8AkBUSUgSdr8kAwORyECmHlQux52KhQv9NaxU4g/+devUdSBJO/fTLcaqC+gC4EXU95OKrXilwJfVIKf94tKv73+f+zdXXsn/zp3L/R47fn4TEU8sYf9djh3zb0NLi3x7xW/78MPQFF0cl3QDgD9BBeGb3+QbETfWK64gWr/ePXOMVcja23naSa8MTX4ZZYKQz/OUl0eO+O8btXgKxwvFaEEWiSIw6O4m+ru/88+4J7TkwQe5htg1SVAoED36aKVzxm1/8f+33yZatEitzWmw8zAOIwB2or6fRCeHTLbMUon7Fk4ZJcPUN1MN1pz0wk13rLz7bvX/R0e5BjllyXTzm4h4gBXUV9q4kQdge/YQvfwy0d13+x/z2mty54am+IMRpYCI6WZPPslv4iefjK7+jg6HZnSUB0m33Vbb49HayntgJyaItm7l21NPcWNfsMB52k7Q6YB+vPWWXJBEpEc8CwUUowVmITsFbeFC7/pgsiM6KvzP/0nU28v/LWNTbr2wzc28HtS+fdXOiNv+4tzvvBOs3TrsPEwttiQL4oLsEfX9JFNEuaPDv6ZX0PpmonaTVw0kcezAgHuNyttucz/eDa9pa27p/8POdGltrUyhli3ue9ZZcvtBUySIac1UouheQOmVSSmKbCJioWDYBAViQeHEBF9MuGkTz3Lnla7b7fMEXcypY/NKKKHyXYnFjDLZwYA+TFyInaY2yyZzcEqPa7dnmexLhYJa+v+REXWbcks64fX68DBjy5aF05KTT9aTAaynp1ZHndKU+yXWcPsdkIGzmjTZowpxtTuu+ynos9NqCz093hrU0VFtLzKZLO1lQoaHeTKYri7+b+vn7uxkLJ8Pph/W5BRe2eVkk2nJ/E6yiSMmJqApfiDrnQWd4iSTSSmKbCI6M7kJR0bG+XHLjqJq+CopyoO0R/W7sos56gzEh4lOTtra7JcevLNTPtuRjBao1Frq6ODntduU6KCRtTEvxyNoqQT7Zi9zEOR38Kv/4tZe2aAVGapqSZs9ypJE1ruo7yfVZ6eTLTh1xtiDl5YWxi65RN7fkLU7xrg2rVyprh9+GTbFdx3U93H7nbz02CnrHTTFGQRKFnSJk0o6yigidV2FITs61IIup54H2REla4rygYHwbZfprXJzXhoba3unQPyY6OSksc1uTsDAgFqK/8lJ73T9Yv/ubjkbLRada6Wo1PLwcjx0aKAOjZbtNJMJpLy+J4xu15JGe5QhDXWUkryf/Oy6p4c/o4PadKFQGTFSTY0dZNRHXMtPb0slrrEq+pXPcy2X/Q7FZi+enbZ7IE0gULKgS5xUp5vprMuhs2ZAU1Ow46yfR3VoP0z7ZSpPC/xEBOKQPCY6OWlts1NvrqxOCXuW3X94mPfqqmqFqsMSR32UsL2putroNrUGo9vepNUe/Uii3Wm5n2QDirB2JT6jaj3IIMsJhoflj/ObYuino7LfoVMHUFrugbQha4/IeqeAanYQndlEdKa9HhsLdpz183hlrBILMzdsqCxs3LcvePsvukhuAbtfgolcji/AXLIECRpANhB1kqyoZryS3X//fqKrr+aJD2TPXS4T3Xijs00yVm2TRFwvtm6Ntj5KW5t7Fi5ZdOkxY5UUveJ3dPpNAQhKWu4nmZTlOmxqzx619Ojiu1mwgKhY5LWNZNm/X37fefN4IprVq+U/5+BgpW0ySSGcPhdReu4BU0HWOwVUs4PozCaShhSOp5xS/X+vugG33Ua0Zk2l/sCaNerXc8t44wZqkQCgnvFKZX8R0Mie+557vIvjCpu85x6iM8/keiGT+laFXI5n0Ovr88/CZcctk51uPU6DvgMQJXHd46edFjw9+s03q19LRT/b24neeKOSGdSPb3yjunYTyggkA0aUFFCpHaDi4MuQhhSOK1bwUSR7ul573YADB4iWL/f/jrwIkp4bIgKAv07Z6wbJ6FqhUNm/VHLvkLCeu1yupBD2o7tbbj9VCgWiL3yB9+Y61YLzYmiotve3VOKfSbcep0HfAfDDLf21zP779kXbNqv2yHaGCrtzsnXZaxGp6W0+T3TLLURf+5qcL0lUqd20bp1c+6AnmolpKmCiZCHrna4U4WHn0/t9Nl1z94MsNlRdmwGSwcT1Baa1WTXbkV8WPSL1TEpJlhBobmZs+fLaJBWy5Rv81lWJ1OsyelwoIEWvbkyzR4Gp7WYsWEIW+/5B03Cr+iYqa6hVMwq7JWFRzS4nU5rBfi6xhgt6ogckc7AQZx2lKLOJqBpWVILkZYhBnSORQjPMYkPUIjEDE50FE9usku3IrzaT3XZkzh20doifTjj927r19PBsUSoJJOzfhcyCaXENt3YUCpUUxUjRqxcT7ZExc9utmpAlSPARRhecdE3G7mQ6du3Hu2lokOxyg4PeGUfd9A16ogcEShaiECeRRWTTJsZ6e/nfOLKJqBhWY6OaARYKjF17rdy+9gx4IqNKV1cwoevp0ff9QETSjYnOgoltZkw+21GQ0Vi/c0cxolQqVVIIF4vOTknQzFBBvgu3mjA9PdXnR4pevZhqj1G1O8qsZqr2JBN82EeWgtZZ9Cv34Wd3srbe2yv33Qb5HYaH1T5zXx/0RBcIlCyYKqpuyBrWHXfI7XfzzRWj3rRJ3lgZ01f4UZxPBxCRdGOiPZrYZhVkR39U7FRmuvCMGfIasWJFrV23tNQ6S2Gn4Kp+F7LOEVL06sNUe4yi3apT4lSJqtyAPfiYnORTZmWOFaO1MnjZXRS6p4rqyLv4nqEn4UF68Awjm5JSdtHyZZfx1JFDQ/LZ6U47je+/bBk337DoXHzolGBCZRE3APWGaqY8GbxKCAhuv11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+      "text/plain": [
+       "<Figure size 1000x1000 with 6 Axes>"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.datasets import make_regression as mr\n",
+    "X,y = mr(n_samples=1000, n_features=6, noise=0)\n",
+    "print(X.shape)\n",
+    "print(y.shape)\n",
+    "\n",
+    "fig2,ax= plt.subplots(2,3,figsize=(10,10))\n",
+    "for i in range(6):\n",
+    "  plt.subplot(231+i)\n",
+    "  plt.scatter(X[:,i],y, color='blue')\n",
+    "\n",
+    "\n",
+    "\"\"\"\n",
+    "# house price dataset\n",
+    "n_features =6 :\n",
+    "area of the plot\n",
+    "number of roo ms\n",
+    "etc etc...\n",
+    "\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "bTd795ffiTy6"
+   },
+   "source": [
+    "### 2. Data For Classification\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 484
     },
+    "id": "QQG3Ho0k0nzo",
+    "outputId": "8814cfa9-eae9-4092-916a-cacc3350f0ed"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "E09pcdfHWb1Q"
-      },
-      "source": [
-        "## Step 1: Understanding the data\n",
-        "\n",
-        "### 1.1 df.head() \n",
-        "is a method used in pandas, a popular data analysis library in Python, to display the first few rows of a DataFrame.\n",
-        "\n",
-        "The method returns a new DataFrame containing the first n rows of the original DataFrame, where n is the number specified in the parentheses. By default, n is set to 5."
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1000, 2)\n",
+      "(1000,)\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 2,
-      "metadata": {
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-          "base_uri": "https://localhost:8080/",
-          "height": 337
-        },
-        "id": "LXVYGuOhmD6t",
-        "outputId": "cd3ee45e-a754-450e-851b-1096fb84afc9"
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-      "outputs": [
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-              "      <td>male</td>\n",
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-              "      <td>113781</td>\n",
-              "      <td>151.5500</td>\n",
-              "      <td>C22 C26</td>\n",
-              "      <td>S</td>\n",
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-              "      <td>135.0</td>\n",
-              "      <td>Montreal, PQ / Chesterville, ON</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>1.0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n",
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-              "\n",
-              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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-            "text/plain": [
-              "   pclass survived                                             name     sex  \\\n",
-              "0     1.0        1                    Allen, Miss. Elisabeth Walton  female   \n",
-              "1     1.0        1                   Allison, Master. Hudson Trevor    male   \n",
-              "2     1.0        0                     Allison, Miss. Helen Loraine  female   \n",
-              "3     1.0        0             Allison, Mr. Hudson Joshua Creighton    male   \n",
-              "4     1.0        0  Allison, Mrs. Hudson J C (Bessie Waldo Daniels)  female   \n",
-              "\n",
-              "       age  sibsp  parch  ticket      fare    cabin embarked  boat   body  \\\n",
-              "0  29.0000    0.0    0.0   24160  211.3375       B5        S     2   None   \n",
-              "1   0.9167    1.0    2.0  113781  151.5500  C22 C26        S    11    NaN   \n",
-              "2   2.0000    1.0    2.0  113781  151.5500  C22 C26        S  None    NaN   \n",
-              "3  30.0000    1.0    2.0  113781  151.5500  C22 C26        S  None  135.0   \n",
-              "4  25.0000    1.0    2.0  113781  151.5500  C22 C26        S  None    NaN   \n",
-              "\n",
-              "                         home.dest  \n",
-              "0                     St Louis, MO  \n",
-              "1  Montreal, PQ / Chesterville, ON  \n",
-              "2  Montreal, PQ / Chesterville, ON  \n",
-              "3  Montreal, PQ / Chesterville, ON  \n",
-              "4  Montreal, PQ / Chesterville, ON  "
-            ]
-          },
-          "execution_count": 2,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "df.head()"
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.datasets import make_classification\n",
+    "X,y= make_classification(n_samples=1000,n_features=2, n_informative=2, n_redundant=0,n_classes=2,n_clusters_per_class=2)\n",
+    "print(X.shape)\n",
+    "print(y.shape)\n",
+    "\n",
+    "plt.scatter(X[:, 0],X[:,1], c=y)\n",
+    "plt.xlabel('Feature 1')\n",
+    "plt.ylabel('Feature 2')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "ncXBbbxCigi7"
+   },
+   "source": [
+    "#### 3. Data For Clustering (Unsupervised Learning)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 484
     },
+    "id": "AyGEnfO61DbO",
+    "outputId": "9ca241bc-d24c-4a9f-8364-7c49abee8f82"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "VAL_93jPs0fN"
-      },
-      "source": [
-        "### 1.2 df.info() \n",
-        "is a method in pandas that provides a summary of the DataFrame's metadata. It returns information about the DataFrame, such as:\n",
-        "\n",
-        "* The number of rows and columns in the DataFrame\n",
-        "* The data type of each column\n",
-        "* The number of non-null values in each column\n",
-        "* The memory usage of the DataFrame\n",
-        "\n",
-        "This method is particularly useful for gaining a quick understanding of the DataFrame's data types and whether there are any missing values. It can also help identify any potential issues with the data, such as columns with mixed data types. Additionally, it can be used to check the memory usage of the DataFrame, which can be important when working with large datasets."
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1000, 2)\n",
+      "(1000,)\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 3,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "2QXcvvQysR26",
-        "outputId": "b3c7fca0-1627-402f-8c94-16c92a119459"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "<class 'pandas.core.frame.DataFrame'>\n",
-            "RangeIndex: 1309 entries, 0 to 1308\n",
-            "Data columns (total 14 columns):\n",
-            " #   Column     Non-Null Count  Dtype   \n",
-            "---  ------     --------------  -----   \n",
-            " 0   pclass     1309 non-null   float64 \n",
-            " 1   survived   1309 non-null   category\n",
-            " 2   name       1309 non-null   object  \n",
-            " 3   sex        1309 non-null   category\n",
-            " 4   age        1046 non-null   float64 \n",
-            " 5   sibsp      1309 non-null   float64 \n",
-            " 6   parch      1309 non-null   float64 \n",
-            " 7   ticket     1309 non-null   object  \n",
-            " 8   fare       1308 non-null   float64 \n",
-            " 9   cabin      295 non-null    object  \n",
-            " 10  embarked   1307 non-null   category\n",
-            " 11  boat       486 non-null    object  \n",
-            " 12  body       121 non-null    object  \n",
-            " 13  home.dest  745 non-null    object  \n",
-            "dtypes: category(3), float64(5), object(6)\n",
-            "memory usage: 116.8+ KB\n"
-          ]
-        }
-      ],
-      "source": [
-        "df.info()"
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.datasets import make_blobs\n",
+    "X,y = make_blobs(n_samples=1000, centers=4, n_features=2)\n",
+    "print(X.shape)\n",
+    "print(y.shape)\n",
+    "\n",
+    "plt.scatter(X[:, 0],X[:,1], c=y)\n",
+    "plt.xlabel('Feature 1')\n",
+    "plt.ylabel('Feature 2')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "aiNTXG2GzMeY"
+   },
+   "source": [
+    "## Web Scraping Example\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "oMKlbtk5ipkx"
+   },
+   "source": [
+    "### Step 1 : Send a \"GET\" request to the URL.\n",
+    "\n",
+    "Status_code 200 indicates that the GET request has been successful. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
     },
+    "id": "inSdmA5Jy1zT",
+    "outputId": "fe6e9967-7939-49fd-d894-86e02545e0e1"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "uJCV-2Num4Tx"
-      },
-      "source": [
-        "### 1.3 df.describe()\n",
-        "is a method to generate descriptive statistics of the numerical columns of the data. It displays the central tendency, dispersion and shape of a dataset's distribution, excluding NaN values."
-      ]
-    },
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "403\n"
+     ]
+    }
+   ],
+   "source": [
+    "import requests as req\n",
+    "url =\"https://www.mykhel.com/football/indian-super-league-table-l750/\"\n",
+    "page= req.get(url)\n",
+    "print(page.status_code)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "35p0kiErjOOq"
+   },
+   "source": [
+    "### Step 2 : Parse the HTML using BeautifulSoup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "id": "OdBVdAYQzSao"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 4,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 300
-        },
-        "id": "Zk_C0941mIz9",
-        "outputId": "666a15a4-3bed-47f0-bab5-1d331a61034c"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/html": [
-              "\n",
-              "  <div id=\"df-29747124-0830-4756-a92c-117908e87e59\">\n",
-              "    <div class=\"colab-df-container\">\n",
-              "      <div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>pclass</th>\n",
-              "      <th>age</th>\n",
-              "      <th>sibsp</th>\n",
-              "      <th>parch</th>\n",
-              "      <th>fare</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>count</th>\n",
-              "      <td>1309.000000</td>\n",
-              "      <td>1046.000000</td>\n",
-              "      <td>1309.000000</td>\n",
-              "      <td>1309.000000</td>\n",
-              "      <td>1308.000000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>mean</th>\n",
-              "      <td>2.294882</td>\n",
-              "      <td>29.881135</td>\n",
-              "      <td>0.498854</td>\n",
-              "      <td>0.385027</td>\n",
-              "      <td>33.295479</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>std</th>\n",
-              "      <td>0.837836</td>\n",
-              "      <td>14.413500</td>\n",
-              "      <td>1.041658</td>\n",
-              "      <td>0.865560</td>\n",
-              "      <td>51.758668</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>min</th>\n",
-              "      <td>1.000000</td>\n",
-              "      <td>0.166700</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>25%</th>\n",
-              "      <td>2.000000</td>\n",
-              "      <td>21.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>7.895800</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>50%</th>\n",
-              "      <td>3.000000</td>\n",
-              "      <td>28.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>14.454200</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>75%</th>\n",
-              "      <td>3.000000</td>\n",
-              "      <td>39.000000</td>\n",
-              "      <td>1.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>31.275000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>max</th>\n",
-              "      <td>3.000000</td>\n",
-              "      <td>80.000000</td>\n",
-              "      <td>8.000000</td>\n",
-              "      <td>9.000000</td>\n",
-              "      <td>512.329200</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>\n",
-              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-29747124-0830-4756-a92c-117908e87e59')\"\n",
-              "              title=\"Convert this dataframe to an interactive table.\"\n",
-              "              style=\"display:none;\">\n",
-              "        \n",
-              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-              "       width=\"24px\">\n",
-              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
-              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
-              "  </svg>\n",
-              "      </button>\n",
-              "      \n",
-              "  <style>\n",
-              "    .colab-df-container {\n",
-              "      display:flex;\n",
-              "      flex-wrap:wrap;\n",
-              "      gap: 12px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert {\n",
-              "      background-color: #E8F0FE;\n",
-              "      border: none;\n",
-              "      border-radius: 50%;\n",
-              "      cursor: pointer;\n",
-              "      display: none;\n",
-              "      fill: #1967D2;\n",
-              "      height: 32px;\n",
-              "      padding: 0 0 0 0;\n",
-              "      width: 32px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert:hover {\n",
-              "      background-color: #E2EBFA;\n",
-              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "      fill: #174EA6;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "      fill: #FFFFFF;\n",
-              "    }\n",
-              "  </style>\n",
-              "\n",
-              "      <script>\n",
-              "        const buttonEl =\n",
-              "          document.querySelector('#df-29747124-0830-4756-a92c-117908e87e59 button.colab-df-convert');\n",
-              "        buttonEl.style.display =\n",
-              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "        async function convertToInteractive(key) {\n",
-              "          const element = document.querySelector('#df-29747124-0830-4756-a92c-117908e87e59');\n",
-              "          const dataTable =\n",
-              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                     [key], {});\n",
-              "          if (!dataTable) return;\n",
-              "\n",
-              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "            + ' to learn more about interactive tables.';\n",
-              "          element.innerHTML = '';\n",
-              "          dataTable['output_type'] = 'display_data';\n",
-              "          await google.colab.output.renderOutput(dataTable, element);\n",
-              "          const docLink = document.createElement('div');\n",
-              "          docLink.innerHTML = docLinkHtml;\n",
-              "          element.appendChild(docLink);\n",
-              "        }\n",
-              "      </script>\n",
-              "    </div>\n",
-              "  </div>\n",
-              "  "
-            ],
-            "text/plain": [
-              "            pclass          age        sibsp        parch         fare\n",
-              "count  1309.000000  1046.000000  1309.000000  1309.000000  1308.000000\n",
-              "mean      2.294882    29.881135     0.498854     0.385027    33.295479\n",
-              "std       0.837836    14.413500     1.041658     0.865560    51.758668\n",
-              "min       1.000000     0.166700     0.000000     0.000000     0.000000\n",
-              "25%       2.000000    21.000000     0.000000     0.000000     7.895800\n",
-              "50%       3.000000    28.000000     0.000000     0.000000    14.454200\n",
-              "75%       3.000000    39.000000     1.000000     0.000000    31.275000\n",
-              "max       3.000000    80.000000     8.000000     9.000000   512.329200"
-            ]
-          },
-          "execution_count": 4,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "df.describe()"
-      ]
+     "ename": "FeatureNotFound",
+     "evalue": "Couldn't find a tree builder with the features you requested: lxml. Do you need to install a parser library?",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mFeatureNotFound\u001b[0m                           Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[14], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mbs4\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m BeautifulSoup\n\u001b[1;32m----> 2\u001b[0m soup \u001b[38;5;241m=\u001b[39m \u001b[43mBeautifulSoup\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpage\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtext\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mlxml\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\bs4\\__init__.py:250\u001b[0m, in \u001b[0;36mBeautifulSoup.__init__\u001b[1;34m(self, markup, features, builder, parse_only, from_encoding, exclude_encodings, element_classes, **kwargs)\u001b[0m\n\u001b[0;32m    248\u001b[0m     builder_class \u001b[38;5;241m=\u001b[39m builder_registry\u001b[38;5;241m.\u001b[39mlookup(\u001b[38;5;241m*\u001b[39mfeatures)\n\u001b[0;32m    249\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m builder_class \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 250\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m FeatureNotFound(\n\u001b[0;32m    251\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCouldn\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt find a tree builder with the features you \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    252\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrequested: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m. Do you need to install a parser library?\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    253\u001b[0m             \u001b[38;5;241m%\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m,\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(features))\n\u001b[0;32m    255\u001b[0m \u001b[38;5;66;03m# At this point either we have a TreeBuilder instance in\u001b[39;00m\n\u001b[0;32m    256\u001b[0m \u001b[38;5;66;03m# builder, or we have a builder_class that we can instantiate\u001b[39;00m\n\u001b[0;32m    257\u001b[0m \u001b[38;5;66;03m# with the remaining **kwargs.\u001b[39;00m\n\u001b[0;32m    258\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m builder \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
+      "\u001b[1;31mFeatureNotFound\u001b[0m: Couldn't find a tree builder with the features you requested: lxml. Do you need to install a parser library?"
+     ]
+    }
+   ],
+   "source": [
+    "from bs4 import BeautifulSoup\n",
+    "soup = BeautifulSoup(page.text, 'lxml')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "52zGR4Lfjd4v"
+   },
+   "source": [
+    "### Step 3 : Find the tag,class name,id of the required item/container/object in the html file.\n",
+    "\n",
+    "In this case, it is a table.\n",
+    "Inspect the webpage to navigate in the HTML document.\n",
+    "As an identification feature, we used the CSS class name of the table. *The word class is followed by a _ .\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
     },
+    "id": "Xme8LfCAzUuY",
+    "outputId": "79dd254e-a093-4532-c70d-3330f6040a91"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "5GdOGiJL5Doh"
-      },
-      "source": [
-        "### Column Names\n",
-        "### 1.4 cols=df.columns\n",
-        "is a method in pandas that returns the column labels of the DataFrame as a pandas Index object. The column labels can be used to subset or manipulate the DataFrame. This method is useful for quickly obtaining the column labels of the DataFrame without having to manually type them out."
-      ]
+     "ename": "NameError",
+     "evalue": "name 'soup' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[15], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m table \u001b[38;5;241m=\u001b[39m \u001b[43msoup\u001b[49m\u001b[38;5;241m.\u001b[39mfind(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtable\u001b[39m\u001b[38;5;124m'\u001b[39m, class_\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mos-football-table\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28mprint\u001b[39m(table)\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'soup' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "table = soup.find('table', class_='os-football-table')\n",
+    "print(table)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "ES4AqVRHkOGA"
+   },
+   "source": [
+    "### Step 4 : Extract the headers/ column names of the table.\n",
+    "\n",
+    "They are present inside the first tag \"tr\" .\n",
+    "\n",
+    "So use table.find('tr') or store all the rows using find_all('tr') and use the first one.\n",
+    "\n",
+    "all_rows= table.find_all('tr') and all_rows[0] .\n",
+    "\n",
+    "Inside the tr container, there are several th containers that contain the text data. Extract data from each of them by iterating over a loop. Store their text data in a list named headers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
     },
-    {
-      "cell_type": "code",
-      "execution_count": 5,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "KvgZi8BK4-Io",
-        "outputId": "80e1d7ec-45a7-4f1a-bb0e-f137031c9320"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Index(['pclass', 'survived', 'name', 'sex', 'age', 'sibsp', 'parch', 'ticket',\n",
-            "       'fare', 'cabin', 'embarked', 'boat', 'body', 'home.dest'],\n",
-            "      dtype='object')\n"
-          ]
-        }
-      ],
-      "source": [
-        "cols = df.columns\n",
-        "print(cols)"
-      ]
+    "id": "tjWCvUCgzYpj",
+    "outputId": "96f3f33b-07cb-4854-acc8-54ee5f936d99"
+   },
+   "outputs": [],
+   "source": [
+    "headers = []\n",
+    "first_row=table.find('tr');\n",
+    "\n",
+    "for i in first_row.find_all('th'):\n",
+    " title = i.text\n",
+    " headers.append(title)\n",
+    "\n",
+    "print(headers)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "mYqe8JZhljUM"
+   },
+   "source": [
+    "### Step 5 : Store the data inside a Pandas DataFrame.\n",
+    "\n",
+    "1. Create a blank DataFrame with the header list as the column names.\n",
+    "2. Iterate over all the rows starting from second.\n",
+    "3. Inside the row containers (enclosed by tr and /tr), the data is present in td containers. Iterate over all of them and store their data in a list.\n",
+    "4. Append the list to the dataframe. This is done by adding the list to that row of the dataframe which has index==current_length of the dataframe. The 'loc' function of pandas come in handy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "sRHcTmcZqfXL"
-      },
-      "source": [
-        "### 1.5 df.unique() and df.nunique()\n",
-        "They are used to display all the unique values in a dataframe and the number of unique values in a dataframe respectively.  df.col_name.unique() and df.col_name.nunique() does the same for a colume.   Note : df.col_name and df[\"col_name\"] are identical."
-      ]
+    "id": "NhSn8aF_zdwI",
+    "outputId": "0b4606b0-68a4-4d15-d411-5f0474d1b78a"
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "mydata = pd.DataFrame(columns = headers)\n",
+    "for j in table.find_all('tr')[1:]:\n",
+    "  row_data = j.find_all('td')\n",
+    "  row = [i.text for i in row_data]\n",
+    "  length = len(mydata)\n",
+    "  mydata.loc[length] = row\n",
+    "print(mydata)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "P30hCJrCmvAG"
+   },
+   "source": [
+    "### This is an additional step to include the data of the column \"Form\".\n",
+    "\n",
+    "On inspecting the website, we realized that each row of the form column consists of 5 containers with class names of os-win, os-draw and os-loss , each of which renders a circle of specific color in the webpage.\n",
+    "\n",
+    "Here, for each row, we went to the last column (the last column had tag td, so we found out all the td and extracted the last one using -1) and then counted the number of containers with class name os-win (find_all os-win and then find the length of that list formed) and put that value in the specific row of the last column."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
     },
-    {
-      "cell_type": "code",
-      "execution_count": 6,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "51ROI2S6CcTR",
-        "outputId": "cb773ad0-6b83-49af-b5d0-369988b52e7c"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "pclass has 3 unique values\n",
-            "survived has 2 unique values\n",
-            "name has 1307 unique values\n",
-            "sex has 2 unique values\n",
-            "age has 98 unique values\n",
-            "sibsp has 7 unique values\n",
-            "parch has 8 unique values\n",
-            "ticket has 929 unique values\n",
-            "fare has 281 unique values\n",
-            "cabin has 186 unique values\n",
-            "embarked has 3 unique values\n",
-            "boat has 27 unique values\n",
-            "body has 121 unique values\n",
-            "home.dest has 369 unique values\n"
-          ]
-        }
-      ],
-      "source": [
-        "for col in cols:\n",
-        "    print(\"{} has {} unique values\".format(col,df[col].nunique()))"
-      ]
+    "id": "N5EJHrWizgXs",
+    "outputId": "6362cc3f-80b6-46a5-e653-0a19a20d198a"
+   },
+   "outputs": [],
+   "source": [
+    "x=0\n",
+    "for j in table.find_all('tr')[1:]:\n",
+    "  data=j.find_all('td')[-1]\n",
+    "  di = data.find('div',class_=\"os-form\")\n",
+    "  spa1 = di.find_all('span',class_='os-win')\n",
+    "  l=len(spa1)\n",
+    "  mydata.Form[x]=l\n",
+    "  x+=1\n",
+    "\n",
+    "print(mydata)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# End of Part 1"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "4cMGeud_slkT"
+   },
+   "source": [
+    "# Data PreProcessing\n",
+    "## Importing data from Sklearn/Kaggle etc.\n",
+    "Many libraries and websites provide pre-made datasets. Visit [Kaggle](https://www.kaggle.com/datasets) to explore some of the datsets it hosts.\n",
+    "\n",
+    "For now, we will be use the Sklearn Library to get the titanic dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
     },
+    "id": "biTuX-N1bpcf",
+    "outputId": "c7475f33-6705-42a8-f833-0d9917ec3601"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "8Z_-k_GFtvAd"
-      },
-      "source": [
-        "# Step 2 : Null Values\n",
-        "\n",
-        "### 2.1 df.isna().sum()\n",
-        "is a chained method that is often used with df.isna(). It calculates the total number of missing values (null or NaN) in each column of the DataFrame. Specifically, it returns a Series object that shows the count of missing values for each column of the DataFrame."
-      ]
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\yash0\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\datasets\\_openml.py:1022: FutureWarning: The default value of `parser` will change from `'liac-arff'` to `'auto'` in 1.4. You can set `parser='auto'` to silence this warning. Therefore, an `ImportError` will be raised from 1.4 if the dataset is dense and pandas is not installed. Note that the pandas parser may return different data types. See the Notes Section in fetch_openml's API doc for details.\n",
+      "  warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.datasets import fetch_openml\n",
+    "data = fetch_openml('titanic', version=1, as_frame=True)\n",
+    "df = data.frame"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "E09pcdfHWb1Q"
+   },
+   "source": [
+    "## Step 1: Understanding the data\n",
+    "\n",
+    "### 1.1 df.head() \n",
+    "is a method used in pandas, a popular data analysis library in Python, to display the first few rows of a DataFrame.\n",
+    "\n",
+    "The method returns a new DataFrame containing the first n rows of the original DataFrame, where n is the number specified in the parentheses. By default, n is set to 5."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 337
     },
+    "id": "LXVYGuOhmD6t",
+    "outputId": "cd3ee45e-a754-450e-851b-1096fb84afc9"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 7,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "cUBUKu4qtH7d",
-        "outputId": "e33c27b3-8071-47d0-aa37-ab3b1bb95a32"
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-        {
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-            "text/plain": [
-              "pclass          0\n",
-              "survived        0\n",
-              "name            0\n",
-              "sex             0\n",
-              "age           263\n",
-              "sibsp           0\n",
-              "parch           0\n",
-              "ticket          0\n",
-              "fare            1\n",
-              "cabin        1014\n",
-              "embarked        2\n",
-              "boat          823\n",
-              "body         1188\n",
-              "home.dest     564\n",
-              "dtype: int64"
-            ]
-          },
-          "execution_count": 7,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
+     "data": {
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+       "      <th>3</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Allison, Mr. Hudson Joshua Creighton</td>\n",
+       "      <td>male</td>\n",
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+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>135.0</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n",
+       "      <td>female</td>\n",
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+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
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+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
       ],
-      "source": [
-        "df.isna().sum()"
+      "text/plain": [
+       "   pclass survived                                             name     sex  \\\n",
+       "0     1.0        1                    Allen, Miss. Elisabeth Walton  female   \n",
+       "1     1.0        1                   Allison, Master. Hudson Trevor    male   \n",
+       "2     1.0        0                     Allison, Miss. Helen Loraine  female   \n",
+       "3     1.0        0             Allison, Mr. Hudson Joshua Creighton    male   \n",
+       "4     1.0        0  Allison, Mrs. Hudson J C (Bessie Waldo Daniels)  female   \n",
+       "\n",
+       "       age  sibsp  parch  ticket      fare    cabin embarked boat   body  \\\n",
+       "0  29.0000    0.0    0.0   24160  211.3375       B5        S    2    NaN   \n",
+       "1   0.9167    1.0    2.0  113781  151.5500  C22 C26        S   11    NaN   \n",
+       "2   2.0000    1.0    2.0  113781  151.5500  C22 C26        S  NaN    NaN   \n",
+       "3  30.0000    1.0    2.0  113781  151.5500  C22 C26        S  NaN  135.0   \n",
+       "4  25.0000    1.0    2.0  113781  151.5500  C22 C26        S  NaN    NaN   \n",
+       "\n",
+       "                         home.dest  \n",
+       "0                     St Louis, MO  \n",
+       "1  Montreal, PQ / Chesterville, ON  \n",
+       "2  Montreal, PQ / Chesterville, ON  \n",
+       "3  Montreal, PQ / Chesterville, ON  \n",
+       "4  Montreal, PQ / Chesterville, ON  "
       ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "VAL_93jPs0fN"
+   },
+   "source": [
+    "### 1.2 df.info() \n",
+    "is a method in pandas that provides a summary of the DataFrame's metadata. It returns information about the DataFrame, such as:\n",
+    "\n",
+    "* The number of rows and columns in the DataFrame\n",
+    "* The data type of each column\n",
+    "* The number of non-null values in each column\n",
+    "* The memory usage of the DataFrame\n",
+    "\n",
+    "This method is particularly useful for gaining a quick understanding of the DataFrame's data types and whether there are any missing values. It can also help identify any potential issues with the data, such as columns with mixed data types. Additionally, it can be used to check the memory usage of the DataFrame, which can be important when working with large datasets."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
     },
+    "id": "2QXcvvQysR26",
+    "outputId": "b3c7fca0-1627-402f-8c94-16c92a119459"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "VGcZ2KFtd-51"
-      },
-      "source": [
-        "## Dropping out columns with excessive null values.\n",
-        "\n",
-        "### 2.2 df.drop()\n",
-        "Sometimes, too much of your data is missing for a particular feature. Eg, try running df.isna().sum() again and you will find that the feature Cabin has 1014 missing values. \n",
-        "\n",
-        "Likewise, the feature \"boat\" has 823 missing values.\n",
-        "It only makes sense to remove such features from our dataset.\n",
-        "\n",
-        "Again, if you find out that some feature name does not make sense in a dataset, that feature should be dropped. Here, name and ticket number has no relation to whether a person survived or not.\n",
-        "\n",
-        "df.drop() can also delete rows. To delete rows, pass in the row indexes as labels and axis=0.\n",
-        "\n",
-        "axis=1 refers to column and axis=0 refers to rows."
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 1309 entries, 0 to 1308\n",
+      "Data columns (total 14 columns):\n",
+      " #   Column     Non-Null Count  Dtype   \n",
+      "---  ------     --------------  -----   \n",
+      " 0   pclass     1309 non-null   float64 \n",
+      " 1   survived   1309 non-null   category\n",
+      " 2   name       1309 non-null   object  \n",
+      " 3   sex        1309 non-null   category\n",
+      " 4   age        1046 non-null   float64 \n",
+      " 5   sibsp      1309 non-null   float64 \n",
+      " 6   parch      1309 non-null   float64 \n",
+      " 7   ticket     1309 non-null   object  \n",
+      " 8   fare       1308 non-null   float64 \n",
+      " 9   cabin      295 non-null    object  \n",
+      " 10  embarked   1307 non-null   category\n",
+      " 11  boat       486 non-null    object  \n",
+      " 12  body       121 non-null    object  \n",
+      " 13  home.dest  745 non-null    object  \n",
+      "dtypes: category(3), float64(5), object(6)\n",
+      "memory usage: 116.8+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "uJCV-2Num4Tx"
+   },
+   "source": [
+    "### 1.3 df.describe()\n",
+    "is a method to generate descriptive statistics of the numerical columns of the data. It displays the central tendency, dispersion and shape of a dataset's distribution, excluding NaN values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 300
     },
+    "id": "Zk_C0941mIz9",
+    "outputId": "666a15a4-3bed-47f0-bab5-1d331a61034c"
+   },
+   "outputs": [
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-              "          const element = document.querySelector('#df-8dd61d97-a5e3-4f45-a987-7e4701cd7f4a');\n",
-              "          const dataTable =\n",
-              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                     [key], {});\n",
-              "          if (!dataTable) return;\n",
-              "\n",
-              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "            + ' to learn more about interactive tables.';\n",
-              "          element.innerHTML = '';\n",
-              "          dataTable['output_type'] = 'display_data';\n",
-              "          await google.colab.output.renderOutput(dataTable, element);\n",
-              "          const docLink = document.createElement('div');\n",
-              "          docLink.innerHTML = docLinkHtml;\n",
-              "          element.appendChild(docLink);\n",
-              "        }\n",
-              "      </script>\n",
-              "    </div>\n",
-              "  </div>\n",
-              "  "
-            ],
-            "text/plain": [
-              "   pclass survived     sex      age  sibsp  parch      fare embarked\n",
-              "0     1.0        1  female  29.0000    0.0    0.0  211.3375        S\n",
-              "1     1.0        1    male   0.9167    1.0    2.0  151.5500        S\n",
-              "2     1.0        0  female   2.0000    1.0    2.0  151.5500        S\n",
-              "3     1.0        0    male  30.0000    1.0    2.0  151.5500        S\n",
-              "4     1.0        0  female  25.0000    1.0    2.0  151.5500        S"
-            ]
-          },
-          "execution_count": 8,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
+     "data": {
+      "text/html": [
+       "\n",
+       "  <div id=\"df-29747124-0830-4756-a92c-117908e87e59\">\n",
+       "    <div class=\"colab-df-container\">\n",
+       "      <div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>pclass</th>\n",
+       "      <th>age</th>\n",
+       "      <th>sibsp</th>\n",
+       "      <th>parch</th>\n",
+       "      <th>fare</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>1309.000000</td>\n",
+       "      <td>1046.000000</td>\n",
+       "      <td>1309.000000</td>\n",
+       "      <td>1309.000000</td>\n",
+       "      <td>1308.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>2.294882</td>\n",
+       "      <td>29.881135</td>\n",
+       "      <td>0.498854</td>\n",
+       "      <td>0.385027</td>\n",
+       "      <td>33.295479</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>0.837836</td>\n",
+       "      <td>14.413500</td>\n",
+       "      <td>1.041658</td>\n",
+       "      <td>0.865560</td>\n",
+       "      <td>51.758668</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.166700</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>21.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>7.895800</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>28.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>14.454200</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>39.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>31.275000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>80.000000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>9.000000</td>\n",
+       "      <td>512.329200</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-29747124-0830-4756-a92c-117908e87e59')\"\n",
+       "              title=\"Convert this dataframe to an interactive table.\"\n",
+       "              style=\"display:none;\">\n",
+       "        \n",
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+       "  </svg>\n",
+       "      </button>\n",
+       "      \n",
+       "  <style>\n",
+       "    .colab-df-container {\n",
+       "      display:flex;\n",
+       "      flex-wrap:wrap;\n",
+       "      gap: 12px;\n",
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+       "\n",
+       "    .colab-df-convert {\n",
+       "      background-color: #E8F0FE;\n",
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+       "      padding: 0 0 0 0;\n",
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+       "\n",
+       "    .colab-df-convert:hover {\n",
+       "      background-color: #E2EBFA;\n",
+       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "      fill: #174EA6;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert {\n",
+       "      background-color: #3B4455;\n",
+       "      fill: #D2E3FC;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert:hover {\n",
+       "      background-color: #434B5C;\n",
+       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+       "      fill: #FFFFFF;\n",
+       "    }\n",
+       "  </style>\n",
+       "\n",
+       "      <script>\n",
+       "        const buttonEl =\n",
+       "          document.querySelector('#df-29747124-0830-4756-a92c-117908e87e59 button.colab-df-convert');\n",
+       "        buttonEl.style.display =\n",
+       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "\n",
+       "        async function convertToInteractive(key) {\n",
+       "          const element = document.querySelector('#df-29747124-0830-4756-a92c-117908e87e59');\n",
+       "          const dataTable =\n",
+       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+       "                                                     [key], {});\n",
+       "          if (!dataTable) return;\n",
+       "\n",
+       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+       "            + ' to learn more about interactive tables.';\n",
+       "          element.innerHTML = '';\n",
+       "          dataTable['output_type'] = 'display_data';\n",
+       "          await google.colab.output.renderOutput(dataTable, element);\n",
+       "          const docLink = document.createElement('div');\n",
+       "          docLink.innerHTML = docLinkHtml;\n",
+       "          element.appendChild(docLink);\n",
+       "        }\n",
+       "      </script>\n",
+       "    </div>\n",
+       "  </div>\n",
+       "  "
       ],
-      "source": [
-        "df = df.drop(['cabin', 'boat', 'name','ticket','body',\"home.dest\"], axis=1)\n",
-        "df.head()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "lSatrB7ruJhU"
-      },
-      "source": [
-        "## Filling the Null Values\n",
-        "\n",
-        "### 2.3.1 For numerical columns.\n",
-        "### df.fillna()\n",
-        "Is a method in pandas that is used to fill missing or null values in a DataFrame. It can be used to fill in missing values with a specified value or with values computed from other data in the DataFrame, such as the mean, median or mode. The method can also be used to forward or backward fill missing values to carry forward the last known value or the next known value, respectively.\n",
-        "\n",
-        "Refer to <a href = \"https://scikit-learn.org/stable/modules/classes.html#module-sklearn.impute\">sklearn.impute</a> for more ways of filling in missing values. "
+      "text/plain": [
+       "            pclass          age        sibsp        parch         fare\n",
+       "count  1309.000000  1046.000000  1309.000000  1309.000000  1308.000000\n",
+       "mean      2.294882    29.881135     0.498854     0.385027    33.295479\n",
+       "std       0.837836    14.413500     1.041658     0.865560    51.758668\n",
+       "min       1.000000     0.166700     0.000000     0.000000     0.000000\n",
+       "25%       2.000000    21.000000     0.000000     0.000000     7.895800\n",
+       "50%       3.000000    28.000000     0.000000     0.000000    14.454200\n",
+       "75%       3.000000    39.000000     1.000000     0.000000    31.275000\n",
+       "max       3.000000    80.000000     8.000000     9.000000   512.329200"
       ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "5GdOGiJL5Doh"
+   },
+   "source": [
+    "### Column Names\n",
+    "### 1.4 cols=df.columns\n",
+    "is a method in pandas that returns the column labels of the DataFrame as a pandas Index object. The column labels can be used to subset or manipulate the DataFrame. This method is useful for quickly obtaining the column labels of the DataFrame without having to manually type them out."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
     },
+    "id": "KvgZi8BK4-Io",
+    "outputId": "80e1d7ec-45a7-4f1a-bb0e-f137031c9320"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 9,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "HLy713BcuCJ9",
-        "outputId": "72cf66c8-7794-4a02-c096-88c75672eda9"
-      },
-      "outputs": [
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "<ipython-input-9-dec89c434036>:2: FutureWarning: The default value of numeric_only in DataFrame.median is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
-            "  df.fillna(df.median(), inplace=True)\n"
-          ]
-        }
-      ],
-      "source": [
-        "# df.fillna(df.mean(), inplace=True)\n",
-        "df.fillna(df.median(), inplace=True)"
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Index(['pclass', 'survived', 'name', 'sex', 'age', 'sibsp', 'parch', 'ticket',\n",
+      "       'fare', 'cabin', 'embarked', 'boat', 'body', 'home.dest'],\n",
+      "      dtype='object')\n"
+     ]
+    }
+   ],
+   "source": [
+    "cols = df.columns\n",
+    "print(cols)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "sRHcTmcZqfXL"
+   },
+   "source": [
+    "### 1.5 df.unique() and df.nunique()\n",
+    "They are used to display all the unique values in a dataframe and the number of unique values in a dataframe respectively.  df.col_name.unique() and df.col_name.nunique() does the same for a colume.   Note : df.col_name and df[\"col_name\"] are identical."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
     },
+    "id": "51ROI2S6CcTR",
+    "outputId": "cb773ad0-6b83-49af-b5d0-369988b52e7c"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 10,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "lrg6gdPwdLA2",
-        "outputId": "202aaaf7-24c3-4c39-fae5-6464a3a12a46"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "pclass      0\n",
-              "survived    0\n",
-              "sex         0\n",
-              "age         0\n",
-              "sibsp       0\n",
-              "parch       0\n",
-              "fare        0\n",
-              "embarked    2\n",
-              "dtype: int64"
-            ]
-          },
-          "execution_count": 10,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "df.isna().sum()"
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "pclass has 3 unique values\n",
+      "survived has 2 unique values\n",
+      "name has 1307 unique values\n",
+      "sex has 2 unique values\n",
+      "age has 98 unique values\n",
+      "sibsp has 7 unique values\n",
+      "parch has 8 unique values\n",
+      "ticket has 929 unique values\n",
+      "fare has 281 unique values\n",
+      "cabin has 186 unique values\n",
+      "embarked has 3 unique values\n",
+      "boat has 27 unique values\n",
+      "body has 121 unique values\n",
+      "home.dest has 369 unique values\n"
+     ]
+    }
+   ],
+   "source": [
+    "for col in cols:\n",
+    "    print(\"{} has {} unique values\".format(col,df[col].nunique()))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "8Z_-k_GFtvAd"
+   },
+   "source": [
+    "# Step 2 : Null Values\n",
+    "\n",
+    "### 2.1 df.isna().sum()\n",
+    "is a chained method that is often used with df.isna(). It calculates the total number of missing values (null or NaN) in each column of the DataFrame. Specifically, it returns a Series object that shows the count of missing values for each column of the DataFrame."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
     },
+    "id": "cUBUKu4qtH7d",
+    "outputId": "e33c27b3-8071-47d0-aa37-ab3b1bb95a32"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "7Qi4CQM__Oep"
-      },
-      "source": [
-        "### 2.3.2 For Categorical columns.\n",
-        "As we can see there are 2 values missing in the feature \"embarked\" and 564 in the feature \"home.dest\"\n",
-        "\n",
-        "One way is to replace the missing values with value with maximum frequence of occurance. This is obviously a method with obvious flaws, but is our best shot.\n",
-        "\n",
-        "The other option is to just drop this feature (as discussed earlier)"
+     "data": {
+      "text/plain": [
+       "pclass          0\n",
+       "survived        0\n",
+       "name            0\n",
+       "sex             0\n",
+       "age           263\n",
+       "sibsp           0\n",
+       "parch           0\n",
+       "ticket          0\n",
+       "fare            1\n",
+       "cabin        1014\n",
+       "embarked        2\n",
+       "boat          823\n",
+       "body         1188\n",
+       "home.dest     564\n",
+       "dtype: int64"
       ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.isna().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "VGcZ2KFtd-51"
+   },
+   "source": [
+    "## Dropping out columns with excessive null values.\n",
+    "\n",
+    "### 2.2 df.drop()\n",
+    "Sometimes, too much of your data is missing for a particular feature. Eg, try running df.isna().sum() again and you will find that the feature Cabin has 1014 missing values. \n",
+    "\n",
+    "Likewise, the feature \"boat\" has 823 missing values.\n",
+    "It only makes sense to remove such features from our dataset.\n",
+    "\n",
+    "Again, if you find out that some feature name does not make sense in a dataset, that feature should be dropped. Here, name and ticket number has no relation to whether a person survived or not.\n",
+    "\n",
+    "df.drop() can also delete rows. To delete rows, pass in the row indexes as labels and axis=0.\n",
+    "\n",
+    "axis=1 refers to column and axis=0 refers to rows."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 206
     },
+    "id": "TTCS5abfd-Js",
+    "outputId": "619aa0fa-5235-45b8-d056-f174daef17e1"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 11,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "Nwtttd25_N5o",
-        "outputId": "734ca5c6-871b-47d1-afa4-53e044c77360"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "<class 'pandas.core.frame.DataFrame'>\n",
-            "RangeIndex: 1309 entries, 0 to 1308\n",
-            "Data columns (total 8 columns):\n",
-            " #   Column    Non-Null Count  Dtype   \n",
-            "---  ------    --------------  -----   \n",
-            " 0   pclass    1309 non-null   float64 \n",
-            " 1   survived  1309 non-null   category\n",
-            " 2   sex       1309 non-null   category\n",
-            " 3   age       1309 non-null   float64 \n",
-            " 4   sibsp     1309 non-null   float64 \n",
-            " 5   parch     1309 non-null   float64 \n",
-            " 6   fare      1309 non-null   float64 \n",
-            " 7   embarked  1309 non-null   category\n",
-            "dtypes: category(3), float64(5)\n",
-            "memory usage: 55.5 KB\n"
-          ]
-        }
+     "data": {
+      "text/html": [
+       "\n",
+       "  <div id=\"df-8dd61d97-a5e3-4f45-a987-7e4701cd7f4a\">\n",
+       "    <div class=\"colab-df-container\">\n",
+       "      <div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>pclass</th>\n",
+       "      <th>survived</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>age</th>\n",
+       "      <th>sibsp</th>\n",
+       "      <th>parch</th>\n",
+       "      <th>fare</th>\n",
+       "      <th>embarked</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>female</td>\n",
+       "      <td>29.0000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>211.3375</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0.9167</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>151.5500</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>female</td>\n",
+       "      <td>2.0000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>151.5500</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>male</td>\n",
+       "      <td>30.0000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>151.5500</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>female</td>\n",
+       "      <td>25.0000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>151.5500</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8dd61d97-a5e3-4f45-a987-7e4701cd7f4a')\"\n",
+       "              title=\"Convert this dataframe to an interactive table.\"\n",
+       "              style=\"display:none;\">\n",
+       "        \n",
+       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+       "       width=\"24px\">\n",
+       "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
+       "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+       "  </svg>\n",
+       "      </button>\n",
+       "      \n",
+       "  <style>\n",
+       "    .colab-df-container {\n",
+       "      display:flex;\n",
+       "      flex-wrap:wrap;\n",
+       "      gap: 12px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert {\n",
+       "      background-color: #E8F0FE;\n",
+       "      border: none;\n",
+       "      border-radius: 50%;\n",
+       "      cursor: pointer;\n",
+       "      display: none;\n",
+       "      fill: #1967D2;\n",
+       "      height: 32px;\n",
+       "      padding: 0 0 0 0;\n",
+       "      width: 32px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert:hover {\n",
+       "      background-color: #E2EBFA;\n",
+       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "      fill: #174EA6;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert {\n",
+       "      background-color: #3B4455;\n",
+       "      fill: #D2E3FC;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert:hover {\n",
+       "      background-color: #434B5C;\n",
+       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+       "      fill: #FFFFFF;\n",
+       "    }\n",
+       "  </style>\n",
+       "\n",
+       "      <script>\n",
+       "        const buttonEl =\n",
+       "          document.querySelector('#df-8dd61d97-a5e3-4f45-a987-7e4701cd7f4a button.colab-df-convert');\n",
+       "        buttonEl.style.display =\n",
+       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "\n",
+       "        async function convertToInteractive(key) {\n",
+       "          const element = document.querySelector('#df-8dd61d97-a5e3-4f45-a987-7e4701cd7f4a');\n",
+       "          const dataTable =\n",
+       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+       "                                                     [key], {});\n",
+       "          if (!dataTable) return;\n",
+       "\n",
+       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+       "            + ' to learn more about interactive tables.';\n",
+       "          element.innerHTML = '';\n",
+       "          dataTable['output_type'] = 'display_data';\n",
+       "          await google.colab.output.renderOutput(dataTable, element);\n",
+       "          const docLink = document.createElement('div');\n",
+       "          docLink.innerHTML = docLinkHtml;\n",
+       "          element.appendChild(docLink);\n",
+       "        }\n",
+       "      </script>\n",
+       "    </div>\n",
+       "  </div>\n",
+       "  "
       ],
-      "source": [
-        "# Fill NaN values with mode for column\n",
-        "df['embarked'] = df['embarked'].fillna(df['embarked'].mode().iloc[0])\n",
-        "\n",
-        "df.info()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "oLn1BftYqQ_a"
-      },
-      "source": [
-        "# Step 3: Duplicate rows\n",
-        "### 3.1 df.duplicated().sum()\n",
-        "The df.duplicated() method returns a Series with True and False values that describe which rows in the DataFrame are duplicated and not. The df.duplicated().sum() returns the number of rows which have more than one occurance in the dataframe."
+      "text/plain": [
+       "   pclass survived     sex      age  sibsp  parch      fare embarked\n",
+       "0     1.0        1  female  29.0000    0.0    0.0  211.3375        S\n",
+       "1     1.0        1    male   0.9167    1.0    2.0  151.5500        S\n",
+       "2     1.0        0  female   2.0000    1.0    2.0  151.5500        S\n",
+       "3     1.0        0    male  30.0000    1.0    2.0  151.5500        S\n",
+       "4     1.0        0  female  25.0000    1.0    2.0  151.5500        S"
       ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = df.drop(['cabin', 'boat', 'name','ticket','body',\"home.dest\"], axis=1)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "lSatrB7ruJhU"
+   },
+   "source": [
+    "## Filling the Null Values\n",
+    "\n",
+    "### 2.3.1 For numerical columns.\n",
+    "### df.fillna()\n",
+    "Is a method in pandas that is used to fill missing or null values in a DataFrame. It can be used to fill in missing values with a specified value or with values computed from other data in the DataFrame, such as the mean, median or mode. The method can also be used to forward or backward fill missing values to carry forward the last known value or the next known value, respectively.\n",
+    "\n",
+    "Refer to <a href = \"https://scikit-learn.org/stable/modules/classes.html#module-sklearn.impute\">sklearn.impute</a> for more ways of filling in missing values. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
     },
+    "id": "HLy713BcuCJ9",
+    "outputId": "72cf66c8-7794-4a02-c096-88c75672eda9"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 12,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "HScSs5hRqiEq",
-        "outputId": "8ff87787-fb8e-44cf-c2dd-b31dfa0055b3"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "202"
-            ]
-          },
-          "execution_count": 12,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "df.duplicated().sum()"
-      ]
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<ipython-input-9-dec89c434036>:2: FutureWarning: The default value of numeric_only in DataFrame.median is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
+      "  df.fillna(df.median(), inplace=True)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# df.fillna(df.mean(), inplace=True)\n",
+    "df.fillna(df.median(), inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
     },
+    "id": "lrg6gdPwdLA2",
+    "outputId": "202aaaf7-24c3-4c39-fae5-6464a3a12a46"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "1MbhUwDl4xl2"
-      },
-      "source": [
-        "### 3.2 df.drop_duplicates()\n",
-        "is a method in pandas that is used to remove duplicate rows from a DataFrame. By default, it removes all rows that are completely identical to a previous row. It can also be used to remove duplicate rows based on a subset of columns. This method is useful when ensuring that each row in the DataFrame is unique and that no data is duplicated."
+     "data": {
+      "text/plain": [
+       "pclass      0\n",
+       "survived    0\n",
+       "sex         0\n",
+       "age         0\n",
+       "sibsp       0\n",
+       "parch       0\n",
+       "fare        0\n",
+       "embarked    2\n",
+       "dtype: int64"
       ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.isna().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "7Qi4CQM__Oep"
+   },
+   "source": [
+    "### 2.3.2 For Categorical columns.\n",
+    "As we can see there are 2 values missing in the feature \"embarked\" and 564 in the feature \"home.dest\"\n",
+    "\n",
+    "One way is to replace the missing values with value with maximum frequence of occurance. This is obviously a method with obvious flaws, but is our best shot.\n",
+    "\n",
+    "The other option is to just drop this feature (as discussed earlier)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
     },
+    "id": "Nwtttd25_N5o",
+    "outputId": "734ca5c6-871b-47d1-afa4-53e044c77360"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 13,
-      "metadata": {
-        "id": "_Lme4NFZx8OZ"
-      },
-      "outputs": [],
-      "source": [
-        "df.drop_duplicates(inplace=True)"
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 1309 entries, 0 to 1308\n",
+      "Data columns (total 8 columns):\n",
+      " #   Column    Non-Null Count  Dtype   \n",
+      "---  ------    --------------  -----   \n",
+      " 0   pclass    1309 non-null   float64 \n",
+      " 1   survived  1309 non-null   category\n",
+      " 2   sex       1309 non-null   category\n",
+      " 3   age       1309 non-null   float64 \n",
+      " 4   sibsp     1309 non-null   float64 \n",
+      " 5   parch     1309 non-null   float64 \n",
+      " 6   fare      1309 non-null   float64 \n",
+      " 7   embarked  1309 non-null   category\n",
+      "dtypes: category(3), float64(5)\n",
+      "memory usage: 55.5 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Fill NaN values with mode for column\n",
+    "df['embarked'] = df['embarked'].fillna(df['embarked'].mode().iloc[0])\n",
+    "\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "oLn1BftYqQ_a"
+   },
+   "source": [
+    "# Step 3: Duplicate rows\n",
+    "### 3.1 df.duplicated().sum()\n",
+    "The df.duplicated() method returns a Series with True and False values that describe which rows in the DataFrame are duplicated and not. The df.duplicated().sum() returns the number of rows which have more than one occurance in the dataframe."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
     },
+    "id": "HScSs5hRqiEq",
+    "outputId": "8ff87787-fb8e-44cf-c2dd-b31dfa0055b3"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 14,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "7sXlqzrOrwOt",
-        "outputId": "047bdda0-c6ba-475a-eeea-1a5f0d249c8b"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "<class 'pandas.core.frame.DataFrame'>\n",
-            "Int64Index: 1107 entries, 0 to 1308\n",
-            "Data columns (total 8 columns):\n",
-            " #   Column    Non-Null Count  Dtype   \n",
-            "---  ------    --------------  -----   \n",
-            " 0   pclass    1107 non-null   float64 \n",
-            " 1   survived  1107 non-null   category\n",
-            " 2   sex       1107 non-null   category\n",
-            " 3   age       1107 non-null   float64 \n",
-            " 4   sibsp     1107 non-null   float64 \n",
-            " 5   parch     1107 non-null   float64 \n",
-            " 6   fare      1107 non-null   float64 \n",
-            " 7   embarked  1107 non-null   category\n",
-            "dtypes: category(3), float64(5)\n",
-            "memory usage: 55.5 KB\n"
-          ]
-        }
-      ],
-      "source": [
-        "df.info()"
+     "data": {
+      "text/plain": [
+       "202"
       ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.duplicated().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "1MbhUwDl4xl2"
+   },
+   "source": [
+    "### 3.2 df.drop_duplicates()\n",
+    "is a method in pandas that is used to remove duplicate rows from a DataFrame. By default, it removes all rows that are completely identical to a previous row. It can also be used to remove duplicate rows based on a subset of columns. This method is useful when ensuring that each row in the DataFrame is unique and that no data is duplicated."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "id": "_Lme4NFZx8OZ"
+   },
+   "outputs": [],
+   "source": [
+    "df.drop_duplicates(inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
     },
+    "id": "7sXlqzrOrwOt",
+    "outputId": "047bdda0-c6ba-475a-eeea-1a5f0d249c8b"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "Qkr02gGI_1NE"
-      },
-      "source": [
-        "# Step 4: Normalization and Standardization of Numerical Data\n",
-        "\n",
-        "<a href=\"https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.Normalizer.html\">Normalization</a> is the process of converting a range of values into a standard range of values, typically in the interval [-1, 1] or [0, 1]. It's not a strict requirement but it improves the speed of learning (e.g. faster convergence in gradient descent) and prevents numerical overflow.\n",
-        "\n",
-        "<a href=\"https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html\">Standardization</a> is the process of rescaling the attributes so that they have mean as 0 and variance as 1. It brings down all the features to a common scale without distorting the differences in the range of the values.\n",
-        "\n",
-        "In this dataset, let's normalize the fare column and standardize the age column. We are doing this randomly just to show how its done!!"
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Int64Index: 1107 entries, 0 to 1308\n",
+      "Data columns (total 8 columns):\n",
+      " #   Column    Non-Null Count  Dtype   \n",
+      "---  ------    --------------  -----   \n",
+      " 0   pclass    1107 non-null   float64 \n",
+      " 1   survived  1107 non-null   category\n",
+      " 2   sex       1107 non-null   category\n",
+      " 3   age       1107 non-null   float64 \n",
+      " 4   sibsp     1107 non-null   float64 \n",
+      " 5   parch     1107 non-null   float64 \n",
+      " 6   fare      1107 non-null   float64 \n",
+      " 7   embarked  1107 non-null   category\n",
+      "dtypes: category(3), float64(5)\n",
+      "memory usage: 55.5 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Qkr02gGI_1NE"
+   },
+   "source": [
+    "# Step 4: Normalization and Standardization of Numerical Data\n",
+    "\n",
+    "<a href=\"https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.Normalizer.html\">Normalization</a> is the process of converting a range of values into a standard range of values, typically in the interval [-1, 1] or [0, 1]. It's not a strict requirement but it improves the speed of learning (e.g. faster convergence in gradient descent) and prevents numerical overflow.\n",
+    "\n",
+    "<a href=\"https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html\">Standardization</a> is the process of rescaling the attributes so that they have mean as 0 and variance as 1. It brings down all the features to a common scale without distorting the differences in the range of the values.\n",
+    "\n",
+    "In this dataset, let's normalize the fare column and standardize the age column. We are doing this randomly just to show how its done!!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
     },
+    "id": "hSXpSHC6DA4x",
+    "outputId": "ad7ac292-a4e2-48fe-db0e-28496e8142b0"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 15,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "hSXpSHC6DA4x",
-        "outputId": "ad7ac292-a4e2-48fe-db0e-28496e8142b0"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "pclass has datatype float64 and has 3 unique values\n",
-            "survived has datatype category and has 2 unique values\n",
-            "sex has datatype category and has 2 unique values\n",
-            "age has datatype float64 and has 98 unique values\n",
-            "sibsp has datatype float64 and has 7 unique values\n",
-            "parch has datatype float64 and has 8 unique values\n",
-            "fare has datatype float64 and has 281 unique values\n",
-            "embarked has datatype category and has 3 unique values\n"
-          ]
-        }
-      ],
-      "source": [
-        "cols=df.columns\n",
-        "for col in cols:\n",
-        "    print(\"{} has datatype {} and has {} unique values\".format(col,df[col].dtype,df[col].nunique()))"
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "pclass has datatype float64 and has 3 unique values\n",
+      "survived has datatype category and has 2 unique values\n",
+      "sex has datatype category and has 2 unique values\n",
+      "age has datatype float64 and has 98 unique values\n",
+      "sibsp has datatype float64 and has 7 unique values\n",
+      "parch has datatype float64 and has 8 unique values\n",
+      "fare has datatype float64 and has 281 unique values\n",
+      "embarked has datatype category and has 3 unique values\n"
+     ]
+    }
+   ],
+   "source": [
+    "cols=df.columns\n",
+    "for col in cols:\n",
+    "    print(\"{} has datatype {} and has {} unique values\".format(col,df[col].dtype,df[col].nunique()))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 206
     },
+    "id": "UK8pcQG9EBBg",
+    "outputId": "a898841d-0aeb-4807-fb2b-b3041fc8e4e0"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 17,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 206
-        },
-        "id": "UK8pcQG9EBBg",
-        "outputId": "a898841d-0aeb-4807-fb2b-b3041fc8e4e0"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/html": [
-              "\n",
-              "  <div id=\"df-30648161-7f74-4fa4-8355-b2925047d03f\">\n",
-              "    <div class=\"colab-df-container\">\n",
-              "      <div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>pclass</th>\n",
-              "      <th>survived</th>\n",
-              "      <th>sex</th>\n",
-              "      <th>age</th>\n",
-              "      <th>sibsp</th>\n",
-              "      <th>parch</th>\n",
-              "      <th>fare</th>\n",
-              "      <th>embarked</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>1.0</td>\n",
-              "      <td>1</td>\n",
-              "      <td>female</td>\n",
-              "      <td>29.0000</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.412503</td>\n",
-              "      <td>S</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>1.0</td>\n",
-              "      <td>1</td>\n",
-              "      <td>male</td>\n",
-              "      <td>0.9167</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>2.0</td>\n",
-              "      <td>0.295806</td>\n",
-              "      <td>S</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>1.0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>female</td>\n",
-              "      <td>2.0000</td>\n",
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-              "      <td>2.0</td>\n",
-              "      <td>0.295806</td>\n",
-              "      <td>S</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>1.0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>male</td>\n",
-              "      <td>30.0000</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>2.0</td>\n",
-              "      <td>0.295806</td>\n",
-              "      <td>S</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>1.0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>female</td>\n",
-              "      <td>25.0000</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>2.0</td>\n",
-              "      <td>0.295806</td>\n",
-              "      <td>S</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>\n",
-              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-30648161-7f74-4fa4-8355-b2925047d03f')\"\n",
-              "              title=\"Convert this dataframe to an interactive table.\"\n",
-              "              style=\"display:none;\">\n",
-              "        \n",
-              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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-              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
-              "  </svg>\n",
-              "      </button>\n",
-              "      \n",
-              "  <style>\n",
-              "    .colab-df-container {\n",
-              "      display:flex;\n",
-              "      flex-wrap:wrap;\n",
-              "      gap: 12px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert {\n",
-              "      background-color: #E8F0FE;\n",
-              "      border: none;\n",
-              "      border-radius: 50%;\n",
-              "      cursor: pointer;\n",
-              "      display: none;\n",
-              "      fill: #1967D2;\n",
-              "      height: 32px;\n",
-              "      padding: 0 0 0 0;\n",
-              "      width: 32px;\n",
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-              "\n",
-              "    .colab-df-convert:hover {\n",
-              "      background-color: #E2EBFA;\n",
-              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "      fill: #174EA6;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "      fill: #FFFFFF;\n",
-              "    }\n",
-              "  </style>\n",
-              "\n",
-              "      <script>\n",
-              "        const buttonEl =\n",
-              "          document.querySelector('#df-30648161-7f74-4fa4-8355-b2925047d03f button.colab-df-convert');\n",
-              "        buttonEl.style.display =\n",
-              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "        async function convertToInteractive(key) {\n",
-              "          const element = document.querySelector('#df-30648161-7f74-4fa4-8355-b2925047d03f');\n",
-              "          const dataTable =\n",
-              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                     [key], {});\n",
-              "          if (!dataTable) return;\n",
-              "\n",
-              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "            + ' to learn more about interactive tables.';\n",
-              "          element.innerHTML = '';\n",
-              "          dataTable['output_type'] = 'display_data';\n",
-              "          await google.colab.output.renderOutput(dataTable, element);\n",
-              "          const docLink = document.createElement('div');\n",
-              "          docLink.innerHTML = docLinkHtml;\n",
-              "          element.appendChild(docLink);\n",
-              "        }\n",
-              "      </script>\n",
-              "    </div>\n",
-              "  </div>\n",
-              "  "
-            ],
-            "text/plain": [
-              "   pclass survived     sex      age  sibsp  parch      fare embarked\n",
-              "0     1.0        1  female  29.0000    0.0    0.0  0.412503        S\n",
-              "1     1.0        1    male   0.9167    1.0    2.0  0.295806        S\n",
-              "2     1.0        0  female   2.0000    1.0    2.0  0.295806        S\n",
-              "3     1.0        0    male  30.0000    1.0    2.0  0.295806        S\n",
-              "4     1.0        0  female  25.0000    1.0    2.0  0.295806        S"
-            ]
-          },
-          "execution_count": 17,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
+     "data": {
+      "text/html": [
+       "\n",
+       "  <div id=\"df-30648161-7f74-4fa4-8355-b2925047d03f\">\n",
+       "    <div class=\"colab-df-container\">\n",
+       "      <div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>pclass</th>\n",
+       "      <th>survived</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>age</th>\n",
+       "      <th>sibsp</th>\n",
+       "      <th>parch</th>\n",
+       "      <th>fare</th>\n",
+       "      <th>embarked</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>female</td>\n",
+       "      <td>29.0000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.412503</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0.9167</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.295806</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>female</td>\n",
+       "      <td>2.0000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.295806</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>male</td>\n",
+       "      <td>30.0000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.295806</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>female</td>\n",
+       "      <td>25.0000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.295806</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-30648161-7f74-4fa4-8355-b2925047d03f')\"\n",
+       "              title=\"Convert this dataframe to an interactive table.\"\n",
+       "              style=\"display:none;\">\n",
+       "        \n",
+       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+       "       width=\"24px\">\n",
+       "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
+       "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+       "  </svg>\n",
+       "      </button>\n",
+       "      \n",
+       "  <style>\n",
+       "    .colab-df-container {\n",
+       "      display:flex;\n",
+       "      flex-wrap:wrap;\n",
+       "      gap: 12px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert {\n",
+       "      background-color: #E8F0FE;\n",
+       "      border: none;\n",
+       "      border-radius: 50%;\n",
+       "      cursor: pointer;\n",
+       "      display: none;\n",
+       "      fill: #1967D2;\n",
+       "      height: 32px;\n",
+       "      padding: 0 0 0 0;\n",
+       "      width: 32px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert:hover {\n",
+       "      background-color: #E2EBFA;\n",
+       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "      fill: #174EA6;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert {\n",
+       "      background-color: #3B4455;\n",
+       "      fill: #D2E3FC;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert:hover {\n",
+       "      background-color: #434B5C;\n",
+       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+       "      fill: #FFFFFF;\n",
+       "    }\n",
+       "  </style>\n",
+       "\n",
+       "      <script>\n",
+       "        const buttonEl =\n",
+       "          document.querySelector('#df-30648161-7f74-4fa4-8355-b2925047d03f button.colab-df-convert');\n",
+       "        buttonEl.style.display =\n",
+       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "\n",
+       "        async function convertToInteractive(key) {\n",
+       "          const element = document.querySelector('#df-30648161-7f74-4fa4-8355-b2925047d03f');\n",
+       "          const dataTable =\n",
+       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+       "                                                     [key], {});\n",
+       "          if (!dataTable) return;\n",
+       "\n",
+       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+       "            + ' to learn more about interactive tables.';\n",
+       "          element.innerHTML = '';\n",
+       "          dataTable['output_type'] = 'display_data';\n",
+       "          await google.colab.output.renderOutput(dataTable, element);\n",
+       "          const docLink = document.createElement('div');\n",
+       "          docLink.innerHTML = docLinkHtml;\n",
+       "          element.appendChild(docLink);\n",
+       "        }\n",
+       "      </script>\n",
+       "    </div>\n",
+       "  </div>\n",
+       "  "
       ],
-      "source": [
-        "import numpy as np\n",
-        "from sklearn.preprocessing import MinMaxScaler\n",
-        "arr= np.array(df['fare'])\n",
-        "minmax= MinMaxScaler()\n",
-        "df['fare']=minmax.fit_transform(arr.reshape(-1,1))\n",
-        "df.head()"
+      "text/plain": [
+       "   pclass survived     sex      age  sibsp  parch      fare embarked\n",
+       "0     1.0        1  female  29.0000    0.0    0.0  0.412503        S\n",
+       "1     1.0        1    male   0.9167    1.0    2.0  0.295806        S\n",
+       "2     1.0        0  female   2.0000    1.0    2.0  0.295806        S\n",
+       "3     1.0        0    male  30.0000    1.0    2.0  0.295806        S\n",
+       "4     1.0        0  female  25.0000    1.0    2.0  0.295806        S"
       ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "arr= np.array(df['fare'])\n",
+    "minmax= MinMaxScaler()\n",
+    "df['fare']=minmax.fit_transform(arr.reshape(-1,1))\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 206
     },
+    "id": "iiPBsiGwIOPW",
+    "outputId": "ef6d4ef1-2cba-4f3d-a03e-5df78b29c2d4"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 18,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 206
-        },
-        "id": "iiPBsiGwIOPW",
-        "outputId": "ef6d4ef1-2cba-4f3d-a03e-5df78b29c2d4"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/html": [
-              "\n",
-              "  <div id=\"df-b754a9fe-818a-49c0-837e-226d3b50973a\">\n",
-              "    <div class=\"colab-df-container\">\n",
-              "      <div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>pclass</th>\n",
-              "      <th>survived</th>\n",
-              "      <th>sex</th>\n",
-              "      <th>age</th>\n",
-              "      <th>sibsp</th>\n",
-              "      <th>parch</th>\n",
-              "      <th>fare</th>\n",
-              "      <th>embarked</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>1.0</td>\n",
-              "      <td>1</td>\n",
-              "      <td>female</td>\n",
-              "      <td>-0.060648</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.412503</td>\n",
-              "      <td>S</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>1.0</td>\n",
-              "      <td>1</td>\n",
-              "      <td>male</td>\n",
-              "      <td>-2.092555</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>2.0</td>\n",
-              "      <td>0.295806</td>\n",
-              "      <td>S</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>1.0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>female</td>\n",
-              "      <td>-2.014175</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>2.0</td>\n",
-              "      <td>0.295806</td>\n",
-              "      <td>S</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>1.0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>male</td>\n",
-              "      <td>0.011705</td>\n",
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-              "      <td>0.295806</td>\n",
-              "      <td>S</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>1.0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>female</td>\n",
-              "      <td>-0.350059</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>2.0</td>\n",
-              "      <td>0.295806</td>\n",
-              "      <td>S</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>\n",
-              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b754a9fe-818a-49c0-837e-226d3b50973a')\"\n",
-              "              title=\"Convert this dataframe to an interactive table.\"\n",
-              "              style=\"display:none;\">\n",
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-              "      </button>\n",
-              "      \n",
-              "  <style>\n",
-              "    .colab-df-container {\n",
-              "      display:flex;\n",
-              "      flex-wrap:wrap;\n",
-              "      gap: 12px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert {\n",
-              "      background-color: #E8F0FE;\n",
-              "      border: none;\n",
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-              "    .colab-df-convert:hover {\n",
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-              "      fill: #174EA6;\n",
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-              "\n",
-              "    [theme=dark] .colab-df-convert {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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-              "      fill: #FFFFFF;\n",
-              "    }\n",
-              "  </style>\n",
-              "\n",
-              "      <script>\n",
-              "        const buttonEl =\n",
-              "          document.querySelector('#df-b754a9fe-818a-49c0-837e-226d3b50973a button.colab-df-convert');\n",
-              "        buttonEl.style.display =\n",
-              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "        async function convertToInteractive(key) {\n",
-              "          const element = document.querySelector('#df-b754a9fe-818a-49c0-837e-226d3b50973a');\n",
-              "          const dataTable =\n",
-              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                     [key], {});\n",
-              "          if (!dataTable) return;\n",
-              "\n",
-              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "            + ' to learn more about interactive tables.';\n",
-              "          element.innerHTML = '';\n",
-              "          dataTable['output_type'] = 'display_data';\n",
-              "          await google.colab.output.renderOutput(dataTable, element);\n",
-              "          const docLink = document.createElement('div');\n",
-              "          docLink.innerHTML = docLinkHtml;\n",
-              "          element.appendChild(docLink);\n",
-              "        }\n",
-              "      </script>\n",
-              "    </div>\n",
-              "  </div>\n",
-              "  "
-            ],
-            "text/plain": [
-              "   pclass survived     sex       age  sibsp  parch      fare embarked\n",
-              "0     1.0        1  female -0.060648    0.0    0.0  0.412503        S\n",
-              "1     1.0        1    male -2.092555    1.0    2.0  0.295806        S\n",
-              "2     1.0        0  female -2.014175    1.0    2.0  0.295806        S\n",
-              "3     1.0        0    male  0.011705    1.0    2.0  0.295806        S\n",
-              "4     1.0        0  female -0.350059    1.0    2.0  0.295806        S"
-            ]
-          },
-          "execution_count": 18,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
+     "data": {
+      "text/html": [
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+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>pclass</th>\n",
+       "      <th>survived</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>age</th>\n",
+       "      <th>sibsp</th>\n",
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+       "      <td>0.295806</td>\n",
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+       "      <td>0</td>\n",
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+       "      <td>0.295806</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0.011705</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.295806</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>female</td>\n",
+       "      <td>-0.350059</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.295806</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b754a9fe-818a-49c0-837e-226d3b50973a')\"\n",
+       "              title=\"Convert this dataframe to an interactive table.\"\n",
+       "              style=\"display:none;\">\n",
+       "        \n",
+       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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+       "  </svg>\n",
+       "      </button>\n",
+       "      \n",
+       "  <style>\n",
+       "    .colab-df-container {\n",
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+       "      flex-wrap:wrap;\n",
+       "      gap: 12px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert {\n",
+       "      background-color: #E8F0FE;\n",
+       "      border: none;\n",
+       "      border-radius: 50%;\n",
+       "      cursor: pointer;\n",
+       "      display: none;\n",
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+       "      height: 32px;\n",
+       "      padding: 0 0 0 0;\n",
+       "      width: 32px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert:hover {\n",
+       "      background-color: #E2EBFA;\n",
+       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "      fill: #174EA6;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert {\n",
+       "      background-color: #3B4455;\n",
+       "      fill: #D2E3FC;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert:hover {\n",
+       "      background-color: #434B5C;\n",
+       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+       "      fill: #FFFFFF;\n",
+       "    }\n",
+       "  </style>\n",
+       "\n",
+       "      <script>\n",
+       "        const buttonEl =\n",
+       "          document.querySelector('#df-b754a9fe-818a-49c0-837e-226d3b50973a button.colab-df-convert');\n",
+       "        buttonEl.style.display =\n",
+       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "\n",
+       "        async function convertToInteractive(key) {\n",
+       "          const element = document.querySelector('#df-b754a9fe-818a-49c0-837e-226d3b50973a');\n",
+       "          const dataTable =\n",
+       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+       "                                                     [key], {});\n",
+       "          if (!dataTable) return;\n",
+       "\n",
+       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+       "            + ' to learn more about interactive tables.';\n",
+       "          element.innerHTML = '';\n",
+       "          dataTable['output_type'] = 'display_data';\n",
+       "          await google.colab.output.renderOutput(dataTable, element);\n",
+       "          const docLink = document.createElement('div');\n",
+       "          docLink.innerHTML = docLinkHtml;\n",
+       "          element.appendChild(docLink);\n",
+       "        }\n",
+       "      </script>\n",
+       "    </div>\n",
+       "  </div>\n",
+       "  "
       ],
-      "source": [
-        "from sklearn.preprocessing import StandardScaler\n",
-        "arr= np.array(df['age']) #This has to be done only if one column is standardized. For more than one column, it is simple.\n",
-        "scaler = StandardScaler()\n",
-        "df['age']=scaler.fit_transform(arr.reshape(-1,1))\n",
-        "df.head()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "Kp4VwHfA6u7-"
-      },
-      "source": [
-        "# Step 5: Encoding Categorical Data\n",
-        "Categorical data is data that represents categories or labels, such as color, type, or sex. In order to use categorical data in machine learning models, it needs to be transformed into numerical data.\n",
-        "\n",
-        "\n",
-        "* Ordinal encoding is a method of encoding categorical data where each unique category is assigned an integer value. The assigned integers are based on the order of the categories, which may not be meaningful in all cases. For example, if we have a categorical variable \"size\" with three categories - small, medium, and large - we could assign the values 0, 1, and 2 respectively. However, this method assumes that there is an inherent order to the categories, which may not always be true.\n",
-        "\n",
-        "* One-hot encoding, on the other hand, is a method of encoding categorical data where each unique category is transformed into a binary vector. Each binary vector has a length equal to the number of unique categories, with a value of 1 in the corresponding index of the category and 0 in all other indices. For example, if we have a categorical variable \"color\" with three categories - red, blue, and green - we could transform it into three binary vectors [1,0,0], [0,1,0], and [0,0,1], respectively. This method avoids the assumption of an inherent order to the categories and works well with machine learning algorithms.\n",
-        "\n",
-        "In Python, you can use the `sklearn.preprocessing` module to perform both ordinal and one-hot encoding. The `OrdinalEncoder` class can be used for ordinal (or label) encoding, while the `OneHotEncoder` class can be used for one-hot encoding. Additionally, pandas provides a `get_dummies()` function which can be used for one-hot encoding of categorical variables.\n",
-        "\n",
-        "\n",
-        "After encoding the categorical data to numbers, we can retrieve the categorical values using the inverse_transform function."
+      "text/plain": [
+       "   pclass survived     sex       age  sibsp  parch      fare embarked\n",
+       "0     1.0        1  female -0.060648    0.0    0.0  0.412503        S\n",
+       "1     1.0        1    male -2.092555    1.0    2.0  0.295806        S\n",
+       "2     1.0        0  female -2.014175    1.0    2.0  0.295806        S\n",
+       "3     1.0        0    male  0.011705    1.0    2.0  0.295806        S\n",
+       "4     1.0        0  female -0.350059    1.0    2.0  0.295806        S"
       ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import StandardScaler\n",
+    "arr= np.array(df['age']) #This has to be done only if one column is standardized. For more than one column, it is simple.\n",
+    "scaler = StandardScaler()\n",
+    "df['age']=scaler.fit_transform(arr.reshape(-1,1))\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Kp4VwHfA6u7-"
+   },
+   "source": [
+    "# Step 5: Encoding Categorical Data\n",
+    "Categorical data is data that represents categories or labels, such as color, type, or sex. In order to use categorical data in machine learning models, it needs to be transformed into numerical data.\n",
+    "\n",
+    "\n",
+    "* Ordinal encoding is a method of encoding categorical data where each unique category is assigned an integer value. The assigned integers are based on the order of the categories, which may not be meaningful in all cases. For example, if we have a categorical variable \"size\" with three categories - small, medium, and large - we could assign the values 0, 1, and 2 respectively. However, this method assumes that there is an inherent order to the categories, which may not always be true.\n",
+    "\n",
+    "* One-hot encoding, on the other hand, is a method of encoding categorical data where each unique category is transformed into a binary vector. Each binary vector has a length equal to the number of unique categories, with a value of 1 in the corresponding index of the category and 0 in all other indices. For example, if we have a categorical variable \"color\" with three categories - red, blue, and green - we could transform it into three binary vectors [1,0,0], [0,1,0], and [0,0,1], respectively. This method avoids the assumption of an inherent order to the categories and works well with machine learning algorithms.\n",
+    "\n",
+    "In Python, you can use the `sklearn.preprocessing` module to perform both ordinal and one-hot encoding. The `OrdinalEncoder` class can be used for ordinal (or label) encoding, while the `OneHotEncoder` class can be used for one-hot encoding. Additionally, pandas provides a `get_dummies()` function which can be used for one-hot encoding of categorical variables.\n",
+    "\n",
+    "\n",
+    "After encoding the categorical data to numbers, we can retrieve the categorical values using the inverse_transform function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 206
     },
+    "id": "DVUTm4Bv5ISn",
+    "outputId": "2cd34bdb-c55b-494a-cd4e-daae0483045c"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 19,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 206
-        },
-        "id": "DVUTm4Bv5ISn",
-        "outputId": "2cd34bdb-c55b-494a-cd4e-daae0483045c"
-      },
-      "outputs": [
-        {
-          "data": {
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-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>1.0</td>\n",
-              "      <td>1</td>\n",
-              "      <td>female</td>\n",
-              "      <td>-0.060648</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.412503</td>\n",
-              "      <td>2</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>1.0</td>\n",
-              "      <td>1</td>\n",
-              "      <td>male</td>\n",
-              "      <td>-2.092555</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>2.0</td>\n",
-              "      <td>0.295806</td>\n",
-              "      <td>2</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>1.0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>female</td>\n",
-              "      <td>-2.014175</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>2.0</td>\n",
-              "      <td>0.295806</td>\n",
-              "      <td>2</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>1.0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>male</td>\n",
-              "      <td>0.011705</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>2.0</td>\n",
-              "      <td>0.295806</td>\n",
-              "      <td>2</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>1.0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>female</td>\n",
-              "      <td>-0.350059</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>2.0</td>\n",
-              "      <td>0.295806</td>\n",
-              "      <td>2</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>\n",
-              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cb363cb2-0067-45c9-b3c3-2978246dd5eb')\"\n",
-              "              title=\"Convert this dataframe to an interactive table.\"\n",
-              "              style=\"display:none;\">\n",
-              "        \n",
-              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-              "       width=\"24px\">\n",
-              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
-              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
-              "  </svg>\n",
-              "      </button>\n",
-              "      \n",
-              "  <style>\n",
-              "    .colab-df-container {\n",
-              "      display:flex;\n",
-              "      flex-wrap:wrap;\n",
-              "      gap: 12px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert {\n",
-              "      background-color: #E8F0FE;\n",
-              "      border: none;\n",
-              "      border-radius: 50%;\n",
-              "      cursor: pointer;\n",
-              "      display: none;\n",
-              "      fill: #1967D2;\n",
-              "      height: 32px;\n",
-              "      padding: 0 0 0 0;\n",
-              "      width: 32px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert:hover {\n",
-              "      background-color: #E2EBFA;\n",
-              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "      fill: #174EA6;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "      fill: #FFFFFF;\n",
-              "    }\n",
-              "  </style>\n",
-              "\n",
-              "      <script>\n",
-              "        const buttonEl =\n",
-              "          document.querySelector('#df-cb363cb2-0067-45c9-b3c3-2978246dd5eb button.colab-df-convert');\n",
-              "        buttonEl.style.display =\n",
-              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "        async function convertToInteractive(key) {\n",
-              "          const element = document.querySelector('#df-cb363cb2-0067-45c9-b3c3-2978246dd5eb');\n",
-              "          const dataTable =\n",
-              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                     [key], {});\n",
-              "          if (!dataTable) return;\n",
-              "\n",
-              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "            + ' to learn more about interactive tables.';\n",
-              "          element.innerHTML = '';\n",
-              "          dataTable['output_type'] = 'display_data';\n",
-              "          await google.colab.output.renderOutput(dataTable, element);\n",
-              "          const docLink = document.createElement('div');\n",
-              "          docLink.innerHTML = docLinkHtml;\n",
-              "          element.appendChild(docLink);\n",
-              "        }\n",
-              "      </script>\n",
-              "    </div>\n",
-              "  </div>\n",
-              "  "
-            ],
-            "text/plain": [
-              "   pclass  survived     sex       age  sibsp  parch      fare  embarked\n",
-              "0     1.0         1  female -0.060648    0.0    0.0  0.412503         2\n",
-              "1     1.0         1    male -2.092555    1.0    2.0  0.295806         2\n",
-              "2     1.0         0  female -2.014175    1.0    2.0  0.295806         2\n",
-              "3     1.0         0    male  0.011705    1.0    2.0  0.295806         2\n",
-              "4     1.0         0  female -0.350059    1.0    2.0  0.295806         2"
-            ]
-          },
-          "execution_count": 19,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
+     "data": {
+      "text/html": [
+       "\n",
+       "  <div id=\"df-cb363cb2-0067-45c9-b3c3-2978246dd5eb\">\n",
+       "    <div class=\"colab-df-container\">\n",
+       "      <div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>pclass</th>\n",
+       "      <th>survived</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>age</th>\n",
+       "      <th>sibsp</th>\n",
+       "      <th>parch</th>\n",
+       "      <th>fare</th>\n",
+       "      <th>embarked</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>female</td>\n",
+       "      <td>-0.060648</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.412503</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>male</td>\n",
+       "      <td>-2.092555</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.295806</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>female</td>\n",
+       "      <td>-2.014175</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.295806</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0.011705</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.295806</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>female</td>\n",
+       "      <td>-0.350059</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.295806</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cb363cb2-0067-45c9-b3c3-2978246dd5eb')\"\n",
+       "              title=\"Convert this dataframe to an interactive table.\"\n",
+       "              style=\"display:none;\">\n",
+       "        \n",
+       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+       "       width=\"24px\">\n",
+       "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
+       "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+       "  </svg>\n",
+       "      </button>\n",
+       "      \n",
+       "  <style>\n",
+       "    .colab-df-container {\n",
+       "      display:flex;\n",
+       "      flex-wrap:wrap;\n",
+       "      gap: 12px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert {\n",
+       "      background-color: #E8F0FE;\n",
+       "      border: none;\n",
+       "      border-radius: 50%;\n",
+       "      cursor: pointer;\n",
+       "      display: none;\n",
+       "      fill: #1967D2;\n",
+       "      height: 32px;\n",
+       "      padding: 0 0 0 0;\n",
+       "      width: 32px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert:hover {\n",
+       "      background-color: #E2EBFA;\n",
+       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "      fill: #174EA6;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert {\n",
+       "      background-color: #3B4455;\n",
+       "      fill: #D2E3FC;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert:hover {\n",
+       "      background-color: #434B5C;\n",
+       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+       "      fill: #FFFFFF;\n",
+       "    }\n",
+       "  </style>\n",
+       "\n",
+       "      <script>\n",
+       "        const buttonEl =\n",
+       "          document.querySelector('#df-cb363cb2-0067-45c9-b3c3-2978246dd5eb button.colab-df-convert');\n",
+       "        buttonEl.style.display =\n",
+       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "\n",
+       "        async function convertToInteractive(key) {\n",
+       "          const element = document.querySelector('#df-cb363cb2-0067-45c9-b3c3-2978246dd5eb');\n",
+       "          const dataTable =\n",
+       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+       "                                                     [key], {});\n",
+       "          if (!dataTable) return;\n",
+       "\n",
+       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+       "            + ' to learn more about interactive tables.';\n",
+       "          element.innerHTML = '';\n",
+       "          dataTable['output_type'] = 'display_data';\n",
+       "          await google.colab.output.renderOutput(dataTable, element);\n",
+       "          const docLink = document.createElement('div');\n",
+       "          docLink.innerHTML = docLinkHtml;\n",
+       "          element.appendChild(docLink);\n",
+       "        }\n",
+       "      </script>\n",
+       "    </div>\n",
+       "  </div>\n",
+       "  "
       ],
-      "source": [
-        "from sklearn.preprocessing import LabelEncoder\n",
-        "le1 = LabelEncoder()\n",
-        "le2 = LabelEncoder()\n",
-        "df[\"survived\"]=le1.fit_transform(df[\"survived\"])\n",
-        "df[\"embarked\"]=le1.fit_transform(df[\"embarked\"])\n",
-        "\n",
-        "df.head()"
+      "text/plain": [
+       "   pclass  survived     sex       age  sibsp  parch      fare  embarked\n",
+       "0     1.0         1  female -0.060648    0.0    0.0  0.412503         2\n",
+       "1     1.0         1    male -2.092555    1.0    2.0  0.295806         2\n",
+       "2     1.0         0  female -2.014175    1.0    2.0  0.295806         2\n",
+       "3     1.0         0    male  0.011705    1.0    2.0  0.295806         2\n",
+       "4     1.0         0  female -0.350059    1.0    2.0  0.295806         2"
       ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "le1 = LabelEncoder()\n",
+    "le2 = LabelEncoder()\n",
+    "df[\"survived\"]=le1.fit_transform(df[\"survived\"])\n",
+    "df[\"embarked\"]=le1.fit_transform(df[\"embarked\"])\n",
+    "\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 423
     },
+    "id": "57qawhpH-boh",
+    "outputId": "1e05b6c9-03f9-4d30-eb9c-1d437c1a90aa"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 22,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 423
-        },
-        "id": "57qawhpH-boh",
-        "outputId": "1e05b6c9-03f9-4d30-eb9c-1d437c1a90aa"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/html": [
-              "\n",
-              "  <div id=\"df-6d6a22cc-645d-439c-98db-1eec31da8352\">\n",
-              "    <div class=\"colab-df-container\">\n",
-              "      <div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
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-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
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-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>female</th>\n",
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-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>0</td>\n",
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-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>1</td>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>0</td>\n",
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-              "      <th>4</th>\n",
-              "      <td>1</td>\n",
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-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>...</th>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1303</th>\n",
-              "      <td>0</td>\n",
-              "      <td>1</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1304</th>\n",
-              "      <td>1</td>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1306</th>\n",
-              "      <td>0</td>\n",
-              "      <td>1</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1307</th>\n",
-              "      <td>0</td>\n",
-              "      <td>1</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1308</th>\n",
-              "      <td>0</td>\n",
-              "      <td>1</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "<p>1107 rows × 2 columns</p>\n",
-              "</div>\n",
-              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6d6a22cc-645d-439c-98db-1eec31da8352')\"\n",
-              "              title=\"Convert this dataframe to an interactive table.\"\n",
-              "              style=\"display:none;\">\n",
-              "        \n",
-              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-              "       width=\"24px\">\n",
-              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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-              "\n",
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-              "\n",
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-              "      fill: #D2E3FC;\n",
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-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
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-              "  </style>\n",
-              "\n",
-              "      <script>\n",
-              "        const buttonEl =\n",
-              "          document.querySelector('#df-6d6a22cc-645d-439c-98db-1eec31da8352 button.colab-df-convert');\n",
-              "        buttonEl.style.display =\n",
-              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "        async function convertToInteractive(key) {\n",
-              "          const element = document.querySelector('#df-6d6a22cc-645d-439c-98db-1eec31da8352');\n",
-              "          const dataTable =\n",
-              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                     [key], {});\n",
-              "          if (!dataTable) return;\n",
-              "\n",
-              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "            + ' to learn more about interactive tables.';\n",
-              "          element.innerHTML = '';\n",
-              "          dataTable['output_type'] = 'display_data';\n",
-              "          await google.colab.output.renderOutput(dataTable, element);\n",
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-              "          element.appendChild(docLink);\n",
-              "        }\n",
-              "      </script>\n",
-              "    </div>\n",
-              "  </div>\n",
-              "  "
-            ],
-            "text/plain": [
-              "      female  male\n",
-              "0          1     0\n",
-              "1          0     1\n",
-              "2          1     0\n",
-              "3          0     1\n",
-              "4          1     0\n",
-              "...      ...   ...\n",
-              "1303       0     1\n",
-              "1304       1     0\n",
-              "1306       0     1\n",
-              "1307       0     1\n",
-              "1308       0     1\n",
-              "\n",
-              "[1107 rows x 2 columns]"
-            ]
-          },
-          "execution_count": 22,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
+     "data": {
+      "text/html": [
+       "\n",
+       "  <div id=\"df-6d6a22cc-645d-439c-98db-1eec31da8352\">\n",
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+       "      <div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "        vertical-align: top;\n",
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+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>female</th>\n",
+       "      <th>male</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
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+       "      <td>1</td>\n",
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+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
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+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1303</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1304</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1306</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1307</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1308</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1107 rows × 2 columns</p>\n",
+       "</div>\n",
+       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6d6a22cc-645d-439c-98db-1eec31da8352')\"\n",
+       "              title=\"Convert this dataframe to an interactive table.\"\n",
+       "              style=\"display:none;\">\n",
+       "        \n",
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+       "  </svg>\n",
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+       "      gap: 12px;\n",
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+       "\n",
+       "    .colab-df-convert {\n",
+       "      background-color: #E8F0FE;\n",
+       "      border: none;\n",
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+       "      padding: 0 0 0 0;\n",
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+       "\n",
+       "    .colab-df-convert:hover {\n",
+       "      background-color: #E2EBFA;\n",
+       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "      fill: #174EA6;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert {\n",
+       "      background-color: #3B4455;\n",
+       "      fill: #D2E3FC;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert:hover {\n",
+       "      background-color: #434B5C;\n",
+       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+       "      fill: #FFFFFF;\n",
+       "    }\n",
+       "  </style>\n",
+       "\n",
+       "      <script>\n",
+       "        const buttonEl =\n",
+       "          document.querySelector('#df-6d6a22cc-645d-439c-98db-1eec31da8352 button.colab-df-convert');\n",
+       "        buttonEl.style.display =\n",
+       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "\n",
+       "        async function convertToInteractive(key) {\n",
+       "          const element = document.querySelector('#df-6d6a22cc-645d-439c-98db-1eec31da8352');\n",
+       "          const dataTable =\n",
+       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+       "                                                     [key], {});\n",
+       "          if (!dataTable) return;\n",
+       "\n",
+       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+       "            + ' to learn more about interactive tables.';\n",
+       "          element.innerHTML = '';\n",
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+       "          docLink.innerHTML = docLinkHtml;\n",
+       "          element.appendChild(docLink);\n",
+       "        }\n",
+       "      </script>\n",
+       "    </div>\n",
+       "  </div>\n",
+       "  "
       ],
-      "source": [
-        "# We prefer this method as it generates the column names itself.\n",
-        "import pandas as pd\n",
-        "df_encoded = pd.get_dummies(df['sex'])\n",
-        "df_encoded"
+      "text/plain": [
+       "      female  male\n",
+       "0          1     0\n",
+       "1          0     1\n",
+       "2          1     0\n",
+       "3          0     1\n",
+       "4          1     0\n",
+       "...      ...   ...\n",
+       "1303       0     1\n",
+       "1304       1     0\n",
+       "1306       0     1\n",
+       "1307       0     1\n",
+       "1308       0     1\n",
+       "\n",
+       "[1107 rows x 2 columns]"
       ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# We prefer this method as it generates the column names itself.\n",
+    "import pandas as pd\n",
+    "df_encoded = pd.get_dummies(df['sex'])\n",
+    "df_encoded"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 206
     },
+    "id": "qKe5uZx8rw4y",
+    "outputId": "2720cdd7-21e5-4645-e9e6-2327d37b95d8"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 23,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 206
-        },
-        "id": "qKe5uZx8rw4y",
-        "outputId": "2720cdd7-21e5-4645-e9e6-2327d37b95d8"
-      },
-      "outputs": [
-        {
-          "data": {
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-              "\n",
-              "    .colab-df-convert {\n",
-              "      background-color: #E8F0FE;\n",
-              "      border: none;\n",
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-              "\n",
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-              "          document.querySelector('#df-d61f1b4f-2632-4ea4-870d-2c0914cde298 button.colab-df-convert');\n",
-              "        buttonEl.style.display =\n",
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-              "          const element = document.querySelector('#df-d61f1b4f-2632-4ea4-870d-2c0914cde298');\n",
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-              "                                                     [key], {});\n",
-              "          if (!dataTable) return;\n",
-              "\n",
-              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "            + ' to learn more about interactive tables.';\n",
-              "          element.innerHTML = '';\n",
-              "          dataTable['output_type'] = 'display_data';\n",
-              "          await google.colab.output.renderOutput(dataTable, element);\n",
-              "          const docLink = document.createElement('div');\n",
-              "          docLink.innerHTML = docLinkHtml;\n",
-              "          element.appendChild(docLink);\n",
-              "        }\n",
-              "      </script>\n",
-              "    </div>\n",
-              "  </div>\n",
-              "  "
-            ],
-            "text/plain": [
-              "   pclass  survived     sex       age  sibsp  parch      fare  embarked  \\\n",
-              "0     1.0         1  female -0.060648    0.0    0.0  0.412503         2   \n",
-              "1     1.0         1    male -2.092555    1.0    2.0  0.295806         2   \n",
-              "2     1.0         0  female -2.014175    1.0    2.0  0.295806         2   \n",
-              "3     1.0         0    male  0.011705    1.0    2.0  0.295806         2   \n",
-              "4     1.0         0  female -0.350059    1.0    2.0  0.295806         2   \n",
-              "\n",
-              "   female  male  \n",
-              "0       1     0  \n",
-              "1       0     1  \n",
-              "2       1     0  \n",
-              "3       0     1  \n",
-              "4       1     0  "
-            ]
-          },
-          "execution_count": 23,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
+     "data": {
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+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>pclass</th>\n",
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+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>male</td>\n",
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+       "              title=\"Convert this dataframe to an interactive table.\"\n",
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+       "\n",
+       "    .colab-df-convert {\n",
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+       "      border-radius: 50%;\n",
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+       "\n",
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+       "      fill: #174EA6;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert {\n",
+       "      background-color: #3B4455;\n",
+       "      fill: #D2E3FC;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert:hover {\n",
+       "      background-color: #434B5C;\n",
+       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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+       "\n",
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+       "        const buttonEl =\n",
+       "          document.querySelector('#df-d61f1b4f-2632-4ea4-870d-2c0914cde298 button.colab-df-convert');\n",
+       "        buttonEl.style.display =\n",
+       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "\n",
+       "        async function convertToInteractive(key) {\n",
+       "          const element = document.querySelector('#df-d61f1b4f-2632-4ea4-870d-2c0914cde298');\n",
+       "          const dataTable =\n",
+       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+       "                                                     [key], {});\n",
+       "          if (!dataTable) return;\n",
+       "\n",
+       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+       "            + ' to learn more about interactive tables.';\n",
+       "          element.innerHTML = '';\n",
+       "          dataTable['output_type'] = 'display_data';\n",
+       "          await google.colab.output.renderOutput(dataTable, element);\n",
+       "          const docLink = document.createElement('div');\n",
+       "          docLink.innerHTML = docLinkHtml;\n",
+       "          element.appendChild(docLink);\n",
+       "        }\n",
+       "      </script>\n",
+       "    </div>\n",
+       "  </div>\n",
+       "  "
       ],
-      "source": [
-        "df=pd.concat([df,df_encoded],axis=1)\n",
-        "df.head()"
+      "text/plain": [
+       "   pclass  survived     sex       age  sibsp  parch      fare  embarked  \\\n",
+       "0     1.0         1  female -0.060648    0.0    0.0  0.412503         2   \n",
+       "1     1.0         1    male -2.092555    1.0    2.0  0.295806         2   \n",
+       "2     1.0         0  female -2.014175    1.0    2.0  0.295806         2   \n",
+       "3     1.0         0    male  0.011705    1.0    2.0  0.295806         2   \n",
+       "4     1.0         0  female -0.350059    1.0    2.0  0.295806         2   \n",
+       "\n",
+       "   female  male  \n",
+       "0       1     0  \n",
+       "1       0     1  \n",
+       "2       1     0  \n",
+       "3       0     1  \n",
+       "4       1     0  "
       ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df=pd.concat([df,df_encoded],axis=1)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 206
     },
+    "id": "HjzlByZkQO2d",
+    "outputId": "66ed6c58-3a4e-4778-b350-418fad944392"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 24,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
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-        },
-        "id": "HjzlByZkQO2d",
-        "outputId": "66ed6c58-3a4e-4778-b350-418fad944392"
-      },
-      "outputs": [
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-              "\n",
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-              "    [theme=dark] .colab-df-convert:hover {\n",
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-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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-              "\n",
-              "      <script>\n",
-              "        const buttonEl =\n",
-              "          document.querySelector('#df-a190c573-4611-4de5-b951-e0bcbe1692e4 button.colab-df-convert');\n",
-              "        buttonEl.style.display =\n",
-              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "        async function convertToInteractive(key) {\n",
-              "          const element = document.querySelector('#df-a190c573-4611-4de5-b951-e0bcbe1692e4');\n",
-              "          const dataTable =\n",
-              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                     [key], {});\n",
-              "          if (!dataTable) return;\n",
-              "\n",
-              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "            + ' to learn more about interactive tables.';\n",
-              "          element.innerHTML = '';\n",
-              "          dataTable['output_type'] = 'display_data';\n",
-              "          await google.colab.output.renderOutput(dataTable, element);\n",
-              "          const docLink = document.createElement('div');\n",
-              "          docLink.innerHTML = docLinkHtml;\n",
-              "          element.appendChild(docLink);\n",
-              "        }\n",
-              "      </script>\n",
-              "    </div>\n",
-              "  </div>\n",
-              "  "
-            ],
-            "text/plain": [
-              "   pclass  survived     sex       age  sibsp  parch      fare  embarked  \\\n",
-              "0     1.0         1  female -0.060648    0.0    0.0  0.412503         2   \n",
-              "1     1.0         1    male -2.092555    1.0    2.0  0.295806         2   \n",
-              "2     1.0         0  female -2.014175    1.0    2.0  0.295806         2   \n",
-              "3     1.0         0    male  0.011705    1.0    2.0  0.295806         2   \n",
-              "4     1.0         0  female -0.350059    1.0    2.0  0.295806         2   \n",
-              "\n",
-              "   female  male  \n",
-              "0     1.0   0.0  \n",
-              "1     0.0   1.0  \n",
-              "2     1.0   0.0  \n",
-              "3     0.0   1.0  \n",
-              "4     1.0   0.0  "
-            ]
-          },
-          "execution_count": 24,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
+     "data": {
+      "text/html": [
+       "\n",
+       "  <div id=\"df-a190c573-4611-4de5-b951-e0bcbe1692e4\">\n",
+       "    <div class=\"colab-df-container\">\n",
+       "      <div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>pclass</th>\n",
+       "      <th>survived</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>age</th>\n",
+       "      <th>sibsp</th>\n",
+       "      <th>parch</th>\n",
+       "      <th>fare</th>\n",
+       "      <th>embarked</th>\n",
+       "      <th>female</th>\n",
+       "      <th>male</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>female</td>\n",
+       "      <td>-0.060648</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.412503</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>male</td>\n",
+       "      <td>-2.092555</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.295806</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>female</td>\n",
+       "      <td>-2.014175</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.295806</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0.011705</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.295806</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>female</td>\n",
+       "      <td>-0.350059</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.295806</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a190c573-4611-4de5-b951-e0bcbe1692e4')\"\n",
+       "              title=\"Convert this dataframe to an interactive table.\"\n",
+       "              style=\"display:none;\">\n",
+       "        \n",
+       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+       "       width=\"24px\">\n",
+       "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
+       "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+       "  </svg>\n",
+       "      </button>\n",
+       "      \n",
+       "  <style>\n",
+       "    .colab-df-container {\n",
+       "      display:flex;\n",
+       "      flex-wrap:wrap;\n",
+       "      gap: 12px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert {\n",
+       "      background-color: #E8F0FE;\n",
+       "      border: none;\n",
+       "      border-radius: 50%;\n",
+       "      cursor: pointer;\n",
+       "      display: none;\n",
+       "      fill: #1967D2;\n",
+       "      height: 32px;\n",
+       "      padding: 0 0 0 0;\n",
+       "      width: 32px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert:hover {\n",
+       "      background-color: #E2EBFA;\n",
+       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "      fill: #174EA6;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert {\n",
+       "      background-color: #3B4455;\n",
+       "      fill: #D2E3FC;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert:hover {\n",
+       "      background-color: #434B5C;\n",
+       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+       "      fill: #FFFFFF;\n",
+       "    }\n",
+       "  </style>\n",
+       "\n",
+       "      <script>\n",
+       "        const buttonEl =\n",
+       "          document.querySelector('#df-a190c573-4611-4de5-b951-e0bcbe1692e4 button.colab-df-convert');\n",
+       "        buttonEl.style.display =\n",
+       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "\n",
+       "        async function convertToInteractive(key) {\n",
+       "          const element = document.querySelector('#df-a190c573-4611-4de5-b951-e0bcbe1692e4');\n",
+       "          const dataTable =\n",
+       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+       "                                                     [key], {});\n",
+       "          if (!dataTable) return;\n",
+       "\n",
+       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+       "            + ' to learn more about interactive tables.';\n",
+       "          element.innerHTML = '';\n",
+       "          dataTable['output_type'] = 'display_data';\n",
+       "          await google.colab.output.renderOutput(dataTable, element);\n",
+       "          const docLink = document.createElement('div');\n",
+       "          docLink.innerHTML = docLinkHtml;\n",
+       "          element.appendChild(docLink);\n",
+       "        }\n",
+       "      </script>\n",
+       "    </div>\n",
+       "  </div>\n",
+       "  "
       ],
-      "source": [
-        "from sklearn.preprocessing import OneHotEncoder\n",
-        "ohe = OneHotEncoder()\n",
-        "arr= np.array(df['sex'])\n",
-        "col=[\"female\",\"male\"] #Here, you need to specify column names.\n",
-        "df[col]=ohe.fit_transform(arr.reshape(-1,1)).toarray()\n",
-        "\n",
-        "df.head()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "K5lXCrsOTPt3"
-      },
-      "source": [
-        "# Looking for imbalance in dataset for classification tasks.\n",
-        "\n",
-        "Consider a classification dataset of 1000 samples and 2 classes such that 900 samples belong to the first class and 100 samples belong to the second. In such situation, the dataset is said to have imbalanced classes. Examples of such datasets are Cancer detection and bank fraudulent transactions.\n",
-        "\n",
-        "When the samples of a certain class are much more than the other class, our model may get biased towards the prediction. To fix it, we try to balance the classes by resampling our dataset. Resampling refers to 2 techniques : oversampling the minority class or undersampling the majority class. <a href=\"https://encord.com/blog/an-introduction-to-balanced-and-imbalanced-datasets-in-machine-learning/\">Read this</a>\n",
-        "\n",
-        "All these comes under Data Augmentation.\n",
-        "\n",
-        "Refer to this <a href=\"https://machinelearningmastery.com/smote-oversampling-for-imbalanced-classification/\">website</a> to learn how to implement SMOTE, an algorithm to oversample minority class. For other methods, refer to this <a href=\"https://imbalanced-learn.org/stable/references/index.html#api\">website</a> .\n"
+      "text/plain": [
+       "   pclass  survived     sex       age  sibsp  parch      fare  embarked  \\\n",
+       "0     1.0         1  female -0.060648    0.0    0.0  0.412503         2   \n",
+       "1     1.0         1    male -2.092555    1.0    2.0  0.295806         2   \n",
+       "2     1.0         0  female -2.014175    1.0    2.0  0.295806         2   \n",
+       "3     1.0         0    male  0.011705    1.0    2.0  0.295806         2   \n",
+       "4     1.0         0  female -0.350059    1.0    2.0  0.295806         2   \n",
+       "\n",
+       "   female  male  \n",
+       "0     1.0   0.0  \n",
+       "1     0.0   1.0  \n",
+       "2     1.0   0.0  \n",
+       "3     0.0   1.0  \n",
+       "4     1.0   0.0  "
       ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
     }
-  ],
-  "metadata": {
-    "colab": {
-      "provenance": []
-    },
-    "kernelspec": {
-      "display_name": "Python 3",
-      "name": "python3"
-    },
-    "language_info": {
-      "name": "python"
-    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import OneHotEncoder\n",
+    "ohe = OneHotEncoder()\n",
+    "arr= np.array(df['sex'])\n",
+    "col=[\"female\",\"male\"] #Here, you need to specify column names.\n",
+    "df[col]=ohe.fit_transform(arr.reshape(-1,1)).toarray()\n",
+    "\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "K5lXCrsOTPt3"
+   },
+   "source": [
+    "# Looking for imbalance in dataset for classification tasks.\n",
+    "\n",
+    "Consider a classification dataset of 1000 samples and 2 classes such that 900 samples belong to the first class and 100 samples belong to the second. In such situation, the dataset is said to have imbalanced classes. Examples of such datasets are Cancer detection and bank fraudulent transactions.\n",
+    "\n",
+    "When the samples of a certain class are much more than the other class, our model may get biased towards the prediction. To fix it, we try to balance the classes by resampling our dataset. Resampling refers to 2 techniques : oversampling the minority class or undersampling the majority class. <a href=\"https://encord.com/blog/an-introduction-to-balanced-and-imbalanced-datasets-in-machine-learning/\">Read this</a>\n",
+    "\n",
+    "All these comes under Data Augmentation.\n",
+    "\n",
+    "Refer to this <a href=\"https://machinelearningmastery.com/smote-oversampling-for-imbalanced-classification/\">website</a> to learn how to implement SMOTE, an algorithm to oversample minority class. For other methods, refer to this <a href=\"https://imbalanced-learn.org/stable/references/index.html#api\">website</a> .\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
   },
-  "nbformat": 4,
-  "nbformat_minor": 0
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
 }
diff --git a/Week 1/Libraries Examples and Resources/Matplotlib_Examples.ipynb b/Week 1/Libraries Examples and Resources/Matplotlib_Examples.ipynb
index 6972db6..ceda1e8 100644
--- a/Week 1/Libraries Examples and Resources/Matplotlib_Examples.ipynb	
+++ b/Week 1/Libraries Examples and Resources/Matplotlib_Examples.ipynb	
@@ -58,7 +58,10 @@
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    }
    },
    "outputs": [],
    "source": [
@@ -75,7 +78,10 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    }
    },
    "source": [
     "# Basic Example\n",
@@ -1058,7 +1064,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -1072,9 +1078,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.12.1"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }
diff --git a/Week 1/ML Assignment/.ipynb_checkpoints/Assignment-checkpoint.ipynb b/Week 1/ML Assignment/.ipynb_checkpoints/Assignment-checkpoint.ipynb
new file mode 100644
index 0000000..a9104a9
--- /dev/null
+++ b/Week 1/ML Assignment/.ipynb_checkpoints/Assignment-checkpoint.ipynb	
@@ -0,0 +1,750 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Y5y_HbJiPKhA"
+   },
+   "source": [
+    "# Linear Regression\n",
+    "## Question 1\n",
+    "Make a class called LinearRegression which provides two functions : fit and predict. Try to implement it from scratch. If stuck, refer to the examples folder."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "id": "gzoG2XilPLFr"
+   },
+   "outputs": [],
+   "source": [
+    "class LinearRegression:\n",
+    "    def __init__(self, learning_rate, epochs):\n",
+    "        self.lr=learning_rate\n",
+    "        self.epochs=epochs\n",
+    "\n",
+    "    def fit(self, X_train, y_train):\n",
+    "        n_samples, n_features = X_train.shape\n",
+    "        y_train=y_train.reshape(-1,1)\n",
+    "        # init parameters\n",
+    "        self.weights = np.zeros((n_features,1))\n",
+    "        self.bias = np.zeros((1,1))\n",
+    "\n",
+    "        # gradient descent\n",
+    "        for i in range(self.epochs):\n",
+    "            delta= -(y_train-np.dot(X_train,self.weights)-self.bias)/n_samples\n",
+    "            dw= np.dot(X_train.T,delta)\n",
+    "            db= np.sum(delta).reshape(1,1)\n",
+    "\n",
+    "            #update weights and biases\n",
+    "            self.weights-= self.lr * dw\n",
+    "            self.bias-= self.lr* db\n",
+    "\n",
+    "    def predict(self, X_test):\n",
+    "        y_predicted = np.dot(X_test,self.weights)+self.bias\n",
+    "        print(self.weights, self.bias)\n",
+    "        return y_predicted\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "PsqoxNag7D3-"
+   },
+   "source": [
+    "## Question 2\n",
+    "\n",
+    "Use the dataset https://www.kaggle.com/datasets/quantbruce/real-estate-price-prediction (*).\n",
+    "1. Read it using pandas.\n",
+    "2. Check for **null values**.\n",
+    "3. For each of the columns (except the first and last), plot the column values in the X-axis against the last column of prices in the Y-axis.\n",
+    "4. Remove the unwanted columns.\n",
+    "5. Split the dataset into train and test data. Test data size = 25% of total dataset.\n",
+    "6. **Normalize** the X_train and X_test using MinMaxScaler from sklearn.preprocessing.\n",
+    "7. Fit the training data into the model created in question 1 and predict the testing data.\n",
+    "8. Use **mean square error and R<sup>2</sup>** from sklearn.metrics as evaluation criterias.\n",
+    "9. Fit the training data into the models of the same name provided by sklearn.linear_model and evaluate the predictions using MSE and R<sup>2</sup>.\n",
+    "10. Tune the hyperparameters of your models (learning rate, epochs) to achieve losses close to that of the sklearn models.\n",
+    "\n",
+    "Note : (*) To solve this question, you may proceed in any of the following ways :\n",
+    "1. Prepare the notebook in Kaggle, download it and submit it separately with the other questions.\n",
+    "2. Download the dataset from kaggle. Upload it to the session storage in Colab.\n",
+    "3. Use Colab data directly in Colab. [Refer here](https://www.kaggle.com/general/74235). For this, you need to create kaggle API token. Before submitting, hide or remove the API token."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "id": "8lupaMcr63QF"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No                                        0\n",
+      "X1 transaction date                       0\n",
+      "X2 house age                              0\n",
+      "X3 distance to the nearest MRT station    0\n",
+      "X4 number of convenience stores           0\n",
+      "X5 latitude                               0\n",
+      "X6 longitude                              0\n",
+      "Y house price of unit area                0\n",
+      "dtype: int64\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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33snV1RVOTk7Ytm2boi61gn799Vc0a9bMomndw8MDQ4cOxenTpxEXF2cRP2jQILi6upq/P3ToEE6cOIFnnnkGV69eNeeenZ2NTp06YceOHeaWFR8fH8TExODixYtW5fjUU08hJSXFYnbPDz/8AKPRaH6N3Ot5AG7/rTZt2lToy9SKZvLrr7/C3t4eI0eOtDj++uuvQwiB3377zerfXdq5seY8l2TSpEmoVKkSgoOD8dBDDyEhIQEzZ85Enz59LOKUXIs2bdqEzMxMjB8/vtDYO9Nr+17zLotr3d1S8n5XQ15eHlatWoWnnnrKfB47duyIwMBArFixosifeeWVVyy+t+Zaduf7OTs7G1euXEGrVq0ghMDBgwetyt3muskA4Ny5c5g0aRLq1auH2NhYzJo1C2+//bZFzJ49e7Bs2TJs3ry50A3WGtWqVbP43nSTu9uLXlHP6e3tDQCoWrVqoeNF/Z5atWoVOnbfffdh9erVAICTJ09CCIGJEydi4sSJReaQkpJiUVBFREQoyv3MmTOoVatWoXNap04d8+PWSkhIgJ2dHSIjI0v8vQBw//33Wxx3cnJC9erVC/3eKlWqFOof9/X1xT///GP+PiAgAJ06dcLq1avx3nvvAbjd/eHg4GBxgT5x4gT++eefYpu+U1JSLL6/m9fMiRMnIIQo8m8LoFBXlZL/X0JCAu6//344OBR/mThx4gTS09MRGBhY5OMF/293cnZ2xsyZM/H6668jKCgILVq0QI8ePfDcc88hODi42J8zOXPmDJo3b17o+J2vpTs/6BR8jZ44cQLA7SKpOOnp6fD19cWsWbMwaNAgVK1aFU2aNEH37t3x3HPPoXr16iXmaBprsmrVKnTq1AnA7ddIw4YNcd9996lyHgCgXbt25g94dyp4oz9z5gxCQ0Ph6elpcfxe3n+lnRtrznNJhg4diieffBJ2dnbw8fExj/0rSMm1yNTFVtwHYTXyLotrnVIF39tK3u9q2LhxIy5fvoxmzZrh5MmT5uMdOnTAt99+i5kzZxY6H0W9L5Vey86ePYt33nkHGzZsKHR9TE9Ptyp3myyGTGs0/PbbbxgzZgymTZuGZ555xuLCNnbsWLRt2xYRERHmwWymtTySkpJw9uzZQjetohT3CUUUM7D5Tvn5+VY9Z1HHlfyegkyfdt544w107dq1yJiaNWtafH9nhV4RKP279e/fHy+88AIOHTqEhg0bYvXq1ejUqZPFjcloNKJLly4YO3Zskc9puila+7vvZDQaYTAY8NtvvxX58x4eHvf8O4r7vSV96itt7MPo0aPRs2dPrF+/Hr///jsmTpyIGTNmYMuWLWjUqJFVuZSm4GvU9Dr/4IMP0LBhwyJ/xnTe+vXrh7Zt22LdunXYuHEjPvjgA8ycORNr164tdswFcLvQ6d27N9atW4cvvvgCly5dwq5duzB9+nSLOC3PgxLFDZQt6ppU2rmx5jyXpFatWujcuXOpcWpdi9TKW23Ozs7FDhDOyckBULgIVuv9XhrTdaBfv35FPr59+3Z06NDB4lhR70sl17L8/Hx06dIFqampGDduHGrXrg13d3dcuHABzz//vNWzRW2uGFq3bh02bNiA2bNno0qVKpgzZw5+//13REVFWTQRnz17FmfOnCnyU0avXr3g7e1daBbS3fL19S30XLdu3UJSUpIqz1+Q6RPPnY4fP24eqGkqCh0dHRVdfKwRFhaGf/75B0aj0eITwrFjx8yPW6tGjRowGo2Ii4sr9qJlet74+HiLovfWrVtITEy86/9n79698fLLL5u7yo4fP44JEyYUyi8rK0v1c1nwdwghEBERUai4upfnjImJQW5ubrGDoGvUqIE//vgDrVu3vuubUI0aNfD666/j9ddfx4kTJ9CwYUN89NFHWL58OYDib8xhYWGIj48vdFzpa8k0ANzLy0vR3yYkJASvvvoqXn31VaSkpKBx48aYNm1aicUQcLurbOnSpdi8eTOOHj0KIYRFN+qd+ZR0HtQQFhaGP/74A5mZmRatQwXPmam1o+B1qbjWjJLOjbXnWQumnGJjYwt9sCsYc7d5l8W1zvRzRb3uAZiP3+1zF2TNUgTZ2dn48ccf8dRTT+GJJ54o9PjIkSOxYsWKQsVQQUqvZUeOHMHx48exdOlSi0lCmzZtUpzznWxqzFBmZiZGjhyJRo0amcdBhIaG4r333kN0dLTF6POFCxdi3bp1Fl+mn/nwww+L/SR8N2rUqFFo3MjChQuLbRm6V+vXr7cY87N3717ExMSYL+qBgYF46KGHsGDBgiILsoJLA1ije/fuSE5Othhnk5eXh08//RQeHh6FZt0o0bt3b9jZ2WHKlClFzp4BgM6dO8PJyQlz5861+DT01VdfIT09HY8++uhd/X98fHzQtWtXrF69Gt999x2cnJzQu3dvi5h+/fph9+7d+P333wv9fFpaGvLy8u7qd9+pT58+sLe3x+TJkwt92hNC4OrVq1Y/Z9++fXHlyhWLJQLufE7g9v8tPz/f3E14p7y8vBI/MOTk5ODGjRsWx2rUqAFPT0/cvHnTfMzd3b3I5+nevTv27t2L3bt3m49lZ2dj4cKFCA8PL7HbFACaNGmCGjVq4MMPP0RWVlahx02v8/z8/EJN7oGBgQgNDbXIszidO3eGn58fVq1ahVWrVqFZs2YWH7KUngc1dO/eHfn5+YX+prNnz4bBYDBfA7y8vBAQEFDouvTFF19YfK/k3Cg9z1p6+OGH4enpiRkzZhQ696bX9r3mXRbXOtPz7tmzB/v377c4npaWhhUrVqBhw4aKu1dL4+7ubn7u0qxbtw7Z2dmIiorCE088UeirR48eWLNmTamvaaXXMlOr0Z0xQohi1wssjU21DL399tu4ePEi1q5da9H8FhUVhaVLl2L06NHo1q0bPD09zdOx72R6QbRv3x5NmzZVLa+XXnoJr7zyCvr27YsuXbrg8OHD+P3334scA6CGmjVrok2bNhg2bBhu3ryJOXPmwN/f36Ib5/PPP0ebNm1Qv359DBkyBNWrV8elS5ewe/dunD9/HocPH76r3z106FAsWLAAzz//PPbv34/w8HD88MMP2LVrF+bMmVNoLIPS/89bb72F9957D23btkWfPn3g7OyMffv2ITQ0FDNmzEClSpUwYcIETJ48Gd26dUOvXr0QHx+PL774Ag8++KDF4EJrPfXUUxg4cCC++OILdO3atdDiaW+++SY2bNiAHj164Pnnn0eTJk2QnZ2NI0eO4IcffsDp06fv+W9do0YNTJ06FRMmTMDp06fRu3dveHp6IjExEevWrcPQoUPxxhtvWPWczz33HL755huMGTMGe/fuRdu2bZGdnY0//vgDr776Kh577DG0b98eL7/8MmbMmIFDhw7h4YcfhqOjI06cOIHvv/8en3zySZGfEoHbrWidOnVCv379EBkZCQcHB6xbtw6XLl1C//79zXFNmjTBvHnzMHXqVNSsWROBgYHo2LEjxo8fj2+//RaPPPIIRo4cCT8/PyxduhSJiYlYs2ZNqWP97Ozs8OWXX+KRRx5B3bp18cILL6By5cq4cOECtm7dCi8vL/z000/IzMxElSpV8MQTT6BBgwbw8PDAH3/8gX379uGjjz4q9Tw6OjqiT58++O6775CdnV1ob0Cl50ENPXv2RIcOHfDWW2/h9OnTaNCgATZu3Igff/wRo0ePtlhL7aWXXsL777+Pl156CU2bNsWOHTsKbWWk5NwoPc9a8vLywuzZs/HSSy/hwQcfNK9zc/jwYeTk5GDp0qX3nHdZXOsAYPz48fj+++/Rrl07vPzyy6hduzYuXryIJUuWICkpCYsXL77b01JIkyZNANxu1enatSvs7e2LfU2uWLEC/v7+aNWqVZGP9+rVC4sWLcIvv/xSaND7nZRey2rXro0aNWrgjTfewIULF+Dl5YU1a9bc/Xhcq+aeSezvv/8W9vb2Yvjw4UU+vnfvXmFnZydGjhxZ7HPczdT6grGmab53Tn3Mz88X48aNEwEBAcLNzU107dpVnDx5stip9QWfs7gl+QcNGiTc3d3N39+5HcdHH30kqlatKpydnUXbtm3F4cOHC/0fEhISxHPPPSeCg4OFo6OjqFy5sujRo4f44Ycf7uqcmFy6dEm88MILIiAgQDg5OYn69esXOYXX2q0Evv76a9GoUSPh7OwsfH19Rfv27cWmTZssYj777DNRu3Zt4ejoKIKCgsSwYcPEtWvXLGLat28v6tatW+j5Bw0aJMLCwgodz8jIEK6uroWm0d4pMzNTTJgwQdSsWVM4OTmJgIAA0apVK/Hhhx+KW7duCSFK3i4FBaYzF5xab7JmzRrRpk0b4e7uLtzd3UXt2rVFVFSUiI+Pv6v/X05OjnjrrbdERESEcHR0FMHBweKJJ54QCQkJFnELFy4UTZo0Ea6ursLT01PUr19fjB07Vly8eLHI8yGEEFeuXBFRUVGidu3awt3dXXh7e4vmzZuL1atXW8QlJyeLRx99VHh6egoAFtPsExISxBNPPCF8fHyEi4uLaNasmfj5558tft70nvv++++LzOPgwYOiT58+wt/fXzg7O4uwsDDRr18/sXnzZiHE7WnIb775pmjQoIHw9PQU7u7uokGDBuKLL74o9v9W0KZNmwQAYTAYxLlz5+7qPBTlbrbjyMzMFK+99poIDQ0Vjo6OolatWuKDDz4otFVFTk6OGDx4sPD29haenp6iX79+IiUlxeK1aM25Ke08F0fpNkIlXYsKTq032bBhg2jVqpVwdXUVXl5eolmzZuLbb79VJW8hyu5ad/78efHSSy+JypUrCwcHB+Hn5yd69Ogh9uzZUyhW6fu9qKn1eXl5YsSIEaJSpUrCYDAUO83+0qVLwsHBQTz77LPF5pyTkyPc3NzE448/LoQo/bWr5FoWFxcnOnfuLDw8PERAQIAYMmSIOHz4cLHLQpTEIITKI6iIiIiIJGJTY4aIiIiICmIxRERERDaNxRARERHZNBZDREREZNNYDBEREZFNYzFERERENs2mFl0sjtFoxMWLF+Hp6WnV8uNERERUfoQQyMzMRGho6D1tqs5iCMDFixcL7fhOREREcjh37hyqVKly1z/PYggwL4t+7tw5eHl5lXM2REREpERGRgaqVq1619ubmLAYwn8783p5ebEYIiIiksy9DnHhAGoiIiKyaSyGiIiIyKaVazG0Y8cO9OzZE6GhoTAYDFi/fr35sdzcXIwbNw7169eHu7s7QkND8dxzz+HixYsWz5GamooBAwbAy8sLPj4+GDx4MLKysjT+nxAREZGsyrUYys7ORoMGDfD5558XeiwnJwcHDhzAxIkTceDAAaxduxbx8fHo1auXRdyAAQPw77//YtOmTfj555+xY8cODB06VKv/AhEREUnOIIQQ5Z0EcHvw07p169C7d+9iY/bt24dmzZrhzJkzqFatGo4ePYrIyEjs27cPTZs2BQBER0eje/fuOH/+PEJDQxX97oyMDHh7eyM9PZ0DqImIiCSh1v1bqjFD6enpMBgM8PHxAQDs3r0bPj4+5kIIADp37gw7OzvExMQU+zw3b95ERkaGxRcRERHZJmmKoRs3bmDcuHF4+umnzdVfcnIyAgMDLeIcHBzg5+eH5OTkYp9rxowZ8Pb2Nn9xwUUiIiLbJUUxlJubi379+kEIgXnz5t3z802YMAHp6enmr3PnzqmQJREREclI94sumgqhM2fOYMuWLRZ9gsHBwUhJSbGIz8vLQ2pqKoKDg4t9TmdnZzg7O5dZzkRERCQPXbcMmQqhEydO4I8//oC/v7/F4y1btkRaWhr2799vPrZlyxYYjUY0b95c63Sll28U2J1wFT8euoDdCVeRb9TF2HoiIqIyVa4tQ1lZWTh58qT5+8TERBw6dAh+fn4ICQnBE088gQMHDuDnn39Gfn6+eRyQn58fnJycUKdOHXTr1g1DhgzB/PnzkZubi+HDh6N///6KZ5LRbdGxSZj8UxyS0m+Yj4V4u2BSz0h0qxdSjpkRERGVrXKdWr9t2zZ06NCh0PFBgwbh3XffRURERJE/t3XrVjz00EMAbi+6OHz4cPz000+ws7ND3759MXfuXHh4eCjOw9an1kfHJmHY8gMo+EIw7fQyb2BjFkRERKQ7at2/dbPOUHmy5WIo3yjQZuYWixahOxkABHu74M9xHWFvd28b4REREanJJtcZIvXtTUwtthACAAEgKf0G9iamapcUERGRhlgM2biUzOILobuJIyIikg2LIRsX6OmiahwREZFsWAzZuGYRfgjxdkFxo4EMuD2rrFmEn5ZpERERaYbFkI2ztzNgUs9IAChUEJm+n9QzkoOniYiowmIxROhWLwTzBjZGsLdlV1iwtwun1RMRUYWn++04SBvd6oWgS2Qw9iamIiXzBgI9b3eNsUWIiIgqOhZDZGZvZ0DLGv6lBxIREVUg7CYjIiIim8ZiiIiIiGwaiyEiIiKyaSyGiIiIyKaxGCIiIiKbxmKIiIiIbBqLISIiIrJpLIaIiIjIprEYIiIiIpvGYoiIiIhsGoshIiIismkshoiIiMimsRgiIiIim8ZiiIiIiGwaiyEiIiKyaSyGiIiIyKaxGCIiIiKbxmKIiIiIbBqLISIiIrJpLIaIiIjIprEYIiIiIpvGYoiIiIhsGoshIiIismkshoiIiMimsRgiIiIim8ZiiIiIiGwaiyEiIiKyaSyGiIiIyKaxGCIiIiKbxmKIiIiIbBqLISIiIrJpLIaIiIjIprEYIiIiIpvGYoiIiIhsGoshIiIismkshoiIiMimsRgiIiIim8ZiiIiIiGwaiyEiIiKyaSyGiIiIyKaxGCIiIiKbxmKIiIiIbFq5FkM7duxAz549ERoaCoPBgPXr11s8LoTAO++8g5CQELi6uqJz5844ceKERUxqaioGDBgALy8v+Pj4YPDgwcjKytLwf0FEREQyK9diKDs7Gw0aNMDnn39e5OOzZs3C3LlzMX/+fMTExMDd3R1du3bFjRs3zDEDBgzAv//+i02bNuHnn3/Gjh07MHToUK3+C0RERCQ5gxBClHcSAGAwGLBu3Tr07t0bwO1WodDQULz++ut44403AADp6ekICgrCkiVL0L9/fxw9ehSRkZHYt28fmjZtCgCIjo5G9+7dcf78eYSGhir63RkZGfD29kZ6ejq8vLzK5P9HRERE6lLr/q3bMUOJiYlITk5G586dzce8vb3RvHlz7N69GwCwe/du+Pj4mAshAOjcuTPs7OwQExNT7HPfvHkTGRkZFl9ERERkm3RbDCUnJwMAgoKCLI4HBQWZH0tOTkZgYKDF4w4ODvDz8zPHFGXGjBnw9vY2f1WtWlXl7ImIiEgWui2GytKECROQnp5u/jp37lx5p0RERETlRLfFUHBwMADg0qVLFscvXbpkfiw4OBgpKSkWj+fl5SE1NdUcUxRnZ2d4eXlZfBEREZFt0m0xFBERgeDgYGzevNl8LCMjAzExMWjZsiUAoGXLlkhLS8P+/fvNMVu2bIHRaETz5s01z5mIiIjk41CevzwrKwsnT540f5+YmIhDhw7Bz88P1apVw+jRozF16lTUqlULERERmDhxIkJDQ80zzurUqYNu3bphyJAhmD9/PnJzczF8+HD0799f8UwyIiIism3lWgz9/fff6NChg/n7MWPGAAAGDRqEJUuWYOzYscjOzsbQoUORlpaGNm3aIDo6Gi4uLuafWbFiBYYPH45OnTrBzs4Offv2xdy5czX/vxAREZGcdLPOUHniOkNERETyqfDrDBERERFpgcUQERER2TQWQ0RERGTTWAwRERGRTWMxRERERDaNxRARERHZNBZDREREZNNYDBEREZFNYzFERERENo3FEBEREdk0FkNERERk01gMERERkU1jMUREREQ2jcUQERER2TQWQ0RERGTTWAwRERGRTWMxRERERDaNxRARERHZNBZDREREZNNYDBEREZFNYzFERERENo3FEBEREdk0FkNERERk01gMERERkU1jMUREREQ2zeFufzAnJwdnz57FrVu3LI4/8MAD95wUERERkVasLoYuX76MF154Ab/99luRj+fn599zUkRERERasbqbbPTo0UhLS0NMTAxcXV0RHR2NpUuXolatWtiwYUNZ5EhERERUZqxuGdqyZQt+/PFHNG3aFHZ2dggLC0OXLl3g5eWFGTNm4NFHHy2LPImIiIjKhNUtQ9nZ2QgMDAQA+Pr64vLlywCA+vXr48CBA+pmR0RERFTGrC6G7r//fsTHxwMAGjRogAULFuDChQuYP38+QkJCVE+QiIiIqCxZ3U02atQoJCUlAQAmTZqEbt26YcWKFXBycsKSJUvUzo+IiIioTBmEEOJeniAnJwfHjh1DtWrVEBAQoFZemsrIyIC3tzfS09Ph5eVV3ukQERGRAmrdv+960cVbt24hPj4eTk5OaNy4sbSFEBEREdk2q4uhnJwcDB48GG5ubqhbty7Onj0LABgxYgTef/991RMkIiIiKktWF0MTJkzA4cOHsW3bNri4uJiPd+7cGatWrVI1OSIiIqKyZvUA6vXr12PVqlVo0aIFDAaD+XjdunWRkJCganJEREREZc3qlqHLly+b1xm6U3Z2tkVxRERERCQDq4uhpk2b4pdffjF/byqAvvzyS7Rs2VK9zIiIiIg0YHU32fTp0/HII48gLi4OeXl5+OSTTxAXF4e//voL27dvL4sciYiIiMqM1S1Dbdq0weHDh5GXl4f69etj48aNCAwMxO7du9GkSZOyyJGIiIiozFjVMpSbm4uXX34ZEydOxKJFi8oqJyIiIiLNWNUy5OjoiDVr1pRVLkRERESas7qbrHfv3li/fn0ZpEJERESkPasHUNeqVQtTpkzBrl270KRJE7i7u1s8PnLkSNWSIyIiIiprVm/UGhERUfyTGQw4derUPSelNW7USkREJB+17t9WtwwlJibe9S8jIiIi0pu73rW+oKNHj+KNN95Q6+mIiIiINHFPxVB2dja++uortGrVCnXr1kV0dLRaeRERERFp4q6KoV27duHFF19EUFAQhg4dilatWiEuLg6xsbFq50dERERUphQXQykpKZg1axZq166NJ554Aj4+Pti2bRvs7Ozw4osvonbt2qonl5+fj4kTJyIiIgKurq6oUaMG3nvvPdw55lsIgXfeeQchISFwdXVF586dceLECdVzISIioopJ8QDqsLAwPPHEE/jkk0/QpUsX2NmpNtyoWDNnzsS8efOwdOlS1K1bF3///TdeeOEFeHt7m6fwz5o1C3PnzsXSpUsRERGBiRMnomvXroiLi4OLi0uZ50hERERys6oY+vPPP1GtWjWEhYWVSUtQQX/99Rcee+wxPProowCA8PBwfPvtt9i7dy+A261Cc+bMwdtvv43HHnsMAPDNN98gKCgI69evR//+/cs8RyIiIpKb4uadY8eOYfny5UhKSsKDDz6IJk2aYPbs2QBury9UFlq1aoXNmzfj+PHjAIDDhw/jzz//xCOPPALg9jT/5ORkdO7c2fwz3t7eaN68OXbv3l0mOREREVHFYtU6Q61bt0br1q0xd+5cfPvtt1i8eDHy8/Px6quv4plnnkHv3r1RqVIl1ZIbP348MjIyULt2bdjb2yM/Px/Tpk3DgAEDAADJyckAgKCgIIufCwoKMj9WlJs3b+LmzZvm7zMyMlTLmYiIiORyVwN/PDw8MGTIEPz111/4999/0aRJE7z99tsIDQ1VNbnVq1djxYoVWLlyJQ4cOIClS5fiww8/xNKlS+/peWfMmAFvb2/zV9WqVVXKmIiIqGzkGwV2J1zFj4cuYHfCVeQbrdpAgkpg9XYcxcnLy8OGDRvQp08fNZ4OAFC1alWMHz8eUVFR5mNTp07F8uXLcezYMZw6dQo1atTAwYMH0bBhQ3NM+/bt0bBhQ3zyySdFPm9RLUNVq1bldhxERKRL0bFJeHfDv0jO+O/eFezljHd71UW3eiHlmFn5Ums7DtWmhDk4OKhaCAFATk5OoVlr9vb2MBqNAG7vkxYcHIzNmzebH8/IyEBMTAxatmxZ7PM6OzvDy8vL4ouIiEiPomOT8MryAxaFEAAkZ9zEK8sPIDo2qZwyqzis3ptMSz179sS0adNQrVo11K1bFwcPHsTHH3+MF198EcDtgdujR4/G1KlTUatWLfPU+tDQUPTu3bt8kyciIrpH+UaB8WuPlBgzfu0RdIkMhr1d2UxmsgW6LoY+/fRTTJw4Ea+++ipSUlIQGhqKl19+Ge+88445ZuzYscjOzsbQoUORlpaGNm3aIDo6mmsMERGR9PYkXEVaTm6JMWk5udiTcBWtawVolFXFo9qYIZmp1edIRESkpg9/P4bPtiaUGje8Qw280bXs1//Tm3IbM/Tiiy8iMzOz0PHs7Gxz9xURERGpQWnXF7vI7oXVxdDSpUtx/fr1QsevX7+Ob775RpWkiIiICGhZw1/VOCqa4jFDGRkZEEJACIHMzEyLMTn5+fn49ddfERgYWCZJEhER2aIW1f3h4+ZY4rghXzdHtKjOYuheKC6GfHx8YDAYYDAYcN999xV63GAwYPLkyaomR0REZMvs7Qx4v099vLL8QLExM/rU50yye6S4GNq6dSuEEOjYsSPWrFkDPz8/82NOTk4ICwtTfQVqIiIiW9etXgjmD2yMdzfEITnjhvl4iLcLJvWMtOlFF9Vi9WyyM2fOoFq1amW2OWt54GwyIiLSu3yjwN7EVKRk3kCgpwuaRfjZfIuQWvdvRS1D//zzD+rVqwc7Ozukp6fjyJHiF4B64IEH7joZIiIiKpq9nYEDpcuIomKoYcOGSE5ORmBgIBo2bAiDwYCiGpQMBgPy8/NVT5KIiIiorCgqhhITE1GpUiXzv4mIqGywK4RIe4qKobCwsCL/TURE6omOTcLkn+KQlM5BskRauqu9yU6cOIGtW7ciJSXFvIO8yZ37hhERkTLRsUkYtvwACg5ASE6/gWHLD2DewMYsiIjKiNXF0KJFizBs2DAEBAQgODjYYlaZwWBgMUREZKV8o8Dkn+IKFUIAIHB7o4XJP8VxZ3KiMmJ1MTR16lRMmzYN48aNK4t8iIhszt7EVIuusYIEgKT0G9ibmMrZRERlwOq9ya5du4Ynn3yyLHIhIrJJKZnFF0J3E0dE1rG6GHryySexcePGssiFiMgmBXq6lB5kRRwRWcfqbrKaNWti4sSJ2LNnD+rXrw9HR0eLx0eOHKlackREtqBZhB9CvF2QnH6jyHFDBgDB3ren2ROR+qzejiMiIqL4JzMYcOrUqXtOSmvcjoOIyptpNhkAi4LINFyas8mICtN0O447cdFFIiL1dasXgqHtIrBoZyLu/IhqMABD2kawECIqQ1aPGSIiIvVFxyZh4Y5EGAu01RsFsHBHIqJjk8onMSIboLhlqE+fPkUe9/b2xn333YeXXnrJvGUHEREpV9I6QyZcZ4io7ChuGfL29i7yKy0tDYsWLcL999+P2NjYssyViKhCsmadISJSn+KWocWLFxf7mNFoxJAhQzBhwgT89NNPqiRGRGQruM4QUflSZcyQnZ0dRo4cif3796vxdERENiXAw1nVOCKyjmoDqN3d3ZGTk6PW0xER2QxjvrIVTpTGEZF1VCuGNm3ahPvuu0+tpyMishkxp6+qGkdE1lE8ZmjDhg1FHk9PT8f+/fvx5Zdf4ssvv1QtMSIi26F0hhhnkhGVBcXFUO/evYs87unpifvvvx9ffvkl+vfvr1ZeREQ2o2UNf3y29aSiOFJHvlFgb2IqUjJvINDz9lYnXLbAdikuhoxGY1nmQURks1pU94ePmyPScnKLjfF1c0SL6iyG1BAdm4TJP8VZLGcQ4u2CST0judK3jeIK1ERE5czezoD3+9QvMWZGn/psuVCBaQ+4gus6JaffwLDlB7jSt41iMUREpAPd6oVg/sDGCPZysTge4u2C+dykVRUlrfRtOjb5pzjkF9wThSo8qzdqJSKistGtXgi6RAZzLEsZsWalb47Psi0shoiIdMTezsAbcRnhSt9UHEXdZGPGjEF2djYAYMeOHcjLyyvTpIiIiNQW6OlSepAVcVRxKCqGPv30U2RlZQEAOnTogNRUbhZIRERyaRbhhxBvl2JXazLg9hitZhF+WqZFOqComyw8PBxz587Fww8/DCEEdu/eDV9f3yJj27Vrp2qCREREarC3M2BSz0gMW34ABsBiILWpQJrUM5JjtGyQQQhR6rD59evX45VXXkFKSgoMBgOK+xGDwYD8/HzVkyxrGRkZ8Pb2Rnp6Ory8vMo7HSIiKkNcZ6jiUOv+ragYMsnKyoKXlxfi4+MRGBhYZIy3t/ddJ1NeWAwREdkWrkBdMah1/7ZqNpmHhwe2bt2KiIgIODhwIhoREcmJs/boTlZXNO3bt0d+fj7WrFmDo0ePAgAiIyPx2GOPwd7eXvUEiYiIiK1ZZcnqYujkyZN49NFHcf78edx///0AgBkzZqBq1ar45ZdfUKNGDdWTJCIismUc51S2rBozBADdu3eHEAIrVqyAn9/t6YdXr17FwIEDYWdnh19++aVMEi1LHDNERHpxK8+IZbtP40xqDsL83PBsy3A4OXDnJLXJ1Mpi2k+t4M3alO08G96upVwGUAOAu7s79uzZg/r1LTcVPHz4MFq3bm1ej0gmLIaISifTzUNWM36Nw6Kdibhzayw7AzCkbQQmdI8sv8QqGJlaWfKNAm1mbil2GxEDgGBvF/w5rqNNvh/LZQA1ADg7OyMzM7PQ8aysLDg5Od11IkSkXzLdPGQ149c4LNiRWOi4UcB8nAXRvSuulcW0a73eWlm4n5o2rG577dGjB4YOHYqYmBgIISCEwJ49e/DKK6+gV69eZZEjEZUj082j4AXZdPOIjk0qp8wqjlt5RizaWbgQutOinYm4lWfUKCPr5BsFdidcxY+HLmB3wlXd7vou46713E9NG1a3DM2dOxeDBg1Cy5Yt4ejoCADIy8tDr1698Mknn6ieIBGVn9JuHgbcvnl0iQy2ySZ6tSzbfRql3X+N4nbc4LbVtUlKIZlaDWVsZZF9PzVZutetLoZ8fHzw448/4uTJk+ap9XXq1EHNmjVVT46oIpLl4gDIefOQ0ZnUHFXjtCJbl5OMrSzNIvzg4+aItJzcYmN83Rx1uZ+aTIXyXa+cWLNmTRZARFaS6eIAyHnzkFGYn5uqcVqQsdVQ9laW4uinU+8/shXKnK9JpBEZx95U1JuH3jzbMhyl1Qt2httxemFNq6FeyLhr/d7E1BJbhQAgLSdXV+dZxrFZLIaINCDjxQGQ8+ZxJ1kG9jo52GFI24gSY4a0jdDVekMythqadq0HUOg1rddd62U8zzIWyvp5ZxFVYDJeHAA5bx4m0bFJaDNzC55etAejvjuEpxftQZuZW3TZAgfcnjbfJbLoDbC7RAbqblq9rK2G3eqFYN7Axgj2tswr2NtFd103gJznWcYCTvfF0IULFzBw4ED4+/vD1dUV9evXx99//21+XAiBd955ByEhIXB1dUXnzp1x4sSJcsyYqDAZLw4mst08ADm7JKNjk/BHXEqRj/0Rl6K7nGVuNexWLwR/juuIb4e0wCf9G+LbIS3w57iOunwty3ieZSzg7qoY2rlzJwYOHIiWLVviwoULAIBly5bhzz//VDW5a9euoXXr1nB0dMRvv/2GuLg4fPTRR/D19TXHzJo1C3PnzsX8+fMRExMDd3d3dO3aFTdu6O+mQrZLxovDnWS6ecjYJVlSziZ6y1nmVkPgv13rH2tYGS1r+Os6T9nOs4wFnNXF0Jo1a9C1a1e4urri4MGDuHnzJgAgPT0d06dPVzW5mTNnomrVqli8eDGaNWuGiIgIPPzww+bNYIUQmDNnDt5++2089thjeOCBB/DNN9/g4sWLWL9+vaq5EN0LGS8OBcly85CxS1LGnAE5Ww1l1K1eCIa2i4ChwFvOYACGtovQ3XmWsYCzuhiaOnUq5s+fj0WLFpkXXQSA1q1b48CBA6omt2HDBjRt2hRPPvkkAgMD0ahRIyxatMj8eGJiIpKTk9G5c2fzMW9vbzRv3hy7d+9WNReieyHjxUFWMnZJypiziUythrKKjk3Cwh2JhRbmNApg4Y5E3XWhAvIVylavMxQfH4927doVOu7t7Y20tDQ1cjI7deoU5s2bhzFjxuB///sf9u3bh5EjR8LJyQmDBg1CcnIyACAoKMji54KCgsyPFeXmzZvmFi3g9kZvRGXNdHEouM5QsI7XGZJRgIezqnFakL0b1dRqSOorrQtVQH/rOZl0qxeCLpHBUiwya3UxFBwcjJMnTyI8PNzi+J9//onq1dVdJt5oNKJp06bm7rdGjRohNjYW8+fPx6BBg+76eWfMmIHJkyerlSaRYjJdHKSldFiNfobfmLtRk9NvFJmWaWdyPXejUtkorQsV0Pcq8LIUylZ3kw0ZMgSjRo1CTEwMDAYDLl68iBUrVuCNN97AsGHDVE0uJCQEkZGW00nr1KmDs2fPArhdmAHApUuXLGIuXbpkfqwoEyZMQHp6uvnr3LlzquZNVBJZxt7ISsYuJ3ajak+WNaiSM5S9TpXGUdGsbhkaP348jEYjOnXqhJycHLRr1w7Ozs544403MGLECFWTa926NeLj4y2OHT9+HGFhYQCAiIgIBAcHY/PmzWjYsCGA211eMTExJRZmzs7OcHbWTxM5EaknNfuWqnFaYTeqdmTaFic162bpQVbEUdGsLoYMBgPeeustvPnmmzh58iSysrIQGRkJDw8P1ZN77bXX0KpVK0yfPh39+vXD3r17sXDhQixcuNCcy+jRozF16lTUqlULERERmDhxIkJDQ9G7d2/V8yEi/fNTOBZIaZyWZO1GlWnzYdn2zPJzd1I1jopmdTGUnp6O/Px8+Pn5WXRhpaamwsHBAV5eXqol9+CDD2LdunWYMGECpkyZgoiICMyZMwcDBgwwx4wdOxbZ2dkYOnQo0tLS0KZNG0RHR8PFRZ8DDYmobAV7KXvvK43TmixjLExkamWRcXNZPzeFxZDCOCqa1WOG+vfvj++++67Q8dWrV6N///6qJHWnHj164MiRI7hx4waOHj2KIUOGWDxuMBgwZcoUJCcn48aNG/jjjz9w3333qZ4HEcnBNBi5JHpf00kWsq30LeN6TpuOXio9yIo4KprVxVBMTAw6dOhQ6PhDDz2EmJgYVZIiIrpb9nYG9GpQcotErwYhuvnkLysZV/qWcXD9mdQcVeOoaFYXQzdv3kReXl6h47m5ubh+/boqSRER3a18o8Cqv8+XGLPq7/O6uknLSMZWFhnXcwr3d1M1jopmdTHUrFkz8wDmO82fPx9NmjRRJSkioru1J+Eq0nJyS4xJy8nFnoSrGmVUMcnYytIkzBelNQjaGW7H6cX/ukeWHmRFHBXN6gHUU6dORefOnXH48GF06tQJALB582bs27cPGzduVD1BWd3KM2LZ7tM4k5qDMD83PNsyHE4Od7UvLlUwMs28kdFfp64ojmtdK6CMs6m4ZGxl2X/mWqEtLQoyittxehnE7uRgB2cHO9zMMxYb4+xgx/vLPbK6GGrdujV2796NDz74AKtXr4arqyseeOABfPXVV6hVq1ZZ5CidGb/GYdFOy31kpv16FEPaRmACq3ebJtPMm4JkKeIuXlPWXa80joom46rZMi5guDcxtcRCCABu5hl1uwK1LKwuhgCgYcOGWLFihdq5VAgzfo3Dgh2JhY4bBczHWRDZJtnWN7mTTEVcwY0h7zWOimZaNfuV5UVv0C2gv1WzZVzAUMbuSBkpale7cyPTjIyMEr9s2a08IxbtLFwI3WnRzkTcKqXKp4pHxpk3JrJNn/ZzV7joosI4rcmyTYSMZFzAUMbuyDvJ8npW1DLk6+uLpKQkBAYGwsfHBwZD4UpfCAGDwYD8/HzVk5TFst2nFfVHL9t9GoPbqrupLembNTNv9NTULeMidb5ujqrGaUmmFjjTa6MkenttBHu7qhqnBRm7I01kej0rKoa2bNkCP7/bJ3rr1q1lmpDMuB4EFUfWpm4Zi7hD59IUxz3RtGrZJmOF4rpRk3TajSrjbuqmwqKkvPW2IKepO3LY8gMwABavDz1v4ivbsABFxVD79u0BAHl5edi+fTtefPFFVKlSpUwTk1GYn7J1HpTGUcUha1O3jEXcJYWDX5XGaaGkFjjg9g1Qb60syenKBqArjdPCnYVFca0seiwsTJv4vrvhXyRn/DeeKcjLGe/2qqurogKQs0XZqrl4Dg4O+OCDD4pcdJGAZ1uGK1rD4tmW4ZrkQ/ph+kRa3MvDAP19IgWAAIWbmSqN04K7s7J5IUrjtGBNK4tepGbfUjVOK93qhWBou4hC12o7AzC0XYTuCgtLBa8g+igkCpJxQU6rFybo2LEjtm/fXha5SM/JwQ5D2kaUGDOkbQTXg7BBpk+kQPGXMz1+Ii22qeJu4zTQt5GyVmulcVqQsZXFT2EBrDROK9GxSVi4I7HQ+E6jABbuSNTdhADgvy6nglP+L2XocxKDjC3KVn80euSRRzB+/HgcOXIETZo0gbu7u8XjvXr1Ui05GZmmzRdcZ8jOAK4zZONMTd0FBxQG63RAIQBcyVY2xVhpnBaaKxyfojROCzK2sgR6KitylMZpobTuSEB/3TdKZ6LqKWcZhwVYXQy9+uqrAICPP/640GO2PpvMZEL3SIzufD+m/xqH01dzEO7vhv91j4Srk315p1YiWRbVk1m3eiHoEhkszXmW8aIWc0rZNhsxp66i7X2VyjgbZaRsZZGw1VDGCQEyD1SXaQac1cWQ0cg1ckpTcDrhzhPAH0dTdPvpH5BrCqTs7O0MurlolUbGi9raAyVv0npnnF6KoWAvhQtFKozTgoythjJ238i4araMM+CsGrxy+vRpLFq0CF988QX+/fffsspJarItUAfImTNpQ8axTlk3lU3wUBqnBVPRWRK9DbCXsdVQxpxlXDUb+G9YQMGV3oO9XXQ3rR6womVo69at6NGjB65fvz2Az8HBAV9//TUGDhxYZsnJRsbphDLmTNqSbaxTJYVjVJTGaeHOT9KAHJ+km0X4wcfNEWk5ucXG+Lo56qqAk7GlU8ZVs01kGhaguGVo4sSJ6NKlCy5cuICrV69iyJAhGDt2bFnmJh0ZpxPKmDNpr1u9EPw5riO+HdICn/RviG+HtMCf4zrqrhACAFd7ZZc1pXFaMU35LrjAv0GKKd9F09FwIQBytnTKuGr2nUzDAh5rWBkta/jr6tzeSfHVIDY2FtOnT0dISAh8fX3xwQcfICUlBVevKhusaAtk7I+WMec7ybLvTUUgy0Xt53+VdesqjdNKdGwSFhQz5XuBDqd8701MLbFVCADScnJ190FKtu4bGbtQZaS4mywjIwMBAQHm793c3ODq6or09HT4+8sxGLSsydgfLWPOJhz0TUXJvqFsLJDSOC3kGwXGrz1SYsyEtUd01V0t8wcpmbpv7O0M6NUgBAt2FL8JeK8GIbrMXSZWzSb7/fff4e3tbf7eaDRi8+bNiI2NNR+z5XWGZOyPlnGvHkC+fW9IO+7ODsi6Vfp6PHpagXrPqaultrJcy8nFnlNX0bpmQIlxWpH5gxQgz6zOfKPAhsMltwpuOJyEsd3q6LIgkmXJFquuBoMGDSp07OWXXzb/29bXGZJxOqGMnzo46JtK8ugDIfh61xlFcXqxO0HZcIPdCfophmT9ICUbGdcZMpGp9V7xmCGj0Vjqly0XQiay9Ucr/dShp7E4HPRNJQnxVrYRstI4bci3gqHpg1RJ9PZBSkYybtUCyLdki37aiSsQmfqjZfzUIfNYBSp7AQqnzCuN00LL6gH4bGuCoji9kL37RhYybtUiY+s9i6EyIkt/tIyFhexjFahsBShcb0VpnBYeVNiVpDROCzJ+kJKRjFu1yLjtib4W2qhAZJnyLWNhYRqrUNznCQM4VsGWHUvOVDVOC/sUdukqjdOCjB+k7nT9Vj4mrj+CZ7+KwcT1R3D9lj6Heci4VYuMrw22DJUBmQaNyTgDTsaB6qSdc9dyVI3Twu5TVxTHta6lj64yGT9ImQz5Zh82xaWYv995Ali25yy6RAZi0XMPlmNmhck4UF3G1wZbhlQm26AxGVdkBeQbqE7aCfNTNjBaaZwWhMKGY6VxWmgS5ltoteyCDIbbcXpSsBC606a4FAz5Zp/GGZXMdI02oOhrtAH6u0Y3CfMttuXexAB9vTbuqhhKS0vDl19+iQkTJiA19Xaz7YEDB3DhwgVVk5NNaYPGgNuDxvTWZSZrYSHTFhEVgSxdv880D1M1Tgs+bsrGLymN08K+06mlFmdC3I7Ti+u38osthEw2xaXorsvMdI0O8pLjGr0vMbXUeY8C+ur2tbqb7J9//kHnzp3h7e2N06dPY8iQIfDz88PatWtx9uxZfPPNN2WRpxRkHDRmItMMuDvJMlBddjJ1/R46l6Y4Ti+vHRk349x1QlnX3q4TV3SzNtL0X+MUx73Xu34ZZ3M3LEsMoaemwjvI2O1rdcvQmDFj8Pzzz+PEiRNwcfmvSu3evTt27NihanKykXHQ2J1k2XvqTrK0VshMtq5fGd+Hqdk3VY3Twj/n01SN08Lpq8rGiSmN04rpPZicYfn3v5RxU5fvwcIdevcaV/asbhnat28fFixYUOh45cqVkZycrEpSspJx0JjMZGqtkJWM64X4KexKUhqnhdK24rA2TguuTvaqxmkhzM8NOxXG6YWM78HmEX74bKuyOL2wumXI2dkZGRkZhY4fP34clSpVUiUpWTWL8IOPm2OJMT5ujroa9S8r2VorZCXjat9HLxa+Pt1LnCbk+yCt+Dqmp+tdl9pBqsZpQcb3oF1pI+utjNOC1cVQr169MGXKFOTm3v6EYjAYcPbsWYwbNw59+/ZVPcGKRj9/+sJk6XKSdaC6jGTscvr7rLKbgtI4Lfi4KhxArTBOC4NaRSiaMTSoVYQW6SiSel3has4K47Qg43tQxpytLoY++ugjZGVlITAwENevX0f79u1Rs2ZNeHp6Ytq0aWWRozT2JqYq2nlaTxW8SXRsElq/vxlPL9qDUd8dwtOL9qD1+5t12cIi4yclWcnY9eviqKxbRmmcFgI8FK6arTBOC04OdhjaruRCZ2i7CDg56GcFFxm3tpDxPXglS9n5UxqnBavHDHl7e2PTpk3YtWsXDh8+jKysLDRu3BidO3cui/ykImM1DNwuhF5ZfqDQ8eSMm3hl+QHM19nUTVnPs4xkXJTTTeEYFaVxWghwV7ifmsI4rUzoHolTV7KLnK7eJTIQE7pHlkNWxZNxawsZ34NpOcqKHKVxWrjrkr1169Z49dVXMXbsWDRt2lTNnKQVoPANpDROC/lGgfFrj5QYM37tEV11Ocn4SUlWMi7KeSVT4adShXFaiEtWNn5JaZxWomOT8EcRhZABwB9xKbprWZZxawvTe7C4K7CA/t6DSocC6WjIkPXF0MyZM7Fq1Srz9/369YO/vz8qV66Mw4cPq5qcbIz5ygoGpXFa2JNwtdSuvbScXOxJuKpRRqXj3mTa+m/BN8siPsjLWZcLvt3IVbZgntI4LfytcGFCpXFakHHsnunaURI9XjvWHDh/T49rrWV1ZWsHKY3TgtXF0Pz581G1alUAwKZNm7Bp0yb89ttveOSRR/Dmm2+qnqBMYk4rKxiUxmnBmsWx9ELG1oqKobizrS/1q3qrGqcFpSse62llZBnH7t157SiO3q4dMq6a3aKGv6KZ1S10sugpcBfFUHJysrkY+vnnn9GvXz88/PDDGDt2LPbt09eeLtqTcH6slDnLu4WIjP5b8M3yxncpQ5/LGLSKUPZpU2mcFh6o4qNqnBZkHbt38Oy1e3pca9asmq0X9nYGvN+n5BW83+9TX1dFp9XFkK+vL86dOwcAiI6ONg+cFkIgP18/lWl5ULq0v162AADkzNmEe5OVPRm7QuzsFa5xojBOC0q3JNDL1gWAnGP3buUZsWhnYokxi3Ym4laeUaOMSifrqtmysboY6tOnD5555hl06dIFV69exSOPPAIAOHjwIGrWrKl6gjJpUd0f7qXMUHF3tkeL6vopLB4M91O0VsiD4frqQzeRcQsRmcjYFZKSqWzLCqVxWngw3E/RDvB6eh8qWWTWV2eLzC7bfRql1e1GcTtOL8L9la2GrTROC6YPUcUxrZqtqw9R1v7A7NmzMXz4cERGRmLTpk3w8PAAACQlJeHVV19VPUHZlPqn1c/fHsD/7zxdSoyAvnaeJu3I2BWSmqVwny+FcVrYf+aaoh3g95/RVxdOaXR2ucPpq9mqxmnhfwqXJ1AapwUZP0RZvc6Qo6Mj3njjjULHX3vtNVUSktmeU1eRU8ogtuxb+dhz6qpudnHedVLhztMn9bPzNGlHxq4QH4V7jimN04KMRaeSRWbT/n+RWb10syvd5F1Pm8G7OtmjS2RgiYOou0QG6moPOBlfz1YXQ998802Jjz/33HN3nYzsZCwsLqZdVzWOKhZTV0hJNz297bcn4w7wMhadMt7wPF2V3fKUxmll0XMPotdnO/HP+cLrTD1QxQuLnnuwHLIqnoyvZ6v/4qNGjbL4Pjc3Fzk5OXBycoKbm5tNF0MyFhah3q6qxlHFk1vKYNLcfP0MNgWAVIVL/CuN00KTMF8YDCW3SBgMt+P0wk/hPmlK47Rgr3CVP6VxWomOTSqyEAKAf85nIDo2SVcTR2RcNdvqMUPXrl2z+MrKykJ8fDzatGmDb7/9tixylIaMhYWvu7ILldI4qlj2nLqK7NK6fm/e7vrViwtpymbVKI3Twr7TqYrGDOlp7N6xS5mqxmnhwTBlN1+lcVrINwqMWV3ygsZjVh/W1WBkGdeCU2UHvVq1auH9998v1Gpka2QsLGTcIFJ2+UaB3QlX8eOhC9idcFVXF7GCditceVxpnBZOXVFW5CiN04KM5/lMqrJBxkrjtBCfrKwwUxqnhb9OXCl1LGrOrXz8dUI/C+MC8q0Fp1rHqIODAy5evKjW00lJxsIiWGErldI4Kll0bBIm/xRnMdMixNsFk3pG6u7icJvSQk0/BV32zTxV47QgFI7YVRqnBRlz/vuswm1PzqZiKGqUcTbKrDmobKuNNQfPo+39lco4G+t0qxeCLpHB2JuYipTMGwj0vN01pqcWIROri6ENGzZYfC+EQFJSEj777DO0bt1atcRkJOOgsSZhvrAzoMS1N+x0NlZBVqaVnAue6uT02ys56/HTUsvqAfhsa4KiOL0I8XZBooIF6Erbo0pLpa3XY22cFjydleWiNE4Lbk7KbnlK47RQWquQtXFaM60Fp3dW/8V79+5t8b3BYEClSpXQsWNHfPTRR2rlJScJd7bYf+aaokXI9p+5JsULWq9KW8nZtAhZl8hgXX1qMu0xVNpsMj3tMdQqwh9/nSq9BaBVhH5y9lU4zV9pnBZKWkfmbuK08HjDylh/qPQejMcbVtYgG2UeDPfDxrhLiuLo7lk9ZshoNFp85efnIzk5GStXrkRISNl+qn3//fdhMBgwevRo87EbN24gKioK/v7+8PDwQN++fXHpUukvnLIg48q3Mk6PvZMs429kXIQMuP2prppfyV2k1fxcdVXA/aVwI2SlcVo4dE7ZYopK47RwKV3ZmCulcVpwcFB2y1Map4VBrcIVrU4+qFW4JvlUVPfUFmjqCzZoMA1x3759WLBgAR544AGL46+99hp++eUXfP/99/D29sbw4cPRp08f7Nq1q8xzKkjGlW9l7NozkWn8jaxF5/Vb+cVO6TX553wGrt/K182ibzK2WFzKUHZNUBqnhfNpys6f0jgtJCtc1kRpnBacHOwwtG0EFuwofk+1oW0j4KSjAk5Gd3X2vvnmG9SvXx+urq5wdXXFAw88gGXLlqmdm1lWVhYGDBiARYsWwdf3v7Er6enp+Oqrr/Dxxx+jY8eOaNKkCRYvXoy//voLe/bsKbN8iuOncJaY0jgtKNlfSG+L6gH/jb8peEMzjb/R207qshad035RthO20jgtBHs5qxqnBQ9nZZ9LlcZpIU9hK6zSOC0cOp+mapxWGlUrecxmaY9T6awuhj7++GMMGzYM3bt3x+rVq7F69Wp069YNr7zyCmbPnl0WOSIqKgqPPvooOnfubHF8//79yM3NtTheu3ZtVKtWDbt37y72+W7evImMjAyLLzVU1JlZ+ukAuU3GndRNA9VLoseB6ocV3hSUxmkh3N9D1Tgt9GlcRdU4LVSv5K5qnBaMCq8JSuO0IOOmpzKyuhj69NNPMW/ePMycORO9evVCr169MGvWLHzxxReYO3eu6gl+9913OHDgAGbMmFHoseTkZDg5OcHHx8fieFBQEJKTk4t9zhkzZsDb29v8VbVqVVVylXEXZyX7C137//2F9ELG8TfWDFTXEy8XZTOBlMZpQsKJDK1qBsC9lG5Gd2d7tNLJNj4AMLhFhKpxWpBxXKeM1zsZWV0MJSUloVWrVoWOt2rVCklJ6nZNnDt3DqNGjcKKFSvg4qJe98GECROQnp5u/jp37pxqz32rlK0LSntcazKOZWHO2hnatrqqcVq4rPBGpjROC/Z2BnzUr0GJMR892UBXA9UTFC6mqDROC5UUdo0qjdOCrNcOE1kmuVhdDNWsWROrV68udHzVqlWoVauWKkmZ7N+/HykpKWjcuDEcHBzg4OCA7du3Y+7cuXBwcEBQUBBu3bqFtLQ0i5+7dOkSgoODi31eZ2dneHl5WXypwZpd6/VCxrEszFk7LRW2RCiN04SMW5Pj9gJ1L7eLKNSdamcAXm4XobtJAX+fUbiAocI4LdQIUNY1qjROCwEeygozpXFaio5NQpuZW/D0oj0Y9d0hPL1oD9rM3KK7MZ3AXcwmmzx5Mp566ins2LHDvMjirl27sHnz5iKLpHvRqVMnHDlyxOLYCy+8gNq1a2PcuHGoWrUqHB0dsXnzZvTt2xcAEB8fj7Nnz6Jly5aq5qKEjLvWy7ihninnkpqOQ3Sas0znGQD2KWx635eYita19PGadlc4yFhpnFaiY5OwcEdiodeHUQALdySiUTVfXRVEMi5g+EzzMLz3y1FFcboh3yLwAORbZNbqlqG+ffsiJiYGAQEBWL9+PdavX4+AgADs3bsXjz/+uKrJeXp6ol69ehZf7u7u8Pf3R7169eDt7Y3BgwdjzJgx2Lp1K/bv348XXngBLVu2RIsWLVTNRYkL1xRuEKkwTgumDfWKex8J6G9DPXs7A3o1KPlN1KtBiO5ylm3jQgDYlXBZ1Tgt1A31VjVOCyVNCgBuvw/1Nki2r8LB3ErjtHBA4Zg8pXFauJKtrDtXaZwWZJzkcldT65s0aYLly5dj//792L9/P5YvX45GjRqpnZsis2fPRo8ePdC3b1+0a9cOwcHBWLt2bbnkYlA4IlNpnFYOni35jV/a41rLNwqs+rvk/XpW/X1eV2804L+NCwM9LZuzg7ycdfcpyeTCNWXrrSiN00IlL2VdjUrjtFDaIFlAf4NkW9UMgFtpg76d9DXoe/cpZa33SuO0IGMXu4yDvhUXQwWnohf3Vda2bduGOXPmmL93cXHB559/jtTUVGRnZ2Pt2rUljhcqS5VLWanX2jgt3MozYtHO4hfzAoBFOxN1NfB7T8LVUmfApeXkYo+Odvg2OXj2Gi4XWHQzJfOm7grO/8g3NctP4f5dSuO0kJyucDFAhXFasLcz4OPSBn3309egb6UfkPT0QcrUxV7cWTRAf8MCZBz0rbgY8vHxga+vb7FfpsdtWQuFex0pjdPCst2nFU35Xrb7tCb5KCFj1w0AzPg1Dgt2JBY630YBLNiRiBm/6mfhQhMZhytsjCt+WY27idNCavYtVeO00q1eCOYPbIygAq2dwV7OmK/D1s7MG3mqxmlBxqEMMrZmKR7ZtnXrVvO/hRDo3r07vvzyS1SurJ8N7cqbncJtSZTGaeFMqrLxS0rjtHDxmrJPE0rjtHArz4iFpbTALdyZiNcfrq2vZfUlnJl1NlVZ64nSOC3IuFGrSbd6IegSGYy9ialIybyBQM/brRR6ujmbKL306ugSLSUZJ4woLobat29v8b29vT1atGiB6tX1s75IeZNxoFuYn5uqcZqQr+cGS/86XWq9IMTtuCHt9POeUno/09N9L9zfDTtPKIvTiysK9ytUGqc1ezsDWtbQT4t3car5KVsNW2mcFpSuQN0lMlg3BaipNWvY8gMwwLLlWK8TRnT0EVR+MjYNPtsyXNE2Ec+2DNckHyUq+ygcm6UwTgv7FO6QrjROK5UVFsFK47Twv+6RqsZp4d+L6arGUdFqB3mqGqcFGQcjA/9NGAn2trzfBXu76HLCiH4WgKgATPtPlTQGR2/7Tzk52GFIKTsiD9HZjsgtIvzx+bYERXF6IeOaLMD/n+utcp1rVyd7PFDFC/+cL35CxwNVvOBaykwoLV3PLXmxVmvjqGip1xWOzVIYpwUZByObyNSFek93OAM7Vi3Iuv/UhO6RJa58O0FHn6ABwM5e4dgshXFakHFNFkDOcXD5RoHLmSXfzC5n3tLVjKEmYcrGTiiN05osWy7I2HovY853MnWhPtawMlrW8NdlIQRY0TLUp08fi+9v3LiBV155Be7uln2r5bXGjx7IXMFP6B6J0Z3vx/Rf43D6ag7C/d3wv+6Ruvr0bCLj+IpWNQPg7GCHmyUsUeDsYKerNVkAOV/T1qzZo5dxLpEhyrYEUhqnpejYJEz+Kc7inId4u2BSz0jddYXIOLBXxpxlpLhl6M5d3r29vTFw4ECEhoYWOm7LZK7go2OT0PGjbVi25yx2nriCZXvOouNH23S5h4ys57m0wlKPhaeMU76T0pTNElMap4XUHIXnWWGcVkxbLhQsPk1bLujt+iHjSvAy5iwjxS1DixcvLss8KgRZK3jZ9pBpFuEHHzfHEhde9HVz1NV53puYqmihSD21VgByTvk+eE5ZN/TBc9fQp4k+uiVlLPBL23JBj7OcgP8G9hZszQrWaWsWIGfOstHXaE3JyTidUNYLWmn0NmJBxhWGAeCqwmUglMZpIV/hmkdK47Qg4+QLa2Y56anAB+Qa2GsiY84y0c8UoQpCtumEMk7btKaVRS9k7G4CUOp5tjZOC3YKF5hSGqcFGSdfyDie7E6yDOy9k4w5y4ItQ2VApgpexguajDl7uSrbB0tpnFaUzhjV08xSLxeF51phnBZkfE3L2LUnu3yjkOK+IiMWQ2VElhVZA9ydSw+yIk4LMl6E/zmfpjjuyaZVyzYZK7Ss4Y/Ptp5UFKcXdgrbu5XGaUHG17SMXXsyk2nWnox0dDmg8mBUOG5CaZwWZNzFWco9RAC0qO4P91Jmubk726NFdf0UQ83DleWiNE4LMr6mZezak5Vss/ZkxGLIxsUoHFejNE4Ld041LY7eBqor3QdLT/tlmZRWBuuoTgYg56KcMk6flrFr706yLBRZ2iQX4PYkF73mLwsWQzZP6RtIX2+0bvVCMLSYVbOHtovQXbPxM83DVI3Typ6Eq8i5VfIWEDm38rEnQT97qsm4KCcg3+QLGbv2TKJjk9Bm5hY8vWgPRn13CE8v2oM2M7fosoVFxkkuMuKYIRvXPNwfn6H0vaf01KUA3L6YFbWfmlEAC3YkolE1X13dPA4o7Co4cOYaWtfSzyrUu09dURynl7xlvknLNPmC66ppQ/YWOFmwZcjWSTiUJd8oMH7tkRJjJqw9oqtm4z9PXlY1TjvyvUBMA3tLwoG9907Grj0Zu5xkLu5lwmLIxsk4ZmjPqaulrmtzLScXe07pp+vGmtlkeqJ0lpieZpPJPLBXpu4b4L+uvSAvObr2ZOxyknFwvYxYDJURWQbnyThmaNdJZV03SuO0cCO3+A1a7yZOKw+G+6G0JYQMhttxeiHrat9yzxiyvD4IvY2q/38ydjnJ2AInIxZDZUCmT3fNIxROQ1YYp4UL13JUjdOCs6OyTViVxmll/5lrpc4WEzprZZFxtW8Zu2+A/wq45AzLweiXMm7qsoCTtctJtsH1MuIAapUVNzgvSaeD8yRsGIJB4fgUpXFaCHBXtpGp0jityPhJ2s9D2QKhSuO0IOM+XzLuayjroG9ArsH1MmLLkIpKujgAty8Qevt0F3Na2bgapXFaCPVxVTVOC1X8lOWiNE4rMn6SDvZSlovSOC3IWHTKOP5G9i4n7k1WdlgMqai0iwOgv4uDhA1D8FXYeqI0TgstI5RNO1capxUZZ2aZPv2XRG8DTmUsOmUs4AB2OVHR2E2mootpygZkKo3Tgrezss0qlcZpIcBDYZeTwjhNyDdDHYB1M7P00n1j+vQ/bPkBAJaFvF4//cvYfSNjAWfCLicqiC1DKjp4VlmLj9I4LVy7rmwQqdI4Lch4EVY6zV9PywEA/PSvFRm7b2Sf8s0uJ7oTW4ZUdClD2fL+SuO0IGNrloytLOdTlc1sUxqnlQCFg4yVxmlJtk//pgKu4M7kwTrdmfzOFjgD5GiBIyoOiyEVuTsrO51K47RwJUtZi4/SOC3IuPfU5SxlLSdK4zQj46CyO5g+/cuCBRxR+dDPXbkC6Nu4CtYfuqgoTi/cnJSta6M0TgsydpNdV7iYotI4rVzJVlh4KozTWr5RSFNYmLCAI9IeiyEVtaoZAGcHO9zMK/6G5uxgh1Y19TNjqFmEPzYdTVEUpxfNIvzg4+ZY4pYcPm6OuhqrcC1H4dgshXFakbHwNImOTSrUYhHCFosyIVsBB8hZKFPZYTGkMlcn+xKLIVcdtbAAwKBW4Zj+69ESezkM/x+nJ7klnGMAyM3XVwuLh8KuUaVxWjFNrS9pRpneptYD8u1MTtpioUwFcTaZivYmppa6gWhaTq6u1hlS+klIT5+Y9py6iuxb+SXGZN/M19XMrAeq+KgapxUZNz2VdWsL0obce8BRWWExpCIZpyHvOJZS6thX8f9xerE7QVmRozROC13rBKsapxUZX9MyroxM2mChTMVhMaQiGcdXfLgpXtU4bcg3xSntZskthtbGaUXG17SMBRxpg4UyFYfFkIpMA3tLoreBvSkKp58rjdNC83BlAzWVxmlBxqICkHNhPVnPtczyjQK7E67ix0MXsDvhqm5bVlgoU3H0NVqzArheyliW0h7XWqCHMy5nlj6DKVBHi+rZ2Ssbv6Q0TguRIV6qxmlFxoX1ZNzaQmYyDUZmoUzFYcuQiv46eaXEmWQAcDPPiL9OXtEoo9I9ovBipTROCzIuuvjG94dUjdOSrFtbFNc2IaC/Ak5Wsg1GlrGlk7TBYkhFaw+cVzVOC9m38lSN04KMn+7OXlO2nYnSOK11qxeCLa8/hGdbVEPbWgF4tkU1bHn9Id0VQqQdGQcjy7gHHGmDxZCKMm8oG/yqNE4LMu5NZlr7piR6W/ummq+rqnFam/FrHOpOisayPWex88QVLNtzFnUnRWPGr3HlnVohppt0cQzQ301aRrIORpatpZO0wTFDapJwA1EZ9yazZu0bvayK++GTDdFgykZFcXoz49c4LNiRWOi4UcB8fEL3SK3TKpY1N2m9vD5kJPNgZG4hQgWxZUhFMnbfyLg3mYwX4bikDFXjtHIrz4hFOwsXQndatDMRt0oZK6clGV8fMpLxencn0xYijzWsjJY1/FkI2TgWQyqScTXnB8OUDRRUGqeFAIUz25TGaeH8tRxV47SybPdpRa1wy3af1iQfJWS/ScuCg5GpImExpKJGVZWNUVEap4U6ocqmciuN04R8ay7i93+VzapRGqeVM6nKijOlcVqQcb0vGXEwMlUkLIZUVMlTWUuE0jgtpCrcJV1pnBaSM5R1byiN00JSmrJclMZpJczPTdU4veDtWR0cjEwVBQdQq+hYsrLxHseSM9D2vkplnI0yMnY5HTqnbFPQQ+euoW+TKmWcjTKlbSxrbZxWnm0Zjmm/Hi111/pnW4ZrllNplGyYfO3/N0zmAOp7x8HIVBGwGFLR6avZqsZpQsIuJxnVCHDH6auldyXVCHDXIBvlnBzsMKRtRJGzyUyGtI2Ak4N+Gpk5gFp7psHIRLLSzxWsArhUwnTeu4nTwpVshas5K4zTQri/soJBaZwWmldXuJ+awjgtNapW8hi30h7XGgdQE5G1WAypKDVb2WKKSuO0IOON49mW4TCU0gJv0FnXTQ0/ZYWZ0jityLiAIWc5EZG1WAypKOuWsiJHaZwWTDeOkujtxmFvZ4CjfckvXSd7O12NWfh6T8lr9VgbpxUZVxnmLCcishaLIRXVDvJUNU4L9nYG9GpQ8oyPXg1CdHXj2JNwtdRF/m7mGbEn4apGGZXuosJZYkrjtCLr+BvOciIia+i6GJoxYwYefPBBeHp6IjAwEL1790Z8fLxFzI0bNxAVFQV/f394eHigb9++uHTpUrnk+1iDyqrGaSHfKLDhcMlr22w4nKSrbpDdp66oGqcFd2dlcxWUxmlFxm5Uk271QvDnuI74dkgLfNK/Ib4d0gJ/juvIQoiICtF1MbR9+3ZERUVhz5492LRpE3Jzc/Hwww8jO/u/2VivvfYafvrpJ3z//ffYvn07Ll68iD59+pRLvluPp6gap4XSukEA/XWDyLgJXOtaygZGK43Tiuzjb7jlAhEpoetiKDo6Gs8//zzq1q2LBg0aYMmSJTh79iz2798PAEhPT8dXX32Fjz/+GB07dkSTJk2wePFi/PXXX9izZ4/m+Z6+qmxnd6VxWpCxG0TpFF49TfVtVytQ1TitcPwNEdkCXRdDBaWnpwMA/Pxufwrdv38/cnNz0blzZ3NM7dq1Ua1aNezevbvY57l58yYyMjIsvtTg6qjsdCqN04KM3SAtqvuXut2Cr5sjWuhomrqMOZtw/A0RVXT6GqBQAqPRiNGjR6N169aoV68eACA5ORlOTk7w8fGxiA0KCkJycnKxzzVjxgxMnjxZ9Rw71q6ETUdL7wLrWFsfq08D/3WDlNRVprduEHs7A97vUx+vLD9QbMyMPvV11VohY8534irDRFSR6aeJohRRUVGIjY3Fd999d8/PNWHCBKSnp5u/zp07p0KGwJELylqYlMZpQcbZZMDtm/P8gY0R7GXZWhHi7YL5Om2tkDHnO3H8DRFVVFK0DA0fPhw///wzduzYgSpV/ttrKjg4GLdu3UJaWppF69ClS5cQHBxc7PM5OzvD2Vn9vbaOJWWqGqcFpbPJxnaro7ubn4ytFd3qhaBj7SAs230aZ1JzEObnhmdbhutqOwsiIluj62JICIERI0Zg3bp12LZtGyIiIiweb9KkCRwdHbF582b07dsXABAfH4+zZ8+iZcuWmucr42Bka2aT6WlAsolseyJFxyZh8k9xFuf8yz8TMalnpO5bhoiIKipdF0NRUVFYuXIlfvzxR3h6eprHAXl7e8PV1RXe3t4YPHgwxowZAz8/P3h5eWHEiBFo2bIlWrRooXm+QuFSPErjtCBjASer6NgkDFt+oNCet8npNzBs+QEORiYiKie6bpufN28e0tPT8dBDDyEkJMT8tWrVKnPM7Nmz0aNHD/Tt2xft2rVDcHAw1q5dWy75ujnZqxqnBRlnk8nItMdXUXWw6Zje9vgiIrIVum4ZEgqaUFxcXPD555/j888/1yCjktUJ8cSJy9mK4vSiYVUfVeOoaNbs8SVTtx8RUUWg65Yh2TzZpJqqcVpYGXNG1TgqGrsjiYj0i8WQiprX8C91AwjD/8fpxZnUHFXjqGjsjiQi0i8WQyraf+ZakWNC7iT+P04vwvzcVI2josm+xxcRUUXGYkhFyRnKujiUxmnh2ZbhKG1ZHjvD7Ti6e9zji4hIv1gMqehSurINWJXGacHJwQ5D2kaUGDOkbQQXBVQB9/giItInXc8mk832+EuK4155qGYZZ6Nco2q+ABJLeZzUIOOq2UREFR2LIRUlXlXW4qM0Tgum9W+KY8Dt9W+6RAbzhq0S2VbNJiKq6Nj3oSIvF2W1pdI4LViz/g0REVFFxGJIRY3DlHUnKY3TAte/ISIiW8diSEW+bk6qxmmB698QEZGtYzGkJqVDanQ09Ma0/k1JuP4NERFVZCyGVJSek6tqnBbs7Qzo1aDkKd29GoRw8DQREVVYLIZUdCnzpqpxWsg3Cmw4nFRizIbDSdxNnYiIKiwWQyrKuXFL1TgtlDabDOBsMiIiqthYDKko80a+qnFa4GwyIiKydSyG1KR0XI2Oxt8EuDurGkdERCQbFkMqaljFR9U4TUg4A46IiEhNLIZUNP6ROqrGaeFKlrLB3ErjiIiIZMNiSEVHLqSrGqcFdpMREZGtYzGkIikHI7ObjIiIbByLIRXJuLUFu8mIiMjWsRhSkWlri+IaUQzQ39YWMhZwREREamIxpCJ7OwMm9YwEULhXyfT9pJ6RutraQsYCjoiISE0shlTWrV4I5g1sjOACm58Ge7tg3sDG6Fav5H3AtCZjAUdERKQmgxDC5jedysjIgLe3N9LT0+Hl5aXKc+YbBfYmpiIl8wYCPW+3rOi5oIiOTcLkn+IstuYI8XbBpJ6RuivgiIiIAPXu3yyGUDbFkIxkK+CIiMi2qXX/dlAxJ5KcvZ0BLWv4l3caRDaNH0qItMdiiIhIJ9hdTVQ+OICaiEgHomOTMGz5AYtCCACS029g2PIDiI5NKqfMiCo+FkNEROUs3ygw+ac4FDWA03Rs8k9xyDfa/BBPojLBYoiIqJztTUwt1CJ0JwEgKf0G9iamapcUkQ1hMUREVM6k3NeQqAJhMUREVM64LQ5R+WIxRERUzrgtDlH5YjFERFTOuC0OUfliMUREpAOy7WtIVJFw0UUiIp3oVi8EXSKDuQI1kcZYDBER6Qi3xSHSHrvJiIiIyKaxGCIiIiKbxmKIiIiIbBqLISIiIrJpLIaIiIjIprEYIiIiIpvGYoiIiIhsGoshIiIismkshoiIiMimcQVqAEIIAEBGRkY5Z0JERERKme7bpvv43WIxBCAzMxMAULVq1XLOhIiIiKyVmZkJb2/vu/55g7jXcqoCMBqNuHjxIjw9PWEwqLchYkZGBqpWrYpz587By8tLteclSzzP2uG51gbPszZ4nrVRludZCIHMzEyEhobCzu7uR/6wZQiAnZ0dqlSpUmbP7+XlxTeaBnietcNzrQ2eZ23wPGujrM7zvbQImXAANREREdk0FkNERERk01gMlSFnZ2dMmjQJzs7O5Z1KhcbzrB2ea23wPGuD51kbMpxnDqAmIiIim8aWISIiIrJpLIaIiIjIprEYIiIiIpvGYoiIiIhsGouhMvT5558jPDwcLi4uaN68Ofbu3VveKVUoM2bMwIMPPghPT08EBgaid+/eiI+PL++0Krz3338fBoMBo0ePLu9UKpwLFy5g4MCB8Pf3h6urK+rXr4+///67vNOqUPLz8zFx4kRERETA1dUVNWrUwHvvvXfPe1sRsGPHDvTs2ROhoaEwGAxYv369xeNCCLzzzjsICQmBq6srOnfujBMnTpRPsgWwGCojq1atwpgxYzBp0iQcOHAADRo0QNeuXZGSklLeqVUY27dvR1RUFPbs2YNNmzYhNzcXDz/8MLKzs8s7tQpr3759WLBgAR544IHyTqXCuXbtGlq3bg1HR0f89ttviIuLw0cffQRfX9/yTq1CmTlzJubNm4fPPvsMR48excyZMzFr1ix8+umn5Z2a9LKzs9GgQQN8/vnnRT4+a9YszJ07F/Pnz0dMTAzc3d3RtWtX3LhxQ+NMiyCoTDRr1kxERUWZv8/PzxehoaFixowZ5ZhVxZaSkiIAiO3bt5d3KhVSZmamqFWrlti0aZNo3769GDVqVHmnVKGMGzdOtGnTprzTqPAeffRR8eKLL1oc69OnjxgwYEA5ZVQxARDr1q0zf280GkVwcLD44IMPzMfS0tKEs7Oz+Pbbb8shQ0tsGSoDt27dwv79+9G5c2fzMTs7O3Tu3Bm7d+8ux8wqtvT0dACAn59fOWdSMUVFReHRRx+1eF2TejZs2ICmTZviySefRGBgIBo1aoRFixaVd1oVTqtWrbB582YcP34cAHD48GH8+eefeOSRR8o5s4otMTERycnJFtcPb29vNG/eXBf3RW7UWgauXLmC/Px8BAUFWRwPCgrCsWPHyimris1oNGL06NFo3bo16tWrV97pVDjfffcdDhw4gH379pV3KhXWqVOnMG/ePIwZMwb/+9//sG/fPowcORJOTk4YNGhQeadXYYwfPx4ZGRmoXbs27O3tkZ+fj2nTpmHAgAHlnVqFlpycDABF3hdNj5UnFkNUIURFRSE2NhZ//vlneadS4Zw7dw6jRo3Cpk2b4OLiUt7pVFhGoxFNmzbF9OnTAQCNGjVCbGws5s+fz2JIRatXr8aKFSuwcuVK1K1bF4cOHcLo0aMRGhrK82zD2E1WBgICAmBvb49Lly5ZHL906RKCg4PLKauKa/jw4fj555+xdetWVKlSpbzTqXD279+PlJQUNG7cGA4ODnBwcMD27dsxd+5cODg4ID8/v7xTrBBCQkIQGRlpcaxOnTo4e/ZsOWVUMb355psYP348+vfvj/r16+PZZ5/Fa6+9hhkzZpR3ahWa6d6n1/sii6Ey4OTkhCZNmmDz5s3mY0ajEZs3b0bLli3LMbOKRQiB4cOHY926ddiyZQsiIiLKO6UKqVOnTjhy5AgOHTpk/mratCkGDBiAQ4cOwd7evrxTrBBat25daGmI48ePIywsrJwyqphycnJgZ2d567O3t4fRaCynjGxDREQEgoODLe6LGRkZiImJ0cV9kd1kZWTMmDEYNGgQmjZtimbNmmHOnDnIzs7GCy+8UN6pVRhRUVFYuXIlfvzxR3h6epr7nb29veHq6lrO2VUcnp6ehcZhubu7w9/fn+OzVPTaa6+hVatWmD59Ovr164e9e/di4cKFWLhwYXmnVqH07NkT06ZNQ7Vq1VC3bl0cPHgQH3/8MV588cXyTk16WVlZOHnypPn7xMREHDp0CH5+fqhWrRpGjx6NqVOnolatWoiIiMDEiRMRGhqK3r17l1/SJuU9na0i+/TTT0W1atWEk5OTaNasmdizZ095p1ShACjya/HixeWdWoXHqfVl46effhL16tUTzs7Oonbt2mLhwoXlnVKFk5GRIUaNGiWqVasmXFxcRPXq1cVbb70lbt68Wd6pSW/r1q1FXpMHDRokhLg9vX7ixIkiKChIODs7i06dOon4+PjyTfr/GYTgsptERERkuzhmiIiIiGwaiyEiIiKyaSyGiIiIyKaxGCIiIiKbxmKIiIiIbBqLISIiIrJpLIaIiIjIprEYIrIRp0+fhsFgwKFDh8o7FbNjx46hRYsWcHFxQcOGDcs7nTK1ZMkS+Pj4lHcaRFQEFkNEBeTn56NVq1bo06ePxfH09HRUrVoVb731VqGfuXr1KqpUqQKDwYC0tDSNMpXfpEmT4O7ujvj4eIs9iyqip556CsePHy/vNO7J888/r4+tE4hUxmKIqAB7e3ssWbIE0dHRWLFihfn4iBEj4Ofnh0mTJhX6mcGDB+OBBx7QMk3duHXr1l3/bEJCAtq0aYOwsDD4+/urmJX+uLq6IjAwsLzT0IV7ec0QlQUWQ0RFuO+++/D+++9jxIgRSEpKwo8//ojvvvsO33zzDZycnCxi582bh7S0NLzxxhulPq+pq2rt2rXo0KED3Nzc0KBBA+zevdsc8+677xbqMpozZw7Cw8PN35s+oU+fPh1BQUHw8fHBlClTkJeXhzfffBN+fn6oUqUKFi9eXCiHY8eOoVWrVnBxcUG9evWwfft2i8djY2PxyCOPwMPDA0FBQXj22Wdx5coV8+MPPfQQhg8fjtGjRyMgIABdu3Yt8v9qNBoxZcoUVKlSBc7OzmjYsCGio6PNjxsMBuzfvx9TpkyBwWDAu+++W+zzzJo1CzVr1oSzszOqVauGadOmmR8/cuQIOnbsCFdXV/j7+2Po0KHIysoqdK4+/PBDhISEwN/fH1FRUcjNzQUA/O9//0Pz5s0L/d4GDRpgypQp5u+//PJL1KlTBy4uLqhduza++OIL82NK/q5FdZP9+OOPaNy4MVxcXFC9enVMnjwZeXl5Fufoyy+/xOOPPw43NzfUqlULGzZssHiOf//9Fz169ICXlxc8PT3Rtm1bJCQkKMq7KD/88APq169vPp+dO3dGdnY23n33XSxduhQ//vgjDAYDDAYDtm3bZtXfYNq0aQgNDcX9998PADh37hz69esHHx8f+Pn54bHHHsPp06fNP7dt2zY0a9YM7u7u8PHxQevWrXHmzJkS8ye6K+W9ORqRXhmNRvHQQw+JTp06icDAQPHee+8Vivn3339FcHCwOHPmjHmTwmvXrhX7nImJiQKAqF27tvj5559FfHy8eOKJJ0RYWJjIzc0VQggxadIk0aBBA4ufmz17tggLCzN/P2jQIOHp6SmioqLEsWPHxFdffSUAiK5du4pp06aJ48ePi/fee084OjqKc+fOWfzuKlWqiB9++EHExcWJl156SXh6eoorV64IIYS4du2aqFSpkpgwYYI4evSoOHDggOjSpYvo0KGD+Xe3b99eeHh4iDfffFMcO3ZMHDt2rMj/68cffyy8vLzEt99+K44dOybGjh0rHB0dxfHjx4UQQiQlJYm6deuK119/XSQlJYnMzMwin2fs2LHC19dXLFmyRJw8eVLs3LlTLFq0SAghRFZWlggJCRF9+vQRR44cEZs3bxYRERHmjSFN58rLy0u88sor4ujRo+Knn34Sbm5u5k1QY2NjBQBx8uRJ88+Yjp04cUIIIcTy5ctFSEiIWLNmjTh16pRYs2aN8PPzE0uWLFH8d128eLHw9vY2/44dO3YILy8vsWTJEpGQkCA2btwowsPDxbvvvmuOMf29Vq5cKU6cOCFGjhwpPDw8xNWrV4UQQpw/f174+fmJPn36iH379on4+Hjx9ddfm/8mpeVd0MWLF4WDg4P4+OOPRWJiovjnn3/E559/LjIzM0VmZqbo16+f6Natm0hKShJJSUni5s2biv8GHh4e4tlnnxWxsbEiNjZW3Lp1S9SpU0e8+OKL4p9//hFxcXHimWeeEffff7+4efOmyM3NFd7e3uKNN94QJ0+eFHFxcWLJkiXizJkzReZOdC9YDBGV4OjRowKAqF+/vvmmZnLjxg3xwAMPiGXLlgkhhFXF0Jdffmk+9u+//woA4ujRo0II5cVQWFiYyM/PNx+7//77Rdu2bc3f5+XlCXd3d/Htt99a/O7333/fHJObmyuqVKkiZs6cKYQQ4r333hMPP/ywxe8+d+6cAGDeXbp9+/aiUaNGxf4fTUJDQ8W0adMsjj344IPi1VdfNX/foEEDMWnSpGKfIyMjQzg7O5uLn4IWLlwofH19RVZWlvnYL7/8Iuzs7ERycrIQ4r9zlZeXZ4558sknxVNPPWWRx5QpU8zfT5gwQTRv3tz8fY0aNcTKlSstfvd7770nWrZsKYRQ9nctWAx16tRJTJ8+3eI5ly1bJkJCQszfAxBvv/22+fusrCwBQPz222/mPCMiIsStW7eKPD+l5V3Q/v37BQBx+vTpIh8fNGiQeOyxxyyOKf0bBAUFWewMv2zZMnH//fcLo9FoPnbz5k3h6uoqfv/9d3H16lUBQGzbtq3IXIjUxG4yohJ8/fXXcHNzQ2JiIs6fP2/x2IQJE1CnTh0MHDjQ6ue9c3xRSEgIACAlJcWq56hbty7s7P57CwcFBaF+/frm7+3t7eHv71/oeVu2bGn+t4ODA5o2bYqjR48CAA4fPoytW7fCw8PD/FW7dm0AsOh6adKkSYm5ZWRk4OLFi2jdurXF8datW5t/lxJHjx7FzZs30alTp2Ifb9CgAdzd3S1+h9FoRHx8vPlY3bp1YW9vb/4+JCTE4rwMGDAAK1euBAAIIfDtt99iwIABAIDs7GwkJCRg8ODBFudl6tSpFucEsO7vevjwYUyZMsXiOYcMGYKkpCTk5OQU+Zzu7u7w8vIyP+ehQ4fQtm1bODo6Fnp+a/I2adCgATp16oT69evjySefxKJFi3Dt2rUiY02U/g3q169v0cV8+PBhnDx5Ep6enubc/Pz8cOPGDSQkJMDPzw/PP/88unbtip49e+KTTz5BUlJSibkQ3S2H8k6ASK/++usvzJ49Gxs3bsTUqVMxePBg/PHHHzAYDACALVu24MiRI/jhhx8A3L6JAkBAQADeeustTJ48udjnvvPmZXo+o9EIALCzszM/l4lpfEtxz2F6nqKOmZ5XiaysLPTs2RMzZ84s9Jjp5g7A4sZXllxdXVV5ntLOy9NPP41x48bhwIEDuH79Os6dO4ennnoKAMxjXxYtWlRobNGdBVbB31Pw71pQVlYWJk+eXGjWIgC4uLgoyr2k82NN3nce37RpE/766y9s3LgRn376Kd566y3ExMQgIiKi2N+lRMHXTFZWFpo0aWIxScGkUqVKAIDFixdj5MiRiI6OxqpVq/D2229j06ZNaNGixT3lQlQQiyGiIuTk5OD555/HsGHD0KFDB0RERKB+/fqYP38+hg0bBgBYs2YNrl+/bv6Zffv24cUXX8TOnTtRo0aNu/7dlSpVQnJyMoQQ5huqmmsD7dmzB+3atQMA5OXlYf/+/Rg+fDgAoHHjxlizZg3Cw8Ph4HD3lwcvLy+EhoZi165daN++vfn4rl270KxZM8XPU6tWLbi6umLz5s146aWXCj1ep04dLFmyBNnZ2eab7a5du2BnZ2cepKtElSpV0L59e6xYsQLXr19Hly5dzDO/goKCEBoailOnTplbi9TQuHFjxMfHo2bNmnf9HA888ACWLl2K3NzcQkXT3eZtMBjQunVrtG7dGu+88w7CwsKwbt06jBkzBk5OTsjPz7eIv9u/QePGjbFq1SoEBgbCy8ur2LhGjRqhUaNGmDBhAlq2bImVK1eyGCLVsZuMqAgTJkyAEALvv/8+ACA8PBwffvghxo4da57tUqNGDdSrV8/8ZfrkXKdOnXuaQv3QQw/h8uXLmDVrFhISEvD555/jt99+u+f/k8nnn3+OdevW4dixY4iKisK1a9fw4osvAgCioqKQmpqKp59+Gvv27UNCQgJ+//13vPDCC4VugqV58803MXPmTKxatQrx8fEYP348Dh06hFGjRil+DhcXF4wbNw5jx47FN998g4SEBOzZswdfffUVgNvdWy4uLhg0aBBiY2OxdetWjBgxAs8++yyCgoKsynfAgAH47rvv8P333xcqHiZPnowZM2Zg7ty5OH78OI4cOYLFixfj448/tup33Omdd97BN998g8mTJ+Pff//F0aNH8d133+Htt99W/BzDhw9HRkYG+vfvj7///hsnTpzAsmXLzN1T1uYdExOD6dOn4++//8bZs2exdu1aXL58GXXq1AFw+33wzz//ID4+HleuXEFubu5d/w0GDBiAgIAAPPbYY9i5cycSExOxbds2jBw5EufPn0diYiImTJiA3bt348yZM9i4cSNOnDhhzoVIVeU6YolIh7Zt2ybs7e3Fzp07Cz328MMPi44dO1oM+jSxZgD1wYMHzceuXbsmAIitW7eaj82bN09UrVpVuLu7i+eee05Mmzat0ADqggNZ27dvL0aNGmVxLCwsTMyePdvid69cuVI0a9ZMODk5icjISLFlyxaLnzl+/Lh4/PHHhY+Pj3B1dRW1a9cWo0ePNv+fi/o9RcnPzxfvvvuuqFy5snB0dBQNGjQwD/w1KW0Atel5pk6dKsLCwoSjo6OoVq2axcDjf/75R3To0EG4uLgIPz8/MWTIEIuZaUWdq1GjRon27dtbHLt27ZpwdnYWbm5uRc5sW7FihWjYsKFwcnISvr6+ol27dmLt2rVCCGV/14IDqIUQIjo6WrRq1Uq4uroKLy8v0axZM/MsNyFuD6Bet26dxc94e3uLxYsXm78/fPiwePjhh4Wbm5vw9PQUbdu2FQkJCYryLiguLk507dpVVKpUSTg7O4v77rtPfPrpp+bHU1JSRJcuXYSHh4fF/+1u/gZC3J5R+Nxzz4mAgADh7OwsqlevLoYMGSLS09NFcnKy6N27twgJCRFOTk4iLCxMvPPOOxaTBojUYhCiwOAEIiIiIhvCbjIiIiKyaSyGiIiIyKaxGCIiIiKbxmKIiIiIbBqLISIiIrJpLIaIiIjIprEYIiIiIpvGYoiIiIhsGoshIiIismkshoiIiMimsRgiIiIim8ZiiIiIiGza/wHmQleYNTrQkwAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "from sklearn.metrics import mean_squared_error, r2_score\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "\n",
+    "df = pd.read_csv('Real estate.csv')\n",
+    "print(df.isnull().sum())\n",
+    "columns = df.columns[1:-1]\n",
+    "for i in columns:\n",
+    "    plt.scatter(df[i], df['Y house price of unit area'])\n",
+    "    plt.xlabel(i)\n",
+    "    plt.ylabel('House Price of Unit Area')\n",
+    "    plt.title(f'{i} vs House Price of Unit Area')\n",
+    "    plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean Squared Error: 66.6733627183596\n",
+      "R2 Score: 0.579662418286177\n"
+     ]
+    }
+   ],
+   "source": [
+    "df = df.drop(columns=['No'])\n",
+    "X = df.drop(columns=['Y house price of unit area'])\n",
+    "y = df['Y house price of unit area']\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)\n",
+    "scaler = MinMaxScaler()\n",
+    "\n",
+    "X_train_scaled = scaler.fit_transform(X_train)\n",
+    "X_test_scaled = scaler.transform(X_test)\n",
+    "model = LinearRegression()\n",
+    "model.fit(X_train_scaled, y_train)\n",
+    "y_pred = model.predict(X_test_scaled)\n",
+    "mse = mean_squared_error(y_test, y_pred)\n",
+    "r2 = r2_score(y_test, y_pred)\n",
+    "\n",
+    "print(f'Mean Squared Error: {mse}')\n",
+    "print(f'R2 Score: {r2}')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "dQ7lQpy-SYCq"
+   },
+   "source": [
+    "# Logistic Regression\n",
+    "## Question 3\n",
+    "\n",
+    "The breast cancer dataset is a binary classification dataset commonly used in machine learning tasks. It is available in scikit-learn (sklearn) as part of its datasets module.\n",
+    "Here is an explanation of the breast cancer dataset's components:\n",
+    "\n",
+    "* Features (X):\n",
+    "\n",
+    " * The breast cancer dataset consists of 30 numeric features representing different characteristics of the FNA images. These features include mean, standard error, and worst (largest) values of various attributes such as radius, texture, smoothness, compactness, concavity, symmetry, fractal dimension, etc.\n",
+    "\n",
+    "* Target (y):\n",
+    "\n",
+    " * The breast cancer dataset is a binary classification problem, and the target variable (y) represents the diagnosis of the breast mass. It contains two classes:\n",
+    "    * 0: Represents a malignant (cancerous) tumor.\n",
+    "    * 1: Represents a benign (non-cancerous) tumor.\n",
+    "\n",
+    "Complete the code given below in place of the \"...\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "auipk-zBpmO-"
+   },
+   "source": [
+    "1. Load the dataset from sklearn.datasets\n",
+    "2. Separate out the X and Y columns.\n",
+    "3. Normalize the X data using MinMaxScaler or StandardScaler.\n",
+    "4. Create a train-test-split. Take any suitable test size."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "id": "0OyGNHNjFh13"
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.datasets import make_classification\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
+    "scaler = MinMaxScaler() \n",
+    "from sklearn.datasets import load_breast_cancer\n",
+    "data = load_breast_cancer()\n",
+    "X = data.data\n",
+    "y = data.target\n",
+    "X_normalized = scaler.fit_transform(X)\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X_normalized, y, test_size=0.2, random_state=42)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "uM-SsSxpqF2o"
+   },
+   "source": [
+    "5. Write code for the sigmoid function and Logistic regression.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "id": "o81LA5MZFoTW"
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "# Sigmoid function\n",
+    "def sigmoid(z):\n",
+    "    return 1.0 / (1.0 + np.exp(-z))\n",
+    "\n",
+    "# Derivative of the sigmoid function\n",
+    "def sigmoid_derivative(z):\n",
+    "    return sigmoid(z) * (1 - sigmoid(z))\n",
+    "\n",
+    "class LogisticRegression:\n",
+    "    def __init__(self, learning_rate, epochs):\n",
+    "        # Initialise the hyperparameters of the model\n",
+    "        self.lr = learning_rate\n",
+    "        self.epochs = epochs\n",
+    "\n",
+    "    def fit(self, X, y):\n",
+    "        n_samples, n_features = X.shape\n",
+    "        y = y.reshape(-1, 1)\n",
+    "        self.weights = np.random.randn(n_features, 1) / np.sqrt(n_features)\n",
+    "        self.bias = np.random.randn(1, 1)\n",
+    "\n",
+    "        # Implement the Gradient Descent algorithm\n",
+    "        for _ in range(self.epochs):\n",
+    "            z = np.dot(X, self.weights) + self.bias\n",
+    "            y_pred = sigmoid(z)\n",
+    "\n",
+    "            dw = -np.dot(X.T, (y - y_pred)) / n_samples\n",
+    "            db = -np.sum(y - y_pred) / n_samples\n",
+    "\n",
+    "            self.weights -= self.lr * dw\n",
+    "            self.bias -= self.lr * db\n",
+    "\n",
+    "    def predict(self, X):\n",
+    "        y_pred = sigmoid(np.dot(X, self.weights) + self.bias)\n",
+    "        y_pred_class = (y_pred > 0.5).astype(int)\n",
+    "        return y_pred_class\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Uo9LNRMzq4K-"
+   },
+   "source": [
+    "6. Fit your model on the dataset and make predictions.\n",
+    "7. Compare your model with the Sklearn Logistic Regression model. Try out all the different penalties.\n",
+    "8. Print accuracy_score in each case using sklearn.metrics ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "id": "DyGsTTOqFphf"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Logistic Regression Accuracy: 0.8771929824561403\n"
+     ]
+    }
+   ],
+   "source": [
+    "lo = LogisticRegression(learning_rate=0.01, epochs=1000)\n",
+    "\n",
+    "lo.fit(X_train, y_train)\n",
+    "\n",
+    "y_pred = lo.predict(X_test)\n",
+    "\n",
+    "from sklearn.metrics import accuracy_score\n",
+    "\n",
+    "accuracy_custom = accuracy_score(y_test, y_pred)\n",
+    "print(f\"Logistic Regression Accuracy: {accuracy_custom}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "AGBkzAO5red4"
+   },
+   "source": [
+    "9. For the best model in each case (yours and scikit-learn), print the classification_report using sklearn.metrics .\n",
+    "10. For the best model in each case (yours and scikit-learn), print the confusion_matrix using sklearn.metrics ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "id": "le-HfABsvnyF"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\yash0\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:1183: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sklearn Logistic Regression with none penalty Accuracy: 0.9122807017543859\n",
+      "Sklearn Logistic Regression with l2 penalty Accuracy: 0.9824561403508771\n",
+      "Sklearn Logistic Regression with l1 penalty Accuracy: 0.956140350877193\n",
+      "Sklearn Logistic Regression with elasticnet penalty Accuracy: 0.9824561403508771\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import LogisticRegression as SklearnLogisticRegression\n",
+    "best_accuracy_sklearn = 0\n",
+    "best_penalty = None\n",
+    "best_sklearn_model = None\n",
+    "y_pred_best_sklearn = None\n",
+    "penalties = ['none', 'l2', 'l1', 'elasticnet']\n",
+    "solver = {\n",
+    "    'none': 'lbfgs',\n",
+    "    'l2': 'lbfgs',\n",
+    "    'l1': 'saga',\n",
+    "    'elasticnet': 'saga'\n",
+    "}\n",
+    "\n",
+    "for penalty in penalties:\n",
+    "    if penalty == 'elasticnet':\n",
+    "        sklearn_model = SklearnLogisticRegression(penalty=penalty, solver=solver[penalty], l1_ratio=0.5, max_iter=10000)\n",
+    "    else:\n",
+    "        sklearn_model = SklearnLogisticRegression(penalty=penalty, solver=solver[penalty], max_iter=10000)\n",
+    "    sklearn_model.fit(X_train, y_train)\n",
+    "    y_pred_sklearn = sklearn_model.predict(X_test)\n",
+    "    accuracy_sklearn = accuracy_score(y_test, y_pred_sklearn)\n",
+    "    print(f\"Sklearn Logistic Regression with {penalty} penalty Accuracy: {accuracy_sklearn}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Best Custom Logistic Regression Model:\n",
+      "Classification Report:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       1.00      0.67      0.81        43\n",
+      "           1       0.84      1.00      0.91        71\n",
+      "\n",
+      "    accuracy                           0.88       114\n",
+      "   macro avg       0.92      0.84      0.86       114\n",
+      "weighted avg       0.90      0.88      0.87       114\n",
+      "\n",
+      "Confusion Matrix:\n",
+      "[[29 14]\n",
+      " [ 0 71]]\n",
+      "\n",
+      "Best Sklearn Logistic Regression Model with elasticnet penalty:\n",
+      "Classification Report:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       1.00      0.95      0.98        43\n",
+      "           1       0.97      1.00      0.99        71\n",
+      "\n",
+      "    accuracy                           0.98       114\n",
+      "   macro avg       0.99      0.98      0.98       114\n",
+      "weighted avg       0.98      0.98      0.98       114\n",
+      "\n",
+      "Confusion Matrix:\n",
+      "[[41  2]\n",
+      " [ 0 71]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
+    "if accuracy_sklearn > best_accuracy_sklearn:\n",
+    "        best_accuracy_sklearn = accuracy_sklearn\n",
+    "        best_penalty = penalty\n",
+    "        best_sklearn_model = sklearn_model\n",
+    "        y_pred_best_sklearn = y_pred_sklearn\n",
+    "\n",
+    "# Print classification report and confusion matrix for the best custom model\n",
+    "print(\"\\nBest Custom Logistic Regression Model:\")\n",
+    "print(\"Classification Report:\")\n",
+    "print(classification_report(y_test, y_pred))\n",
+    "print(\"Confusion Matrix:\")\n",
+    "print(confusion_matrix(y_test, y_pred))\n",
+    "\n",
+    "# Print classification report and confusion matrix for the best sklearn model\n",
+    "print(f\"\\nBest Sklearn Logistic Regression Model with {best_penalty} penalty:\")\n",
+    "print(\"Classification Report:\")\n",
+    "print(classification_report(y_test, y_pred_best_sklearn))\n",
+    "print(\"Confusion Matrix:\")\n",
+    "print(confusion_matrix(y_test, y_pred_best_sklearn))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "6OQ2tSp0MO6n"
+   },
+   "source": [
+    "# KNN\n",
+    "## Question 4\n",
+    "\n",
+    "How accurately can a K-Nearest Neighbors (KNN) model classify different types of glass based on a glass classification dataset consisting of 214 samples and 7 classes? Use the kaggle dataset \"https://www.kaggle.com/datasets/uciml/glass\".\n",
+    "\n",
+    "Context: This is a Glass Identification Data Set from UCI. It contains 10 attributes including id. The response is glass type(discrete 7 values)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "iMGxbtX-zfsI"
+   },
+   "source": [
+    "1. Load the data as you did in the 2nd question.\n",
+    "2. Extract the X and Y columns.\n",
+    "3. Split it into training and testing datasets."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "id": "p0SfLB7pO7_z"
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "from sklearn.metrics import mean_squared_error, r2_score\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "\n",
+    "df = pd.read_csv('glass.csv')\n",
+    "X = df.iloc[:, :-1].values\n",
+    "y = df['Type']\n",
+    "scaler = StandardScaler()\n",
+    "X = scaler.fit_transform(X)\n",
+    "X=np.array(X)\n",
+    "y=np.array(y)\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "qtyZJXh9zoh5"
+   },
+   "source": [
+    "4. Define Euclidean distance.\n",
+    "5. Build the KNN model.\n",
+    "6. Fit the model on the training data. (Note : you may require to change the type of the data from pandas dataframe to numpy arrays. To do that, just do this X=np.array(X) and so on...)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "id": "YJkhLORLzn6r"
+   },
+   "outputs": [],
+   "source": [
+    "from collections import Counter\n",
+    "def euclidean_distance(x1,x2):\n",
+    "    return np.sqrt(np.sum((x1-x2)**2))\n",
+    "\n",
+    "class KNN(object):\n",
+    "    def __init__(self,k):\n",
+    "        self.k=k\n",
+    "    def fit(self,x_train,y_train):\n",
+    "        self.x_train=x_train\n",
+    "        self.y_train=y_train\n",
+    "    def predict(self,x_test):\n",
+    "        predictions=[self._helper(x) for x in x_test]\n",
+    "        return np.array(predictions)\n",
+    "    def _helper(self,x):\n",
+    "        prediction=[euclidean_distance(x,x1) for x1 in self.x_train]\n",
+    "        indices= np.argsort(prediction)[:self.k]\n",
+    "        labels= [self.y_train[i] for i in indices]\n",
+    "        c=Counter(labels).most_common()\n",
+    "        return c[0][0]\n",
+    "        \n",
+    "def accuracy(predictions,y_test):\n",
+    "    return np.sum(predictions==y_test)/len(y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.6976744186046512\n",
+      "Classification Report:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           1       0.62      0.91      0.74        11\n",
+      "           2       0.62      0.57      0.59        14\n",
+      "           3       0.00      0.00      0.00         3\n",
+      "           5       0.50      0.25      0.33         4\n",
+      "           6       1.00      1.00      1.00         3\n",
+      "           7       0.89      1.00      0.94         8\n",
+      "\n",
+      "    accuracy                           0.70        43\n",
+      "   macro avg       0.60      0.62      0.60        43\n",
+      "weighted avg       0.64      0.70      0.66        43\n",
+      "\n",
+      "Confusion Matrix:\n",
+      "[[10  1  0  0  0  0]\n",
+      " [ 5  8  0  1  0  0]\n",
+      " [ 1  2  0  0  0  0]\n",
+      " [ 0  2  0  1  0  1]\n",
+      " [ 0  0  0  0  3  0]\n",
+      " [ 0  0  0  0  0  8]]\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\yash0\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
+      "  _warn_prf(average, modifier, msg_start, len(result))\n",
+      "C:\\Users\\yash0\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
+      "  _warn_prf(average, modifier, msg_start, len(result))\n",
+      "C:\\Users\\yash0\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
+      "  _warn_prf(average, modifier, msg_start, len(result))\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "clf=KNN(k=5)\n",
+    "clf.fit(X_train,y_train)\n",
+    "predictions=clf.predict(X_test)\n",
+    "print(accuracy(predictions,y_test))\n",
+    "print(\"Classification Report:\")\n",
+    "print(classification_report(y_test, predictions))\n",
+    "print(\"Confusion Matrix:\")\n",
+    "print(confusion_matrix(y_test, predictions))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "E9rxZpPB0pVS"
+   },
+   "source": [
+    "7. Make predictions. Find their accuracy using accuracy_score. Try different k values. k=3 worked well in our case.\n",
+    "8. Compare with the sklearn model (from sklearn.neighbors import KNeighborsClassifier)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "id": "ernfjaZJ0pAh"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "KNN Model Accuracy: 0.6977\n",
+      "Classification Report:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           1       0.62      0.91      0.74        11\n",
+      "           2       0.62      0.57      0.59        14\n",
+      "           3       0.00      0.00      0.00         3\n",
+      "           5       0.50      0.25      0.33         4\n",
+      "           6       1.00      1.00      1.00         3\n",
+      "           7       0.89      1.00      0.94         8\n",
+      "\n",
+      "    accuracy                           0.70        43\n",
+      "   macro avg       0.60      0.62      0.60        43\n",
+      "weighted avg       0.64      0.70      0.66        43\n",
+      "\n",
+      "Confusion Matrix:\n",
+      "[[10  1  0  0  0  0]\n",
+      " [ 5  8  0  1  0  0]\n",
+      " [ 1  2  0  0  0  0]\n",
+      " [ 0  2  0  1  0  1]\n",
+      " [ 0  0  0  0  3  0]\n",
+      " [ 0  0  0  0  0  8]]\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\yash0\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
+      "  _warn_prf(average, modifier, msg_start, len(result))\n",
+      "C:\\Users\\yash0\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
+      "  _warn_prf(average, modifier, msg_start, len(result))\n",
+      "C:\\Users\\yash0\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
+      "  _warn_prf(average, modifier, msg_start, len(result))\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "knn = KNeighborsClassifier(n_neighbors=5) \n",
+    "knn.fit(X_train, y_train)\n",
+    "y_pred = knn.predict(X_test)\n",
+    "accuracy = accuracy_score(y_test, y_pred)\n",
+    "print(f\"KNN Model Accuracy: {accuracy:.4f}\")\n",
+    "print(\"Classification Report:\")\n",
+    "print(classification_report(y_test, y_pred))\n",
+    "\n",
+    "# Print confusion matrix\n",
+    "print(\"Confusion Matrix:\")\n",
+    "print(confusion_matrix(y_test, y_pred))"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Week 1/ML Assignment/Assignment.ipynb b/Week 1/ML Assignment/Assignment.ipynb
index 9ff3c94..a9104a9 100644
--- a/Week 1/ML Assignment/Assignment.ipynb	
+++ b/Week 1/ML Assignment/Assignment.ipynb	
@@ -1,295 +1,750 @@
 {
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "Y5y_HbJiPKhA"
-      },
-      "source": [
-        "# Linear Regression\n",
-        "## Question 1\n",
-        "Make a class called LinearRegression which provides two functions : fit and predict. Try to implement it from scratch. If stuck, refer to the examples folder."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "gzoG2XilPLFr"
-      },
-      "outputs": [],
-      "source": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "PsqoxNag7D3-"
-      },
-      "source": [
-        "## Question 2\n",
-        "\n",
-        "Use the dataset https://www.kaggle.com/datasets/quantbruce/real-estate-price-prediction (*).\n",
-        "1. Read it using pandas.\n",
-        "2. Check for **null values**.\n",
-        "3. For each of the columns (except the first and last), plot the column values in the X-axis against the last column of prices in the Y-axis.\n",
-        "4. Remove the unwanted columns.\n",
-        "5. Split the dataset into train and test data. Test data size = 25% of total dataset.\n",
-        "6. **Normalize** the X_train and X_test using MinMaxScaler from sklearn.preprocessing.\n",
-        "7. Fit the training data into the model created in question 1 and predict the testing data.\n",
-        "8. Use **mean square error and R<sup>2</sup>** from sklearn.metrics as evaluation criterias.\n",
-        "9. Fit the training data into the models of the same name provided by sklearn.linear_model and evaluate the predictions using MSE and R<sup>2</sup>.\n",
-        "10. Tune the hyperparameters of your models (learning rate, epochs) to achieve losses close to that of the sklearn models.\n",
-        "\n",
-        "Note : (*) To solve this question, you may proceed in any of the following ways :\n",
-        "1. Prepare the notebook in Kaggle, download it and submit it separately with the other questions.\n",
-        "2. Download the dataset from kaggle. Upload it to the session storage in Colab.\n",
-        "3. Use Colab data directly in Colab. [Refer here](https://www.kaggle.com/general/74235). For this, you need to create kaggle API token. Before submitting, hide or remove the API token."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "8lupaMcr63QF"
-      },
-      "outputs": [],
-      "source": []
-    },
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Y5y_HbJiPKhA"
+   },
+   "source": [
+    "# Linear Regression\n",
+    "## Question 1\n",
+    "Make a class called LinearRegression which provides two functions : fit and predict. Try to implement it from scratch. If stuck, refer to the examples folder."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "id": "gzoG2XilPLFr"
+   },
+   "outputs": [],
+   "source": [
+    "class LinearRegression:\n",
+    "    def __init__(self, learning_rate, epochs):\n",
+    "        self.lr=learning_rate\n",
+    "        self.epochs=epochs\n",
+    "\n",
+    "    def fit(self, X_train, y_train):\n",
+    "        n_samples, n_features = X_train.shape\n",
+    "        y_train=y_train.reshape(-1,1)\n",
+    "        # init parameters\n",
+    "        self.weights = np.zeros((n_features,1))\n",
+    "        self.bias = np.zeros((1,1))\n",
+    "\n",
+    "        # gradient descent\n",
+    "        for i in range(self.epochs):\n",
+    "            delta= -(y_train-np.dot(X_train,self.weights)-self.bias)/n_samples\n",
+    "            dw= np.dot(X_train.T,delta)\n",
+    "            db= np.sum(delta).reshape(1,1)\n",
+    "\n",
+    "            #update weights and biases\n",
+    "            self.weights-= self.lr * dw\n",
+    "            self.bias-= self.lr* db\n",
+    "\n",
+    "    def predict(self, X_test):\n",
+    "        y_predicted = np.dot(X_test,self.weights)+self.bias\n",
+    "        print(self.weights, self.bias)\n",
+    "        return y_predicted\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "PsqoxNag7D3-"
+   },
+   "source": [
+    "## Question 2\n",
+    "\n",
+    "Use the dataset https://www.kaggle.com/datasets/quantbruce/real-estate-price-prediction (*).\n",
+    "1. Read it using pandas.\n",
+    "2. Check for **null values**.\n",
+    "3. For each of the columns (except the first and last), plot the column values in the X-axis against the last column of prices in the Y-axis.\n",
+    "4. Remove the unwanted columns.\n",
+    "5. Split the dataset into train and test data. Test data size = 25% of total dataset.\n",
+    "6. **Normalize** the X_train and X_test using MinMaxScaler from sklearn.preprocessing.\n",
+    "7. Fit the training data into the model created in question 1 and predict the testing data.\n",
+    "8. Use **mean square error and R<sup>2</sup>** from sklearn.metrics as evaluation criterias.\n",
+    "9. Fit the training data into the models of the same name provided by sklearn.linear_model and evaluate the predictions using MSE and R<sup>2</sup>.\n",
+    "10. Tune the hyperparameters of your models (learning rate, epochs) to achieve losses close to that of the sklearn models.\n",
+    "\n",
+    "Note : (*) To solve this question, you may proceed in any of the following ways :\n",
+    "1. Prepare the notebook in Kaggle, download it and submit it separately with the other questions.\n",
+    "2. Download the dataset from kaggle. Upload it to the session storage in Colab.\n",
+    "3. Use Colab data directly in Colab. [Refer here](https://www.kaggle.com/general/74235). For this, you need to create kaggle API token. Before submitting, hide or remove the API token."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "id": "8lupaMcr63QF"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "dQ7lQpy-SYCq"
-      },
-      "source": [
-        "# Logistic Regression\n",
-        "## Question 3\n",
-        "\n",
-        "The breast cancer dataset is a binary classification dataset commonly used in machine learning tasks. It is available in scikit-learn (sklearn) as part of its datasets module.\n",
-        "Here is an explanation of the breast cancer dataset's components:\n",
-        "\n",
-        "* Features (X):\n",
-        "\n",
-        " * The breast cancer dataset consists of 30 numeric features representing different characteristics of the FNA images. These features include mean, standard error, and worst (largest) values of various attributes such as radius, texture, smoothness, compactness, concavity, symmetry, fractal dimension, etc.\n",
-        "\n",
-        "* Target (y):\n",
-        "\n",
-        " * The breast cancer dataset is a binary classification problem, and the target variable (y) represents the diagnosis of the breast mass. It contains two classes:\n",
-        "    * 0: Represents a malignant (cancerous) tumor.\n",
-        "    * 1: Represents a benign (non-cancerous) tumor.\n",
-        "\n",
-        "Complete the code given below in place of the \"...\""
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No                                        0\n",
+      "X1 transaction date                       0\n",
+      "X2 house age                              0\n",
+      "X3 distance to the nearest MRT station    0\n",
+      "X4 number of convenience stores           0\n",
+      "X5 latitude                               0\n",
+      "X6 longitude                              0\n",
+      "Y house price of unit area                0\n",
+      "dtype: int64\n"
+     ]
     },
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "auipk-zBpmO-"
-      },
-      "source": [
-        "1. Load the dataset from sklearn.datasets\n",
-        "2. Separate out the X and Y columns.\n",
-        "3. Normalize the X data using MinMaxScaler or StandardScaler.\n",
-        "4. Create a train-test-split. Take any suitable test size."
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "0OyGNHNjFh13"
-      },
-      "outputs": [],
-      "source": [
-        "\n"
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "uM-SsSxpqF2o"
-      },
-      "source": [
-        "5. Write code for the sigmoid function and Logistic regression.\n",
-        ""
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "o81LA5MZFoTW"
-      },
-      "outputs": [],
-      "source": [
-        "def sigmoid(z):\n",
-        "   \"...\"\n",
-        "\n",
-        "def sigmoid_derivative(z):\n",
-        "    \"...\"\n",
-        "\n",
-        "class LogisticRegression:\n",
-        "    def __init__(self, learning_rate, epochs):\n",
-        "      #Initialise the hyperparameters of the model\n",
-        "        self.lr = \"...\"\n",
-        "        self.epochs = \"...\"\n",
-        "\n",
-        "    def fit(self, X, y):\n",
-        "        n_samples, n_features = X.shape\n",
-        "        y = y.reshape(-1, 1)\n",
-        "        self.weights = \"...\"\n",
-        "        self.bias = \"...\"\n",
-        "\n",
-        "        #Implement the GD algortihm\n",
-        "        for _ in range(self.epochs):\n",
-        "            z = \"...\"\n",
-        "            y_pred = \"...\"\n",
-        "\n",
-        "            dw = \"...\"\n",
-        "            db = \"...\"\n",
-        "\n",
-        "            self.weights -= \"...\"\n",
-        "            self.bias -= \"...\"\n",
-        "\n",
-        "    def predict(self, X):\n",
-        "      #Write the predict function\n",
-        "        \"...\"\n",
-        "        return y_pred"
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "Uo9LNRMzq4K-"
-      },
-      "source": [
-        "6. Fit your model on the dataset and make predictions.\n",
-        "7. Compare your model with the Sklearn Logistic Regression model. Try out all the different penalties.\n",
-        "8. Print accuracy_score in each case using sklearn.metrics ."
+     "data": {
+      "image/png": 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vj0IIo7V9dLU1pFRFNrVvUFAQKleujL179xps/+9//1uqXd26dcOGDRtw+fJl/fbTp09j69atBvv27dsXHh4emD59eqlvwUIIpKWlWWxvTEwMQkJCsHr1aqxevRotW7Y06LrPzc3FvXv3DO5Tq1YtBAYGIi8vz+Lx5dAV5fv8888Nts+dOxcqlUo/M0fqc1lYWFhquCA0NBQRERH6trdo0QK1atXCxx9/jOzs7FJtkjOLTimPP/44AgMDMWvWrFLPve51trXdPXr0QGpqKlavXq3fVlBQgM8++wwBAQGlZuhJ1aNHDxw8eBBHjhwx2J6RkYHly5ejadOmkoc/LZHzvly/fj1ycnIwZswY9O/fv9TPk08+ibVr11r8n5b6npPz+UJlhz075BDvvPMOrl27hnXr1hl0CY8ZMwZLly7FhAkTEBsbi8DAQGzatAkzZsxA//79ER0djfT0dKxYsQKJiYmYOXOm7A/QevXqoVatWnj99ddx9epVBAUFYe3atUaTcFu0aAGg6Ntft27d4OHhgYEDBxo9rm7ff//73xg4cCA8PT3Rs2dP+Pv748UXX8Ts2bPx4osv4uGHH8bevXuNLpUxffp0xMXF4dFHH8Xo0aP1F6GGDRsa5DHUqlULM2bMwOTJk5GcnIw+ffogMDAQSUlJWL9+PUaOHInXX3/d7PPg6emJvn37YtWqVcjJycHHH39scPvZs2fRpUsXDBgwAA0aNECFChWwfv16XL9+3eRzYK2ePXuiU6dO+Pe//43k5GQ0adIEv/76KzZu3IgJEyboezMASHou79y5g+rVq6N///5o0qQJAgICsH37dhw+fBj/+c9/AABqtRpff/01unfvjoYNG+L5559HtWrVcPXqVezatQtBQUH46aefFH2clgQFBWHu3Ll48cUX8cgjj+jrvJw4cQK5ublYunSpze0eOXIkvvzySwwbNgxHjhxBVFQUfvzxRxw4cADz5s0rlTcl1VtvvYUffvgBHTp0wEsvvYR69erh2rVrWLJkCVJSUrB48WJrn5ZS5Lwvly9fjkqVKqFt27ZGb+/VqxcWLVqEzZs3l0oqL07qe07O5wuVIUdP/6Ly5/fffxceHh5i7NixRm8/dOiQUKvVYvz48fr9e/bsKapVqya8vLxEQECAaN++vVizZo2k8xmben7q1CkRExMjAgICROXKlcWIESPEiRMnSk1xLSgoEOPGjRNVqlQRKpXKYLorSkx1FkKI999/X1SrVk2o1WqDKcW5ubli+PDhQqPRiMDAQDFgwABx48YNo8fYs2ePaNGihfDy8hIPPPCAWLhwoX46bElr164V7du3F/7+/sLf31/Uq1dPjBkzRpw5c0bSc7Nt2zYBQKhUKnHlyhWD227duiXGjBkj6tWrJ/z9/YVGoxGtWrWS9Lzr2nvz5k2jtxub0nznzh0xceJEERERITw9PUWdOnXERx99ZDC9Wghpz2VeXp544403RJMmTURgYKDw9/cXTZo0Ef/9739LteXYsWOib9++olKlSsLb21vUrFlTDBgwQOzYscPsY9RNibY0NV83vfzw4cMmbytZrmDTpk2ibdu2wtfXVwQFBYmWLVuKlStXKtJuIYS4fv26eP7550XlypWFl5eXaNy4sdHp/HKmngshxN9//y1efPFFUa1aNVGhQgUREhIinnzySXHw4MFS+3bs2FE0bNiw1PahQ4eKmjVr6v82NvXc3Puy5OOsUKGCePbZZ022OTc3V/j5+Yl//etfQgjL/7tS3nNSP1+o7KiEUDgzjIiIiMiJMGeHiIiI3BqDHSIiInJrDHaIiIjIrTHYISIiIrfGYIeIiIjcGoMdIiIicmssKoiiNVCuXbuGwMBAWWXJiYiIqOwIIXDnzh1ERESUWty1OAY7AK5du6ZfZJKIiIhcy5UrV1C9enWTtzPYAfTl0q9cuYKgoKAybg0RERFJkZWVhcjISIvLnjDYwT8r6gYFBTHYISIicjGWUlCYoExERERujcEOERERubUyDXb27t2Lnj17IiIiAiqVChs2bNDflp+fj0mTJqFx48bw9/dHREQEnnvuOVy7ds3gGOnp6Rg8eDCCgoIQHByM4cOHIzs728GPhIiIiJxVmQY7OTk5aNKkCb744otSt+Xm5uLo0aOYMmUKjh49inXr1uHMmTPo1auXwX6DBw/Gn3/+iW3btuHnn3/G3r17MXLkSEc9BCIiInJyKiGEKOtGAEXJRevXr0efPn1M7nP48GG0bNkSly5dQo0aNXD69Gk0aNAAhw8fxsMPPwwAiIuLQ48ePfD3338jIiJC0rmzsrKg0WiQmZnJBGUiIiIXIfX67VI5O5mZmVCpVAgODgYAxMfHIzg4WB/oAEBMTAzUajUSEhJMHicvLw9ZWVkGP0REROSeXCbYuXfvHiZNmoRBgwbpo7fU1FSEhoYa7FehQgWEhIQgNTXV5LFmzZoFjUaj/2FBQSIiIvflEsFOfn4+BgwYACEEFixYYPPxJk+ejMzMTP3PlStXFGglEREROSOnLyqoC3QuXbqEnTt3GozJhYWF4caNGwb7FxQUID09HWFhYSaP6e3tDW9vb7u1mYiIiJyHU/fs6AKdc+fOYfv27ahUqZLB7W3atEFGRgaOHDmi37Zz505otVq0atXK0c0lIhdSqBWIv5CGjcevIv5CGgq1TjFXg4jsoEx7drKzs3H+/Hn930lJSTh+/DhCQkIQHh6O/v374+jRo/j5559RWFioz8MJCQmBl5cX6tevj9jYWIwYMQILFy5Efn4+xo4di4EDB0qeiUVE5U9cYgqm/3QKKZn39NvCNT6Y2rMBYhuFl2HLiMgeynTq+e7du9GpU6dS24cOHYpp06YhOjra6P127dqFxx57DEBRUcGxY8fip59+glqtRr9+/fDpp58iICBAcjs49Zyo/IhLTMGoZUdR8oNPt7LOgiHNGfAQuQip12+nqbNTlhjsEJUPhVqB9nN2GvToFKcCEKbxwf5JneGhNr+wIBGVPbess0NEZItDSekmAx0AEABSMu/hUFK64xpFRHbHYIeIyo0bd0wHOtbsR0SugcEOEZUboYE+iu5HRK6BwQ4RlRsto0MQrvGBqWwcFYpmZbWMDnFks4jIzhjsEFG54aFWYWrPBgBQKuDR/T21ZwMmJxO5GQY7RFSuxDYKx4IhzRGmMRyqCtP4cNo5kZty+uUiiIiUFtsoHF0bhOFQUjpu3LmH0MCioSv26BC5JwY7RFQueahVaFOrkuUdicjlcRiLiIiI3BqDHSIiInJrDHaIiIjIrTHYISIiIrfGYIeIiIjcGoMdIiIicmsMdoiIiMitMdghIiIit8Zgh4iIiNwagx0iIiJyawx2iIiIyK0x2CEiIiK3xmCHiIiI3BqDHSIiInJrDHaIiIjIrTHYISIiIrfGYIeIiIjcGoMdIiIicmsMdoiIiMitMdghIiIit8Zgh4iIiNwagx0iIiJyawx2iIiIyK0x2CEiIiK3xmCHiIiI3BqDHSIiInJrDHaIiIjIrTHYISIiIrfGYIeIiIjcGoMdIiIicmsMdoiIiMitMdghIiIit8Zgh4iIiNwagx0iIiJyawx2iIiIyK0x2CEiIiK3xmCHiIiI3BqDHSIiInJrDHaIiIjIrTHYISIiIrfGYIeIiIjcGoMdIiIicmtlGuzs3bsXPXv2REREBFQqFTZs2GBwuxAC7777LsLDw+Hr64uYmBicO3fOYJ/09HQMHjwYQUFBCA4OxvDhw5Gdne3AR0FERETOrEyDnZycHDRp0gRffPGF0ds//PBDfPrpp1i4cCESEhLg7++Pbt264d69e/p9Bg8ejD///BPbtm3Dzz//jL1792LkyJGOeghERETk5FRCCFHWjQAAlUqF9evXo0+fPgCKenUiIiLw2muv4fXXXwcAZGZmomrVqliyZAkGDhyI06dPo0GDBjh8+DAefvhhAEBcXBx69OiBv//+GxEREZLOnZWVBY1Gg8zMTAQFBdnl8REREZGypF6/nTZnJykpCampqYiJidFv02g0aNWqFeLj4wEA8fHxCA4O1gc6ABATEwO1Wo2EhASTx87Ly0NWVpbBDxEREbknpw12UlNTAQBVq1Y12F61alX9bampqQgNDTW4vUKFCggJCdHvY8ysWbOg0Wj0P5GRkQq3noiIiJyF0wY79jR58mRkZmbqf65cuVLWTSIiIiI7cdpgJywsDABw/fp1g+3Xr1/X3xYWFoYbN24Y3F5QUID09HT9PsZ4e3sjKCjI4IeIiIjck9MGO9HR0QgLC8OOHTv027KyspCQkIA2bdoAANq0aYOMjAwcOXJEv8/OnTuh1WrRqlUrh7eZiIiInE+Fsjx5dnY2zp8/r/87KSkJx48fR0hICGrUqIEJEyZgxowZqFOnDqKjozFlyhREREToZ2zVr18fsbGxGDFiBBYuXIj8/HyMHTsWAwcOlDwTi4iIiNxbmQY7v//+Ozp16qT/+9VXXwUADB06FEuWLMGbb76JnJwcjBw5EhkZGWjfvj3i4uLg4+Ojv8/y5csxduxYdOnSBWq1Gv369cOnn37q8MdCREREzslp6uyUJdbZISIicj0uX2eHiIiISAkMdoiIiMitMdghIiIit8Zgh4iIiNwagx0iIiJyawx2iIiIyK0x2CEiIiK3xmCHiIiI3BqDHSIiInJrDHaIiIjIrTHYISIiIrfGYIeIiIjcGoMdIiIicmsMdoiIiMitMdghIiIit8Zgh4iIiNwagx0iIiJyawx2iIiIyK0x2CEiIiK3xmCHiIiI3BqDHSIiInJrDHaIiIjIrTHYISIiIrfGYIeIiIjcGoMdIiIicmsVrL1jbm4uLl++jPv37xtsf+ihh2xuFBEREZFSZAc7N2/exPPPP49ffvnF6O2FhYU2N4qIiIhIKbKHsSZMmICMjAwkJCTA19cXcXFxWLp0KerUqYNNmzbZo41EREREVpPds7Nz505s3LgRDz/8MNRqNWrWrImuXbsiKCgIs2bNwhNPPGGPdhIRERFZRXbPTk5ODkJDQwEAFStWxM2bNwEAjRs3xtGjR5VtHREREZGNZAc7Dz74IM6cOQMAaNKkCb788ktcvXoVCxcuRHh4uOINJCIiIrKF7GGsV155BSkpKQCAqVOnIjY2FsuXL4eXlxeWLFmidPuIiIiIbKISQghbDpCbm4u//voLNWrUQOXKlZVql0NlZWVBo9EgMzMTQUFBZd0cIiIikkDq9dvqooL379/HmTNn4OXlhebNm7tsoENERETuTXawk5ubi+HDh8PPzw8NGzbE5cuXAQDjxo3D7NmzFW8gERERkS1kBzuTJ0/GiRMnsHv3bvj4+Oi3x8TEYPXq1Yo2joiIiMhWshOUN2zYgNWrV6N169ZQqVT67Q0bNsSFCxcUbRwRERGRrWT37Ny8eVNfZ6e4nJwcg+CHiIiIyBnIDnYefvhhbN68Wf+3LsD5+uuv0aZNG+VaRkRERKQA2cNYM2fORPfu3XHq1CkUFBRg/vz5OHXqFH777Tfs2bPHHm0kIiIisprsnp327dvjxIkTKCgoQOPGjfHrr78iNDQU8fHxaNGihT3aSERERGQ1WT07+fn5eOmllzBlyhQsWrTIXm0iIiIiUoysnh1PT0+sXbvWXm0hIiIiUpzsYaw+ffpgw4YNdmgKERERkfJkJyjXqVMH7733Hg4cOIAWLVrA39/f4Pbx48cr1jgiIiIiW8leCDQ6Otr0wVQqXLx40eZGORoXAiUiInI9Uq/fsnt2kpKSbGoYERERkSNZvep5SadPn8brr7+u1OGIiIiIFGFTsJOTk4NvvvkGbdu2RcOGDREXF6dUu4iIiIgUYVWwc+DAAbzwwguoWrUqRo4cibZt2+LUqVNITExUun1ERERENpEc7Ny4cQMffvgh6tWrh/79+yM4OBi7d++GWq3GCy+8gHr16ineuMLCQkyZMgXR0dHw9fVFrVq18P7776N4TrUQAu+++y7Cw8Ph6+uLmJgYnDt3TvG2EBERkWuSnKBcs2ZN9O/fH/Pnz0fXrl2hViuW7mPSnDlzsGDBAixduhQNGzbE77//jueffx4ajUY/xf3DDz/Ep59+iqVLlyI6OhpTpkxBt27dcOrUKfj4+Ni9jUREROTcZAU7+/fvR40aNVCzZk279OSU9Ntvv6F379544oknAABRUVFYuXIlDh06BKCoV2fevHl455130Lt3bwDAd999h6pVq2LDhg0YOHCg3dtIREREzk1y98xff/2FZcuWISUlBY888ghatGiBuXPnAiiqr2MPbdu2xY4dO3D27FkAwIkTJ7B//350794dQNE0+NTUVMTExOjvo9Fo0KpVK8THx9ulTURERORaZNXZadeuHdq1a4dPP/0UK1euxOLFi1FYWIjRo0fjmWeeQZ8+fVClShXFGvfWW28hKysL9erVg4eHBwoLC/HBBx9g8ODBAIDU1FQAQNWqVQ3uV7VqVf1txuTl5SEvL0//d1ZWlmJtJiIiIudiVeJNQEAARowYgd9++w1//vknWrRogXfeeQcRERGKNm7NmjVYvnw5VqxYgaNHj2Lp0qX4+OOPsXTpUpuOO2vWLGg0Gv1PZGSkQi0mIndWqBWIv5CGjcevIv5CGgq1sgrQE1EZkb1chCkFBQXYtGkT+vbtq8ThAACRkZF46623MGbMGP22GTNmYNmyZfjrr79w8eJF1KpVC8eOHUPTpk31+3Ts2BFNmzbF/PnzjR7XWM9OZGQkl4sgckKFWoFDSem4ceceQgN90DI6BB5q+wydmxOXmILpP51CSuY9/bZwjQ+m9myA2EbhDm8PEdlxuQiTB6pQQdFABwByc3NLzfry8PCAVqsFULROV1hYGHbs2KEPdrKyspCQkIBRo0aZPK63tze8vb0VbSsRKc9ZAoy4xBSMWnYUJb8Zpmbew6hlR7FgSHMGPEROzP7zx23Qs2dPfPDBB9i8eTOSk5Oxfv16fPLJJ/jXv/4FoCgxesKECZgxYwY2bdqEkydP4rnnnkNERAT69OlTto0nIpvoAozigQ7wT4ARl5jikHYUagWm/3SqVKADQL9t+k+nOKRF5MQU69mxh88++wxTpkzB6NGjcePGDUREROCll17Cu+++q9/nzTffRE5ODkaOHImMjAy0b98ecXFxrLFD5MIsBRgqFAUYXRuE2X1I61BSeqmAq2R7UjLv4VBSOtrUqmTXthCRdRTL2XFlUsf8iMgx4i+kYdCigxb3Wzmitd0DjI3Hr+KVVcct7jd/YFP0blrNrm0hIkNSr9+yh7FeeOEF3Llzp9T2nJwcvPDCC3IPR0RUyo07pntSrNnPFqGB0nqJpe5HRI4nO9hZunQp7t69W2r73bt38d133ynSKCIq35wpwGgZHYJwjQ9MDZapUJQ03TI6xO5tISLrSA52srKykJmZCSEE7ty5g6ysLP3P7du3sWXLFoSGhtqzrURUTjhTgOGhVmFqzwb685ZsBwBM7dmgTKbDE5E0khOUg4ODoVKpoFKpULdu3VK3q1QqTJ8+XdHGEVH5pAswRi07ChVgkKhcFgFGbKNwLBjSvNQ0+DDW2SFyCZITlPfs2QMhBDp37oy1a9ciJOSfb1ReXl6oWbOm4hWUHYUJykTOyVnq7Og4S4FDIioi9fotezbWpUuXUKNGDbst/lkWGOwQOS8GGERkiqIVlP/44w80atQIarUamZmZOHnypMl9H3roIfmtJSIywUOtYv0aIrKJpGCnadOmSE1NRWhoKJo2bQqVSgVjHUIqlQqFhYWKN5KIiIjIWpKCnaSkJFSpUkX/OxFRecahNSLXIinYqVmzptHfiYjKG2dLmiYiy6xaG+vcuXPYtWsXbty4oV+BXKf4ulVERO6Eq58TuSbZwc6iRYswatQoVK5cGWFhYQazslQqFYMdInJLzrQ4KRHJIzvYmTFjBj744ANMmjTJHu0hInJKXP2cyHXJXhvr9u3beOqpp+zRFiIip+VMi5MSkTyyg52nnnoKv/76qz3aQkTktJxpcVIikkf2MFbt2rUxZcoUHDx4EI0bN4anp6fB7ePHj1escUREzkK3OGlq5j2jeTsqFK2VxdXPiZyP7OUioqOjTR9MpcLFixdtbpSjcbkIIpJCNxsLML44KWdjETmWostFFMeigkRUXsU2CsfIDtFYtC8Jxb8mqlTAiEejGegQOSnZOTtEROVVXGIKvtqbBG2J/nCtAL7am4S4xJSyaRgRmSW5Z6dv375Gt2s0GtStWxcvvviifkkJIiJ3Y67Ojg7r7BA5J8k9OxqNxuhPRkYGFi1ahAcffBCJiYn2bCsRUZmRU2eHiJyL5J6dxYsXm7xNq9VixIgRmDx5Mn766SdFGkZE5ExYZ4fIdSmSs6NWqzF+/HgcOXJEicMRETmdygHeiu5HRI6jWIKyv78/cnNzlTocEZFT0RZKq9IhdT8ichzFgp1t27ahbt26Sh2OiMipJCSnKbofETmO5JydTZs2Gd2emZmJI0eO4Ouvv8bXX3+tWMOIiJyL1BlWnIlF5GwkBzt9+vQxuj0wMBAPPvggvv76awwcOFCpdhGRGyjUChxKSseNO/cQGli0lIKrTstuU6sSPt91XtJ+RORcJAc7Wq3Wnu0gIjcTl5iC6T+dMpiuHa7xwdSeDVyy0nDrByoh2M8TGbn5Jvep6OeJ1g8w2CFyNqygTESK060hVbIuTWrmPYxadtQlKw17qFWY3bex2X1m9W3ssj1XRO6MwQ4RKcpcpWHdtuk/nUJhyTUXXEBso3AsHNIcYUE+BtvDNT5YyEVAiZyW7IVAiYjMkVNp2BXzW2IbhaNrgzC3yUUiKg8Y7BCRospDpWEPtcolAzWi8krSMNarr76KnJwcAMDevXtRUFBg10YRkesKDfSxvJOM/YiIbCUp2Pnss8+QnZ0NAOjUqRPS07nQHREZ1zI6BOEaH5PVZlQoynFpGR3iyGYRUTkmaRgrKioKn376KR5//HEIIRAfH4+KFSsa3bdDhw6KNpCIXIuHWoWpPRtg1LKjUAEGicq6AGhqzwbMcSEih1EJISxOidiwYQNefvll3LhxAyqVCqbuolKpUFhYqHgj7S0rKwsajQaZmZkICgoq6+YQuQV3q7NDRM5H6vVbUrCjk52djaCgIJw5cwahoaFG99FoNPJbW8YY7BDZhztVUCYi5yP1+i1rNlZAQAB27dqF6OhoVKjAiVxEZB5nLRGRM5AdsXTs2BGFhYVYu3YtTp8+DQBo0KABevfuDQ8PD8UbSESkJPY2EZU/soOd8+fP44knnsDff/+NBx98EAAwa9YsREZGYvPmzahVq5bijSQiUgLziIjKJ1k5OwDQo0cPCCGwfPlyhIQUTR1NS0vDkCFDoFarsXnzZrs01J6Ys0Pknor34iTfysW87WdLLWOh69NZ8P/lHqT0/LB3iMg52CVnBwD27NmDgwcP6gMdAKhUqRJmz56Ndu3aWddaIiKFGevFMUagKOCZ/tMpaLUC728+bbbnx1Tv0JQnGqCivxcDICInJDvY8fb2xp07d0ptz87OhpeXlyKNIiKyhW7Vdand1rr1ukavOFbqNt1K7QuGNAcAo8ctuu9Rg20cHiNyHrJXPX/yyScxcuRIJCQkQAgBIQQOHjyIl19+Gb169bJHG4ncWqFWIP5CGjYev4r4C2kuuRq4MzG36ro1dMeZtulPTNsk/bi6ICkuMUWhlhCRtWT37Hz66acYOnQo2rRpA09PTwBAQUEBevXqhfnz5yveQCJ3xoRZ5Vladd0aAkBqVp7s++iGx7o2CFN0SIs5Q0TyyA52goODsXHjRpw/f14/9bx+/fqoXbu24o0jcmemhlqKD5sw4JHPmVZT1w2PHUpKV6zeEANkIvmsrgxYu3ZtBjhEVjI31GLPHoHywBlXU1cqAGOATGQd2Tk7RGQ7S0MtxXsESB5Lq65bKyzIG2FB1h1XiQDMUoAMFAXIzPkiKo3BDlEZkPpN35mGZKRwhmRr3arrABQNeN59siGm9ZJ3XBWKhphaRoeY3Efqc8YAmch6XOCKqAxI/abvjEMypjhTLklso3AsGNJcUp0dqSr6e6FNrUqSj6sLiKb2bGByKFLOc+auATKRIzh9z87Vq1cxZMgQVKpUCb6+vmjcuDF+//13/e1CCLz77rsIDw+Hr68vYmJicO7cuTJsMZFlloZapPQIOBNdLknJAKAsp1/HNgrH/kmdsXJEa8x9uilC/D1tOp4uiCh+3PkDm2LliNb47zPNEK4xDEzDND5mc2jkPmfuGCATOYpVPTv79u3Dl19+iQsXLuDHH39EtWrV8P333yM6Ohrt27dXrHG3b99Gu3bt0KlTJ/zyyy+oUqUKzp07h4oVK+r3+fDDD/Hpp59i6dKliI6OxpQpU9CtWzecOnUKPj5805Nz0g21jFp2FCrAIA9DSo+AM3HmZOviq677eqoxatlRfbvkKh5EGFvNvVujcMnTwaU+Z4HenriVk4fQQB+0qFkR4RofpGbeM3o/FYoCLFcJkIkcSXaws3btWjz77LMYPHgwjh07hry8otoTmZmZmDlzJrZs2aJY4+bMmYPIyEgsXrxYvy06Olr/uxAC8+bNwzvvvIPevXsDAL777jtUrVoVGzZswMCBAxVrC5HSTA21hLnYNGI5uSRKTb+2RmyjcIzsEI1F+5JQfEVAtQrw8fTA3fuFNgURxgIgU6Q+Z4O/SdBvC9f4oFeTcHy1N8nlA2QiR5M9jDVjxgwsXLgQixYt0hcVBIB27drh6NGjZu4p36ZNm/Dwww/jqaeeQmhoKJo1a4ZFixbpb09KSkJqaipiYmL02zQaDVq1aoX4+HhF20JkD8aGRPZP6uwygQ7gOrkkcYkp+GpvEkrm/2oFkPv/QKdkmGCvIMKa5yI18x6+2puEkR2iESZzyIyovJPds3PmzBl06NCh1HaNRoOMjAwl2qR38eJFLFiwAK+++irefvttHD58GOPHj4eXlxeGDh2K1NRUAEDVqlUN7le1alX9bcbk5eXpe6SAolVTicqKnB4BZ+QKuSRSlpCo6OcJ7wpqg0rJ9upls+a50AVjm06kYM8bnXDk0m1WUCaSSHawExYWhvPnzyMqKspg+/79+/HAAw8o1S4AgFarxcMPP4yZM2cCAJo1a4bExEQsXLgQQ4cOtfq4s2bNwvTp05VqJlG5pku2duZcEilLSNzOzcfyF1tBrVLZPYhoGR2CYD9PZOTmy7qfbnjryKXbLh0gEzma7GGsESNG4JVXXkFCQgJUKhWuXbuG5cuX4/XXX8eoUaMUbVx4eDgaNGhgsK1+/fq4fPkygKLACwCuX79usM/169f1txkzefJkZGZm6n+uXLmiaLuJbOUM9WqkMlfXxllySVKzpA615aFNrUro3bQa2tSq5LS9JWU9JEjkamT37Lz11lvQarXo0qULcnNz0aFDB3h7e+P111/HuHHjFG1cu3btcObMGYNtZ8+eRc2aNQEUJSuHhYVhx44daNq0KYCiIamEhASzgZe3tze8vb0VbSuRUpypXo1Uzp5snZ4tbRFPqfvZ6lBSuuxeneI4vZxIHtnBjkqlwr///W+88cYbOH/+PLKzs9GgQQMEBAQo3riJEyeibdu2mDlzJgYMGIBDhw7hq6++wldffaVvy4QJEzBjxgzUqVNHP/U8IiICffr0Ubw9RPbmymsfxTYKR9cGYU65GneIv5ek/ZLTchB/Ic3u7ba2Z8YZhgSJXJHsYCczMxOFhYUICQkxGGJKT09HhQoVEBQUpFjjHnnkEaxfvx6TJ0/Ge++9h+joaMybNw+DBw/W7/Pmm28iJycHI0eOREZGBtq3b4+4uDjW2CGX48z1aqRy1mTrED9pwc73By/j+4OXEezriefbRWNs59p2ea6t6ZlxliFBIlekEkLISgbo3r07evbsidGjRxtsX7hwITZt2qRonR1HycrKgkajQWZmpqLBGpEc8RfSMGjRQYv7rRzR2ikDCqAoYHPGnp131p/EsoTLsu8X7OeJ2X0bK96bVqgVaD9np8mkbqCo/k/xVC1nH8okKgtSr9+ye3YSEhLwySeflNr+2GOP4d///rfcwxHR/7lKvRpTnDnX6FJ6rlX3y8jNx8vLjmKhwsOHUipofz6oOSr6ezld4EjkimTPxsrLy0NBQUGp7fn5+bh7964ijSIqj1yhXo0pzrg2VnFRlfxsuv/0n04pPiNOV9FZVSJ+UamAkR2i0eOhcJtmhrnSjD4ie5Md7LRs2VKfIFzcwoUL0aJFC0UaRVQetahZEZauZ2pV0X7OxFKuEWCfYEGOt3s0sLyTGbrlLpRkrqLzV3uTbAoQ4xJT0H7OTgxadBCvrDqOQYsOov2cnWUedBKVFdnDWDNmzEBMTAxOnDiBLl26AAB27NiBw4cP49dff1W8gUTlxZFLt0td+ErSCjhdQTlHrY11v0CL7+OTcSk9FzVD/PBsmyh4VZD2fc2rghreFdTIK9BafX4lhw+lVHS2NhndlWf0EdmL7GCnXbt2iI+Px0cffYQ1a9bA19cXDz30EL755hvUqVPHHm0kKhdcNWfHEe2eteUUFu0z7AX5YMtpjHg0GpMl9NocSkq3KdABlB0+lBogLjmQhMqB3pJydgq1AgcvpOGttSddekYfkT3IDnYAoGnTpli+fLnSbSEq11w1Z6eyv7QCnVL3K2nWllP4cm9Sqe1aAf12SwGPLYGWPWrbSG3P+5tP6383l+xtLDncGCm9bM46o47IFpKCnaysLP2ULkuLZnLqNpE8uotLauZdhPh74XbOfaddY0qn+AXxRpbEqsNWXC/vF2ixaF/pQKe4RfuS8Nrj9cwOadkaICpd28aa9pQchtK9BttPpeKbA8myjmUq2HLmGXVEtpAU7FSsWBEpKSkIDQ1FcHAwVCWnDwAQQkClUqGwsFDxRhK5K6nfyJ2poJzUNpd0y4qlGL6PT5aUx/R9fDKGP2p6IeKW0SEIC/KRvEaWToi/J2b+S/k6O7pkdDk528WHobRagfc3n5b9GugYC7aY60PuTFKws3PnToSEFH2b3LVrl10bRFRemLq4GOMsa0zJaXNJ1vRmSK2PY2k/D7UKg1rWwNztZ2Wdf1DLGujawPSiwtaSkoxujG4YavSKY1ad11TvoDtU7yYyR1Kw07FjRwBAQUEB9uzZgxdeeAHVq1e3a8OI3JmU2Tgh/p6Y8mRDhAU5R96ElDYbY8vwW80QafVxpOwXVVl+rZ0vdl3AuqNXFQ80yyLJ3FzvoKNm1BGVFVl1dipUqICPPvrIaFFBIpLO0sUFANJz8hEW5GNVQTl7kNLmkmwdfnu2TZSk2kPPtomyeKzKAdYlSNujMGJZJJmHaXxMDkW56kxAIqlkFxXs3Lkz9uzZY4+2EJUbrnhxsaYt5i6wUnhVUGPEo9Fm9xnxaLSkejuHrSwKaI/CiC2jQxCu8bEmZ1u2YF9PLH+xFfZP6mzydXDVmYBEUsmeet69e3e89dZbOHnyJFq0aAF/f3+D23v16qVY44jcldReBmt7I+xB6oXu2dY1oFKpZBf+M0U3rbxknR21CpLr7BRqBZb8lmx1G5QexjG3NpZSdIHU7H6N0a52ZbP76oIvUwuTOtNMQCJryA52dKudG1sMlLOxiCSSenVzouWMLF0QgaIA5PuD/6wu/vX+JEXyXSb3aIAJMQ9i5pZTSE7LRVQlP7zdowF8vTwk3f9QUjoy7ubb1AbAut4tU3VrYhuFY8GQ5lbNbJNCTlK7lIVJnWEmIJG1ZAc7Wq1tVUiJyhNTF7pbOdKmYUvdzxGk9EaUHOVRatpyyenu+84B20/fkHwxlzvl3BS5wziW6tbENgpH1wZhmLvtDD7fdUHWsY0FJQLAC+2i0LVBmOykdlPBl7PMBCSyhaxgJzk5Gdu2bUN+fj46duyIhg0b2qtdRC7P3IXOVXMkTF0QTdWMKZ7vYu20ZSXqv6RbUeOnOGuGcaS220OtQrvaVWQFOxXUQICPJzJy/+mtUiIo0QVfrKBM7kZysLNr1y48+eSTuHv3btEdK1TAt99+iyFDhtitcUSuytKF7otnmrtsjkTJC+KtO3kGyxoYY22+i1L1X0L8vWSdtzipwzjFe/EqB3hj2qY/JbdbN0QodTirQAtk5uZjYkxdRFX2UzQo8VCrOL2c3I7kzMEpU6aga9euuHr1KtLS0jBixAi8+eab9mwbkUuydIEGgPc3n8KUJ+oDKL2KgqvlSKRl35e0X0pG0RelQq1A/IU0bDx+FfEX0szOcJJT/8WcMI2vpDYav6/lGWVxiSloP2cnBi06iFdWHcfgrxOQamYZjZLt1g0RyrXq8GU8+VCE05QnIHJWknt2EhMT8dtvvyE8vOgN/9FHH+HLL79EWloaKlXitwAiHakX6Ir+3i6ZI2HtchHHrtyGn7eHrLWXlJqiL7fnpLiP+zdBuzqmZzPZUlW6eLtjG4XjlS51MH/HOUn3ZaE/IukkBztZWVmoXPmfN7yfnx98fX2RmZnJYIeoGDkX6N5Nq7lUjoQtF/bLaTlYdvCyrNwbJXObBj4SibnbpQUSxZlLEre2qrROyXYXFMqfAOJMtZiInJWsBOWtW7dCo9Ho/9ZqtdixYwcSExP121hnh8o7uRdoV8mRsPXCnpCcLjv3Ron6L9b2ROmYez2tqSoNmG73tf8P9clx7no24i+kOXWQTFTWZAU7Q4cOLbXtpZde0v/OOjtE7lugzdoLu869fNNhkqkhGVvrv9jSEyXldbKlV8VYuyMqys8t+nzXeXy+67zZ4UCi8k5ygrJWq7X4w0CHyDDZ1NWTj4tzxHCJsXPopruHaQx7WCwlDtvSEyX1dbKmNIBaBYzsEG203W0fMF/p2Bx7rOFF5C5kFxUkIsvcsUCbI2r+mDqHNfVfbOmJkvo6tYwOQbCfYb0bS4QAvtqbhGY1KpY6futalWQfT39cSJ+KT1TeMNghshN3K9Am5cKu8amAsZ3rICEpDdtP35B8bClDRnJzm6zpiXquTU10bxRu19fJVFCiq9PTr1k1fHMg2epjc4YWUWkMdojsyFWSj6Uo1AoUFJofFFKrVagW7IPDybdlHVsAmPJEfdkBhqnlOADreqK6NwqX9XodSkq3uhemeFBiaxJ1ScYCPXPPFZG7Y7BDRBbFJabg7fUnkZ1XYHa/27n5GL3imFXneH/zaaj/v0Cm1DZN23TKYN2rsCAfTOtVNPwkZeFSHRWKqiynZt6VNbPJ1jymG3fu2ZREbUrJQM/SGl1E7k5ygjIRlU9xiSl4edlRpOfYvmq4OXISbHVtKrnAZ2rWPbz8/2OYSxQvSQBIy7mPiWtOYNCig2g/Z6ekdtiax1Q5wNum6fwlqVAUxBQfDtQFUyV7jZjQTOWJVcFORkYGvv76a0yePBnp6UXlzo8ePYqrV68q2jgiKluFWoG31p10yLmKLxpqbgkJKW2avO4kCrXC5EwuS0wFAiWXumhRsyLCNT4Wg6mSdEEJBBQbujI2g0zK0iUln285y3kQuQrZw1h//PEHYmJioNFokJycjBEjRiAkJATr1q3D5cuX8d1339mjnURUBj7fed6qnBRrSUmwPXgxzWKbbufm4+DFNLSrXVmfKP75znNYfCAZGXf/uW/J2j3F21EyidjUUFCvJuH4am+SyWOVpAtKBj5SA1tPpUq4hzTGZpDJWVvMVO4Qh7vIHcju2Xn11VcxbNgwnDt3Dj4+/3xb6tGjB/bu3ato44io7BRqBRYfSCqTc5vLhYm/kCbpGMX323YqFfO2nzMIdADzwUnxQMDcUNBXe5MwskN0qd6jcI0PujYIRanUHxXg6+WBudvP4rv4S5IeiyVPPhSO/ZM6lwpI5CxdwuEu+dgL5jpk9+wcPnwYX375Zant1apVQ2qqct9SiKhsHUpKLxUcOIr5XBipF5Si/Wxd5iI18y4+3HrGbA/QphMp2PNGJxy5dFs/2+l2zn2MWVE68VgIIPe+sgVYt5xMwScDmpZKqpaaU1Q5wBuv/3BC9nIe5Rl7wVyL7J4db29vZGVlldp+9uxZVKlSRZFGEbkbV/wGWBYLTBpLsC2pjcQqw7r9bF3mIj3nvqShoO/jk/WBTouaFfH+ZuUSjy3RCuD7+ORS23Uz0kyFJ1Jzh4r3chGTvl2R7J6dXr164b333sOaNWsAFK2HdfnyZUyaNAn9+vVTvIFErs5VvwE6omJycZaWaNDXicnOQ4C3B7LzTPeOBPt5ovX/c35sCdrCgrwREuAtad/3N5/W/x7i74X0nPtWn9caSWk5pbZJXVvM3MruxXGFdctJ3+wFc06ye3b+85//IDs7G6Ghobh79y46duyI2rVrIzAwEB988IE92ug2XPHbPdnGlb8BWuoVsJbueMF+ngbbza11FZeYgvZzdmLQooOYuPq42UAHAGb3bWxTcUGdewVaXE7LlX0/Rwc6gOnp9VLWFpP6HDk6AHZGcpK+yXnI7tnRaDTYtm0bDhw4gBMnTiA7OxvNmzdHTEyMPdrnNlz12z1Zz9W/AZrrFbBF1SBvDGpZAzUq+SM9Ow8h/l4I0/iaLOQnp+heWJA3pvVqaPCe0gVt1gxlZebmY972swj280Rmbr7DhqWs0SyyosnbLC1d0qJmRahVRcNhpqhVRfuVd3KSvsl5WF1BuV27dmjXrh2Aoro7ZJqpD2vdt3tzKzeT65I77dcZmVrQVCpd6DIhpi6iKvsh+VYuVh66jLnbz+n30QX9poauzCUX6yofv/NEfZMBk4dahV5NwvHlXvkzy3RBqarY784a8IQH+5q93dzSJUcu3TYb6ABFgdCRS7ed9n/VUdgL5ppkD2PNmTMHq1ev1v89YMAAVKpUCdWqVcOJEycUbZw7sKaoF7kHd/kGGNuoaFrz8uGtEODtIeu+uqGSV2LqwLuCGvO2ny1d9djMkJ6UgDEt5z7CNL5oU6uSyYBp0wnrhwsFiur29G9eHaGB0vJ3HM1SUrcl7vK/6ghSk75teT1IebKDnYULFyIyMhIAsG3bNmzbtg2//PILunfvjjfeeEPxBro6ju+WX+70DdBDrQJUsJgro/Ncm5pYOaK1vvaLtUG/EhdhW2dj6fx49G/czJaWyFtciL+n5Z1sNPCRGjbd353+V+3N3DIklpLsqezIHsZKTU3VBzs///wzBgwYgMcffxxRUVFo1aqV4g10dfzGVH5JyRVxlW+AcYkpeGut9GUjuv9/IU5djsitO3lWDekpcRHermCVYrkdsOEaH4P6O+euZ+PzXecVa4/O3O1nserwZatzAC0tmqpCUS+dK/yvOoKp4V1jVazJOcgOdipWrIgrV64gMjIScXFxmDFjBgBACIHCQmULZbkDfmMqv6TkivRqEu703wDlrsod4u+J2zl5aD9np+weFV3Qr5tmnpp1DyH+niYXIbV0ES7UCqw/XnZr9t3NL8T2U9dR0d8LAFDRz369PJZyAPVT940kKEudou7s/6uOZCnpm5yL7GCnb9++eOaZZ1CnTh2kpaWhe/fuAIBjx46hdu3aijfQ1fEbU/klJVdk04kUvBlb32k/IK2pPtz2gRCMXnHMqvOFBvoYnblojKmLcPGL+q07eXZfrd2cjNx8jF5x1GCbpVlP1jI3w0/KbFD2VshnLumbnIvsYGfu3LmIiorClStX8OGHHyIgIAAAkJKSgtGjRyveQFfHb0zll5RcEWefjSU338XPywM/n7wu+zy6oP92Th7GrDgmbZq5kYuw1ECpLNlzLoKx4UA5s0HZW0HuSnaw4+npiddff73U9okTJyrSIHfEb0zlk9x8LXPDDGVF6mPw9FAhv1BYteaT7hFOeaKB2SUWLE0zlzvcVtbs1cMDGP5Pya31xN4Kckeyg53vvvvO7O3PPfec1Y1xZ/zGVP7Iyddy1qKTUh9DfqH1V21d0K/x9ZI1zby4Qq3AtE2OW4tKCcUDHe8Katwv0CrWft3rZk2tJ2cMuolsJTvYeeWVVwz+zs/PR25uLry8vODn58dgxwx+YypfpOZrmRq6cYaiky1qVrRLIT2fCmrM6vcQwoL+uZhulJhIXLy3SXdh/j4+qVT9HleiC3T6N6+Gm9n3sefsTZP7mqvmXDIHUG7vorMG3US2kl1n5/bt2wY/2dnZOHPmDNq3b4+VK1fao41ELklKPY6ioZvTTlt08nBSul16S+4VaBEa6G1QCFBqL1LyraIFL4uvl7UlUX6ekJKC/TxtWkNMN6S0//wtnEm9Y3K/ktWcS94GGOYAVvaXVgSxsr+3S6/jJhfXKSx/rF4uorg6depg9uzZGDJkCP766y8lDknk9KR091vK15IydFOWSczxF2/Z79gX0tCudmX93y2jQxAW5GOxh2blocuoExqIMSucIz+neABiCwEgNct80UJdNeeJMXWw6vAVyzmAEiMwrXDtddzkYO9V+aRIsAMAFSpUwLVr15Q6HJFTk/OBaS5fy5qhG3srHsRdvX3XjmcyvLR6qFV4+pFIzN9xzsT+RVKz8vDOxkSnCHSAfwKQAO8KyM4rcMg5oyr7Y/+kzhaD7VsSKz4nuME6blJwncLyS3aws2nTJoO/hRBISUnB559/rl8YlMidWfOBaSpfy9mKTjpy6nabByob/B2XmIKlvyVLum96zn07tMg2jgp0gKJhJyk5gNL/b6SFjqmZdxF/Ic0lk5etmZlG7kN2sNOnTx+Dv1UqFapUqYLOnTvjP//5j1LtInJKSn9gOlPRSUdO3Q7280TrYhdqV5s2XuYkXoul/n+1eaAyPt91weLx3t982iDQDAvywbRerjH8Y83MNHIfshOUtVqtwU9hYSFSU1OxYsUKhIfb9x9+9uzZUKlUmDBhgn7bvXv3MGbMGFSqVAkBAQHo168frl8v22RFcl9KL+zqLIsKWlMp2Raz+zbWPyY55y6qtWP/hTWd3a3sPElJtlL+vwY+UgM3svMkPa8le9RSs+7hZRdJXuY6heWb7GCnOCEEhHDMx+Phw4fx5Zdf4qGHHjLYPnHiRPz000/44YcfsGfPHly7dg19+/Z1SJuo/LHHB6YuiTlMYzjkEKbxcVgOgVIrg5fkV8HwEhsW5I2FJR6T3HP3a14N5X2UIflWjn4m2iurjmPQooNoP2en0aDD1P9XsJ8nNH6emLv9LCauPm7TshqT1510+hlNzjZkTI5lVYLyd999h48++gjnzhUlEtatWxdvvPEGnn32WUUbp5OdnY3Bgwdj0aJF+oVHASAzMxPffPMNVqxYgc6dOwMAFi9ejPr16+PgwYNo3bq1XdpD5Ze9PjDLuuikvb7NLhrWEmqVyuxjknruYF9PPP1IdXy1N6ncDnepUBSkzN1eOonbXM5Yyf+v5Fs5Ro9hTEU/T9zONR8I3c7Nx8GLhrPrnI0zDRmT48nu2fnkk08watQo9OjRA2vWrMGaNWsQGxuLl19+GXPnzrVHGzFmzBg88cQTiImJMdh+5MgR5OfnG2yvV68eatSogfj4eJPHy8vLQ1ZWlsEPkRS6D0xTIYgKRbOyrPnA1CWc9m5azaD+jCPY49tsuMYHrR+oZPExST33Z4OaYdOJlHId6ACmU4ml1mXSCmBpfLLZ81Ty98LcAU2wckRrPNOqhqT2xV9Ik7RfWXGWIWMqG7J7dj777DMsWLDAoFJyr1690LBhQ0ybNk3xNbJWrVqFo0eP4vDhw6VuS01NhZeXF4KDgw22V61aFampqSaPOWvWLEyfPl3RdlL54K4Lu1r61muN5jU0ip07XOMDtUrl1At82luYxgcDH6mBudvPmtzHVJKtnFl2JZflOHDedDXn0vd0blynsPyS3bOTkpKCtm3bltretm1bpKQom6R25coVvPLKK1i+fDl8fJT75jl58mRkZmbqf65cuaLYscn9OUOOjdLMfeu11uaT19FutvE8Ernnzr1fgO2nTX+BcVch/p6Y+3RTrBzRGvsndUaNSn6S7ld8aNBUZWSpxyhZIsAUqfuVtdhG4dg/qTNWjmiN+QP/eW5d8X1L0snu2alduzbWrFmDt99+22D76tWrUadOHcUaBhQNU924cQPNmzfXbyssLMTevXvx+eefY+vWrbh//z4yMjIMeneuX7+OsLAwk8f19vaGt7e0MupExpR1jo09mPrWa4vULGnF2nTnfmvdSWQYyQ/JvFuAxb9dknTOQJ8KuHPPcTVv7EH3XzTzX431z1tcYgre//lPSffXDQ3aMstOd4zWtSrBz8vD7Ir2/l4eBqUEnB3XKSx/ZAc706dPx9NPP429e/fqiwgeOHAAO3bswJo1axRtXJcuXXDy5EmDbc8//zzq1auHSZMmITIyEp6entixYwf69esHADhz5gwuX76MNm3aKNoWopKs+cB0phWljbWlZBC3668b2HDc9sroUmoPdW0QhrfW/mHzuT4f2Axnb2Tjgy2nJd8nLMgb9wq0RgMtqXwqqFHBQ61IccGSwypS6xCVTLK1ZpadsURdrwpqs8GOZwWbJvYS2Z3sYKdfv35ISEjA3LlzsWHDBgBA/fr1cejQITRr1kzRxgUGBqJRo0YG2/z9/VGpUiX99uHDh+PVV19FSEgIgoKCMG7cOLRp04YzscjpONOaPJbaogviKgd42xzsSC3WdvBCGjLu2h4oHLqUjrpVAyXvH+LvhZ5NIvDDkb9tOu+9Ai1QoJV1nwDvCvBQAZnFeqJC/D0x5Yn6+v8Jqb0zxnLG5M6yM3aMQ0npFoPAjNx8FuMjp2bV1PMWLVpg2bJlSrfFKnPnzoVarUa/fv2Ql5eHbt264b///W9ZN4vIgDOtySOlLf/07uTB39sDOXmmv9VLZenCq9Sio0sOJGNkh1qS90/PuY9F+5IUObdcxnqB0nPyMXrFMSxUqxDbKFxy70yIvxc++Fcjg/8jubPsjCXqpmZKWx9Nyn7O1LNJ5YvkYEfq9OygoCCrGyPF7t27Df728fHBF198gS+++MKu5yWyljOtySOlLZPXncTUjYm4fkfZ9aeSb+UYtKPkRU+p+qQ59wsxb/tZ+HupkXNfXk+LKUqsai7XW+tOomuDMMm9M+8U6w3SkVJbJsTfC+88UR9hGl+jwYfUdcgs7edMPZtU/kgOdoKDg6FSmf4gFkJApVKhsND2b4BE7sSZ1uSR0hZLBeR0wjU+ePKhMPx45G/czrU8/LTy0GWM7VwH206lGr3otVX4sRcUKhielEG0k5Gbj893npdcsylM41tqm5RSCSV7g0oKCZA2mcPcfs7Us0nlk+RgZ9euXfrfhRDo0aMHvv76a1SrVs0uDSNyF860Jo+t5wjwroD3ezc06AV4q3sDTPrxBH48etXsfVOz8vD5zvOYt/2s0YveWgv3l0MAyFMw2HHQqjilLP4tCaMeq2Wxd6ZqkDe0QmDj8aulhodsrS0TFiRtKMzUfs7Us0nll+Rgp2PHjgZ/e3h4oHXr1njggQcUbxSRO3GmNXlsPUd2XgFCA30MeqA81Co8WreKxWAHABYfML7Ug/OXoysbGbn5OHLpttneGYGi5OjBXyfot5ccHrKlVIJuKMxcj6C5quHO1LNJ5RfnCxLZmT2XmFC6LVIYSySWGkRl3LV+and5dePOPZOFLH29PACg1Gwp3fBQ8YKO1i5HohsKU8H4MgsqmK8a7kw9m1R+MdghslKhViD+Qho2Hr+K+AtpJtcjcqY1eZSplFz6nlICumBfT6vPaC13GBTRBZK6yr8TY+rqn0tTtW+krpMllS1Vw52pZ5PKL6umnuuYS1gmcmdyZ5Y405o8sY3CMbJDNBbtSzLIRZGag2tsqMFcIiz+//ewtlGYt0PaStuWhGt80KJmRfz8h/mlKFx5eMxYcb9tp1KN5jwZo/TwkLVDYVxtnJyB5GCnb9++Bn/fu3cPL7/8Mvz9/Q22r1u3TpmWETkpUzNLUizMLHGWJSbiElPw1d7SuTNSLqAV/TzR+gHjF05Ly02sOnwZwX6eyMzNN3nRU6mKVuU2JdjXE18Mbo7WD1TCz39csxjsAMDz7aLww+9XkK1ArSClmXo+jPX4Wbv0g9zhIXO1cKypGu6hVqFXk3B8udd0LSNXXDyXXIvkYEejMVzBeMiQIYo3hsjZWbrgCJSeWeJMhdRsWSsJAGb1bWy27bGNwqHVCoxecazUbdez8vTnNTUNesSj0fjq/xdFY7fP7tcY7WoXLTgpddhjze9XbC6KqGuvv5cHcswsmyDX7L6NAUBSj581Sz8A8oaH7FELRxdcmxLTIJTTzsnuJAc7ixcvtmc7iFyClAtO8aEDZyukJrkar58X0nP/KRIntc2FWoH3Nxtfk0o3zVjj5wmfCh5IzTJ+cW9Wo6Kki7+l4REdJao/h2l8MPCRSMzdrswwHABMjKmjfzxSevysWfpBzvCQPWrhSAmut526gS1/pKDHQwx4yH5sytkhKm/klM53xkJqUi+YhVotJsbUQVRlf1m9UVKmGWfk5mP58OZQq1VGL+5Sh/uK5wkpzVhl4Z//sH1BVJ1wjQ/Gdq6j/1vK8JCcHhq5ie/2qoUjNbiesjER3Rqxzg7ZD2djEckgtXT+rez7Zi8egHIzZeSQesHMvFeAedvPwbuCWtY0ZanB1K2cPLPToKVOk45tFI4JMXUlnVMOASAt5z7CNL7680t97ibG1IXf/6eEG2NpqrYpcsoGSJklVZycWjhySP1/SMu5L/vYRHIw2CGSQWrp/Izc+3a5eNiqRc2KkHONlRuQlcU046jKfoodq6TiF2up9ZLqhAbgrpm8nhcfjYbG18tiyYKSpQ0AWCwbMLxdFFaOaI39kzrL6jW0Vy0cOa8z6+yQPXEYi0gGqaXzpVZlcPQH/JFLt83OdirOmqnLZTHN2J71WYofW8o6U1OeqI/3N5vPUflmf5LBKuvG8qHM5XoZm/Fmax6YvYLUltEhCPH3RHqO5WKSrLND9sSeHSIZpPSMqFVAqyhpwYGjP+CtCa7k3MfRBRQLtQJaIRQvWGiqqrWl4noV/b0t5qiUDDZLVjvW5XqVPI5uPwDYP6kzVo5ojfkDm1rVk1OSvap8e6hVmNG7kcX9HFVBnMov9uxQuSdnariUnhGtANQeKqcspGZNcFXZv2joTurzZE0BRWum5xvr/VCKADDwkRpGbzOXQL3xuPzFTIsnAHeuV1VyorCS60hJ6bWyNkhVS7gP6+yQvTHYoXJN7tRwyQm42Xl2u3jYokXNipIrJeu89sMJ9G4ajk0nUmRVjJZaQNHUazDlifqo6O9t9P6mZropae72s1h1+LLRx2hq9pS1PXW6IcPv45MdvmimLtDMK9BiQkwdrDx0GalZefrbbanyrZvlZU5FP090bRAm+9hEcjDYoXLLmqnhcnIb2tSq5DRLROgcTkqXHSCkZt0zWv3W0hR6c9OpdRfY7adS8c2B5FK3p2TeK1WYMNjXE8+3i8aox2pZrN0S7OeJMY/Vwgdb/jL72ICiKebPtq6B+TvOl7pNbpkAqbV/TLmUnitpP6VyvYwFmmFBPpgYUxdRlf1sLoIpZer57dx8rnhOdsdgh8ola+uKyE3AdZYlInQOXLip2LGKP0+B3p64lZMn6fFZO/yUcTcfc7efxVf7LlgsFJiRm48GERqEa3wsnmd6r4aYucV8IUTdENORS7cl1/6R24MGADVDpM0sUyLXy1Swfz3rHuZtP4sFQ5rbHIBwxXNyFgx2qFySU1ek+Ae+NbkN1qwnZC/XMpS9qOiep8HfJOi3mRveUmL4SWpF5OJDiabO9+RD4Qjx85L0v9B61naDWUWmHqepnCW1mXW/dEHys22i8PX+JLvnetmriGBJXPGcnAVnY1G5JPWb5PZTqaW2WZqR48zr/ERofO1+jpKzi3RsXZdLrtBAH/1rFa4xfjH9+Y8UjFkhrQJzyenTph4nUPQ/UnLG1OeDmhUtdlpi3+JBslcFtUNms9mriGBJ9prlRSQXe3aoXJL6TfKbA8l4JDrE6Ld3ZxqeMqf4TKese5brndjKVM+AtQtZymVqKPHznecxd/vZUvtn3LXuObHUA2KsR2+BWmUxh8ua2WxyOWp4yZ6zvIjkYLBjJ1Km0jrTatjljZxEUjkXM2djz+nZ5hgbBnREXoapC2ihVmBpvOmVt60ld3aU1CDZ3sG0I4eXHBG8EVnCYMcOpExndrbVsMsb3TfOlyUsIqn0VF9HccT0bEuKBzj2yMsI8fcyWK8sxN8L7/duVKoa8dvrEyVV8bWW3MKLUv6X7BlMO7rStSv1hJJ7Ys6OwixVP41LTJG0D9lfbKNwPN8uStK+qVmuNVvE0fkxpugKEgLyFrK0RJfr8V7PBgjx/6d6clrOfby/+VSpasRSF3C1lqsl2Dq60rXunFIWdyWyBwY7CrI0wwEoGhKZtulPp1sNu7yqHiwtYTc9O8/yTk7EnvkxwX6emNy9nrSdi13PpFxgX2gXhYkxdRHsZ3r5B92+vZqEY9yq4yYTh7f8cc3uAZ8rJ9i6cqI9kVwcxlKQ1BkO5tijQiqZJnVNJaXXXrI3e+XHqADM7tsYeQVaSfvfKhEkWsrfAGBx6C1M44MpTzQwueCmLnH4nY3Sh65UKBoCS5PRA+QOCbYcXqLygsGOgpS8wLDIlmOk50q7GErdz1nYa1hlQkxdxDYKR/yFNKvbYeoCCwDt5+w0G+hU8vfCnjc64cil2xa/WMjJ0REA3u/dCO9vPiW5+rG7JNi6QqI9ka0Y7ChIyQuMq+UAuKqMu9K+yUvdz96kzuCzddkCU6Iq+0k6vqUEV2MX2PgLaRZ7PtNy7uurGCtpYkwd9HgoHGo1TE6TFv/fL6qyf5n3gHAmJ7kKZ/lfZbCjIKkXACEErmflOdVq2OVRoVbg6u27kva15q2p9Jtczgw+W5ctMEUXhNujfoqc2i9Svwz4e3tIqrgcVdkfhVoBja8Xnm8XhQ3HrxkkNTtTLw5ncpKrcKb/VQY7CpJ6AQBMf3vU7cNvafYlt/5Mmwcq23x8W97k1ixaaio/xhrGgnAl66cUagVu3ZGWBK4LHKV8sXj64UjM23HO4jGTbmbjkQ+2GQx9hfh74l9NqyGmQZjT9JxY839AVBac7X9VJYQo99N+srKyoNFokJmZiaCgIJuPxzo7zk1u/ZlgP08ceaer5IudqePr7i33TV6oFWg/Z6fJgEV3Yd8/qbPRNhbvYfr5xDVsO31D8rmltNvWHiypgWfJx6l7ngHjXxoWDGmOrg3C0GLGNmRYmXOlgvzXy15s/T8gchRH/q9KvX6zZ8cOpMxw4CyIsmFN/ZmnH64u+XWxxwKL1i5aqlM8P+bWnTzZwU7F//dwaHy9UKgVilaSlhp4Guv1lNqzNLNPY4yWuP5VSQLKLIipBFv/D4gcxRn/Vxns2ImUCwBnQTieNfVnVh++gjdj60u62NnjTS41l+WX/xfS0wXNxnpcnm0ThQ+2nDa5+nZxnR6sghN/ZyI95z6+OZCMbw4kK9r7KCfwNDU0JuVLQ0V/L5va6SwBhKPWsyKylTP+rzLYIZdi65CJNW+ujLsFOHghDe3qWM7bscebXGoy7nfxl/Bd/CWEa3zQq0k4Np1IMTpMOuLRaHy51/w6UV0bhGL7qRt2HW+XGnhOeaI+hrWLNvk6W/rSoMQHqjMEEI5cz4rIFs74v8pgh1yGEnlO1r654i/ekhTs2ONNLncaeUrmPaPBjC5QGdkh2uIxfjufpuhQnDFSA4jKgd42nUeJD1RnCCBa1KxYai2w4jiTk5yFo9dek4LLRZBLUGo9sRY1K8K662bpFevjL6Rh4/GrOHD+Fg6cu4WNx6+ioECLihaWOgjX+ECrFdh4/CriL6RZXBrE3DILcujO8pWFXh0AyLlverp28aE4Wzjq25+ta3KF+HuWeQARl5iCjh/tMhvoAJzJSc6hLNZes4Q9O+T0lEz6PXLptqR8lZJaFbvYyZ22XpwAcDe/EIO/SdBvs9Q7Za7+izXnV4qtQzuO+vZna82hGb0blWkAISWJ25nqABEBypamUAKDHXJ6Sib9WnuBVquKLnZyp60bU3IatLk8GGOBVYi/Jx6qpsHus7dsaIXtbO1xsUdhQlNMffAG+3manZb+Uodo9HgowubzW0tKEneIvyf2vNEJXhXYUU/OxZlmHTPYIaenZNKvtRfoG9l5Vk1bl8JU75SpwOp2Tn6ZBjpSe1ykJJObCkKqBnljUMsayCvQIv5CmiIfkKY+eLedSi11/kr+Xni/dyP0eKhse0qkJHGn5+TjyKXbVs8Wc5Zy/uSenGXWMYMdcnpK5nbohk7kDkGlZ+dZNW1dqpK9U5aG7hxBd7mzpsdFTjJ5ySAk+VYuVh66jLnbz1m8r1zGPnid6dtnSfaewsviplResN+TnJ6lBFNd0q+U3I7iiXNyhPh7OWT6se4c9gyspOj0YBWM71IHwSWSrcM0PhannVuTTK4LQrwrqDFv+1mkZtmWiC6X7vy9m1ZDm1qVnCLQAeybxK1U0r9O8aR9KYn3RI7EYIecnpzMfikfuLGNwvHfZ5rJmpUVpvF1yPRj3TnKuq7LrjM3MX/HOdzOLb5WlBemPGH+G7+UHqnpP50y+rrYcl93pWSgX5zSz3VcYgraz9mJQYsO4pVVxzFo0UG0n7PTbsEpkVwMdsgl6HI7wjSGAUfxngY5H7g9HorA54OaSzq37mJi6xRmc0petCr7e9vhLLa5nXMfY1aY/8YvJ5lcyfu6K3tN4VXyuVa6h4jIHhjskMuIbRSO/ZM6Y+WI1pg/sCmWv9gKH/dvgrwCLeZvPyf7A7fHQ+FYOKR5qaGa4lT452KiVL0bSRct5xhFMSDlG78tOSbOWGLeGUgJ9OVS6rlmbxy5CiYok0vR5VbEJabg9R9OWMxrsVSHR5ec+vnOc1h8IBkZd/8ZtjGWqGlq9pAlwX6emN23MfD/tliqO3ErO0/ysR3J0jR/W3JMrL1vydlELWpWxJFLt50u2dgWSidRK5UL5IwLPhIZw2CHXI7cWjdSVgV/JaYuxnauI+liorvwHLyYhhHf/Y5cM9WGdXw9PfTBlpSLljMsT2COqW/8LaNDLNauqehnvCKxNUUGjc0mUqtgUDgyLMgb03o1dPnZRUpO4VWqoCN748hVcBiLXIottW4sfeDKmZHjoVah9QOVJBdyK57/IOU89swPUoItwZip105ufoqpXJGSIyapWXl4mbkjBpTKBXLGBR+JjGGwQ4q4X6DFN/su4t2Nifhm30XcL9Da5Ty2TMlW8gO3UCuw5ECS2R6MkuR8u7V0MVIBCPYtm45ZUz0zQNHrY+k5ycjNN5n4KjU/xZqg9611J5k7UowSuUD2mi1GpDQOY5HNZm05hUX7kgy+UX+w5TRGPBqNyT3k17Qxx5rucKVX2LV2bSy5wZaltWUAYNSyowAcV2gQAG7n5mPbqVSjF0MlhjWk5KdYE/Rm5Obj4IU0SavXlxe25gI5cskPIlsw2CFJTJWUn7XlFL40soq2VkC/XcmAR27AoPQHrrVrYwWb6Q0xx9LFyJpkaVuZS/iWOmXe0n6W8lOszQGJv3iLwU4JtuYCOduCj0TGOHWwM2vWLKxbtw5//fUXfH190bZtW8yZMwcPPvigfp979+7htddew6pVq5CXl4du3brhv//9L6pWrVqGLXcvpkrK/7t7PSzaVzrQKW7RviS89ng9xRYptJRYWZKSH7i25As93zba6mDL3MVIFwwtOZCE9zeftnisPk0jsOH4NavaoWM24VvqQ7Qx7rR+SJI9DPbgzEtuEAFOnrOzZ88ejBkzBgcPHsS2bduQn5+Pxx9/HDk5Ofp9Jk6ciJ9++gk//PAD9uzZg2vXrqFv375l2GrnZU05d3MFw8auOl4qGbQkrQC+j0+2odWGpNS6eaVLbcwf2BQrR7TG/kmdFftmaW2+ULCfJ8Z2rq1IG4zxUKswrF20pNyJD/s3USzx2VjvitQp87ZOrbc2gZvTn+3HWZfcIAKcPNiJi4vDsGHD0LBhQzRp0gRLlizB5cuXceTIEQBAZmYmvvnmG3zyySfo3LkzWrRogcWLF+O3337DwYMHy7j1zsWacu5KLUZ5KT1XdnvNMZVYqfPtgSR4AIp/4Fo7dDK7b2O7f/BLnV3jVUFt1dpgxihZK0cuawo8VvTzROsHGOwQlUdOHeyUlJmZCQAICSnKfThy5Ajy8/MRExOj36devXqoUaMG4uPjTR4nLy8PWVlZBj/uzNpy7kotRlkzxM/mY5QU2ygcU54wftG+c68QY1cdx4jvDhu93doFC+VeoMM1PlhoZYVba0idXaPbL8Tfy6rzmJth48jZOZaC3pJmOSDoJCLn5NQ5O8VptVpMmDAB7dq1Q6NGjQAAqamp8PLyQnBwsMG+VatWRWpqqsljzZo1C9OnT7dnc52G1HLuxpJNlSgEplYBz7aJsvk4JRVqBd7ffMrsPttO3cAHm0/h38WCIlP5R1LyeqTkC4X4e2LKkw0RFlQ2OQtScydiG4Wjc72qaD1rO9JzpE+ft5Tw7ejZOSUfb/KtHKxIuIzrd/4ZJpP6+hKR+3KZYGfMmDFITEzE/v37bT7W5MmT8eqrr+r/zsrKQmRkpM3HdUZSemdMJZsqUZdmxKPRiiUnFye11+mb/Ul4o1tRgrSpmVS6Hi5LtUWkXMhn/qtxmV9Upc6u8aqgxsx/NTY7fb1kNWQpCd+Onp1T8vFKrYRNROWHSwQ7Y8eOxc8//4y9e/eievXq+u1hYWG4f/8+MjIyDHp3rl+/jrCwMJPH8/b2hre3860qbQ+pWdJ6Z4zt16JmxVKl90tSqQBh5vZmNSpKOr9cUnuddAnSw9pFm+3hMjedujhnnWZrqjSAJaYej643xNoZNmU5O0fJZRWIyD04dbAjhMC4ceOwfv167N69G9HR0Qa3t2jRAp6entixYwf69esHADhz5gwuX76MNm3alEWTnU66xFkvxvY7cum2xdlW5gIdAJi87qTFAMIacnqdktNycfBCmmILFjrbNFtbhuYAy4/H2sCBQQcROQunDnbGjBmDFStWYOPGjQgMDNTn4Wg0Gvj6+kKj0WD48OF49dVXERISgqCgIIwbNw5t2rRB69aty7j1zkFqEqqx/ZTI2bmdm4+DF9PQrrayhdxaRofA11ONu/mWl6W4lJaDMSuOSjqu1MfsLBdyW4fmdJzl8RAR2YNTz8ZasGABMjMz8dhjjyE8PFz/s3r1av0+c+fOxZNPPol+/fqhQ4cOCAsLw7p168qw1c4lTONr9X5KrSUVfyFNkeMU56FW4amHq1veEcDec7eQcVdaEq4rLVgoNfmc60ERUXnn1MGOEMLoz7Bhw/T7+Pj44IsvvkB6ejpycnKwbt06s/k65Y1uBpE5lqYR284+F9vujSIUPZ5aVZSn5CosJWkXH5ojIirPnDrYIdvpZhCZq3tibhpxrya2J9y2ecA+axEpF4wV0YqiPCVXocSim0RE5QGDnXJAN+OmZGAQXqLYXEmFWoFNJ0xXWAaKZmOZE+znidZ2zAUZ+IiyJQNcKTBwVLViIiJX59QJyqQca2YQSallY2k2lr2WSjA2A0kJrhQYWCpyqELRlHglqhUTEbkyBjvliNwZN1J7OYa3i8LmkylIzfpn+npYkDem9Wpol7ozpmYg2aIsAwNra+Q4uloxEZGrYrBDJoX4SZu2XjXIB3vf7Iwjl27bve6MuRlI1irLwECJGjnOWOSQiMiZqISwNBDh/rKysqDRaJCZmYmgoKCybo7T+HLPecz65Yykfe29/pCu9+PA+Zv4fNcFRY9dVmsnmeqh0oVbUmvkANb3DhERuTKp12/27JBJRy5lSN5XbhE7OZTKz5nyRH1UDvRGZX9vQAXcys4rs8DAUo0cqctX6LAoIBGRaQx2yCR/Lw/J+1pzgZZCqfwcjU8FDGsX7TS9HXJq5DCIISKyDaeek0l9m0urUKyjdBG7+wVavL0+UZH8nKx7Bfgw7rQCR1IGa+QQETkOe3bKEbl5HW1rV4a/lwdy7hfKOo8SF+i4xBS8vf4k0nOkLfNgiQDw5d4kAMDkHg0UOaYtWCOHiMhxGOyUE9bM+vFQq/CfAU3w8jJpi2jq3LqTh0KtsHrIyB5Ty3UW7UvCa4/Xg1eFsu3UZI0cIiLH4TBWOaALHkrmiOiSiuMSTVdJjm0Ujpc6RENO3PL+5tNoP2en2eOaYs3U8rGdaqPnQ9LWQ9MK4Pv4ZNntUpquRg6AUkt5sEYOEZGyGOy4OVtXxo5LTMFXe5Mgd+Hs1Mx7eHnZUbz305+Iv5AmeeVtKVWbdVQo6p0a36UOfpcxc+xSeq7kfe1JVyMnrMQyHmEWlvEgIiJ5OIzl5myZ9WNLAT/dfb49kIxvDyRLrmUjN99nas8GOHLptqxp6TVD/GSdw56sWcaDiIjkYc+Om7Nl1o+cXhZLpAyZAdITciv5e+l7P+QESCoAz7aJkry/I+hq5PRuWg1talVioENEpDD27Lg5W2b9KDntWWodHkuJuwAQ4u+J+Mld9EnGcmYsda5XpcyTk3VY9ZiIyDEY7Lg5W2b9KD3tWUqhPCmLW878V2ODgKVldAhC/D0lTVN/8dFaVrdfSbauiUVERNI5x1dcshtbZv3oAiWl+xos9RjJTdz1UKswo3cji+cNd5Kp3LbMjiMiIvnYs1MOWLsytqVeFmvr4EjpMZKbuNvjoQi89HeGvnBgSSo4x1RupdfEIiIiy7jqOcrPqufW5oiYGnLp3igM3x5Ilnx+3ZDZ/kmd7XYh3/JHCt7ZmIj0nPv6bc40PBR/IQ2DFh20uN/KEa25JhYRkQVc9ZxKsXZlbFO9LIeS0iUHO44qlNfjoXB0a+S8U7m5JhYRkeMx2CFJjAVKUmZO6VgaMlOStUGdI3BNLCIix2OwQ1aTktPzQrsodG0Q5lS9K2WJa2IRETkeZ2ORTczNnFo4pDne7dmQhfKK4ZpYRESOxwRllJ8EZXtigTx5WGeHiMh2Uq/fDHbAYIfKBgNEIiLbcDYWkZNzRCI1AyoiIgY7RG6LQ2VEREWYoEzkhrgkBRHRPxjsELkZS0tSAEVLUhRqy326HhGVEwx2iNzMoaT0Uj06xRVffZ6IqDxgsEPkZrgkBRGRIQY7RG6GS1IQERlisEPkZnRLUpiaYK5C0awsLklBROUFgx0iN8MlKYiIDDHYIXJD5tYsWzCkOevsEFG5wqKCRG4qtlE4ujYIYwVlIir3GOwQuTFHLElBROTsOIxFREREbo3BDhEREbk1BjtERETk1hjsEBERkVtjsENERERujcEOERERuTUGO0REROTWGOwQERGRW2OwQ0RERG6NFZQBCCEAAFlZWWXcEiIiIpJKd93WXcdNYbAD4M6dOwCAyMjIMm4JERERyXXnzh1oNBqTt6uEpXCoHNBqtbh27RoCAwOhUpXfRRKzsrIQGRmJK1euICgoqKybQxLwNXM9fM1cD18z5yWEwJ07dxAREQG12nRmDnt2AKjValSvXr2sm+E0goKC+IZ2MXzNXA9fM9fD18w5mevR0WGCMhEREbk1BjtERETk1hjskJ63tzemTp0Kb2/vsm4KScTXzPXwNXM9fM1cHxOUiYiIyK2xZ4eIiIjcGoMdIiIicmsMdoiIiMitMdghIiIit8Zgxw3MmjULjzzyCAIDAxEaGoo+ffrgzJkzRvcVQqB79+5QqVTYsGGD2eNev34dw4YNQ0REBPz8/BAbG4tz587ZfFwq+9csPj4enTt3hr+/P4KCgtChQwfcvXtXiYfmtsryNUtNTcWzzz6LsLAw+Pv7o3nz5li7dq1SD82tSXndHnvsMahUKoOfl19+2exxhRB49913ER4eDl9fX8TExJR63dLT0zF48GAEBQUhODgYw4cPR3Z2tuKPkSxjsOMG9uzZgzFjxuDgwYPYtm0b8vPz8fjjjyMnJ6fUvvPmzZO0JIYQAn369MHFixexceNGHDt2DDVr1kRMTIxNx6UiZfmaxcfHIzY2Fo8//jgOHTqEw4cPY+zYsWZLrVPZvmbPPfcczpw5g02bNuHkyZPo27cvBgwYgGPHjin6GN2R1NdtxIgRSElJ0f98+OGHZo/74Ycf4tNPP8XChQuRkJAAf39/dOvWDffu3dPvM3jwYPz555/Ytm0bfv75Z+zduxcjR460y+MkCwS5nRs3bggAYs+ePQbbjx07JqpVqyZSUlIEALF+/XqTxzhz5owAIBITE/XbCgsLRZUqVcSiRYusPi4Z58jXrFWrVuKdd95R/DGUN458zfz9/cV3331ncN+QkJBS70WyzNjr1rFjR/HKK69IPoZWqxVhYWHio48+0m/LyMgQ3t7eYuXKlUIIIU6dOiUAiMOHD+v3+eWXX4RKpRJXr161/YGQLPwq54YyMzMBACEhIfptubm5eOaZZ/DFF18gLCzM4jHy8vIAAD4+PvptarUa3t7e2L9/v9XHJeMc9ZrduHEDCQkJCA0NRdu2bVG1alV07NjR4DUlaRz5Pmvbti1Wr16N9PR0aLVarFq1Cvfu3cNjjz2m0KMpP4y9bgCwfPlyVK5cGY0aNcLkyZORm5tr8hhJSUlITU1FTEyMfptGo0GrVq0QHx8PoKgHNTg4GA8//LB+n5iYGKjVaiQkJCj5kEgCBjtuRqvVYsKECWjXrh0aNWqk3z5x4kS0bdsWvXv3lnScevXqoUaNGpg8eTJu376N+/fvY86cOfj777+RkpJi9XGpNEe+ZhcvXgQATJs2DSNGjEBcXByaN2+OLl26mMzHotIc/T5bs2YN8vPzUalSJXh7e+Oll17C+vXrUbt2bcUfmzsz9bo988wzWLZsGXbt2oXJkyfj+++/x5AhQ0weJzU1FQBQtWpVg+1Vq1bV35aamorQ0FCD2ytUqICQkBD9PuQ4XPXczYwZMwaJiYkG3wo3bdqEnTt3yhrf9/T0xLp16zB8+HCEhITAw8MDMTEx6N69O8T/i25bc1wqzZGvmVarBQC89NJLeP755wEAzZo1w44dO/Dtt99i1qxZCj4y9+XI1wwApkyZgoyMDGzfvh2VK1fGhg0bMGDAAOzbtw+NGzdW9LG5M2OvGwCDPJrGjRsjPDwcXbp0wYULF1CrVi1HN5PsoYyH0UhBY8aMEdWrVxcXL1402P7KK68IlUolPDw89D8AhFqtFh07drR43IyMDHHjxg0hhBAtW7YUo0ePVuS45PjX7OLFiwKA+P777w32HzBggHjmmWeUeVBuztGv2fnz50vl9QghRJcuXcRLL72kzIMqB0y9bsZkZ2cLACIuLs7o7RcuXBAAxLFjxwy2d+jQQYwfP14IIcQ333wjgoODDW7Pz88XHh4eYt26ddY9CLIagx03oNVqxZgxY0RERIQ4e/ZsqdtTUlLEyZMnDX4AiPnz50t64+ucPXtWqNVqsXXrVkWPWx6V1Wum1WpFREREqQTlpk2bismTJ9v2oNxcWb1mf/zxhwAgTp06ZbDf448/LkaMGGHbgyoHLL1uxuzfv18AECdOnDB5zLCwMPHxxx/rt2VmZhpNUP7999/1+2zdupUJymWEwY4bGDVqlNBoNGL37t0iJSVF/5Obm2vyPjAyS+TBBx80+MaxZs0asWvXLnHhwgWxYcMGUbNmTdG3b1+zbTF2XCqtLF+zuXPniqCgIPHDDz+Ic+fOiXfeeUf4+PiI8+fPK/oY3U1ZvWb3798XtWvXFo8++qhISEgQ58+fFx9//LFQqVRi8+bNij9Od2PpdTt//rx47733xO+//y6SkpLExo0bxQMPPCA6dOhgcJySr9vs2bNFcHCw2Lhxo/jjjz9E7969RXR0tLh7965+n9jYWNGsWTORkJAg9u/fL+rUqSMGDRrkmAdOBhjsuAEARn8WL15s9j4lP4RL3mf+/PmievXqwtPTU9SoUUO88847Ii8vz2JbGOxYVtav2axZs0T16tWFn5+faNOmjdi3b59Cj8x9leVrdvbsWdG3b18RGhoq/Pz8xEMPPVRqKjoZZ+l1u3z5sujQoYMICQkR3t7eonbt2uKNN94QmZmZpY5T/HXTarViypQpomrVqsLb21t06dJFnDlzxuA+aWlpYtCgQSIgIEAEBQWJ559/Xty5c8feD5mMUAlRLAuOiIiIyM1w6jkRERG5NQY7RERE5NYY7BAREZFbY7BDREREbo3BDhEREbk1BjtERETk1hjsEBERkVtjsENELiE5ORkqlQrHjx93iuOYolKpsGHDBrscm4isw2CHiBRTWFiItm3bom/fvgbbMzMzERkZiX//+9/6bSqVqtTPqlWrFG3PsGHD0KdPH4NtkZGRSElJQaNGjQAAu3fvhkqlQkZGhqLnJiLnUaGsG0BE7sPDwwNLlixB06ZNsXz5cgwePBgAMG7cOISEhGDq1KkG+y9evBixsbH6v4ODgx3SxrCwMLufh4icB3t2iEhRdevWxezZszFu3DikpKRg48aNWLVqFb777jt4eXkZ7BscHIywsDD9j4+Pj+TzFBYWYvjw4YiOjoavry8efPBBzJ8/X3/7tGnTsHTpUmzcuFHfc7R7926DYazk5GR06tQJAFCxYkWoVCoMGzYMABAVFYV58+YZnLNp06aYNm2a/u9z586hQ4cO8PHxQYMGDbBt27ZS7bxy5QoGDBiA4OBghISEoHfv3khOTpb8OInIdgx2iEhx48aNQ5MmTfDss89i5MiRePfdd9GkSZNS+40ZMwaVK1dGy5Yt8e2330LOUn1arRbVq1fHDz/8gFOnTuHdd9/F22+/jTVr1gAAXn/9dQwYMACxsbFISUlBSkoK2rZta3CMyMhIrF27FgBw5swZpKSkGARMls7ft29feHl5ISEhAQsXLsSkSZMM9snPz0e3bt0QGBiIffv24cCBAwgICEBsbCzu378v+bESkW04jEVEilOpVFiwYAHq16+Pxo0b46233iq1z3vvvYfOnTvDz88Pv/76K0aPHo3s7GyMHz9e0jk8PT0xffp0/d/R0dGIj4/HmjVrMGDAAAQEBMDX1xd5eXkmh608PDwQEhICAAgNDZU1jLZ9+3b89ddf2Lp1KyIiIgAAM2fORPfu3fX7rF69GlqtFl9//TVUKhWAoqG74OBg7N69G48//rjk8xGR9RjsEJFdfPvtt/Dz80NSUhL+/vtvREVFGdw+ZcoU/e/NmjVDTk4OPvroI8nBDgB88cUX+Pbbb3H58mXcvXsX9+/fR9OmTRV6BOadPn0akZGR+kAHANq0aWOwz4kTJ3D+/HkEBgYabL937x4uXLjgkHYSEYexiMgOfvvtN8ydOxc///wzWrZsieHDh1scomrVqhX+/vtv5OXlSTrHqlWr8Prrr2P48OH49ddfcfz4cTz//POKDQ+p1epSbc7Pz5d1jOzsbLRo0QLHjx83+Dl79iyeeeYZRdpJRJaxZ4eIFJWbm4thw4Zh1KhR6NSpE6Kjo9G4cWMsXLgQo0aNMnm/48ePo2LFivD29pZ0ngMHDqBt27YYPXq0flvJ3hIvLy8UFhaaPY4uabrkflWqVEFKSor+76ysLCQlJen/rl+/Pq5cuYKUlBSEh4cDAA4ePGhwjObNm2P16tUIDQ1FUFCQpMdFRMpjzw4RKWry5MkQQmD27NkAimY1ffzxx3jzzTf1s5B++uknfP3110hMTMT58+exYMECzJw5E+PGjZN8njp16uD333/H1q1bcfbsWUyZMgWHDx822CcqKgp//PEHzpw5g1u3bhntmalZsyZUKhV+/vln3Lx5E9nZ2QCAzp074/vvv8e+fftw8uRJDB06FB4eHvr7xcTEoG7duhg6dChOnDiBffv2GdQRAoDBgwejcuXK6N27N/bt24ekpCTs3r0b48ePx99//y35sRKRjQQRkUJ2794tPDw8xL59+0rd9vjjj4vOnTsLrVYrfvnlF9G0aVMREBAg/P39RZMmTcTChQtFYWGhyWMnJSUJAOLYsWNCCCHu3bsnhg0bJjQajQgODhajRo0Sb731lmjSpIn+Pjdu3BBdu3YVAQEBAoDYtWtXqeMIIcR7770nwsLChEqlEkOHDhVCCJGZmSmefvppERQUJCIjI8WSJUtEkyZNxNSpU/X3O3PmjGjfvr3w8vISdevWFXFxcQKAWL9+vX6flJQU8dxzz4nKlSsLb29v8cADD4gRI0aIzMxMa55iIrKCSggZcz2JiIiIXAyHsYiIiMitMdghIiIit8Zgh4iIiNwagx0iIiJyawx2iIiIyK0x2CEiIiK3xmCHiIiI3BqDHSIiInJrDHaIiIjIrTHYISIiIrfGYIeIiIjcGoMdIiIicmv/AyrQ7Cv+MGOgAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "DyGsTTOqFphf"
-      },
-      "outputs": [],
-      "source": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "AGBkzAO5red4"
-      },
-      "source": [
-        "9. For the best model in each case (yours and scikit-learn), print the classification_report using sklearn.metrics .\n",
-        "10. For the best model in each case (yours and scikit-learn), print the confusion_matrix using sklearn.metrics ."
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
       ]
-    },
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "from sklearn.metrics import mean_squared_error, r2_score\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "\n",
+    "df = pd.read_csv('Real estate.csv')\n",
+    "print(df.isnull().sum())\n",
+    "columns = df.columns[1:-1]\n",
+    "for i in columns:\n",
+    "    plt.scatter(df[i], df['Y house price of unit area'])\n",
+    "    plt.xlabel(i)\n",
+    "    plt.ylabel('House Price of Unit Area')\n",
+    "    plt.title(f'{i} vs House Price of Unit Area')\n",
+    "    plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "le-HfABsvnyF"
-      },
-      "outputs": [],
-      "source": []
-    },
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean Squared Error: 66.6733627183596\n",
+      "R2 Score: 0.579662418286177\n"
+     ]
+    }
+   ],
+   "source": [
+    "df = df.drop(columns=['No'])\n",
+    "X = df.drop(columns=['Y house price of unit area'])\n",
+    "y = df['Y house price of unit area']\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)\n",
+    "scaler = MinMaxScaler()\n",
+    "\n",
+    "X_train_scaled = scaler.fit_transform(X_train)\n",
+    "X_test_scaled = scaler.transform(X_test)\n",
+    "model = LinearRegression()\n",
+    "model.fit(X_train_scaled, y_train)\n",
+    "y_pred = model.predict(X_test_scaled)\n",
+    "mse = mean_squared_error(y_test, y_pred)\n",
+    "r2 = r2_score(y_test, y_pred)\n",
+    "\n",
+    "print(f'Mean Squared Error: {mse}')\n",
+    "print(f'R2 Score: {r2}')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "dQ7lQpy-SYCq"
+   },
+   "source": [
+    "# Logistic Regression\n",
+    "## Question 3\n",
+    "\n",
+    "The breast cancer dataset is a binary classification dataset commonly used in machine learning tasks. It is available in scikit-learn (sklearn) as part of its datasets module.\n",
+    "Here is an explanation of the breast cancer dataset's components:\n",
+    "\n",
+    "* Features (X):\n",
+    "\n",
+    " * The breast cancer dataset consists of 30 numeric features representing different characteristics of the FNA images. These features include mean, standard error, and worst (largest) values of various attributes such as radius, texture, smoothness, compactness, concavity, symmetry, fractal dimension, etc.\n",
+    "\n",
+    "* Target (y):\n",
+    "\n",
+    " * The breast cancer dataset is a binary classification problem, and the target variable (y) represents the diagnosis of the breast mass. It contains two classes:\n",
+    "    * 0: Represents a malignant (cancerous) tumor.\n",
+    "    * 1: Represents a benign (non-cancerous) tumor.\n",
+    "\n",
+    "Complete the code given below in place of the \"...\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "auipk-zBpmO-"
+   },
+   "source": [
+    "1. Load the dataset from sklearn.datasets\n",
+    "2. Separate out the X and Y columns.\n",
+    "3. Normalize the X data using MinMaxScaler or StandardScaler.\n",
+    "4. Create a train-test-split. Take any suitable test size."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "id": "0OyGNHNjFh13"
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.datasets import make_classification\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
+    "scaler = MinMaxScaler() \n",
+    "from sklearn.datasets import load_breast_cancer\n",
+    "data = load_breast_cancer()\n",
+    "X = data.data\n",
+    "y = data.target\n",
+    "X_normalized = scaler.fit_transform(X)\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X_normalized, y, test_size=0.2, random_state=42)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "uM-SsSxpqF2o"
+   },
+   "source": [
+    "5. Write code for the sigmoid function and Logistic regression.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "id": "o81LA5MZFoTW"
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "# Sigmoid function\n",
+    "def sigmoid(z):\n",
+    "    return 1.0 / (1.0 + np.exp(-z))\n",
+    "\n",
+    "# Derivative of the sigmoid function\n",
+    "def sigmoid_derivative(z):\n",
+    "    return sigmoid(z) * (1 - sigmoid(z))\n",
+    "\n",
+    "class LogisticRegression:\n",
+    "    def __init__(self, learning_rate, epochs):\n",
+    "        # Initialise the hyperparameters of the model\n",
+    "        self.lr = learning_rate\n",
+    "        self.epochs = epochs\n",
+    "\n",
+    "    def fit(self, X, y):\n",
+    "        n_samples, n_features = X.shape\n",
+    "        y = y.reshape(-1, 1)\n",
+    "        self.weights = np.random.randn(n_features, 1) / np.sqrt(n_features)\n",
+    "        self.bias = np.random.randn(1, 1)\n",
+    "\n",
+    "        # Implement the Gradient Descent algorithm\n",
+    "        for _ in range(self.epochs):\n",
+    "            z = np.dot(X, self.weights) + self.bias\n",
+    "            y_pred = sigmoid(z)\n",
+    "\n",
+    "            dw = -np.dot(X.T, (y - y_pred)) / n_samples\n",
+    "            db = -np.sum(y - y_pred) / n_samples\n",
+    "\n",
+    "            self.weights -= self.lr * dw\n",
+    "            self.bias -= self.lr * db\n",
+    "\n",
+    "    def predict(self, X):\n",
+    "        y_pred = sigmoid(np.dot(X, self.weights) + self.bias)\n",
+    "        y_pred_class = (y_pred > 0.5).astype(int)\n",
+    "        return y_pred_class\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Uo9LNRMzq4K-"
+   },
+   "source": [
+    "6. Fit your model on the dataset and make predictions.\n",
+    "7. Compare your model with the Sklearn Logistic Regression model. Try out all the different penalties.\n",
+    "8. Print accuracy_score in each case using sklearn.metrics ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "id": "DyGsTTOqFphf"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "6OQ2tSp0MO6n"
-      },
-      "source": [
-        "# KNN\n",
-        "## Question 4\n",
-        "\n",
-        "How accurately can a K-Nearest Neighbors (KNN) model classify different types of glass based on a glass classification dataset consisting of 214 samples and 7 classes? Use the kaggle dataset \"https://www.kaggle.com/datasets/uciml/glass\".\n",
-        "\n",
-        "Context: This is a Glass Identification Data Set from UCI. It contains 10 attributes including id. The response is glass type(discrete 7 values)"
-      ]
-    },
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Logistic Regression Accuracy: 0.8771929824561403\n"
+     ]
+    }
+   ],
+   "source": [
+    "lo = LogisticRegression(learning_rate=0.01, epochs=1000)\n",
+    "\n",
+    "lo.fit(X_train, y_train)\n",
+    "\n",
+    "y_pred = lo.predict(X_test)\n",
+    "\n",
+    "from sklearn.metrics import accuracy_score\n",
+    "\n",
+    "accuracy_custom = accuracy_score(y_test, y_pred)\n",
+    "print(f\"Logistic Regression Accuracy: {accuracy_custom}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "AGBkzAO5red4"
+   },
+   "source": [
+    "9. For the best model in each case (yours and scikit-learn), print the classification_report using sklearn.metrics .\n",
+    "10. For the best model in each case (yours and scikit-learn), print the confusion_matrix using sklearn.metrics ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "id": "le-HfABsvnyF"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "iMGxbtX-zfsI"
-      },
-      "source": [
-        "1. Load the data as you did in the 2nd question.\n",
-        "2. Extract the X and Y columns.\n",
-        "3. Split it into training and testing datasets."
-      ]
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\yash0\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:1183: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
+      "  warnings.warn(\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "p0SfLB7pO7_z"
-      },
-      "outputs": [],
-      "source": []
-    },
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sklearn Logistic Regression with none penalty Accuracy: 0.9122807017543859\n",
+      "Sklearn Logistic Regression with l2 penalty Accuracy: 0.9824561403508771\n",
+      "Sklearn Logistic Regression with l1 penalty Accuracy: 0.956140350877193\n",
+      "Sklearn Logistic Regression with elasticnet penalty Accuracy: 0.9824561403508771\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import LogisticRegression as SklearnLogisticRegression\n",
+    "best_accuracy_sklearn = 0\n",
+    "best_penalty = None\n",
+    "best_sklearn_model = None\n",
+    "y_pred_best_sklearn = None\n",
+    "penalties = ['none', 'l2', 'l1', 'elasticnet']\n",
+    "solver = {\n",
+    "    'none': 'lbfgs',\n",
+    "    'l2': 'lbfgs',\n",
+    "    'l1': 'saga',\n",
+    "    'elasticnet': 'saga'\n",
+    "}\n",
+    "\n",
+    "for penalty in penalties:\n",
+    "    if penalty == 'elasticnet':\n",
+    "        sklearn_model = SklearnLogisticRegression(penalty=penalty, solver=solver[penalty], l1_ratio=0.5, max_iter=10000)\n",
+    "    else:\n",
+    "        sklearn_model = SklearnLogisticRegression(penalty=penalty, solver=solver[penalty], max_iter=10000)\n",
+    "    sklearn_model.fit(X_train, y_train)\n",
+    "    y_pred_sklearn = sklearn_model.predict(X_test)\n",
+    "    accuracy_sklearn = accuracy_score(y_test, y_pred_sklearn)\n",
+    "    print(f\"Sklearn Logistic Regression with {penalty} penalty Accuracy: {accuracy_sklearn}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "qtyZJXh9zoh5"
-      },
-      "source": [
-        "4. Define Euclidean distance.\n",
-        "5. Build the KNN model.\n",
-        "6. Fit the model on the training data. (Note : you may require to change the type of the data from pandas dataframe to numpy arrays. To do that, just do this X=np.array(X) and so on...)"
-      ]
-    },
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Best Custom Logistic Regression Model:\n",
+      "Classification Report:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       1.00      0.67      0.81        43\n",
+      "           1       0.84      1.00      0.91        71\n",
+      "\n",
+      "    accuracy                           0.88       114\n",
+      "   macro avg       0.92      0.84      0.86       114\n",
+      "weighted avg       0.90      0.88      0.87       114\n",
+      "\n",
+      "Confusion Matrix:\n",
+      "[[29 14]\n",
+      " [ 0 71]]\n",
+      "\n",
+      "Best Sklearn Logistic Regression Model with elasticnet penalty:\n",
+      "Classification Report:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       1.00      0.95      0.98        43\n",
+      "           1       0.97      1.00      0.99        71\n",
+      "\n",
+      "    accuracy                           0.98       114\n",
+      "   macro avg       0.99      0.98      0.98       114\n",
+      "weighted avg       0.98      0.98      0.98       114\n",
+      "\n",
+      "Confusion Matrix:\n",
+      "[[41  2]\n",
+      " [ 0 71]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
+    "if accuracy_sklearn > best_accuracy_sklearn:\n",
+    "        best_accuracy_sklearn = accuracy_sklearn\n",
+    "        best_penalty = penalty\n",
+    "        best_sklearn_model = sklearn_model\n",
+    "        y_pred_best_sklearn = y_pred_sklearn\n",
+    "\n",
+    "# Print classification report and confusion matrix for the best custom model\n",
+    "print(\"\\nBest Custom Logistic Regression Model:\")\n",
+    "print(\"Classification Report:\")\n",
+    "print(classification_report(y_test, y_pred))\n",
+    "print(\"Confusion Matrix:\")\n",
+    "print(confusion_matrix(y_test, y_pred))\n",
+    "\n",
+    "# Print classification report and confusion matrix for the best sklearn model\n",
+    "print(f\"\\nBest Sklearn Logistic Regression Model with {best_penalty} penalty:\")\n",
+    "print(\"Classification Report:\")\n",
+    "print(classification_report(y_test, y_pred_best_sklearn))\n",
+    "print(\"Confusion Matrix:\")\n",
+    "print(confusion_matrix(y_test, y_pred_best_sklearn))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "6OQ2tSp0MO6n"
+   },
+   "source": [
+    "# KNN\n",
+    "## Question 4\n",
+    "\n",
+    "How accurately can a K-Nearest Neighbors (KNN) model classify different types of glass based on a glass classification dataset consisting of 214 samples and 7 classes? Use the kaggle dataset \"https://www.kaggle.com/datasets/uciml/glass\".\n",
+    "\n",
+    "Context: This is a Glass Identification Data Set from UCI. It contains 10 attributes including id. The response is glass type(discrete 7 values)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "iMGxbtX-zfsI"
+   },
+   "source": [
+    "1. Load the data as you did in the 2nd question.\n",
+    "2. Extract the X and Y columns.\n",
+    "3. Split it into training and testing datasets."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "id": "p0SfLB7pO7_z"
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "from sklearn.metrics import mean_squared_error, r2_score\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "\n",
+    "df = pd.read_csv('glass.csv')\n",
+    "X = df.iloc[:, :-1].values\n",
+    "y = df['Type']\n",
+    "scaler = StandardScaler()\n",
+    "X = scaler.fit_transform(X)\n",
+    "X=np.array(X)\n",
+    "y=np.array(y)\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "qtyZJXh9zoh5"
+   },
+   "source": [
+    "4. Define Euclidean distance.\n",
+    "5. Build the KNN model.\n",
+    "6. Fit the model on the training data. (Note : you may require to change the type of the data from pandas dataframe to numpy arrays. To do that, just do this X=np.array(X) and so on...)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "id": "YJkhLORLzn6r"
+   },
+   "outputs": [],
+   "source": [
+    "from collections import Counter\n",
+    "def euclidean_distance(x1,x2):\n",
+    "    return np.sqrt(np.sum((x1-x2)**2))\n",
+    "\n",
+    "class KNN(object):\n",
+    "    def __init__(self,k):\n",
+    "        self.k=k\n",
+    "    def fit(self,x_train,y_train):\n",
+    "        self.x_train=x_train\n",
+    "        self.y_train=y_train\n",
+    "    def predict(self,x_test):\n",
+    "        predictions=[self._helper(x) for x in x_test]\n",
+    "        return np.array(predictions)\n",
+    "    def _helper(self,x):\n",
+    "        prediction=[euclidean_distance(x,x1) for x1 in self.x_train]\n",
+    "        indices= np.argsort(prediction)[:self.k]\n",
+    "        labels= [self.y_train[i] for i in indices]\n",
+    "        c=Counter(labels).most_common()\n",
+    "        return c[0][0]\n",
+    "        \n",
+    "def accuracy(predictions,y_test):\n",
+    "    return np.sum(predictions==y_test)/len(y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "YJkhLORLzn6r"
-      },
-      "outputs": [],
-      "source": []
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.6976744186046512\n",
+      "Classification Report:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           1       0.62      0.91      0.74        11\n",
+      "           2       0.62      0.57      0.59        14\n",
+      "           3       0.00      0.00      0.00         3\n",
+      "           5       0.50      0.25      0.33         4\n",
+      "           6       1.00      1.00      1.00         3\n",
+      "           7       0.89      1.00      0.94         8\n",
+      "\n",
+      "    accuracy                           0.70        43\n",
+      "   macro avg       0.60      0.62      0.60        43\n",
+      "weighted avg       0.64      0.70      0.66        43\n",
+      "\n",
+      "Confusion Matrix:\n",
+      "[[10  1  0  0  0  0]\n",
+      " [ 5  8  0  1  0  0]\n",
+      " [ 1  2  0  0  0  0]\n",
+      " [ 0  2  0  1  0  1]\n",
+      " [ 0  0  0  0  3  0]\n",
+      " [ 0  0  0  0  0  8]]\n"
+     ]
     },
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "E9rxZpPB0pVS"
-      },
-      "source": [
-        "7. Make predictions. Find their accuracy using accuracy_score. Try different k values. k=3 worked well in our case.\n",
-        "8. Compare with the sklearn model (from sklearn.neighbors import KNeighborsClassifier)"
-      ]
-    },
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\yash0\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
+      "  _warn_prf(average, modifier, msg_start, len(result))\n",
+      "C:\\Users\\yash0\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
+      "  _warn_prf(average, modifier, msg_start, len(result))\n",
+      "C:\\Users\\yash0\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
+      "  _warn_prf(average, modifier, msg_start, len(result))\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "clf=KNN(k=5)\n",
+    "clf.fit(X_train,y_train)\n",
+    "predictions=clf.predict(X_test)\n",
+    "print(accuracy(predictions,y_test))\n",
+    "print(\"Classification Report:\")\n",
+    "print(classification_report(y_test, predictions))\n",
+    "print(\"Confusion Matrix:\")\n",
+    "print(confusion_matrix(y_test, predictions))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "E9rxZpPB0pVS"
+   },
+   "source": [
+    "7. Make predictions. Find their accuracy using accuracy_score. Try different k values. k=3 worked well in our case.\n",
+    "8. Compare with the sklearn model (from sklearn.neighbors import KNeighborsClassifier)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "id": "ernfjaZJ0pAh"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "ernfjaZJ0pAh"
-      },
-      "outputs": [],
-      "source": []
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "KNN Model Accuracy: 0.6977\n",
+      "Classification Report:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           1       0.62      0.91      0.74        11\n",
+      "           2       0.62      0.57      0.59        14\n",
+      "           3       0.00      0.00      0.00         3\n",
+      "           5       0.50      0.25      0.33         4\n",
+      "           6       1.00      1.00      1.00         3\n",
+      "           7       0.89      1.00      0.94         8\n",
+      "\n",
+      "    accuracy                           0.70        43\n",
+      "   macro avg       0.60      0.62      0.60        43\n",
+      "weighted avg       0.64      0.70      0.66        43\n",
+      "\n",
+      "Confusion Matrix:\n",
+      "[[10  1  0  0  0  0]\n",
+      " [ 5  8  0  1  0  0]\n",
+      " [ 1  2  0  0  0  0]\n",
+      " [ 0  2  0  1  0  1]\n",
+      " [ 0  0  0  0  3  0]\n",
+      " [ 0  0  0  0  0  8]]\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "h2tZQg4L09wn"
-      },
-      "outputs": [],
-      "source": []
-    }
-  ],
-  "metadata": {
-    "colab": {
-      "provenance": []
-    },
-    "kernelspec": {
-      "display_name": "Python 3",
-      "name": "python3"
-    },
-    "language_info": {
-      "name": "python"
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\yash0\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
+      "  _warn_prf(average, modifier, msg_start, len(result))\n",
+      "C:\\Users\\yash0\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
+      "  _warn_prf(average, modifier, msg_start, len(result))\n",
+      "C:\\Users\\yash0\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
+      "  _warn_prf(average, modifier, msg_start, len(result))\n"
+     ]
     }
+   ],
+   "source": [
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "knn = KNeighborsClassifier(n_neighbors=5) \n",
+    "knn.fit(X_train, y_train)\n",
+    "y_pred = knn.predict(X_test)\n",
+    "accuracy = accuracy_score(y_test, y_pred)\n",
+    "print(f\"KNN Model Accuracy: {accuracy:.4f}\")\n",
+    "print(\"Classification Report:\")\n",
+    "print(classification_report(y_test, y_pred))\n",
+    "\n",
+    "# Print confusion matrix\n",
+    "print(\"Confusion Matrix:\")\n",
+    "print(confusion_matrix(y_test, y_pred))"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
   },
-  "nbformat": 4,
-  "nbformat_minor": 0
-}
\ No newline at end of file
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Week 1/ML Assignment/Real estate.csv b/Week 1/ML Assignment/Real estate.csv
new file mode 100644
index 0000000..6f25811
--- /dev/null
+++ b/Week 1/ML Assignment/Real estate.csv	
@@ -0,0 +1,415 @@
+No,X1 transaction date,X2 house age,X3 distance to the nearest MRT station,X4 number of convenience stores,X5 latitude,X6 longitude,Y house price of unit area
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+2,2012.917,19.5,306.5947,9,24.98034,121.53951,42.2
+3,2013.583,13.3,561.9845,5,24.98746,121.54391,47.3
+4,2013.500,13.3,561.9845,5,24.98746,121.54391,54.8
+5,2012.833,5,390.5684,5,24.97937,121.54245,43.1
+6,2012.667,7.1,2175.03,3,24.96305,121.51254,32.1
+7,2012.667,34.5,623.4731,7,24.97933,121.53642,40.3
+8,2013.417,20.3,287.6025,6,24.98042,121.54228,46.7
+9,2013.500,31.7,5512.038,1,24.95095,121.48458,18.8
+10,2013.417,17.9,1783.18,3,24.96731,121.51486,22.1
+11,2013.083,34.8,405.2134,1,24.97349,121.53372,41.4
+12,2013.333,6.3,90.45606,9,24.97433,121.5431,58.1
+13,2012.917,13,492.2313,5,24.96515,121.53737,39.3
+14,2012.667,20.4,2469.645,4,24.96108,121.51046,23.8
+15,2013.500,13.2,1164.838,4,24.99156,121.53406,34.3
+16,2013.583,35.7,579.2083,2,24.9824,121.54619,50.5
+17,2013.250,0,292.9978,6,24.97744,121.54458,70.1
+18,2012.750,17.7,350.8515,1,24.97544,121.53119,37.4
+19,2013.417,16.9,368.1363,8,24.9675,121.54451,42.3
+20,2012.667,1.5,23.38284,7,24.96772,121.54102,47.7
+21,2013.417,4.5,2275.877,3,24.96314,121.51151,29.3
+22,2013.417,10.5,279.1726,7,24.97528,121.54541,51.6
+23,2012.917,14.7,1360.139,1,24.95204,121.54842,24.6
+24,2013.083,10.1,279.1726,7,24.97528,121.54541,47.9
+25,2013.000,39.6,480.6977,4,24.97353,121.53885,38.8
+26,2013.083,29.3,1487.868,2,24.97542,121.51726,27
+27,2012.667,3.1,383.8624,5,24.98085,121.54391,56.2
+28,2013.250,10.4,276.449,5,24.95593,121.53913,33.6
+29,2013.500,19.2,557.478,4,24.97419,121.53797,47
+30,2013.083,7.1,451.2438,5,24.97563,121.54694,57.1
+31,2013.500,25.9,4519.69,0,24.94826,121.49587,22.1
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+33,2012.750,37.9,488.5727,1,24.97349,121.53451,34.2
+34,2013.250,16.5,323.655,6,24.97841,121.54281,49.3
+35,2012.750,15.4,205.367,7,24.98419,121.54243,55.1
+36,2013.500,13.9,4079.418,0,25.01459,121.51816,27.3
+37,2012.917,14.7,1935.009,2,24.96386,121.51458,22.9
+38,2013.167,12,1360.139,1,24.95204,121.54842,25.3
+39,2012.667,3.1,577.9615,6,24.97201,121.54722,47.7
+40,2013.167,16.2,289.3248,5,24.98203,121.54348,46.2
+41,2013.000,13.6,4082.015,0,24.94155,121.50381,15.9
+42,2013.500,16.8,4066.587,0,24.94297,121.50342,18.2
+43,2013.417,36.1,519.4617,5,24.96305,121.53758,34.7
+44,2012.750,34.4,512.7871,6,24.98748,121.54301,34.1
+45,2013.583,2.7,533.4762,4,24.97445,121.54765,53.9
+46,2013.083,36.6,488.8193,8,24.97015,121.54494,38.3
+47,2013.417,21.7,463.9623,9,24.9703,121.54458,42
+48,2013.583,35.9,640.7391,3,24.97563,121.53715,61.5
+49,2013.417,24.2,4605.749,0,24.94684,121.49578,13.4
+50,2012.667,29.4,4510.359,1,24.94925,121.49542,13.2
+51,2013.417,21.7,512.5487,4,24.974,121.53842,44.2
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+53,2013.583,32.1,1438.579,3,24.97419,121.5175,27
+54,2013.083,13.3,492.2313,5,24.96515,121.53737,38.9
+55,2013.083,16.1,289.3248,5,24.98203,121.54348,51.7
+56,2012.833,31.7,1160.632,0,24.94968,121.53009,13.7
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+59,2013.500,30.3,4510.359,1,24.94925,121.49542,22.6
+60,2013.083,13.3,336.0532,5,24.95776,121.53438,42.4
+61,2013.417,11,1931.207,2,24.96365,121.51471,21.3
+62,2013.500,5.3,259.6607,6,24.97585,121.54516,63.2
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+64,2013.583,2.6,533.4762,4,24.97445,121.54765,55
+65,2013.333,17.5,995.7554,0,24.96305,121.54915,25.3
+66,2013.417,40.1,123.7429,8,24.97635,121.54329,44.3
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+72,2013.083,35.5,640.7391,3,24.97563,121.53715,40.8
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+75,2012.917,6.8,379.5575,10,24.98343,121.53762,54.4
+76,2013.500,12.3,1360.139,1,24.95204,121.54842,29.5
+77,2013.583,35.9,616.4004,3,24.97723,121.53767,36.8
+78,2012.833,20.5,2185.128,3,24.96322,121.51237,25.6
+79,2012.917,38.2,552.4371,2,24.97598,121.53381,29.8
+80,2013.000,18,1414.837,1,24.95182,121.54887,26.5
+81,2013.500,11.8,533.4762,4,24.97445,121.54765,40.3
+82,2013.000,30.8,377.7956,6,24.96427,121.53964,36.8
+83,2013.083,13.2,150.9347,7,24.96725,121.54252,48.1
+84,2012.917,25.3,2707.392,3,24.96056,121.50831,17.7
+85,2013.083,15.1,383.2805,7,24.96735,121.54464,43.7
+86,2012.750,0,338.9679,9,24.96853,121.54413,50.8
+87,2012.833,1.8,1455.798,1,24.9512,121.549,27
+88,2013.583,16.9,4066.587,0,24.94297,121.50342,18.3
+89,2012.917,8.9,1406.43,0,24.98573,121.52758,48
+90,2013.500,23,3947.945,0,24.94783,121.50243,25.3
+91,2012.833,0,274.0144,1,24.9748,121.53059,45.4
+92,2013.250,9.1,1402.016,0,24.98569,121.5276,43.2
+93,2012.917,20.6,2469.645,4,24.96108,121.51046,21.8
+94,2012.917,31.9,1146.329,0,24.9492,121.53076,16.1
+95,2012.917,40.9,167.5989,5,24.9663,121.54026,41
+96,2012.917,8,104.8101,5,24.96674,121.54067,51.8
+97,2013.417,6.4,90.45606,9,24.97433,121.5431,59.5
+98,2013.083,28.4,617.4424,3,24.97746,121.53299,34.6
+99,2013.417,16.4,289.3248,5,24.98203,121.54348,51
+100,2013.417,6.4,90.45606,9,24.97433,121.5431,62.2
+101,2013.500,17.5,964.7496,4,24.98872,121.53411,38.2
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+109,2013.417,31.4,592.5006,2,24.9726,121.53561,34.1
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+111,2013.083,8.1,104.8101,5,24.96674,121.54067,51.6
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+113,2013.417,9.9,2102.427,3,24.96044,121.51462,23.1
+114,2013.333,14.8,393.2606,6,24.96172,121.53812,7.6
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+116,2013.083,20.6,737.9161,2,24.98092,121.54739,46.4
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+120,2013.500,16.6,289.3248,5,24.98203,121.54348,59.6
+121,2013.167,13.3,492.2313,5,24.96515,121.53737,31.3
+122,2013.500,13.6,492.2313,5,24.96515,121.53737,48
+123,2013.250,31.5,414.9476,4,24.98199,121.54464,32.5
+124,2013.417,0,185.4296,0,24.9711,121.5317,45.5
+125,2012.917,9.9,279.1726,7,24.97528,121.54541,57.4
+126,2013.167,1.1,193.5845,6,24.96571,121.54089,48.6
+127,2013.083,38.6,804.6897,4,24.97838,121.53477,62.9
+128,2013.250,3.8,383.8624,5,24.98085,121.54391,55
+129,2013.083,41.3,124.9912,6,24.96674,121.54039,60.7
+130,2013.417,38.5,216.8329,7,24.98086,121.54162,41
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+132,2013.500,4,2147.376,3,24.96299,121.51284,30.7
+133,2013.167,26.6,482.7581,5,24.97433,121.53863,37.5
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+137,2012.750,11.4,390.5684,5,24.97937,121.54245,46.8
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+145,2013.083,11.9,1360.139,1,24.95204,121.54842,28.4
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+152,2013.500,4.9,387.7721,9,24.98118,121.53788,44.7
+153,2013.333,12,1360.139,1,24.95204,121.54842,28.9
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+158,2013.250,16.1,815.9314,4,24.97886,121.53464,35.6
+159,2013.000,11.6,390.5684,5,24.97937,121.54245,39.4
+160,2012.667,15.5,815.9314,4,24.97886,121.53464,37.4
+161,2012.917,3.5,49.66105,8,24.95836,121.53756,57.8
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+163,2012.750,16,4066.587,0,24.94297,121.50342,11.6
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+176,2013.083,30.2,472.1745,3,24.97005,121.53758,36.5
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+184,2013.500,17,4082.015,0,24.94155,121.50381,18.8
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+191,2013.500,35.3,616.5735,8,24.97945,121.53642,42.3
+192,2013.167,13.2,750.0704,2,24.97371,121.54951,37.8
+193,2013.167,43.8,57.58945,7,24.9675,121.54069,42.7
+194,2013.417,9.7,421.479,5,24.98246,121.54477,49.3
+195,2013.500,15.2,3771.895,0,24.93363,121.51158,29.3
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+199,2013.083,34,157.6052,7,24.96628,121.54196,39.1
+200,2013.417,18.2,451.6419,8,24.96945,121.5449,31.6
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diff --git a/Week 1/ML Assignment/glass.csv b/Week 1/ML Assignment/glass.csv
new file mode 100644
index 0000000..10b1f43
--- /dev/null
+++ b/Week 1/ML Assignment/glass.csv	
@@ -0,0 +1,215 @@
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diff --git a/Week 1/ML Examples/.ipynb_checkpoints/KNN-checkpoint.ipynb b/Week 1/ML Examples/.ipynb_checkpoints/KNN-checkpoint.ipynb
new file mode 100644
index 0000000..20651b1
--- /dev/null
+++ b/Week 1/ML Examples/.ipynb_checkpoints/KNN-checkpoint.ipynb	
@@ -0,0 +1,132 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ZngIov7TOyqG"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "from collections import Counter\n",
+        "# Counter class is imported from the collections module. It is used later\n",
+        "# to count occurrences of labels in the nearest neighbors.\n",
+        "\n",
+        "def euclidean_distance(x1,x2):\n",
+        "    return np.sqrt(np.sum((x1-x2)**2))\n",
+        "\n",
+        "class KNN(object):\n",
+        "    def __init__(self,k):\n",
+        "        self.k=k\n",
+        "    def fit(self,x_train,y_train):\n",
+        "        self.x_train=x_train\n",
+        "        self.y_train=y_train\n",
+        "    def predict(self,x_test):\n",
+        "        predictions=[self._helper(x) for x in x_test]\n",
+        "        return np.array(predictions)\n",
+        "    def _helper(self,x):\n",
+        "        prediction=[euclidean_distance(x,x1) for x1 in self.x_train]\n",
+        "        indices= np.argsort(prediction)[:self.k]\n",
+        "        labels= [self.y_train[i] for i in indices]\n",
+        "        c=Counter(labels).most_common()\n",
+        "        return c[0][0]\n",
+        "        \n",
+        "def accuracy(predictions,y_test):\n",
+        "    return np.sum(predictions==y_test)/len(y_test)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn import datasets\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "import matplotlib.pyplot as plt\n",
+        "from matplotlib.colors import ListedColormap as lcm\n",
+        "colormap=lcm(['red','blue','yellow'])\n",
+        "\n",
+        "iris = datasets.load_iris()\n",
+        "x,y = iris.data,iris.target\n",
+        "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2)"
+      ],
+      "metadata": {
+        "id": "f_uJHTmzO1lO"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Use the Model\n",
+        "clf=KNN(k=3)\n",
+        "clf.fit(x_train,y_train)\n",
+        "predictions=clf.predict(x_test)\n",
+        "print(accuracy(predictions,y_test))"
+      ],
+      "metadata": {
+        "id": "mH0PZqloHtgz",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "febaf18d-69cf-424b-cb41-1e0004ac58bc"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "0.9666666666666667\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Plotting the data\n",
+        "plt.scatter(x[:, 0], x[:, 1], c=y, cmap=colormap)\n",
+        "plt.xlabel('Sepal Length')\n",
+        "plt.ylabel('Sepal Width')\n",
+        "plt.title('Iris Dataset')\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "zngx3d7FKlJA",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 472
+        },
+        "outputId": "cf5a8567-f6e1-42d2-816d-9558e49a27b7"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Week 1/ML Examples/.ipynb_checkpoints/LinReg-checkpoint.ipynb b/Week 1/ML Examples/.ipynb_checkpoints/LinReg-checkpoint.ipynb
new file mode 100644
index 0000000..da515cd
--- /dev/null
+++ b/Week 1/ML Examples/.ipynb_checkpoints/LinReg-checkpoint.ipynb	
@@ -0,0 +1,256 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "FC5yDl9FZlPV"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "from sklearn.datasets import make_regression\n",
+        "from sklearn.metrics import mean_squared_error\n",
+        "from sklearn.model_selection import train_test_split"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "KAQqig5QZ9R3"
+      },
+      "source": [
+        "## You just made your own Linear Regresion Library (Sort of)\n",
+        "You can use this model for any linear regression problem you have. Make a new notebook and call this class using \"from LinRegScratch import LinearRegression\""
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "SyfD3VBYZ1KU"
+      },
+      "outputs": [],
+      "source": [
+        "class LinearRegression:\n",
+        "    def __init__(self, learning_rate, epochs):\n",
+        "        self.lr=learning_rate\n",
+        "        self.epochs=epochs\n",
+        "\n",
+        "    def fit(self, X_train, y_train):\n",
+        "        n_samples, n_features = X_train.shape\n",
+        "        y_train=y_train.reshape(-1,1)\n",
+        "        # init parameters\n",
+        "        self.weights = np.zeros((n_features,1))\n",
+        "        self.bias = np.zeros((1,1))\n",
+        "\n",
+        "        # gradient descent\n",
+        "        for i in range(self.epochs):\n",
+        "            delta= -(y_train-np.dot(X_train,self.weights)-self.bias)/n_samples\n",
+        "            dw= np.dot(X_train.T,delta)\n",
+        "            db= np.sum(delta).reshape(1,1)\n",
+        "\n",
+        "            #update weights and biases\n",
+        "            self.weights-= self.lr * dw\n",
+        "            self.bias-= self.lr* db\n",
+        "\n",
+        "    def predict(self, X_test):\n",
+        "        y_predicted = np.dot(X_test,self.weights)+self.bias\n",
+        "        print(self.weights, self.bias)\n",
+        "        return y_predicted\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "0fYDHhVmHq63"
+      },
+      "outputs": [],
+      "source": [
+        "\n",
+        "X, y = make_regression(n_samples=1000, n_features=1, noise=10, random_state= 42 )  \n",
+        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "7BUJWqSBHysf",
+        "outputId": "b07142d4-1f41-4772-ff63-8e2d52f2cea3"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "[[15.07321206]] [[0.06528862]]\n"
+          ]
+        }
+      ],
+      "source": [
+        "lr_model = LinearRegression(0.001,2500)\n",
+        "lr_model.fit(X_train, y_train)\n",
+        "y_predicted = lr_model.predict(X_test)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 467
+        },
+        "id": "Mlqs_OsBMxqn",
+        "outputId": "bb8cc34d-8454-491b-c2e6-5e201366dbd5"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "[[15.07321206]] [[0.06528862]]\n"
+          ]
+        },
+        {
+          "data": {
+            "text/plain": [
+              "[<matplotlib.lines.Line2D at 0x7f3cc217d480>]"
+            ]
+          },
+          "execution_count": 9,
+          "metadata": {},
+          "output_type": "execute_result"
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "plt.scatter(X_train, y_train, color='blue', label='Training Data')\n",
+        "plt.scatter(X_test, y_test, color='yellow', label='Testing Data')\n",
+        "plt.plot(X_test, y_predicted, color='red', linewidth=2, label='Line of Best Fit')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 449
+        },
+        "id": "p3BE_JQYJP_P",
+        "outputId": "2387087f-de42-4de3-a3f5-b40d05971953"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "plt.scatter(X_train, y_train, color='blue', label='Training Data')\n",
+        "plt.scatter(X_test, y_test, color='yellow', label='Testing Data')\n",
+        "plt.plot(X_test, y_predicted, color='red', linewidth=2, label='Line of Best Fit')\n",
+        "plt.xlabel('X')\n",
+        "plt.ylabel('y')\n",
+        "plt.legend()\n",
+        "plt.show()\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "NlfXI8HGK2hq",
+        "outputId": "2c3a43e3-aca9-4338-a609-79aca7e66c00"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "[[15.07321206]] [[0.06528862]]\n",
+            "Mean Squared Error: 99.23101918789216\n"
+          ]
+        }
+      ],
+      "source": [
+        "mse = mean_squared_error(y_train, lr_model.predict(X_train))\n",
+        "print('Mean Squared Error:', mse)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "vUW2VPQIkIxI",
+        "outputId": "2fc9a39d-d6a3-473c-f57d-47148f84125a"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "[[15.07321206]] [[0.06528862]]\n",
+            "Mean Squared Error: 110.42416649233857\n"
+          ]
+        }
+      ],
+      "source": [
+        "mse = mean_squared_error(y_test, lr_model.predict(X_test))\n",
+        "print('Mean Squared Error:', mse)"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3.7 (pytorch)",
+      "language": "python",
+      "name": "torch"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.17"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/Week 1/ML Examples/.ipynb_checkpoints/LogReg-checkpoint.ipynb b/Week 1/ML Examples/.ipynb_checkpoints/LogReg-checkpoint.ipynb
new file mode 100644
index 0000000..c8e73ff
--- /dev/null
+++ b/Week 1/ML Examples/.ipynb_checkpoints/LogReg-checkpoint.ipynb	
@@ -0,0 +1,245 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "BXSifC2Nbv60"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "from sklearn.datasets import make_classification\n",
+        "from sklearn.linear_model import LogisticRegression\n",
+        "from sklearn.model_selection import train_test_split"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "xeDXxs9kb13H"
+      },
+      "source": [
+        "##You just made your own Logistic Regresion Library (Sort of)\n",
+        "You can use this model for any logistic regression problem you have. Make a new notebook and call this class using \"from LogRegScratch import LogisticRegression\""
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "EMWnEVDbborl"
+      },
+      "outputs": [],
+      "source": [
+        "class sigmoid:\n",
+        "    def sigmoid(z):\n",
+        "        a=1.0/(1.0+ np.exp(-z))\n",
+        "        return a\n",
+        "    def derivative(self,z):\n",
+        "        return self.sigmoid(z)*(1-self.sigmoid(z))\n",
+        "\n",
+        "class LogisticRegression:\n",
+        "    def __init__(self, learning_rate, epochs):\n",
+        "        self.lr=learning_rate\n",
+        "        self.epochs=epochs\n",
+        "\n",
+        "    def fit(self, X, y):\n",
+        "        n_samples, n_features = X.shape\n",
+        "        y = y.reshape(-1, 1)\n",
+        "        self.weights=np.random.randn(n_features,1)/np.sqrt(n_features)\n",
+        "        self.bias= np.random.randn(1,1)\n",
+        "\n",
+        "        for i in range(self.epochs):\n",
+        "            z = np.dot(X,self.weights) + self.bias\n",
+        "            y_pred = sigmoid(z)\n",
+        "            #cross-entropy loss function\n",
+        "            dw = -np.dot(X.T,(y - y_pred))/n_samples\n",
+        "            db = -np.sum(y - y_pred)/n_samples\n",
+        "            self.weights -= self.lr* dw\n",
+        "            self.bias-= self.lr* db\n",
+        "\n",
+        "    def predict(self, X):\n",
+        "        y_pred = np.dot(X,self.weights)+self.bias\n",
+        "\n",
+        "        for i in range(len(y_pred)):\n",
+        "            if y_pred[i]<= 0.5:\n",
+        "                y_pred[i] = 0\n",
+        "            else:\n",
+        "                y_pred[i] = 1   \n",
+        "        return y_pred"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 75
+        },
+        "id": "cq4zAghD-z0A",
+        "outputId": "7855a22c-e161-464c-b39c-61ca46f71804"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
+            ],
+            "text/plain": [
+              "LogisticRegression()"
+            ]
+          },
+          "execution_count": 8,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "from sklearn.datasets import make_classification\n",
+        "from sklearn.linear_model import LogisticRegression\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "\n",
+        "# Generate classification data\n",
+        "X, y = make_classification(n_samples=1000, n_features=2, n_informative=2, n_redundant=0, random_state=42)\n",
+        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1)\n",
+        "\n",
+        "# Create an instance of LogisticRegression\n",
+        "logreg = LogisticRegression()\n",
+        "\n",
+        "# Fit the model\n",
+        "logreg.fit(X_train, y_train)\n",
+        "\n",
+        "# Predict the output\n",
+        "y_pred = logreg.predict(X_test)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "TOaFPsXEBUD-",
+        "outputId": "66e3fc78-8e46-41e5-a31e-3d7ae711b90e"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "0.92\n",
+            "[[39  4]\n",
+            " [ 4 53]]\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Evaluate the output\n",
+        "from sklearn.metrics import accuracy_score, confusion_matrix\n",
+        "print(accuracy_score(y_test,y_pred))\n",
+        "print(confusion_matrix(y_test,y_pred))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 448
+        },
+        "id": "vjyfqDL_CDgB",
+        "outputId": "3e6a9b73-62d4-45f9-e92c-d6c979bb7b00"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "<matplotlib.collections.PathCollection at 0x7f037bbc5420>"
+            ]
+          },
+          "execution_count": 16,
+          "metadata": {},
+          "output_type": "execute_result"
+        },
+        {
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 640x480 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "plt.subplot(1,2,1)\n",
+        "plt.scatter(X_test[:,0],X_test[:,1],c=y_test)\n",
+        "plt.subplot(1,2,2)\n",
+        "plt.scatter(X_test[:,0],X_test[:,1],c=y_pred)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "background_save": true,
+          "base_uri": "https://localhost:8080/",
+          "height": 472
+        },
+        "id": "qi9xSQYxF0b-",
+        "outputId": "4a01b401-fb01-4e33-e6cd-4a84ff1c0450"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "plt.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap='bwr')\n",
+        "\n",
+        "# Plot the decision boundary\n",
+        "x1_min, x1_max = X_train[:, 0].min() - 1, X_train[:, 0].max() + 1\n",
+        "x2_min, x2_max = X_train[:, 1].min() - 1, X_train[:, 1].max() + 1\n",
+        "xx1, xx2 = np.meshgrid(np.arange(x1_min, x1_max, 0.1), np.arange(x2_min, x2_max, 0.1))\n",
+        "Z = logreg.predict(np.c_[xx1.ravel(), xx2.ravel()])\n",
+        "Z = Z.reshape(xx1.shape)\n",
+        "plt.contour(xx1, xx2, Z, levels=[0.5], colors='black')\n",
+        "\n",
+        "plt.xlabel('Feature 1')\n",
+        "plt.ylabel('Feature 2')\n",
+        "plt.title('Logistic Regression Decision Boundary')\n",
+        "plt.show()\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/Week 1/ML Examples/.ipynb_checkpoints/Machine Learning-checkpoint.pdf b/Week 1/ML Examples/.ipynb_checkpoints/Machine Learning-checkpoint.pdf
new file mode 100644
index 0000000..5a0caef
Binary files /dev/null and b/Week 1/ML Examples/.ipynb_checkpoints/Machine Learning-checkpoint.pdf differ
diff --git a/Week 1/ML Examples/KNN.ipynb b/Week 1/ML Examples/KNN.ipynb
index 20651b1..292f41e 100644
--- a/Week 1/ML Examples/KNN.ipynb	
+++ b/Week 1/ML Examples/KNN.ipynb	
@@ -1,132 +1,142 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "ZngIov7TOyqG"
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from collections import Counter\n",
+    "# Counter class is imported from the collections module. It is used later\n",
+    "# to count occurrences of labels in the nearest neighbors.\n",
+    "\n",
+    "def euclidean_distance(x1,x2):\n",
+    "    return np.sqrt(np.sum((x1-x2)**2))\n",
+    "\n",
+    "class KNN(object):\n",
+    "    def __init__(self,k):\n",
+    "        self.k=k\n",
+    "    def fit(self,x_train,y_train):\n",
+    "        self.x_train=x_train\n",
+    "        self.y_train=y_train\n",
+    "    def predict(self,x_test):\n",
+    "        predictions=[self._helper(x) for x in x_test]\n",
+    "        return np.array(predictions)\n",
+    "    def _helper(self,x):\n",
+    "        prediction=[euclidean_distance(x,x1) for x1 in self.x_train]\n",
+    "        indices= np.argsort(prediction)[:self.k]\n",
+    "        labels= [self.y_train[i] for i in indices]\n",
+    "        c=Counter(labels).most_common()\n",
+    "        return c[0][0]\n",
+    "        \n",
+    "def accuracy(predictions,y_test):\n",
+    "    return np.sum(predictions==y_test)/len(y_test)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "f_uJHTmzO1lO"
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn import datasets\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib.colors import ListedColormap as lcm\n",
+    "colormap=lcm(['red','blue','yellow'])\n",
+    "\n",
+    "iris = datasets.load_iris()\n",
+    "x,y = iris.data,iris.target\n",
+    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
     "colab": {
-      "provenance": []
+     "base_uri": "https://localhost:8080/"
     },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    },
-    "language_info": {
-      "name": "python"
+    "id": "mH0PZqloHtgz",
+    "outputId": "febaf18d-69cf-424b-cb41-1e0004ac58bc"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.9666666666666667\n"
+     ]
     }
+   ],
+   "source": [
+    "# Use the Model\n",
+    "clf=KNN(k=3)\n",
+    "clf.fit(x_train,y_train)\n",
+    "predictions=clf.predict(x_test)\n",
+    "print(accuracy(predictions,y_test))"
+   ]
   },
-  "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "ZngIov7TOyqG"
-      },
-      "outputs": [],
-      "source": [
-        "import numpy as np\n",
-        "from collections import Counter\n",
-        "# Counter class is imported from the collections module. It is used later\n",
-        "# to count occurrences of labels in the nearest neighbors.\n",
-        "\n",
-        "def euclidean_distance(x1,x2):\n",
-        "    return np.sqrt(np.sum((x1-x2)**2))\n",
-        "\n",
-        "class KNN(object):\n",
-        "    def __init__(self,k):\n",
-        "        self.k=k\n",
-        "    def fit(self,x_train,y_train):\n",
-        "        self.x_train=x_train\n",
-        "        self.y_train=y_train\n",
-        "    def predict(self,x_test):\n",
-        "        predictions=[self._helper(x) for x in x_test]\n",
-        "        return np.array(predictions)\n",
-        "    def _helper(self,x):\n",
-        "        prediction=[euclidean_distance(x,x1) for x1 in self.x_train]\n",
-        "        indices= np.argsort(prediction)[:self.k]\n",
-        "        labels= [self.y_train[i] for i in indices]\n",
-        "        c=Counter(labels).most_common()\n",
-        "        return c[0][0]\n",
-        "        \n",
-        "def accuracy(predictions,y_test):\n",
-        "    return np.sum(predictions==y_test)/len(y_test)\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "from sklearn import datasets\n",
-        "from sklearn.model_selection import train_test_split\n",
-        "import matplotlib.pyplot as plt\n",
-        "from matplotlib.colors import ListedColormap as lcm\n",
-        "colormap=lcm(['red','blue','yellow'])\n",
-        "\n",
-        "iris = datasets.load_iris()\n",
-        "x,y = iris.data,iris.target\n",
-        "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2)"
-      ],
-      "metadata": {
-        "id": "f_uJHTmzO1lO"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "# Use the Model\n",
-        "clf=KNN(k=3)\n",
-        "clf.fit(x_train,y_train)\n",
-        "predictions=clf.predict(x_test)\n",
-        "print(accuracy(predictions,y_test))"
-      ],
-      "metadata": {
-        "id": "mH0PZqloHtgz",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "febaf18d-69cf-424b-cb41-1e0004ac58bc"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "0.9666666666666667\n"
-          ]
-        }
-      ]
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 472
     },
+    "id": "zngx3d7FKlJA",
+    "outputId": "cf5a8567-f6e1-42d2-816d-9558e49a27b7"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "source": [
-        "# Plotting the data\n",
-        "plt.scatter(x[:, 0], x[:, 1], c=y, cmap=colormap)\n",
-        "plt.xlabel('Sepal Length')\n",
-        "plt.ylabel('Sepal Width')\n",
-        "plt.title('Iris Dataset')\n",
-        "plt.show()"
-      ],
-      "metadata": {
-        "id": "zngx3d7FKlJA",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 472
-        },
-        "outputId": "cf5a8567-f6e1-42d2-816d-9558e49a27b7"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 640x480 with 1 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {}
-        }
+     "data": {
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ts8w/06yZGsEUGem38IiISOFJxkTOGDAAOHcuN7kBABF12bkTGD3af7EREZHLmOBQ0bV9O5CUpF3Y0mQC5sxRh7CIiKhQYYJDRVdSUsHlEPLKygJ27PBJOERE5DlMcKjoMhjUoSh7jJzwm4iosGGCQ0VXly7224SHAy1aeD8WIiLyKCY4VHTFxgK9e1sXtMxLpwNGjgTCwnwbFxERuY0JDhVts2YBzZur65ZEx3JI6p57gFdf9UtYRETkHp5cQEVb6dLAunXAokXA//6nhozXrAkMG6YOYdk7CZmIiAISExyikBDgvvvUhYiIggIPUREREVHQYYJDREREQYcJDhEREQUdJjhE5Lj0dFVpvVEjVYS0Vi1ViZ3lLIgowARMgvPWW29Bp9Phqaeestnuhx9+QL169VC8eHE0atQIixcv9k2AREVdcrKqvJ6YCOzdC6SkAEeOABMnAk2aqMrsREQBIiASnM2bN2Pq1Klo3LixzXYbNmzAgAED8Mgjj2D79u3o06cP+vTpgz179vgoUqIibPRoYNeu3GrrFmazGl4/YID/YiMiysfvCU5aWhoGDhyIadOmoUyZMjbbfvjhh+jRowfGjBmD+vXr47XXXkNcXBw+/vhjH0VLVERdvqwqq5tMBd+fna2Kl27f7tu4iIg0+D3BGTFiBHr16oWuXbvabbtx48ab2nXv3h0bN27UXCcjIwMpKSlWFyJy0o4dqrK6LTodYOOzSETkS36d6G/u3LnYtm0bNm/e7FD7s2fPomLFilbLKlasiLNnz2quM2nSJLzyyituxUlU5GnV68pLhJXXiShg+G0PzokTJ5CYmIivv/4axYsX91o/48ePR3Jycs7lxIkTXuuLKGi1aKEqq9ui0zlWoZ2IyAf8luBs3boV58+fR1xcHIxGI4xGI1avXo2PPvoIRqMRpgKO9VeqVAnnzp2zWnbu3DlUqlRJs5/Q0FBERERYXYjISWFhqrK6Vm0ugwG46y5VoZ2IKAD4LcHp0qULdu/ejR07duRcWrRogYEDB2LHjh0wFLBLvHXr1lixYoXVsmXLlqF169a+Cpuo6Hr1VVVhHcg9FGX5nMbFAbNn+ycuIqIC+O2AealSpdCwYUOrZWFhYShXrlzO8kGDBqFKlSqYNGkSACAxMREdOnTAe++9h169emHu3LnYsmULvvjiC5/HT1TkhIQA8+YBK1YA06erOXAqVgQGDQJ691b3ExEFiIA+I/D48ePQ63N3MrVp0wbffPMNXnjhBTz//POoU6cOFi5ceFOiREReotMBXbuqCxFRANOJ5J2xK/ilpKQgMjISycnJPB+HiIiokHD299vv8+AQEREReRoTHCIiIgo6THCIfOHYMaBnT6BYMXUeS2ioOjH39Gl/R0ZEFJSY4BB527ZtQO3awJIlueUOMjOBn34CatYEDhzwb3xEREGICQ6Rt8XHq2KUBcnM5Oy/RERewASHyJuWLVOVuG05dQpwsB4bERE5hgkOkTfNn+/ZdkRE5BAmOETe5Gh1bc4CTETkUUxwiLzpkUccazd0qHfjICIqYpjgEHlT06ZqpJQtDRvab0NERE5hgkPkbRs2AJGRBd9Xvjywdq1v4yEiKgKY4BB5W6VKwPnzwPjxQHQ0EBYGVKkCTJwInDkDlC7t7wiJiIIOi20SERFRwGOxTSIiIirymOAQERFR0GGCQ0REREGHCQ4FnyFDcqt263RAqVLA5Mn+jiowbd4MDByoRnOVLasqnK9c6e+oiMijrgH4EEBDAJEAYgG8AcBOGRmnXAfwGYAmN/qoCWAigPMe7MM5PMmYgkvVqqq2U0G6d1cVvUmZMQMYNgwwGHKLgRqN6vobbwDPP+/f+IjIA64A6ABgz43blp98PYAqANYBqOZmH2kAugL4M18fBgDlAawFUMfNPniSMRVlAwdqJzcAsHQp8NNPvosnkB04ADz6KCBiXenccn3CBGDNGv/ERkQeNArAPqikI+/+DDOAMwAe9EAfYwFsKaAPE4BLAO7Pt9w3mOBQ8Pj+e/ttRo70fhyFwaefAnobH3+jEZgyxXfxEJEXnAcwFyrRKEg21N6VPRr3OyIFwAw7fewEsMmNPlzDBIeCR949EVpOn/Z+HIXBunW2n6/sbM6wTFTobYdKMOzZ6EYfu6HOv7FF72YfrmGCQ0WLTufvCAKDwWC/jaOV0IkoQDnwOQcAuPNZd7QPR9t5DhMcCh4lSthvU7u29+MoDHr2tH+IqkcP38VDRF7QEoC970UdgM5u9NEMQGk7bcwA4t3owzVMcCh4jB5tv83s2d6PozAYPhwIDdXeoyUCJCb6NiYi8rBSAJ6E9k+9AUBfANXd6CMUwFNQiZJWH90A1HejD9cwwaHg8cYbwB13aN//zDPA7bf7Lp5AVrky8PPPaq9X3j05BoPae/P110CjRv6Lj4g85E0Ad964bsj39zYA0z3QxwQA/W9ctxzusnyvNATwjQf6cB7nwaHg87//Ac89B1y4oPZQxMaqOV/atvV3ZIHn7Flg+nTg998Bsxlo107t3alRw9+REZHHmAEsB/AlgKMAogEMAnA33Dv/Ji8BsBIqYToMoALUEPR7ARTzSA/O/n4zwSEiIqKAx4n+iIiIqMhjgkNERERBhwkOERERBR0mOBR8li4FEhJUdewKFYChQ4EdOzzbx8qVqvJ22bKqEvd//qMqc2u5elVVNK9TB4iMBBo0AN5/H0hN9WxcREQEgCcZ+zsc8rTnnwcmTVLDnU03aqMYjWpelzlzgAcecL+PN99UxSgtlbctfZhMakTSww9btz99Wg1fP3ZMjVQCcuefqVdPFbUsX979uIiIghhHUdnBBCeILV4M9Oqlfb/RCPzzDxAT43ofa9cC7dtr36/XA/v2AbfckrusWze1x6eg2k8GA9CnDzBvnusxEREVARxFRUXXf/9ru8aSCPDFF+718dFHtms06fXAZ5/l3j54EFi2TLuwpckELFgAnDzpXlxERGSFCQ4Fj40bcw9LFcRkAtavd6+PtWvtV+Fesyb39qZN9h/TbAa2bHEvLiIissIEh4KHreKRFo5U0XZ3/ZAQ5/tj5W4iIo9igkPBo2dP+4ePund3r4+EBPt95K3C3bGj/SQnNJRlJIiIPIwJDgWP0aO1D1Hp9UB4uBoy7o5Ro9S5PAXR6VSyMnx47rLoaGDAAO0kR68HHnkEKFPGvbiIiMgKExwKHi1bAl9+qZKGvAmFXg+EhalRVuXKuddHo0aq0rbReHMfJUqoCt2VK1uv89lnuXtoLOtY/nbrBrz3nnsxERHRTThMnILPoUPA558DGzao82ESEtTcNBUqeK6Po0fViKw1a1RyEx8PPPooUKlSwe1NJuCXX4DZs4FTp4Bq1dTepB49HDt3iIioiOM8OHYwwSEiIip8OA8OERERFXlMcIiIiCjoMMEhIiKioMMEJ1gdOwY88wxQpQpQujTQqpUqNmlrpl9vE1GjjOLj1bDoqCjgsceAvXu11zl3DnjpJaBGDVWFu1kzYOpUICPDZ2ETUbBYAeAuAGUBlAfwIICtfo2IvIcnGQejTZuArl2Bf//NTWj0elUSoHdvVdjR1zPnigD/93/AJ5/cXOkbAH78Ebj7but1DhwA2rUDLl/ObW+pwt26NfD772r4NxGRXa8AmAjACMBSbsUIwARgFoBBfomKHMeTjIu6zEyVKKSnW++tMZvV359+At5/3/dxff+9Sm4A67iys9Xt/v2BCxdyl4sAfftaJzeW5SIqiZswwTexE1EhtwIquQFykxvLdQHwMIDDPo6JvI0JTrCZPx84fz43oclPBPjwQ98fqvrvf7XnexFRidnMmbnL1q5Vh6604jSZgGnTgLQ0j4dKRMHmQ6i9NbZ87otAyIeY4ASbpCTrYo8FOX0aOHPGN/EAKoH580/tpMvSZsOG3NtJSfZrOKWnA/v2eSZGIgpi62G95yY/E4C1PoqFfIUJTrAxGLRrJeXl63Nw7CUrOp11TIG6HURUCNn5/gEA2PnHkAodJjjBJj5endeiRacD6tUDKlb0XUw6nTrp2VaSI6Jit4iPt73HB1B1pRo29EyMRBTEEmD7EJUeQHcfxUK+wgQn2HTrBtSvr71nQwQYOzZ3NJKvPPus9vk0BgNQtiwwcGDussaNgU6dtJMinQ54+mmgWDHPx0pEQSYRgNY/THoAJQA86rtwyCeY4AQbvR749Vc1/w2Qm8hYEp7nngMGD/Z9XJ07A1Om3HwoSqdT89ssXQqEh1uv8913QIMG6rrlBGXLug89pBI1IiK7mgH4CupQVd5/mvQAigP4BYAP92qTT3AenGB17Rrw7bdqeHZKijqU8/jjQIsW/o3rr79Upe9Nm4DixdWQ9iFD1MR/BcnMVHPkzJkDXLwI1KkDDBsGdOjg+71QRFTIHQEwFcAaqENW3QEMA5ObwoHVxO0oMgkOERFREOFEf0RERFTkMcEhIiKioMMEh4iIiIIOExwKbHv2AO3bq9FTOh1QsqQaTp6Sor3OG28AFSqokVd6PVC7thqRpeXSJeC114BatdSIrkaN1Iiv9HTPb48z1q8H+vVT8/2UKwfcf79aRkREdvk1wfnss8/QuHFjREREICIiAq1bt8Zvv/2m2X7WrFnQ6XRWl+LFi/swYvKp335T8+GsXZs7h86//wLffANUrapqbuXXpg3wwgtqxJWlMOfhw8ADDwCjRt3c/uhRoEkTYOJE4MgRlTjt3QskJqpK5snJ3txCbR99BNxxB7BggSo4evkysHChWvbRR/6JiYioEPFrglO1alW89dZb2Lp1K7Zs2YLOnTujd+/e2Lt3r+Y6EREROHPmTM7l2LFjPoyYfMZsBu65R7tcQ2oq0D3fzKOvvAJs3Kj9mFOmqOHpeQ0YAJw7Zz1rsiUx2rkTGD3atfjdsW2bSrAA61mpLdcTE1UbIiLS5NcE56677kJCQgLq1KmDunXr4o033kB4eDiSkpI019HpdKhUqVLOpaIvSw6Q73zyCZCRYbvNjh3A2bO5tx3Zs/Hss7nXt29XRT21SluYTGr+nUuX7D+uJ02ZYrvGltEIfPyx7+IhIiqEAuYcHJPJhLlz5+LatWto3bq1Zru0tDRUr14dMTExdvf2AEBGRgZSUlKsLlQI2DhUqdnu8mX77ffsyb2elGR/ssCsLJVI+dK6dbbriWVnA2vW+C4eIqJCyO8Jzu7duxEeHo7Q0FA8/vjjWLBgARpYpufP55ZbbsGMGTOwaNEizJkzB2azGW3atMHJkyc1H3/SpEmIjIzMucTExHhrU8iT7FUft3C2FpU+z1s+UCuWO7LtIax8TERki99nMs7MzMTx48eRnJyMefPmYfr06Vi9erVmkpNXVlYW6tevjwEDBuC1114rsE1GRgYy8hzqSElJQUxMDGcyDnTz5qlRQ7bodOqkYEsNq2rVgBMnbK/Tqxfwyy/q+uHDaoSVLeHh6jBYWJhjcXvC00+rw222ipOOGgW8/77vYiIi8jOflGq4evUq/vzzT5w/fx5ms3WF1kGDBjn7cFa6du2K2NhYTJ061aH2999/P4xGI7799luH2rNUQyFSpgxw9ar2/fHxwO+/597+8ktVp8qWv/9W9aws+vRRCU9ByYROpwp6TprkTNTuO3xYVYTPzr55D5NOp/be7NsHxMb6Ni4iIj9y9vfb6X3vP//8MwYOHIi0tDRERERAl+ccBp1O53aCYzabrfa42GIymbB7924kJCS41ScFqA0bgGbNCj7ZuFat3D0xFo88ouaJmTmz4Mf79FPr5AYAZs1So7H+/FPtGTGZ1CGp7Gw1iuvVVz2yKU6JjVUFRu+7T8VjSb4MBnWZN4/JDRGRHU7vwalbty4SEhLw5ptvomTJkm51Pn78ePTs2RPVqlVDamoqvvnmG0yePBlLly5FfHw8Bg0ahCpVqmDSjf+gX331VbRq1Qq1a9fG1atX8c4772DhwoXYunWrQ4e0AO7BKXTS0oDx49VEfenpagK/xER1iEavcQrZihVqz8uBAyohaN1aHc6pX7/g9llZwKJFwP/+p4aM16yp9gR16eLfiuUnTwLTpgF//KFud+oEPPaYmgOIiKiI8fohqrCwMOzevRu1atVyOUiLRx55BCtWrMCZM2cQGRmJxo0bY+zYsYiPjwcAdOzYETVq1MCsWbMAAKNHj8b8+fNx9uxZlClTBs2bN8frr7+OZs2aOdwnExwiIqLCx+sJzr333osHHngA/fr1czlIf2KCQ0REVPh45Rycn376Ked6r169MGbMGOzbtw+NGjVCSL7hqnfffbeTIRMRERF5lkN7cPRa5zrkfzCdDiatoa0BgntwiIiICh9nf78dylzMZrNDl0BPboqUY8eAZ54BqlQBSpcGWrVSZQc8+Rpt2aJO4LVU+g4LA4YO9WwVbleqiS9dCiQkAGXLqpOShw61PRuxL6qJHzsG9OypJibU6YDQUKB3b+D0ac/1ESQyMoCpU9UAushIoEYN4KWX1PnfnpMFYAaAFgAiAcQAGAfglI11/gYwEkAlAKUBdAQwD4BfpxIjIi3ipNmzZ8v169dvWp6RkSGzZ8929uF8Ljk5WQBIcnKyv0PxnqQkkfBwEYPBUjZSRK9Xf3v3FsnKcr+P774T0elyHz/vpWxZEU88v4sXa/dRqpTIuXM3rzN+vLo/77Ybjer2t9/e3P7IEZEqVXKfH0D1qdOJxMWJXL3q/nZs3apiKGg7ihUT+esv9/sIEmlpIm3a5L4ElqfJYBCpUMFTT9V1EekqIhAR/Y2/EBGDiJQRkR0FrLNMRIqLiDFfe4jIYBExeSIwIrLB2d9vp08yNhgMOHPmDKKioqyWX7p0CVFRUQG/FyfoD1FlZgIxMcDFi9YVsi10OuCtt4DnnnOvj7Aw2/WS7rgDWLvW9T7MZrW3xtacSE2bqoKZFosXq5mKtRiNwD//qOfHonVrtSeqoG0xGIBBg4AZM5wO30q5crbrZFWpooaEE556StURLehrxGAAbrlF7dRzb/T+iwDeBFDA5wMGAFUBHL5xHQBSAFQBkK6xDgBMA2BnkkkicotXDlHlJSJWk/tZnDx5EpGRkc4+HHna/PnA+fMFJzeA+of4ww/dO1T15pu2kxtATbiXluZ6H65UE//vf23XcRIBvvgi97YvqokvW2a/COipU8Dmza73ESTS0tS0P1pvTZNJTeDsTt4MZAL4BNqJignAMQBL8yybA+CajXV0AD5wJygi8gKHE5xmzZohLi4OOp0OXbp0QVxcXM6lSZMmaNeuHbp27erNWMkRSUn2CzGePg2cOeN6HytW2G8jAqxa5XofrlQT37jRduJmMqnEy8IX1cTnz/dsuyC2f7/9054MBvUyu+4fAFfstDEC2JDndhJsf1UKgH1Qe3iIKFA4XKqhT58+AIAdO3age/fuCLcUOARQrFgx1KhRA3379vV4gOQkX1TIdrTSd2io9/vIW03ckdF+eR/XF8+Vo+uyOrhDL7mIu8XdHXxfWX01GqD20njqsYnIFxz+qnj55ZcBADVq1ED//v1RvHhxrwVFboiPt11lWqdTJzJUrOh6H/37A6tX225jMAAdOrjex+DBN9eayk+nUyORLHr2VDWctA456fWq7pRFly724wgPB1q0sN9OyyOPqJNK7Bk61PU+gkTDhup0JVtHBM1m9RZ3XSzUiClbVeezAeTtJB7ALBvtDQBaAnAjoScij3P6HJzBgwczuQlk3bqpmkta/+aKqDpN7pylOXy4OgHYlj59rPeuOOu++9Twdlu6dlUJiMXo0dqHqPR61TZvIhEbqxIkrV0HOh0wcqQ6odpVTZuq2la2NGxov00RUKwY8PTT2m9No1GV42rc2J1e9ADG2LjfCDV0vE2eZX2hTjLW2kNjAjDWnaCIyAscSnDKlCmDsmXLOnQhP9PrgV9/VSNzgNxfC0vC89xzau+Iu32sXq19WOXWW4Hvv3evD0BVE9c6zFVQNfGWLYEvv1Tx5U1a9HqVpCxerHYR5DVrFtC8ubpuWcfyXHmqmviGDWpCl4KUL+/uWbNBZexY4KGH1HXLy2A58li/vqq56r6RAJ64cd3yj4Dlq7AmgIWwPiQVCnXScfkby3X51p0EgDO4EwUah4aJz549O+f6pUuX8Prrr6N79+5o3bo1AGDjxo1YunQpXnzxRYwePdp70XpA0A8Tt7h2Dfj2W5VopKSovQSPP+7e4Zb8Ll9Wv0gLFgDXrwOVKgFjxqg9PJ7iSjXxQ4eAzz9XiUVIiJr07+GH1boF8UU18cxMYOJElVClpKi9U48+CkyY4O5JJUFHROXP06cDBw+qHHDgQKBvX/dO67rZBqjh3fuhJu4bAKAfgBIa7VMAfAVgPtQJxXEAhgNwa5cSETnI68U2+/bti06dOmHkyJFWyz/++GMsX74cCxcudCpgXysyCQ4REVEQ8fo8OEuXLkWPHj1uWt6jRw8sX77c2YcjIiIi8jinE5xy5cph0aJFNy1ftGgRyuU/v4GIiIjID5w++P/KK69g2LBhWLVqFVq2bAkA2LRpE5YsWYJp06Z5PEAiIiIiZzm9B2fIkCFYv349IiIiMH/+fMyfPx8RERFYt24dhgwZ4oUQqUg7d06Vkq5RQ41EatZMlZq2VcbB2WriRA44cuRvrF49EufPV0Jycmns2NERGzfOg5OnMQaATQBuR+4EhuFQdbSu+zMoIo9z+iTjwo4nGRciBw4A7dqp0VqW+W0so5patwZ+//3mOWqefx6YNEkN+basYzSqoTlz5gAPPOC7+ClobNu2HA0a3AWDIRshIWoiyexsA4xGE9auHYy2bWdA78hM2n73NYAHNe4rD+AIVMJDFHi8MooqJSUl58FSUlJstg30pIEJTiEhAjRqBPz1l3Zp6ZEjVYFNC1eqiRPZkZKSAp2uCkqWTIfBcHPBTRFg3bppaNcu0KuJX4dKXmwV2u0E4A/fhEPkJK+MoipTpgzOnz8PAChdujTKlClz08WynMgj1q4F9u61XVp62jTriuXOVhMncsD27XMQFnatwOQGAMxmHaKjC0M18VdgO7kBgFVg0VAKFg6dZPzHH3/kzFL8xx9/QOepyc+ItCQlWR9mKkh6OrBvH3D77eq2s9XEiRxgMCTBbNZDry/4vWUwCGrX3of09HSUtFfCxK9WOtBGAGwE4ECdNqIA51CC06FDBxw5cgQ1a9ZEx44dvRwSEVyr9O1sNXEiB4gYIGL/nzpDwL+3HI2PRUMpODh8VlxsbCxq1qyJhx9+GHPmzMHJkye9GRcVdfHxqnS0LeXKqRIUFj172i57kL+aOJED9Pr4nBOLC5KdbcCuXW0Q6tk6El5wvwNtjABaeTsQIp9wOMH5448/MHjwYPzzzz949NFHUb16ddSpUwfDhw/H3Llzce7cOW/GSUVN48aqdLStSt9PP21dsdzZauJEDmjRoi/OnKmC7OyC34tGown//lsYqomPAlDcTpv74cL0aEQByaVh4tevX8eGDRuwatUqrFq1Cn/++SeysrJQr1497N271xtxegxHURUiFy6ogpe7d6sExWxWe2iys4FBg4AZM25OgGbOVIUydbrcZMdSTfy334C2bX2/HVToHTq0FxERXVC+vBpsodcLsrKMCAnJxqpVk9Cx4zg/R+ioTQDuAFDQHqkmALbBhenRiHzC68U288rMzMT69evx22+/YerUqUhLS4PJ1kmeAYAJTiGTmQn8+KOaw+biRaBOHZXAdOigXenb2WriRA5ISUnB9u1foVSp+QgJScfly3GIjh6OunULWzXxywCeBfAT1NDxaABjoSb7IwpcXk1wMjMzkZSUhJUrV2LVqlXYtGkTYmJi0L59e7Rv3x4dOnRAtWrV3NoAb2OCQ0REVPg4+/vt8MHWzp07Y9OmTahZsyY6dOiA4cOH45tvvkF0dLRbARMRERF5msMJztq1axEdHY3OnTujY8eO6NChA6uHExERUUBy+Gyyq1ev4osvvkDJkiUxefJkVK5cGY0aNcLIkSMxb948XLhwwZtxEhERETnM4QQnLCwMPXr0wFtvvYVNmzbh4sWLePvtt1GyZEm8/fbbqFq1KhrmnZOEtB06BIwaBURHA6VLA+3bA99/b3/eF2ccOwY88wxQpYrqo1UrdaKu1kngZrNqX7q0OnnXYFBDtZcu9VxMrnClmjgFlB071Oj8ChVUgfeEBGDJEn9H5X0mkwnr18/B7t1tkJxcGmfOVMGqVc/g5MmjNtY6AmA0gMoASgNoA+Ab2C+x4E0CYCGAzgDKAKgI4HEA+22scwbACwCqA4gEEAdgGoDMgnsQVUquRw+gTBkgKkqNC9i1y2MbccMKAHcBKAtVXPRBAFs93QkFCnGRyWSSpKQkmTRpknTr1k1Kliwper3e1YfzmeTkZAEgycnJ/glg5UqREiVEDAYR9bkW0evV3wEDREwm9/tIShIJDy+4j969RbKyrNtnZYnExua2zX+ZPNn9mFzx118iFSpYb4dOpy5t2oikpfknLnLYt9+ql89ozH0JLS/n+PH+js57srKyJCmpt4hAsrP1IgIRgWRlGSQ1NVx2795YwFrrRSRMRAw57UUs6/YVkWyfxG7NLCKP3Yghb1xGEQkRkcUFrLNXRMrla6+78be9iKRb92AWefZZ6/cGoN4zRqPIvHme2paJeWLPux06EZntqU7Ii5z9/XY4wTGZTLJp0yaZPHmy9OjRQ0qVKiV6vV5iYmJk0KBBMnPmTDl69KjLgfuKXxOctDSRyMjcZKOgy6efutdHRoZIVJR2HzrdzQnLoEHa8Vgup065F5ezzGaRW2+1/sbLezEYRBITfRsTOeX4cZGQENtvq19/9XeU3rFy5WQxmSw/6taXrCyDnDsXJRkZGXnW+FdEyktuQpP/ohOR9324BRZfacRjiamkiFzK094sInXFOrnJe9GLyLNWPSxcqP3+0OnUe8j9r5/lNrbDkrwdcrcT8jKvJTiWhKZy5coycOBAmT59uhw6VPjeEH5NcL74wva3vU4nUqeO+nF31bff2k9WKlcWyc7z32CxYvbXeeAB97ffGatX24+pZEmR1FTfxkUOmzBBOz+15Kjx8f6O0vOys7PlzJnKYjbb+kGFrF8/N89a/7PZVl2qiYgH9vA6JU60k66CEi97iQREpJSIXMtZo0MH2+8TvV7klVfc3Y67xHrPTUEJzrOaa1NgcPb32+FRVO+88w46deqEunXreudYWVGQlJQ7E29BRICDB4GUFHW+iat9hIQAWVnabU6fBs6cAapWBS5fVpPp2bNpk2vxuMqVauIUUDZssF/cfeNG38XjK+fPn0V09GmbbTIzQ5CVtQFA/xtLkgCEALDxucVxABegzoHxBRPUzMa26KCqj4++cTsJanCudu0uIBXAAQDNAKivFlvvE7NZvZfcs95OTCYAa93thAKMwwnO8OHDvRlH0eBotWF3qhI7W4XbVnFKT8XkCleqiVNAceQtE/AFuF3gaFVx6/8vHX0ifPl+10GNQ7E3+CH/djgyd2zuOno7Q110Ok98zB15fkPc7YQCDIuO+FJ8vPbeG0B90m+7TRWF9FYfOh1Qrx5Q8cZ/gRERQKlS9h+3WzfXY3KFK9XEKaB07277x8toDM7i7hUqVMThw/VgNmuUEgFQrFgWwsPj8yyJh+29N3oAjaBG//iKHmrklK3kwAyga57b8bA/4qsigPo5t7p1s5/AxMfbvt++BNhODvUAgvDNWMQxwfGlPn2A6tW1/201m4GxblYl7tYNqF9f+xtDRPWRt47T//2f7cfU64FJk9yLy1muVBOngPLwwypX10pyTCb1EgYbnU6HM2fGQq8veE9GVpYRhw/XQ1xc3n8aEgDUhvaPsBmqXpR20uQdz0I7YTEAiALwQJ5lzaGKedpKJp6xuv/ZZ7UPUen16mj94MGOR1ywRGjvidIDKAHgUXc7oQDDBMeXQkLUBCBRUeoH2pJkWJKRV18F+vZ1rw+9Hvj1VzX/DXBzH889d/O3xRtvqAkoCqLTAfPnqz09vvbdd0CDBuq65VfSsh0PPeR+MkheVbasmtskLMw6yTEY1O0vvwRatvRffN7Utu1grFr1HACV0ACAyaSDCHDuXBUUK7YYeqvMzwDgNwCVoJIYSyJjSQQmAPiPL0LPpzuA9/PFAqj4SgP4HUDJfOvMA2A5V9OyjZZ1H4ZKcHK1baumttLrrf+f0evVzuUlS9T0XO5pBuArqOc57z9NegDFAfwC353bRL7iVjXxwiggim2mpqpJ9378Ebh2DWjaFBg+XP31lGvXgG+/VRMIpqSoQzmPPw60aKG9zo8/AhMnAkePqkSia1fgvfcAfxZQdaWaOAWUixeBGTNU3p2VBbRpo96KtWv7OzLv27dvCy5e/Bxly+5BRkYE0tP7IS5uAMLCwjTWSIOa2O8HqJNxGwF4AmqiPH/aB+BzAJuhEpq7AQyGSnIKkgG1Dd9AVS+vA7WHpB209kL9/bdKdDZsAEJDgTvvVBNEerYi0BEAUwGsgUq6ukNVUWdyUxh4pZr4Tz/95HAAd999t8Nt/SEgEhwiIiJyileqiffp08ehznU6HUy2xvsRERER+YBDCY7ZkzWSiIiIiLyMJxkTERFR0HEpwbl27RoWL16Mzz//HB999JHVhQKEs9XEXXHqFDB+PBATo8ZytmihzibVmkXZbAZefFGdNajTqWES9esDixZ5LiaigGECMAeqInhpAFWgRhAd9V9IALZvP4GvvnoOJ09WxdWrpbFly+2YMWMWrl+3NdOvcy5c2IFjx+5AdrYRIjpcv14Sf/89CJmZaR7rg8gep0dRbd++HQkJCUhPT8e1a9dQtmxZXLx4ESVLlkRUVBT++ecfb8XqEUXiJONNm9QIqH//zU1o9HqVYPTuDcyb5/7UoDt3qnlqUlJu7qNrV+CXX9RQCAuzWY0S27274Md7/nk1XJ0oKGQDuA/AIljPBmyAmnNlGYBWPo9q8eJtaNOmM8LD02A0qs+tyaSHwWDG8uU90arVQoSHuze31LFjPyEmpg90OskZ6Gj5lbl2LRIGwz8oUcKXExZSsHD299vpPTijR4/GXXfdhStXrqBEiRJISkrCsWPH0Lx5c7z77rsuBU0elJkJ3H23qtOUd2+N5Tyqn34C3n+/4HUdZTKpSQvzJjd5+/jjD+D1163XGT1aO7kBgDffBPbvdy8uooDxPgDL6NO85zCaAPwLoDcAB2rAedD169lo1Ki3VXIDAAaDiq9Tp6X44Ye33OpDxIzo6Puskhsgd9qvsLBkXLjAGYPJN5xOcHbs2IFnnnkGer0eBoMBGRkZiImJwdtvv43nn3/eGzGSM+bPB86f1y5zIAJ8+KF7h6qWLFFz5Wg9htkMfPKJdRHPGTPsP+4zz9hvQxTwTAA+hHZNJhOA8wAW+CwiAPjhh18QE3PSKrnJy2Aw4847pyA93VbJCNsOHXoPxYplaU5RpdMBMTFbkJ5+0eU+iBzldIITEhKSMwNnVFQUjh8/DgCIjIzEiRMnPBsdOc9STdwWSzVxd/qwd4jryhXg8GF13WwG0hw49r59u+sxEQWMswBsVxNXhR3dLpHtFJEkZGba/m6oUOEiduw45nIfBsPvdmvk6nTAuXPLXO6DyFFOn4jRrFkzbN68GXXq1EGHDh3w0ksv4eLFi/jqq6/QkIUP/c8XVbgdLQHtbB/BWFqaiqBArAwOiBig09n/bggJcScux7Zdp2MNOfI+p/fgvPnmm4iOjgYAvPHGGyhTpgyeeOIJXLhwAV988YXHAyQnOVtN3Bt9AGpkVa1a6rpeD5Qvb/9xO3RwPSaigFERQD3YLoyZBVV523fCwuIREqL9uTWbdThypCaaNXO9NItO96DdCiomkx5Vq/ZyuQ8iRzmd4LRo0QKdOnUCoA5RLVmyBCkpKdi6dSuaNGni8QDJSa5UE3dWmzZqSLitPTRjxljvkRk3zvZj6nSq7hVRoaeDqvyttbfECJUAddO43zvuvbcDdu5sklP8Mz+9XvDHH8/BaHR9erSaNR9EWlopzZ3IIsDRoz1gNBZ3uQ8iR7n8Tj5//jzWrl2LtWvX4sKFC56MidzhSjVxZ+l0wMKF1nto8vbxxBPAyJHW6zzzDHDffdqP97//AZUquRcXUcAYDOC5G9ctCYXln4oqABbD1/Os6vU6mM0/49ix6hBRe1KA3GrnX389CkOHDne7n+vXNyAzUx2CsiQ6lr/nztVBzZqc94p8w+l5cFJTU/Hkk09i7ty5OXWnDAYD+vfvj08++QSRkZFeCdRTisQ8OIBr1cSd9e+/6vG//Ra4elXtOXr0UbWHR8vixcCECcChQyoh6tAB+OADoGZNz8VFFDC2QFXh3gMgAkA/AAMAaFUT977Ll//FwoVzUb36dyhV6iqOH6+PkJDh6N3bc/PyZGSk4NixsYiK+hHFiqUjLS0KKSmjERs7AjodJ9An13ilmnhe/fv3x/bt2zFlyhS0bt0aALBx40YkJiaiadOmmDt3rmuR+0iRSXCIiIiCiNcTnLCwMCxduhR33HGH1fK1a9eiR48euHbtmnMR+xgTHCIiosLH6zMZlytXrsDDUJGRkShTpoyzD0dERETkcU4nOC+88AKefvppnD17NmfZ2bNnMWbMGLz44oseDY6IiIjIFU4nOJ999hmSkpJQrVo11K5dG7Vr10a1atWwYcMGTJ06FXFxcTmXIiE7G5g1C7j9dlW1u2pVNVLJ1qzOhw4Bo0YB0dFqnfbt1cm6WuUVAtWWLUDr1upkYVVoBhg6VNXBKogI8PPPah6dMmWAqCjgsceAvXu1+zh3DnjpJaBGDVWxvFkzYOpUICPDK5vkLVevApMnA3XqqM1o0ECVBEtN9Vwfp0+rWqqhoerlKFYM6NFDFZbXsnkzMHCgmqaobFm1/sqVnosJAA4c2IG1a4fi4sUKuHKlLDZvTsCWLUtsrJEBYCqAZgAiAdQA8BKAc5prHDq0D2vWPIYLF6Jw9WoZbN3aFZs2/QQnj8AHgO8AREGNuNJBTZwXD1W/6mZms/rqaN9efZVER6uvlkOHtHs4ffoEVq16DmfOVEVycmns3Xs71q2bhWzNua0EwEIAnQGUgZrn53EA/q4ddwHAKwBqQr1PGgP4BFrPlbICwF0AygIoD+BBAFu9G6ZXbALwAIByUNtyD4DVNtpfgyof0hDquYoF8AaAy94N09/ESRMnTnT4Ys+nn34qjRo1klKlSkmpUqWkVatWsnjxYpvrfP/993LLLbdIaGioNGzYUH799Ven4k9OThYAkpyc7NR6BcrIEOnZUwQQ0evVX0DEYBCJjBTZuvXmdVauFClRQrWxtLesO2CAiMnkfly+8N13Ijpd7jbkvZQtK5L/+TWbRUaMyH1+LG2NRnVZtOjmPv76S6RCBev2Op26tGkjkpbmm21106lTIjVrWr9FLJtRv77IhQvu9/HXXyLFihX8chiNBb8Vv/xSxWA0WrcFRN54w/2YRETWr/9WsrIMkplpFBGICCQz0yAikJUrxxewRpqItBER3Y0LblwMIlJBRP66aY1Nm36SzEyjVR9ZWaqPVatGiNls9szGeN0Yyd3e/JfiIpJu1dpkUl8ZBX39lCihvmry27dvq1y9Gpnz/IhAsrP1IgL588+ekpGRkW8Ns4g8JrmvgSUeo4iEiIjt72vvOSQilfLFZHnP3CYiKQWsM1FyY8+7HToRme39kD3mc1Ex598OiMjbBbS/LCKN5ObPlF5EYkTkmPdD9hBnf7+dTnA86aeffpJff/1V/v77bzlw4IA8//zzEhISInv27Cmw/fr168VgMMjbb78t+/btkxdeeEFCQkJk9+7dDvfp0QTnlVesv1nyXgwGkSpVRLKyctunpanER2sdQOTTT92Py9syMqx/FQu63HGH9Tpz52q31elEihcXOX8+t73ZLHLrrdbJTf7nNzHRp5vtqvh47afLYBDp29f9PqpUsf1ylC1r3f6vv2y/DQGR1avdi+nUqeOSmRkiJpPWjzbkzz/z/4OSKNY/WnkvBhFpIOpHV7lw4bykpxcXk0mnsQ5k/fq57m2IT5wR7eTGcmlotcann2q/dnq9+qrJ+z9AVlaWnDpV1Sq5yXvJztbLypWv5IvrKxvx6ESkpIhc8tzT4BCziMSJ7ffJY/nWWa7RNu86h3wQu7v2iHWSUtBlQ751HhTt58ooIu18EbhH+CTBuXLlikybNk3GjRsnly6pN/fWrVvl5MmTrjyclTJlysj06dMLvK9fv37Sq1cvq2UtW7aU4cOHO/z4HktwMjNFype3/QsBiCxYkLvOF1/YbqvTidSpo37cA9nLL9vfbp1OJDU1d51WrWz/our1IpMn57Zfvdp+HyVLWvcRgP7+2/5m6PUiJ0643sfmzfb7AESWLMldZ9Qo2zmq0Shy333ubfvKlRM0f0zlxl6WzZvj86yRKuoH094P/eo8fbyVswei4D70snt3K/c2xCc6if3thoioPSxms/qq0NqJarlMm5bbQ1LSAruPf+FCecnMzMwTV5yo//S11tGJyPuefzps2mQjHsslVESu5FnnLrHe45H/YhCRZ30SvXueENvbYRSRAXnan7PT3nJxfCeBPzn7++30OTi7du1C3bp1MXnyZLz77ru4evUqAGD+/PkYP368y4fKTCYT5s6di2vXruXMr5Pfxo0b0bVrV6tl3bt3x8aNGzUfNyMjAykpKVYXjzh+HLh40XabkBAgb2z2qnCLAAcPqon5AtmKFfbbiACrVuVe//NP2+cYiQAb8lRXTkqyX3wzPR3Yt89+LH60aZP9NmazOp3JVfPnO9ZuwYLc6+vW2S4nlp0NrF3rekwAEBm5AUajSfN+o9GEevXyfnb3A9A4fyuHAUDuOsWLJ9ksIGk0mlG//p8Q0W4TGHY62E59RlJS1FeFrc0yGq2/fv7913418fLlL+L0actJWyYA2wDYOjdQh7yvh28kwf7poxkAduW5vR6Arfp5JgBuvuF9Yi1sb0c2rLdju532Fr5+DX3D6QTn6aefxpAhQ3Dw4EEUL55bTyQhIQFr1qxxOoDdu3cjPDwcoaGhePzxx7FgwQI0aNCgwLZnz55FxXxFIitWrGg1oiu/SZMmITIyMucSExPjdIwFcqTytYh1QuNotexAr6rtaHyhoY6vo9Pd/Fw58qPkTlV0H/BW4XVX1g3J89vmSFzuPrVms8HuS2gy5Q3EkSdLkLcKt4izfQQqR2vDlQTg6vvKsWriBkPe0hKO/ET4+jNoADTrfOWVNy5HnjDbyV9gcOS5dna7HX3cwsfpBGfz5s0YPvzmeiVVqlSxmWhoueWWW7Bjxw5s2rQJTzzxBAYPHox9HvyvfPz48UhOTs65nLA1uskZ1aqp8gK2ilZmZ6sRQxb2qnDr9cBttwHh4Z6J0Vv697ffxmDIrQ6u0wFdu9r+Vha5+bmyN6qsXDlVgiKAdexo/8coNBRo29b1PoYOdazdI4/kXu/ZM7eEWEGMRjUCyx2pqd1hNmt3kpVlxN693fMsaQg1KsQWM/JW4c7Kirf5EczKMmDnzi7QuVNc1ie6ONBGB6A5APUVcdtttl/D7Gz1sbMoXdp+NfHjx2uicmVLNXE91MgpW29gM4CuNu73hq6wn+BEQo3Cs0iA7R9xPYDuNu4PFD1h+/Uw3mhj0RJACTuPqYN6nYOQs8fAKlSoINu2bRMRkfDwcDl8+LCIiPz+++9StWpVZx/uJl26dJHHHst/gpgSExMjH3zwgdWyl156SRo3buzw43v0JOPPPrN9EkOTJtbn02RmilSvrn3iLCAyb577cXmbyaTOf7F18D//mbMrVmi3NRhEypW7+XyaTp20nyudznNDfbzswQe1N0OvF3nySff7aNjQ9stRs6Z1+1On1EgbrXM4DAaRXbvci+ny5UuSnBxR4DkyZjPEZNLJ7t1J+dZ6Q7RPojSKOlclV2pqqly8WN7muT5bty53b0N8Il3snyfRxWqNefNsf6SqVVNfORZms1n2729iNdos/2X16s/yxbXERjwGEYkSkWueexoc1lO0T5zViciL+dpvE+1zifQiEiYiZ30RuJuOixpRV9BnxDKyal++dZ4R7W03iIibJ9v5kNdPMn7kkUekT58+kpmZKeHh4fLPP//IsWPHpFmzZpLogVEtnTp1ksGDBxd4X79+/eTOO++0Wta6dWv/nGQsopKXUaNyExrLLxYgEhsrcqyA4Xf794tER+eOE8677quvuh+Tr2zeLBISUvC36623FjzcfcqUm8cl63RqiM+WLTe3P39epFEj6+fVsu6gQSLZ2d7fTg9ITRVp3z73hyfv3x49RP791/0+rlzRPuc9MlLkzJmb11m+XOWp+YcYG41q0Jsn7Ny5TlJSSkl2tl7MZvWlmpVlkOxsvaxZM6OANbJFZJDkJjSWHyCIGup6/qY19u3bKpcvlxWTSZczYisz0ygmk05WrZrimQ3xidminUxUFfXcWHv1VeuPheVrpVIl9VWT36lTx+Xo0Vgxm3OHh1sSnpUrR2kMqX9frF8Py49pORHZ4fZWu+aSqBOgLT/SeePrLyJZBazz9Y22eRMjvagT21d6PWLPWSIiJcQ6aTGIGrZf0D/IGSJyt1g/V5a/rUTkqvdD9hCvJzhXr16Vrl27SunSpcVgMEhMTIyEhIRI+/btJc3JeUnGjRsnq1evliNHjsiuXbtk3LhxotPp5PfffxcRkYceekjGjRuX0379+vViNBrl3Xfflf3798vLL7/s32HiFhs3igwZItKypUj37iIzZoikp2u3T0lRYzy7dFGjix5/XGT7ds/F4yuXLokMG6b2voSFqaTu889tr7N/vxre3aqVSMeOIu+/L3L5snb7jAyRb74RSUgQuf12kYED1QQfgT7SLJ/sbJGFC0XuuUdtxn33ifz6q2enPcrKEpk4UQ0ZDwtTefT48eop1HLmjMhrr4m0ayfStq3IuHEiR454LiYRkYsXL8jKlZNl+/b2snNna1m58hk5evSgjTXMon5wBorI7SKSIOrH6brmGleuXJaVK9+Xbds6yq5drWTVqlFy+HABv/AB75CobTaK+gGLFJHXbK6xY4f6CmnVSn2lfPLJzdNQ5ZWeni5r1syQzZu7y+7dLWXNmiGye/dGO3HtFZH/E/WD2FlE/ivWo5T8IUNEvhORO0U9ZwNEDQe39d3wj4iMFZHWooZHvy6FY89NfqdFzetzh4i0FZHnxfZ8NiYRWSoi/UQ9V71F5EcpOBEMXM7+fjtdbNNi/fr12LlzJ9LS0hAXF3fT6CZHPPLII1ixYgXOnDmDyMhING7cGGPHjkX8jXMxOnbsiBo1amDWrFk56/zwww944YUXcPToUdSpUwdvv/02EhISHO6TxTaJiIgKH69XEy/smOAQEREVPl6rJr5x40b88ssvVsv+97//oWbNmoiKisJjjz2GjEJWH4iIiIiCk8MJzquvvoq9eYoi7t69G4888gi6du2KcePG4eeff8akSZO8EiQRERGRMxxOcHbs2IEuXXLnapg7dy5atmyJadOm4emnn8ZHH32E77//3itBBp1gqSZOASU1VVUob9BAVSyvUwd46y1VyVyLL6qJOysjQxWMb9ZMbUeNGqqg/DntYuLYt08Vpo+KUoXqu3YFfvpJe67IrCxgxgygRQvVR0wMMG4ccOqUrcj+BjASQCUApQF0BDAPWnOymEzAnDlAmzbqY16lCvDMM8DRo7a33xmuVBMnZ1wBMAlAbai5dW4F8F8AaX6MiRzm6NnLoaGhcvz48Zzbbdu2lddffz3n9pEjRyQ8PNzRh/Mbr4yickawVBOngHLhgki9etazD1jeWjVrqnlv8vNFNXFnpaWpQvH5t8NgUIXl/7q5mLj89FNuUfq87QFVwD7/gLvr10W6drX+6FnWKVNGjUq62TJR84/kHSptGWo7WNQolVxZWSK9exfcR3i4GnjpLleqiZMzTohIdbEejm2pyN1QfF9klLw2TLxatWqy+kZ54YyMDClRooQsX547gdauXbukTJkyTobre35NcIKlmjgFnL59tScTNBpVRfO8fFFN3BWJibYLyDdoYJ2wnD+vCtHbKjqZf06fF17Q3naDQc3FaT3FUrKIhIvtopPTrPqYPNn2JIpRUbaH7zvC2Wri5KyOol2o0iDWRS3JF7xWbDMhIQHjxo3D2rVrMX78eJQsWRLt2rXLuX/Xrl2IjY31+B6moPLNN0BysvahKJ0O+OAD7f3qRAU4dUoV0jRp1LXMzgaWLQP+/jt32aef2i/VMGWKZ+O0Jy0NmDZNeztMJnUoKm8R0BkzgMxM7Y+MXg/897+5tzMzgU8+0f4ImkzAsWPA0qV5l84BcA3aRSd1AD6weowPP9SOyWQCzp+3Ln7qLBH1VaFVgcJsVl81337reh9F234Aq6BdqNIE4HsAzpcnIt9xOMF57bXXYDQa0aFDB0ybNg3Tpk1DsWLFcu6fMWMGunXr5pUgg0awVBOngLJli2Onb+WtbO6LauLO2r9fFYi3xWCwrpCdlGT7/wGzWRWyt7T55x/gyhXbfRiN1oXt7VevFgD7YKmEfvYscPq07T5CQvL34RxXqomTM5IcaGMCsNXbgZAbHC4hWr58eaxZswbJyckIDw+HIV8FwR9++AHhgV4k0t+CpZo4BRRXKkv7opq4sxyJScT57cjbxtUq3I5V+za40YdzfNFH0Va0q3AHC6eriUdGRt6U3ABA2bJlrfboUAGCpZo4BZS2bVVFclsMBlXZ3MIX1cSd1bChKhBvi9l8c9F5WwwGoEuX3EM5sbFqxJQt2dn5Hzce2ocqAPVj2AaAehEqVgTq1dM+fASoUVz2YrfFlWri5IxOsJ/UFgfQygexkKucTnDIDX36ANWra//7ZTYDY8f6NCQq/MqUAR55RPvHzmAABgxQQ4gthg9XSZHWj7AIkJjo+VhtKVYMePpp7ZiMRqBTJ6Bx49xlAweqpEjrI2UyAc8+m3tbrwfGjNGOwWhUQ8fbtMm7tC+AKtD+r94EIPdzq9Opj7HW4SOjUSVA7h7RHztW+9CkwQBUq6a+csgVMQD6Qfs11wN4HGroOAUsL5/0HHD8Pkw8WKqJU0D5919VmbygiuXt26uK5vn5opq4s7KzVaH4vB8LS3yNGqlRU/lt3aoK0ucdtWQ0qttTCigmbjaLPPFEwX3UqSNy8mRBke0RkYqSO0wYkjvCZlKBfTz3nHUflviqVxf55x+XnyIrzlYTJ2ekiEgbkQIrlt8ptoq/knf4rNhmYRUQtahSU9UMYD/+CFy7BjRtqv6lbtrUP/FQUDCbgSVLgJkzgePH1cRygwcDd96pvYfj7Flg+nTg99/V+u3aqbdijRo+Dd2KCLB6tYrr4EE1CeHAgUDfvtqH4q5cAWbNUpP7Xb8O3H478MQTak+Jlg0b1Kit/fvVJHkDBgD9+gElSmitkQLgKwDzoU4ojgMwHEBjrRWwZQvw+efAnj1ARIR6/AEDgLAwu0+Dw3buVH3s2KEe9957gQcfVP2Ru7IB/AxgNoAzAKoDeATqsCUPgPgai23aERAJDhERETnFa8U2iYiIiAoLJjhEREQUdJjgEBERUdBhgkMUNFIBvA+gAdTw1ToA3gJwVXON2bPVCcV6vbpUqqQqkmtLBzAFQKMbfdQC8BqASx6I33U//QTUr6+2QadTldEnTLA1w3MWgBkAWkBtRwyAcQBslRN3rpo4kAngCQCloOZUMdzob51mD0eOAKNHA5UrqxOf27RRFV60ylf4ggiwcCHQubOakqBiReDxx9XJ2VSQtQDuBVDuxqU/AH9PKX0NwIcAGkK932MBvAHgsj+D8j4vjugKSH4fJk7kFRdEpJ5YD2OGqAKRNUXk5nLiQ4ZoF2vMX5xTuSoizTT6qCIiRzy9UQ554QXt7WjUSFXdtnZdRLpKbux5CyiWEZEdBfTiXDVxkX9FpFKetraLc4qIrF8vEhZmXWzUMny9b9/8BUB9w2wWeewx62kHLMPSQ0JEFi/2fUyB7T2xHk6e97q/CilfFpFGUvDnNkZEjvkpLud5rZp4sGCCQ8Gpr+T+4Oa/GEXEOmNZvFg7KbBcPvoofx9D7fTRymtbp2XfPvvbMWpU/rVeEO3K4AYRqS4iebMJ56uJi/S00RaifmhyJyf691+R8uW1q5zrdCLvv+/GE+Wir77Sfl51OjWP0qVLvo8rMG0S+6/5Tj/E9aDY/ty280NMrvFaNXEiClSnACyAmlG3INkAlkEdYlHGj7f/qJMn5711Gaqqtq0+kgBst//AHvT00/bbzJiR91YmgE+gXRncBOAYgLzlxJ2rJq76WKrR1kIATMi59cMPwMWL2ofURFRVdEeKqnrSBx9oz5AtAvz7rzrMSQDwEWzXpjIA+NRHsVicBzAXtj+3awHs8VlEvsQEh6jQ2wLtH9+8csuJ//23jWY3nDmT99YOqPNWbNHB1+ca7Nxpv01aWt7E4B8AdsqJwwggb6lv56qJA7vg2OuxOreHJFVh3Jbjx4ELFxx4WA8xmYBt22wnVTodK5bnWgfbNcuyAazxUSwW22E7JovgfBGZ4BAVes5XPrZVpNH1PgS+rq7sne0ArLfDuWrigKNFh3P7CMTq4DqdY88vK5ZbOPIi+vrJKtpV0ZngEBV6bWGpZK3NADXqR2ne3P6j1q6d91YLAPaq3OsAdLH/wB7UoYP9NuXL5/2hjoUaMWVLNtRU/BbOVRNXI1UcSXL65PYQryqMa9HrgUaN1OgwX9Hr1cgpW8mX2cyK5bkSYDuhMADo6aNYLFoC0Kw9coMOQGcfxOJ7THCICr0yUPVxtD7OBgADAOSWE//gA42mebz5Zt5bYVBDpLX2ZBgA3AWVQPjOe+9pVx+3eO65vLf0AGyUE4cRKpnLW07cuWriqo+HbAeFYlDD0pWEBJVQau0NMZtV9XB72+ppzz6rPUTdYACiooAHHvBtTIHL1udDB/XeesJ34QBQUxQ8CdvfDX2hamwFHyY4REHhPQDdblw35PvbFsBnVq3j4oC339Z+tFGjVHFLa68CuOfGdcsvsaWPOKiChL5VqRLw1VfaP/z33QeMuSmfGYncHxrLdli+CmsCWAjrH6pQqJOGy99Yrsu37iQAd+fr4wsAt2tEbYA66dv6ENVvv6nt0elyt8eS8EyYAPznPxoP50Xdu+fOi5Q3+dLp1Dw9v/8OlCzp+7gC0y0AfgAQAutk2AD1HloAoIbvw8KbAO7ME0vev7cBmO7ziHyFxTaJgoYZwBIAMwEch9rrMBjqy63gvQ+7dqmRSJs3q1Ext94KvPMOcMcdWn0IgBVQX4pHAFQEMAhAb6gvdv+wTJC3ejWQnQ3Exqo9UAkJttbaAGAagP1QE/cNANAP2rv0na8mDvwPakK1U1A/cncCeAdAVIGt09LUxH4//ACkpqrDUk88oRJSf9q3T1Us37xZJTR3360q1Zcu7d+4AtMJAFMBrELuYdvHAFT2Y0xmAMsBfAngKNTe3EFQiXnhOf+G1cTtYIJDRERU+LCaOBERERV5THCIiIgo6DDBISIioqDDBIeKvEuXgNdeA2rVAiIj1YmdU6YA6en21w0kqalqxEuDBmo76tQB3noLuHrVc31cvHgRGzfeh+vXi0NEh6ysEGzb1gVHjhz0WB8ZGcDUqUCzZmo7atQAXnoJOHfOY124yNlq4iaoMg9tbrSvAuAZqJM8icjbeJIxFWlHj6oRQ2fO5E5Jbxmi26wZ8Mcf6kc20F28CLRrBxw4oG5bPtV6PVC9OrBuHVDZzUEcJ08eR9my9VCixL8A1PNk6cdk0mPv3tVo0kRz+JVDrl0DunXLnf7f8vgGg5rkbu1a4JZb3OrCRcuh5vnJRu6kfwaoJGYwgBmw/n8xG8B9ABbdWG7Os04JqGHirbweNVEw4UnGRE4YMEDtGchbb8dSL3nnTjX0uDB4/HHg4MHc2C3MZuDECWDIEPf7yMrqihIl/rWap8Vy3WAwIzbW/VlaJ0wANm26eTtMJuDyZeDee62X+0YK1Pw/mbCe0dgyA95sqAQnr/cB/HTjet5iTiYA/0INq8/0eKRElIsJDhVZ27erIofZGrPwm0zAnDnqEFYgO3UKWLBAe8bZ7Gxg2TLHCmxqOXbsMGrUOKg5oZ5OB4SHp2H9+m9d7iMtDZg2TXs7TCY1H8vatS534SJnq4mbAHwI24euzkNN/EZE3sIEh4qspCT7U99nZQE7dvgkHJdt2WK74rPFpk3222g5cmSh3edKBNDpFrncx/799s97Mhj8Ub3a2WriZwGctvOYIbCuWE5EnsYEh4osg8Gxwx2BXi3ZN5WoHVvZbHa9E0e2Q8Qfr4ez1cSLdgVnokDBBIeKrC4OFL4ODwdatPB+LO5o2xYItVNM3GAAOnZ0vY8GDQY4lAyWLPmwy300bAiUK2e7jdmsKm/7lrPVxCsCqAfbSVEWrCuWE5GnMcGhIis2FujdW3vPgU4HjBwJhIX5Ni5nlSkDPPKIGjFVEINBnUwdHV3w/Y6IiorCnj2tNJMcEeD8+YqIi+vsch/Fiqm6WFqHwoxGoFMnoLGt0k9e4Ww1cd2N21oZoREqAeqmcT8ReQITHCrSZs0CmjdX1y2JjuUQyD33AK++6pewnPbee2p4NZC7HZa/bdsCn31W8HrOiI1dgbNn1VhzS6Jj+ZueXhKZmW6c5HPD2LHAQw+p65bXwZK41a8PfPed2124wJVq4oMBPJevnWW9KgAWg1+/RN7FeXCoyMvKAhYtAv73PzVkvGZNYNgwdQjL3om1gcRsBpYsAWbOBI4fB6pUURWf77zT8fN07Pdhxpo176J27Y9RuvRlXLsWjv37B+L2219DyZIlPdKHiKoKPn26GvpevjwwcCDQt6/9Q3He5Uo18S0APgewB0AEVLXyAQACfLcgUQBiNXE7mOAQEREVPpzoj4iIiIo8JjhEREQUdJjgEBERUdBhgkMUoNavB/r1U3PDlCsH3H+/WuZJmzerE3jLl1fFLHv3Blau1G6fnq4qrTdqpIqQ1qqlKrH7v5zFPgCPAYgCUAZAV6haUEXqFEMiDdegyoc0BBAJIBbAGwAu+zMor+NJxkQB6KOPgMRENVTaUivLcv3DD4FRo9zvY8YMNVrMYLi5jzfeAJ5/3rp9crKah8ZSuiJvxfLoaFWxvEYN9+Ny3s8A7r1xPX+l7xEApsCxmYiJgtEVAB2gRvIBuUm/HmrKgnUAqvkhLudxFJUdTHAo0G3bljs3j5atW4G4ONf7OHAAaNDAdg2r1auB9u1zbz/8sBpKX1AxTKNRzfjs+zpRF6C+nDOgvbdmLoD+PouIKLA8BOBbqIQ/PyOA1gDW+DQiV3EUFVEhN2WK7XpLRiPw8cfu9fHpp9ozH1v6mDIl9/bly6qyuq2K5UlJqkK7b80AkAnt5EYP4L8+i4YosJyHSvA1PrjIBrAWuXt3ggsTHKIAs25d7iGjgmRnA2vc/IfLkT7Wrs29vWOHmhDRFp3OX5W+be2ENgP4004bomC1HbbrqFn4/IPrE0xwiAKMI7MOh4R4v4+8e5ECu9K3J9oQBaOiXdmeCQ5RgElIsJ1QGAxAz57u9dGzp/1DVD165N5u0UJVVrdFp3OsQrtn2avIbQDQBTzJmIqmlgBK2GmjA+B6kdxAxgSHKMCMGKGSj4LqYOl0KsEZMcK9PoYPV3WdtGptiahRXBZhYaqyulZ7gwG46y5Vod23BgIoB9uVvp/1XThEAaUUgCeh/VNvANAXQHWfReRLTHCIAkxsLPDjj+owVN49OQaDWjZvnvuJROXKwM8/AyVKWO/JMRjU3puvv1Zz3eT16quqwjqQeyjKEl9cHDB7tnsxuSYcqtJ3JKz30hhv3J4CtQeHqKh6E8CdN64b8v29DcB0n0fkKxwmThSgTp4Epk0D/vhD3e7UCXjsMaBqVc/1cfasqtr9++9qyHi7dmrvjtZ8NiLAihVqnSNHgIoVgUGD1ASB7p4X5J4rAGZBTe53HcDtAJ4AUM+PMREFCjOA5QC+BHAUQDSAQQDuRmE6/4bz4NjBBIeIiKjw4Tw4REREVOQxwSEiIqKgwwSHiIiIgg4THPIhgSqMGA9V8TkKqgL0Xo/2snSpmkumbFmgQgVg6NDcApGFibPVxFNTgfffVzWmIiOBOnWAt94Crl611ctmqKHW5QGUBdAbgI1y4kXYkSN/Y/XqkTh/vhKSk0tjx46O2LhxHgrfaYwnADwHoCqA0lAnZM+CYzPeEhUi4kdvvvmmtGjRQsLDw6VChQrSu3dv+euvv2yuM3PmTIH6pcy5hIaGOtxncnKyAJDk5GR3wyenmEVkhIhARAw3/kJEjDcuizzSy/jxIoCIwaD+AiJGo7r97bce6cInPvwwN/a82wGo+/K7cEGkXj0RnU5dLOvo9SI1a4qcOlVQL1+KiE7U85/39YCIvOG1bSuMtm5dJv/+W1wyM3Ofq6ws9T5es2awmEwmP0foqK0iEinWn0H9jb89RSTDb5ER2ePs77df9+CsXr0aI0aMQFJSEpYtW4asrCx069YN165ds7leREQEzpw5k3M5duyYjyIm130P4JMb1/MWfsu+cbs/VGVo1y1eDEyadKOHPF1kZ6vbDz0EnDjhVhc+sW1b7iR7eetFWa4nJqo2eT3+OHDwoCWtyV1uNqttHjIkfy8HADwK9T9C3v/cLdcnoLBUGPa2lJQU1KlzD0JCMhESkvtcGY3qTXbHHbOxfv0Mf4XnhGyoPXRpsP4MWkrKLwXwlq+DIvIavyY4S5YswZAhQ3DrrbeiSZMmmDVrFo4fP46tW7faXE+n06FSpUo5l4oVK/ooYnLdf6H9dhOoitAz3evhv7ZLHIgAX3zhVhc+4Ww18VOngAULbFf6XrYM+PvvvEs/he2PvxFqkjzavn0OwsKuwWAwF3i/2axDdPQHPo7KFb8AOAntytJmqNfcTlVVokIioM7BSU5OBgCULVvWZru0tDRUr14dMTEx6N27N/bu1T6HIyMjAykpKVYX8jWBquhc8A9EbpsNbvWycaP2jzyg7rN1DkugcLaa+JYtak+NPZs2WfUC2+dcZANYa+P+osNgSILZrP1VaTAIatfeh/T0dB9G5YokAPZmY7wIgHvEKTgETIJjNpvx1FNPoW3btmjYsKFmu1tuuQUzZszAokWLMGfOHJjNZrRp0wYnT54ssP2kSZMQGRmZc4mJifHWJpBN9qra6uDujJq2ikfmRFEICks7W03c0W2y3ivkyEqFZ4ZTbxIxQMR+sU5DwL+5DFD/SNjD152CQ8AkOCNGjMCePXswd+5cm+1at26NQYMGoWnTpujQoQPmz5+PChUqYOrUqQW2Hz9+PJKTk3MuJwrDSRhBRwegK2z/qArsV4a2rWdP24d29Hqge3e3uvAJZ6uJt22rCmfaYjAAHTvmXdIT9g9R9bBxf9Gh18dbnXuTX3a2Abt2tUGovRfB7+Jhe6+dDkBNANV8Ew6RlwVEgjNy5Ej88ssvWLlyJao6WWgnJCQEzZo1w6FDhwq8PzQ0FBEREVYX8odnoX3s3wA1RHmgWz2MHq19iEqvB8LD1ZDxQOdsNfEyZYBHHtHeg2UwAAMGANHReZcOBxAK6wKVeQmARI37ipYWLfrizJkqyM4uOOs0Gk3499+xPo7KFR0ANIH2HhqBGj4eED8LRG7z6ztZRDBy5EgsWLAAf/zxB2rWrOn0Y5hMJuzevRvR1t/eFHA6Q53AmP9QlA6qEvRSqMrQrmvZEvjyS/VDn3cPiF4PhIWpUVblyrnVhU+4Uk38vfeAbt1y2+X927Yt8Nln+XupDDUnUQlYfw0YoF6frwE0yr9SkRQaGopr15bi8uXyMJt1MJtVUpiVpd7Hq1ZNQsuWd/szRAfpoF7z6jduW153y+dxFFTiSxQc/Fps88knn8Q333yDRYsW4ZZbbslZHhkZiRIlSgAABg0ahCpVqmDSjfG/r776Klq1aoXatWvj6tWreOedd7Bw4UJs3boVDRo0sNsni236218APgewCUBxqGq2Q6Am/vOMQ4eAzz8HNmxQCUFCAvDww2rSv8LE2WriZjOwZAkwcyZw/DhQpQoweDBw5522DnmdBTAdwO9QJ4G3g/qRq+HRbQkGKSkp2L79K5QqNR8hIem4fDkO0dHDUbduY3+H5qR/AcwF8B2AqwDqQ73mrfwYE5F9haqauK6gffAAZs6ciSE3Ju7o2LEjatSogVmzZgEARo8ejfnz5+Ps2bMoU6YMmjdvjtdffx3NmjVzqE8mOERERIVPoUpw/IEJDhERUeHj7O83zyYjIiKioMMEh4iIiIIOExzyGRHg55+B+Hg1tDkqSp00a2Mi6iLts8+AmBg1CkyvVycX3zwaioiICsJzcMgnRID/+z/gk0/UiB7LfDWWifl+/BG4uzCMtPWRvn2B+fMLvu/ee9XzRURUlPAcHApI33+vkhug4Erf/fsDF9wrJh40vv5aO7kB1H1ff+27eIiICiMmOOQT//2v9ky7IkBmppq/hYCJE+23eeUVr4dBRFSoMcEhrxMB/vzTdsVrETUxHwHHHCjmfOSI9+MgIirMmOCQT9grtKzT2S6USdYcqZxORFSU8WuSvE6nA7p2tZ3kiKjRVQQ0cqAEVMOG3o+DiKgwY4JDPvHss9qVvg0GoGxZYKB7xcSDxjvveKYNEVFRxgSHfKJzZ2DKlJsPRel0QGQksHQpEO5eMfGg0bkz8MIL2ve/8IJqQ0RE2jgPDvnUX3+pSt+bNgHFi6u5b4YMURP/kbXNm9Wer+3b1e2mTYH33gNuu82vYRER+QWLbdrBBIeIiKjw4UR/REREVOQxwSEiIqKgwwSHiIiIgg4TnMLAZAK++QZo0wYoXRqoXBkYPbpITGd77hzw0ktAjRpqtFWzZsDUqUBGhr8j877164F+/YBy5dTl/vvVMvKPv/8GRo4EKlVSH8OOHYF589QcTkQUeHiScaCzVKL88Uc1fa2l3oHBAISGAr//DrRt698YveTAAaBdO+Dy5dw5dHQ69bd1a7XpYWH+i8+bPvoISExUQ+qzs9Uyy/UPPwRGjfJvfEXN8uXAXXep59/yehgM6n05eDAwYwZnlybyNp5kHGw++ii3tHTeYk4mE3D9OtC7t/obZESAvn2tkxvLchE1zHzCBP/F503btqnkBsj9Mc17PTFRtSHfSEkB7rlHFYTN+3pY3pezZ6sEh4gCCxOcQGY2qzLcWjvZzGbg0iXghx98GpYvrF0L7N2rPfuxyQRMmwakpfk2Ll+YMsV2XS6jEfj4Y9/FU9TNmQNcu6ZdLFanAz74wLcxEZF9THAC2cWLwPHjttuEhAAbN/omHh9KSrJfoDM9Hdi3zzfx+NK6ddZ7CvLLzgbWrPFdPEVdUpLtw08i6n2Ynu67mIjIPiY4gczeL7xFEJbhNhgcO3kzCDfdoZc9JMT7cZBiMOSe+2WvHREFDiY4gaxsWVVa2ta/j1lZQVmGOz5e+5CARblywVlVOyHB9o+lwQD07Om7eIq6+Hjbe9QMBjXAMTTUdzERkX1McAKZTgeMHav9S28wALVrq1/EINO4MdCpk/YPvU4HPP00UKyYb+PyhREjVE5b0F4DnU49JyNG+D6uoqpvX6BKFe33osmkPqZEFFiY4AS6//wnd7iQ5XiMTqcu0dHAb78F7b7x774DGjRQ1y07sSxPwUMPBe+PSmysmhUgJMT6pTUY1LJ581Qb8o3QUFXtvnz53I8ekPtenDRJFY0losDCeXAKi23bgM8+A3bvBkqVUrO+/ec/QHi4vyPzqsxM9WM/Z44657pOHWDYMKBDB8fOiyjMTp5UI8X++EPd7tQJeOwxoGpV/8ZVVKWkAF99pWZtSE8H4uKA4cPV3kYi8j5WE7ej0CY4RERERRgn+iMiIqIijwkOERERBR0mOERERBR0mOAQUUDJyMjAmjVT8ddfzZCSEomTJ2tg1aqXcOHCOY/1kZWl6ke1aKGq1MfEAOPGAadOeawLIvIznmRMRAHj2rVrOHy4Gxo23AgRwGBQX0/Z2QZcuVIWqalrUavWLW71kZEB3HmnqhCu1+dOM2UwABERwMqVQJMm7m4JEXkaTzImokJry5YJaNBgE/R6yUluAMBoNKF06cswm++Fu/+Tvf567tD7vHNomkxqKHjv3tpFXomo8GCCQ0QBIS0tDS1aTIPRWHB2ERJiQu3a+7Br11qX+8jMBD75RHtycJMJOHZMTexHRIUbExwiCgjHj+9HWJjtktzqUNVGl/v45x/gyhXbbYxGYMMGl7sgogDBBIeIAoJOZ7/kiE4nAFwvIe9oVZNgrFJPVNQwwSGigBAb2xCXLpWz2cZgMKNy5Xg3+lAjpmzJzlYVxImocGOCQ0QBoVixYti9+2mYzQUXGcvKMmL79k6oW9f14k96PTBmjPb9RqMaOt6mjctdEFGAYIJDRAGjXbux2LDhIQAqoQEAk0l9TR05Uh8xMd+53cfIkcATT6jrlkNRlmr1NWsCCxcGfyFXoqKA8+AQUUAREezcuRppadNRpsxBXLtWHllZA9GiRV+EhoZ6rJ8NG1S19v37gdKlgQEDgH79gBIlPNYFEXkQq4nbwQSHiIio8OFEf0RERFTkMcEhIiKioMMEh4iIiIIOExwiIiIKOkxwiIiIKOgwwSEiIqKgwwSHiIiIgg4THCIiIgo6THCIiIgo6DDBISIioqDDBIeIiIiCDhMcIiIiCjpMcIiIiCjoMMEhIiKioMMEh4iIiIIOExwiIiIKOkxwiIiIKOgwwaGAdu4c8NJLQI0aQGQk0KwZMHUqkJHh78iIiCiQ+TXBmTRpEm677TaUKlUKUVFR6NOnDw4cOGB3vR9++AH16tVD8eLF0ahRIyxevNgH0ZKvHTgANGoEvPkmcOwYkJIC7NwJPPEE0LkzcO2avyMkIqJA5dcEZ/Xq1RgxYgSSkpKwbNkyZGVloVu3brhm45drw4YNGDBgAB555BFs374dffr0QZ8+fbBnzx4fRk7eJgL07QtcvgyYTNbLRYBNm4AJE/wXHxERBTadiIi/g7C4cOECoqKisHr1arRv377ANv3798e1a9fwyy+/5Cxr1aoVmjZtis8//9xuHykpKYiMjERycjIiIiI8Fjt51po1QIcOttuULKkOYYWH+yYmIiLyH2d/vwPqHJzk5GQAQNmyZTXbbNy4EV27drVa1r17d2zcuLHA9hkZGUhJSbG6UOBLSgIMBttt0tOBfft8Ew8RERUuAZPgmM1mPPXUU2jbti0aNmyo2e7s2bOoWLGi1bKKFSvi7NmzBbafNGkSIiMjcy4xMTEejZu8w2BQh6LsMRq9HwsRERU+AZPgjBgxAnv27MHcuXM9+rjjx49HcnJyzuXEiRMefXzyjvh4wGy23aZcOcBGLkxEREVYQPz/O3LkSPzyyy9Ys2YNqlatarNtpUqVcO7cOatl586dQ6VKlQpsHxoaitDQUI/FSr7RuDHQqZM6FyfvScYWOh3w9NNAsWK+j42IiAKfX/fgiAhGjhyJBQsW4I8//kDNmjXtrtO6dWusWLHCatmyZcvQunVrb4VJfvLdd0CDBuq6/sY71XJI6qGHgLFj/RMXEREFPr/uwRkxYgS++eYbLFq0CKVKlco5jyYyMhIlSpQAAAwaNAhVqlTBpEmTAACJiYno0KED3nvvPfTq1Qtz587Fli1b8MUXX/htO8g7KlQAtmwBfvwRmDMHuHgRqFMHGDZMjbDS6fwdIRERBSq/DhPXafxCzZw5E0OGDAEAdOzYETVq1MCsWbNy7v/hhx/wwgsv4OjRo6hTpw7efvttJCQkONQnh4kTEREVPs7+fgfUPDi+wASHiIio8CnU8+AQEREReQITHCIiIgo6THCIiIgo6DDBISIioqDDBIeIiIiCDhMcIiIiCjpMcIiIiCjoMMEhIiKioMMEh4iIiIJOQFQT9yXLxM0pKSl+joSIiIgcZfnddrQAQ5FLcFJTUwEAMTExfo6EiIiInJWamorIyEi77YpcLSqz2YzTp0+jVKlSmsU+A1lKSgpiYmJw4sSJIlVLq6huN8BtL4rbXlS3G+C2F8Vtd3S7RQSpqamoXLky9Hr7Z9gUuT04er0eVatW9XcYbouIiChSHwCLorrdALe9KG57Ud1ugNteFLfdke12ZM+NBU8yJiIioqDDBIeIiIiCDhOcQiY0NBQvv/wyQkND/R2KTxXV7Qa47UVx24vqdgPc9qK47d7a7iJ3kjEREREFP+7BISIioqDDBIeIiIiCDhMcIiIiCjpMcIiIiCjoMMEJUG+99RZ0Oh2eeuopzTazZs2CTqezuhQvXtx3QXrIxIkTb9qOevXq2Vznhx9+QL169VC8eHE0atQIixcv9lG0nuXstgfLa25x6tQpPPjggyhXrhxKlCiBRo0aYcuWLTbXWbVqFeLi4hAaGoratWtj1qxZvgnWg5zd7lWrVt30uut0Opw9e9aHUbuvRo0aBW7HiBEjNNcJls+6s9seLJ91k8mEF198ETVr1kSJEiUQGxuL1157zW49KU98zovcTMaFwebNmzF16lQ0btzYbtuIiAgcOHAg53ZhLD8BALfeeiuWL1+ec9to1H5rbtiwAQMGDMCkSZNw55134ptvvkGfPn2wbds2NGzY0BfhepQz2w4Ez2t+5coVtG3bFp06dcJvv/2GChUq4ODBgyhTpozmOkeOHEGvXr3w+OOP4+uvv8aKFSswbNgwREdHo3v37j6M3nWubLfFgQMHrGZ6jYqK8maoHrd582aYTKac23v27EF8fDzuv//+AtsH02fd2W0HguOzPnnyZHz22WeYPXs2br31VmzZsgVDhw5FZGQkRo0aVeA6HvucCwWU1NRUqVOnjixbtkw6dOggiYmJmm1nzpwpkZGRPovNW15++WVp0qSJw+379esnvXr1slrWsmVLGT58uIcj8z5ntz1YXnMRkbFjx8odd9zh1DrPPfec3HrrrVbL+vfvL927d/dkaF7lynavXLlSAMiVK1e8E5SfJCYmSmxsrJjN5gLvD6bPen72tj1YPuu9evWShx9+2GrZvffeKwMHDtRcx1Ofcx6iCjAjRoxAr1690LVrV4fap6WloXr16oiJiUHv3r2xd+9eL0foHQcPHkTlypVRq1YtDBw4EMePH9dsu3Hjxpuen+7du2Pjxo3eDtMrnNl2IHhe859++gktWrTA/fffj6ioKDRr1gzTpk2zuU4wvPaubLdF06ZNER0djfj4eKxfv97LkXpXZmYm5syZg4cfflhzz0QwvN4FcWTbgeD4rLdp0wYrVqzA33//DQDYuXMn1q1bh549e2qu46nXnQlOAJk7dy62bduGSZMmOdT+lltuwYwZM7Bo0SLMmTMHZrMZbdq0wcmTJ70cqWe1bNkSs2bNwpIlS/DZZ5/hyJEjaNeuHVJTUwtsf/bsWVSsWNFqWcWKFQvd+QiA89seLK85APzzzz/47LPPUKdOHSxduhRPPPEERo0ahdmzZ2uuo/Xap6Sk4N9///V2yB7hynZHR0fj888/x48//ogff/wRMTEx6NixI7Zt2+bDyD1r4cKFuHr1KoYMGaLZJpg+63k5su3B8lkfN24cHnjgAdSrVw8hISFo1qwZnnrqKQwcOFBzHY99zp3a30Nec/z4cYmKipKdO3fmLLN3iCq/zMxMiY2NlRdeeMELEfrOlStXJCIiQqZPn17g/SEhIfLNN99YLfvkk08kKirKF+F5lb1tz68wv+YhISHSunVrq2X/93//J61atdJcp06dOvLmm29aLfv1118FgKSnp3slTk9zZbsL0r59e3nwwQc9GZpPdevWTe68806bbYL1s+7ItudXWD/r3377rVStWlW+/fZb2bVrl/zvf/+TsmXLyqxZszTX8dTnnHtwAsTWrVtx/vx5xMXFwWg0wmg0YvXq1fjoo49gNBqtTk7TYsmODx065IOIvad06dKoW7eu5nZUqlQJ586ds1p27tw5VKpUyRfheZW9bc+vML/m0dHRaNCggdWy+vXr2zxEp/XaR0REoESJEl6J09Nc2e6C3H777YXydQeAY8eOYfny5Rg2bJjNdsH4WXd02/MrrJ/1MWPG5OzFadSoER566CGMHj3a5pEKT33OmeAEiC5dumD37t3YsWNHzqVFixYYOHAgduzYAYPBYPcxTCYTdu/ejejoaB9E7D1paWk4fPiw5na0bt0aK1assFq2bNkytG7d2hfheZW9bc+vML/mbdu2tRohAgB///03qlevrrlOMLz2rmx3QXbs2FEoX3cAmDlzJqKiotCrVy+b7YLh9c7P0W3Pr7B+1tPT06HXW6caBoMBZrNZcx2Pve4u73cir8t/iOqhhx6ScePG5dx+5ZVXZOnSpXL48GHZunWrPPDAA1K8eHHZu3evH6J13TPPPCOrVq2SI0eOyPr166Vr165Svnx5OX/+vIjcvN3r168Xo9Eo7777ruzfv19efvllCQkJkd27d/trE1zm7LYHy2suIvLnn3+K0WiUN954Qw4ePChff/21lCxZUubMmZPTZty4cfLQQw/l3P7nn3+kZMmSMmbMGNm/f7988sknYjAYZMmSJf7YBJe4st0ffPCBLFy4UA4ePCi7d++WxMRE0ev1snz5cn9sgltMJpNUq1ZNxo4de9N9wfxZF3Fu24Plsz548GCpUqWK/PLLL3LkyBGZP3++lC9fXp577rmcNt76nDPBCWD5E5wOHTrI4MGDc24/9dRTUq1aNSlWrJhUrFhREhISZNu2bb4P1E39+/eX6OhoKVasmFSpUkX69+8vhw4dyrk//3aLiHz//fdSt25dKVasmNx6663y66+/+jhqz3B224PlNbf4+eefpWHDhhIaGir16tWTL774wur+wYMHS4cOHayWrVy5Upo2bSrFihWTWrVqycyZM30XsIc4u92TJ0+W2NhYKV68uJQtW1Y6duwof/zxh4+j9oylS5cKADlw4MBN9wXzZ13EuW0Pls96SkqKJCYmSrVq1aR48eJSq1YtmTBhgmRkZOS08dbnXCdiZzpBIiIiokKG5+AQERFR0GGCQ0REREGHCQ4REREFHSY4REREFHSY4BAREVHQYYJDREREQYcJDhEREQUdJjhEVGjodDosXLjQ32G4ZMiQIejTp4+/wyAqMpjgEJFNFy5cwBNPPIFq1aohNDQUlSpVQvfu3bF+/Xp/h3aTQEgijh49Cp1Ohx07dvg1DqKizujvAIgosPXt2xeZmZmYPXs2atWqhXPnzmHFihW4dOmSv0MjItLEPThEpOnq1atYu3YtJk+ejE6dOqF69eq4/fbbMX78eNx9991W7YYNG4YKFSogIiICnTt3xs6dO3PunzhxIpo2bYqpU6ciJiYGJUuWRL9+/ZCcnJzTZvPmzYiPj0f58uURGRmJDh06YNu2bR7dnj179qBnz54IDw9HxYoV8dBDD+HixYs593fs2BGjRo3Cc889h7Jly6JSpUqYOHGi1WP89ddfuOOOO1C8eHE0aNAAy5cvtzp0VrNmTQBAs2bNoNPp0LFjR6v13333XURHR6NcuXIYMWIEsrKyPLqNRKQwwSEiTeHh4QgPD8fChQuRkZGh2e7+++/H+fPn8dtvv2Hr1q2Ii4tDly5dcPny5Zw2hw4dwvfff4+ff/4ZS5Yswfbt2/Hkk0/m3J+amorBgwdj3bp1SEpKQp06dZCQkIDU1FSPbMvVq1fRuXNnNGvWDFu2bMGSJUtw7tw59OvXz6rd7NmzERYWhk2bNuHtt9/Gq6++imXLlgEATCYT+vTpg5IlS2LTpk344osvMGHCBKv1//zzTwDA8uXLcebMGcyfPz/nvpUrV+Lw4cNYuXIlZs+ejVmzZmHWrFke2T4iysetMqFEFPTmzZsnZcqUkeLFi0ubNm1k/PjxsnPnzpz7165dKxEREXL9+nWr9WJjY2Xq1KkiIvLyyy+LwWCQkydP5tz/22+/iV6vlzNnzhTYr8lkklKlSsnPP/+cswyALFiwQDPWwYMHS+/evQu877XXXpNu3bpZLTtx4oRVdecOHTrIHXfcYdXmtttuk7Fjx+bEbDQarWJetmyZVVxHjhwRALJ9+/abYqtevbpkZ2fnLLv//vulf//+mttDRK7jHhwisqlv3744ffo0fvrpJ/To0QOrVq1CXFxczp6HnTt3Ii0tDeXKlcvZ4xMeHo4jR47g8OHDOY9TrVo1VKlSJed269atYTabceDAAQDAuXPn8Oijj6JOnTqIjIxEREQE0tLScPz4cY9sx86dO7Fy5UqrGOvVqwcAVnE2btzYar3o6GicP38eAHDgwAHExMSgUqVKOffffvvtDsdw6623wmAwFPjYRORZPMmYiOwqXrw44uPjER8fjxdffBHDhg3Dyy+/jCFDhiAtLQ3R0dFYtWrVTeuVLl3a4T4GDx6MS5cu4cMPP0T16tURGhqK1q1bIzMz0yPbkJaWhrvuuguTJ0++6b7o6Oic6yEhIVb36XQ6mM1mj8TgzccmImtMcIjIaQ0aNMg5qTYuLg5nz56F0WhEjRo1NNc5fvw4Tp8+jcqVKwMAkpKSoNfrccsttwAA1q9fj08//RQJCQkAgBMnTlidAOyuuLg4/Pjjj6hRowaMRte++m655RacOHEC586dQ8WKFQGok6PzKlasGAB1vg4R+Q8PURGRpkuXLqFz586YM2cOdu3ahSNHjuCHH37A22+/jd69ewMAunbtitatW6NPnz74/fffcfToUWzYsAETJkzAli1bch6rePHiGDx4MHbu3Im1a9di1KhR6NevX87hnjp16uCrr77C/v37sWnTJgwcOBAlSpRwOubk5GTs2LHD6nLixAmMGDECly9fxoABA7B582YcPnwYS5cuxdChQx1ORuLj4xEbG4vBgwdj165dWL9+PV544QUAam8MAERFRaFEiRI5JzHnHSlGRL7DBIeINIWHh6Nly5b44IMP0L59ezRs2BAvvvgiHn30UXz88ccA1A/74sWL0b59ewwdOhR169bFAw88gGPHjuXs5QCA2rVr495770VCQgK6deuGxo0b49NPP825/8svv8SVK1cQFxeHhx56CKNGjUJUVJTTMa9atQrNmjWzurzyyiuoXLky1q9fD5PJhG7duqFRo0Z46qmnULp0aej1jn0VGgwGLFy4EGlpabjtttswbNiwnFFUxYsXBwAYjUZ89NFHmDp1KipXrpyTCBKRb+lERPwdBBEFt4kTJ2LhwoVBObvv+vXrcccdd+DQoUOIjY31dzhEdAPPwSEicsKCBQsQHh6OOnXq4NChQ0hMTETbtm2Z3BAFGCY4REROSE1NxdixY3H8+HGUL18eXbt2xXvvvefvsIgoHx6iIiIioqDDk4yJiIgo6DDBISIioqDDBIeIiIiCDhMcIiIiCjpMcIiIiCjoMMEhIiKioMMEh4iIiIIOExwiIiIKOkxwiIiIKOj8Pw7gIX2NBIowAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
-  ]
-}
\ No newline at end of file
+   ],
+   "source": [
+    "# Plotting the data\n",
+    "plt.scatter(x[:, 0], x[:, 1], c=y, cmap=colormap)\n",
+    "plt.xlabel('Sepal Length')\n",
+    "plt.ylabel('Sepal Width')\n",
+    "plt.title('Iris Dataset')\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Week 1/ML Examples/LinReg.ipynb b/Week 1/ML Examples/LinReg.ipynb
index da515cd..f91e091 100644
--- a/Week 1/ML Examples/LinReg.ipynb	
+++ b/Week 1/ML Examples/LinReg.ipynb	
@@ -1,256 +1,256 @@
 {
-  "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": 1,
-      "metadata": {
-        "id": "FC5yDl9FZlPV"
-      },
-      "outputs": [],
-      "source": [
-        "import numpy as np\n",
-        "import matplotlib.pyplot as plt\n",
-        "from sklearn.datasets import make_regression\n",
-        "from sklearn.metrics import mean_squared_error\n",
-        "from sklearn.model_selection import train_test_split"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "KAQqig5QZ9R3"
-      },
-      "source": [
-        "## You just made your own Linear Regresion Library (Sort of)\n",
-        "You can use this model for any linear regression problem you have. Make a new notebook and call this class using \"from LinRegScratch import LinearRegression\""
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "SyfD3VBYZ1KU"
-      },
-      "outputs": [],
-      "source": [
-        "class LinearRegression:\n",
-        "    def __init__(self, learning_rate, epochs):\n",
-        "        self.lr=learning_rate\n",
-        "        self.epochs=epochs\n",
-        "\n",
-        "    def fit(self, X_train, y_train):\n",
-        "        n_samples, n_features = X_train.shape\n",
-        "        y_train=y_train.reshape(-1,1)\n",
-        "        # init parameters\n",
-        "        self.weights = np.zeros((n_features,1))\n",
-        "        self.bias = np.zeros((1,1))\n",
-        "\n",
-        "        # gradient descent\n",
-        "        for i in range(self.epochs):\n",
-        "            delta= -(y_train-np.dot(X_train,self.weights)-self.bias)/n_samples\n",
-        "            dw= np.dot(X_train.T,delta)\n",
-        "            db= np.sum(delta).reshape(1,1)\n",
-        "\n",
-        "            #update weights and biases\n",
-        "            self.weights-= self.lr * dw\n",
-        "            self.bias-= self.lr* db\n",
-        "\n",
-        "    def predict(self, X_test):\n",
-        "        y_predicted = np.dot(X_test,self.weights)+self.bias\n",
-        "        print(self.weights, self.bias)\n",
-        "        return y_predicted\n"
-      ]
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "id": "FC5yDl9FZlPV"
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.datasets import make_regression\n",
+    "from sklearn.metrics import mean_squared_error\n",
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "KAQqig5QZ9R3"
+   },
+   "source": [
+    "## You just made your own Linear Regresion Library (Sort of)\n",
+    "You can use this model for any linear regression problem you have. Make a new notebook and call this class using \"from LinRegScratch import LinearRegression\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "SyfD3VBYZ1KU"
+   },
+   "outputs": [],
+   "source": [
+    "class LinearRegression:\n",
+    "    def __init__(self, learning_rate, epochs):\n",
+    "        self.lr=learning_rate\n",
+    "        self.epochs=epochs\n",
+    "\n",
+    "    def fit(self, X_train, y_train):\n",
+    "        n_samples, n_features = X_train.shape\n",
+    "        y_train=y_train.reshape(-1,1)\n",
+    "        # init parameters\n",
+    "        self.weights = np.zeros((n_features,1))\n",
+    "        self.bias = np.zeros((1,1))\n",
+    "\n",
+    "        # gradient descent\n",
+    "        for i in range(self.epochs):\n",
+    "            delta= -(y_train-np.dot(X_train,self.weights)-self.bias)/n_samples\n",
+    "            dw= np.dot(X_train.T,delta)\n",
+    "            db= np.sum(delta).reshape(1,1)\n",
+    "\n",
+    "            #update weights and biases\n",
+    "            self.weights-= self.lr * dw\n",
+    "            self.bias-= self.lr* db\n",
+    "\n",
+    "    def predict(self, X_test):\n",
+    "        y_predicted = np.dot(X_test,self.weights)+self.bias\n",
+    "        print(self.weights, self.bias)\n",
+    "        return y_predicted\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "0fYDHhVmHq63"
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "X, y = make_regression(n_samples=1000, n_features=1, noise=10, random_state= 42 )  \n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
     },
+    "id": "7BUJWqSBHysf",
+    "outputId": "b07142d4-1f41-4772-ff63-8e2d52f2cea3"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "0fYDHhVmHq63"
-      },
-      "outputs": [],
-      "source": [
-        "\n",
-        "X, y = make_regression(n_samples=1000, n_features=1, noise=10, random_state= 42 )  \n",
-        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1)\n"
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[15.07321206]] [[0.06528862]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "lr_model = LinearRegression(0.001,2500)\n",
+    "lr_model.fit(X_train, y_train)\n",
+    "y_predicted = lr_model.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 467
     },
+    "id": "Mlqs_OsBMxqn",
+    "outputId": "bb8cc34d-8454-491b-c2e6-5e201366dbd5"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "7BUJWqSBHysf",
-        "outputId": "b07142d4-1f41-4772-ff63-8e2d52f2cea3"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "[[15.07321206]] [[0.06528862]]\n"
-          ]
-        }
-      ],
-      "source": [
-        "lr_model = LinearRegression(0.001,2500)\n",
-        "lr_model.fit(X_train, y_train)\n",
-        "y_predicted = lr_model.predict(X_test)"
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[15.07321206]] [[0.06528862]]\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 467
-        },
-        "id": "Mlqs_OsBMxqn",
-        "outputId": "bb8cc34d-8454-491b-c2e6-5e201366dbd5"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "[[15.07321206]] [[0.06528862]]\n"
-          ]
-        },
-        {
-          "data": {
-            "text/plain": [
-              "[<matplotlib.lines.Line2D at 0x7f3cc217d480>]"
-            ]
-          },
-          "execution_count": 9,
-          "metadata": {},
-          "output_type": "execute_result"
-        },
-        {
-          "data": {
-            "image/png": 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",
-            "text/plain": [
-              "<Figure size 640x480 with 1 Axes>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "plt.scatter(X_train, y_train, color='blue', label='Training Data')\n",
-        "plt.scatter(X_test, y_test, color='yellow', label='Testing Data')\n",
-        "plt.plot(X_test, y_predicted, color='red', linewidth=2, label='Line of Best Fit')"
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f3cc217d480>]"
       ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
     },
     {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 449
-        },
-        "id": "p3BE_JQYJP_P",
-        "outputId": "2387087f-de42-4de3-a3f5-b40d05971953"
-      },
-      "outputs": [
-        {
-          "data": {
-            "image/png": 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",
-            "text/plain": [
-              "<Figure size 640x480 with 1 Axes>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "plt.scatter(X_train, y_train, color='blue', label='Training Data')\n",
-        "plt.scatter(X_test, y_test, color='yellow', label='Testing Data')\n",
-        "plt.plot(X_test, y_predicted, color='red', linewidth=2, label='Line of Best Fit')\n",
-        "plt.xlabel('X')\n",
-        "plt.ylabel('y')\n",
-        "plt.legend()\n",
-        "plt.show()\n"
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(X_train, y_train, color='blue', label='Training Data')\n",
+    "plt.scatter(X_test, y_test, color='yellow', label='Testing Data')\n",
+    "plt.plot(X_test, y_predicted, color='red', linewidth=2, label='Line of Best Fit')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 449
     },
+    "id": "p3BE_JQYJP_P",
+    "outputId": "2387087f-de42-4de3-a3f5-b40d05971953"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "NlfXI8HGK2hq",
-        "outputId": "2c3a43e3-aca9-4338-a609-79aca7e66c00"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "[[15.07321206]] [[0.06528862]]\n",
-            "Mean Squared Error: 99.23101918789216\n"
-          ]
-        }
-      ],
-      "source": [
-        "mse = mean_squared_error(y_train, lr_model.predict(X_train))\n",
-        "print('Mean Squared Error:', mse)"
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(X_train, y_train, color='blue', label='Training Data')\n",
+    "plt.scatter(X_test, y_test, color='yellow', label='Testing Data')\n",
+    "plt.plot(X_test, y_predicted, color='red', linewidth=2, label='Line of Best Fit')\n",
+    "plt.xlabel('X')\n",
+    "plt.ylabel('y')\n",
+    "plt.legend()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
     },
+    "id": "NlfXI8HGK2hq",
+    "outputId": "2c3a43e3-aca9-4338-a609-79aca7e66c00"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "vUW2VPQIkIxI",
-        "outputId": "2fc9a39d-d6a3-473c-f57d-47148f84125a"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "[[15.07321206]] [[0.06528862]]\n",
-            "Mean Squared Error: 110.42416649233857\n"
-          ]
-        }
-      ],
-      "source": [
-        "mse = mean_squared_error(y_test, lr_model.predict(X_test))\n",
-        "print('Mean Squared Error:', mse)"
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[15.07321206]] [[0.06528862]]\n",
+      "Mean Squared Error: 99.23101918789216\n"
+     ]
     }
-  ],
-  "metadata": {
+   ],
+   "source": [
+    "mse = mean_squared_error(y_train, lr_model.predict(X_train))\n",
+    "print('Mean Squared Error:', mse)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
     "colab": {
-      "provenance": []
+     "base_uri": "https://localhost:8080/"
     },
-    "kernelspec": {
-      "display_name": "Python 3.7 (pytorch)",
-      "language": "python",
-      "name": "torch"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.9.17"
+    "id": "vUW2VPQIkIxI",
+    "outputId": "2fc9a39d-d6a3-473c-f57d-47148f84125a"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[15.07321206]] [[0.06528862]]\n",
+      "Mean Squared Error: 110.42416649233857\n"
+     ]
     }
+   ],
+   "source": [
+    "mse = mean_squared_error(y_test, lr_model.predict(X_test))\n",
+    "print('Mean Squared Error:', mse)"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
   },
-  "nbformat": 4,
-  "nbformat_minor": 0
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
 }
diff --git a/Week 1/ML Examples/LogReg.ipynb b/Week 1/ML Examples/LogReg.ipynb
index c8e73ff..b81dbe6 100644
--- a/Week 1/ML Examples/LogReg.ipynb	
+++ b/Week 1/ML Examples/LogReg.ipynb	
@@ -1,245 +1,241 @@
 {
-  "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "BXSifC2Nbv60"
-      },
-      "outputs": [],
-      "source": [
-        "import numpy as np\n",
-        "import matplotlib.pyplot as plt\n",
-        "from sklearn.datasets import make_classification\n",
-        "from sklearn.linear_model import LogisticRegression\n",
-        "from sklearn.model_selection import train_test_split"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "xeDXxs9kb13H"
-      },
-      "source": [
-        "##You just made your own Logistic Regresion Library (Sort of)\n",
-        "You can use this model for any logistic regression problem you have. Make a new notebook and call this class using \"from LogRegScratch import LogisticRegression\""
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "EMWnEVDbborl"
-      },
-      "outputs": [],
-      "source": [
-        "class sigmoid:\n",
-        "    def sigmoid(z):\n",
-        "        a=1.0/(1.0+ np.exp(-z))\n",
-        "        return a\n",
-        "    def derivative(self,z):\n",
-        "        return self.sigmoid(z)*(1-self.sigmoid(z))\n",
-        "\n",
-        "class LogisticRegression:\n",
-        "    def __init__(self, learning_rate, epochs):\n",
-        "        self.lr=learning_rate\n",
-        "        self.epochs=epochs\n",
-        "\n",
-        "    def fit(self, X, y):\n",
-        "        n_samples, n_features = X.shape\n",
-        "        y = y.reshape(-1, 1)\n",
-        "        self.weights=np.random.randn(n_features,1)/np.sqrt(n_features)\n",
-        "        self.bias= np.random.randn(1,1)\n",
-        "\n",
-        "        for i in range(self.epochs):\n",
-        "            z = np.dot(X,self.weights) + self.bias\n",
-        "            y_pred = sigmoid(z)\n",
-        "            #cross-entropy loss function\n",
-        "            dw = -np.dot(X.T,(y - y_pred))/n_samples\n",
-        "            db = -np.sum(y - y_pred)/n_samples\n",
-        "            self.weights -= self.lr* dw\n",
-        "            self.bias-= self.lr* db\n",
-        "\n",
-        "    def predict(self, X):\n",
-        "        y_pred = np.dot(X,self.weights)+self.bias\n",
-        "\n",
-        "        for i in range(len(y_pred)):\n",
-        "            if y_pred[i]<= 0.5:\n",
-        "                y_pred[i] = 0\n",
-        "            else:\n",
-        "                y_pred[i] = 1   \n",
-        "        return y_pred"
-      ]
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "BXSifC2Nbv60"
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.datasets import make_classification\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "xeDXxs9kb13H"
+   },
+   "source": [
+    "##You just made your own Logistic Regresion Library (Sort of)\n",
+    "You can use this model for any logistic regression problem you have. Make a new notebook and call this class using \"from LogRegScratch import LogisticRegression\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "EMWnEVDbborl"
+   },
+   "outputs": [],
+   "source": [
+    "class sigmoid:\n",
+    "    def sigmoid(z):\n",
+    "        a=1.0/(1.0+ np.exp(-z))\n",
+    "        return a\n",
+    "    def derivative(self,z):\n",
+    "        return self.sigmoid(z)*(1-self.sigmoid(z))\n",
+    "\n",
+    "class LogisticRegression:\n",
+    "    def __init__(self, learning_rate, epochs):\n",
+    "        self.lr=learning_rate\n",
+    "        self.epochs=epochs\n",
+    "\n",
+    "    def fit(self, X, y):\n",
+    "        n_samples, n_features = X.shape\n",
+    "        y = y.reshape(-1, 1)\n",
+    "        self.weights=np.random.randn(n_features,1)/np.sqrt(n_features)\n",
+    "        self.bias= np.random.randn(1,1)\n",
+    "\n",
+    "        for i in range(self.epochs):\n",
+    "            z = np.dot(X,self.weights) + self.bias\n",
+    "            y_pred = sigmoid(z)\n",
+    "            #cross-entropy loss function\n",
+    "            dw = -np.dot(X.T,(y - y_pred))/n_samples\n",
+    "            db = -np.sum(y - y_pred)/n_samples\n",
+    "            self.weights -= self.lr* dw\n",
+    "            self.bias-= self.lr* db\n",
+    "\n",
+    "    def predict(self, X):\n",
+    "        y_pred = np.dot(X,self.weights)+self.bias\n",
+    "\n",
+    "        for i in range(len(y_pred)):\n",
+    "            if y_pred[i]<= 0.5:\n",
+    "                y_pred[i] = 0\n",
+    "            else:\n",
+    "                y_pred[i] = 1   \n",
+    "        return y_pred"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 75
     },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 75
-        },
-        "id": "cq4zAghD-z0A",
-        "outputId": "7855a22c-e161-464c-b39c-61ca46f71804"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/html": [
-              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
-            ],
-            "text/plain": [
-              "LogisticRegression()"
-            ]
-          },
-          "execution_count": 8,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "import numpy as np\n",
-        "import matplotlib.pyplot as plt\n",
-        "from sklearn.datasets import make_classification\n",
-        "from sklearn.linear_model import LogisticRegression\n",
-        "from sklearn.model_selection import train_test_split\n",
-        "\n",
-        "# Generate classification data\n",
-        "X, y = make_classification(n_samples=1000, n_features=2, n_informative=2, n_redundant=0, random_state=42)\n",
-        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1)\n",
-        "\n",
-        "# Create an instance of LogisticRegression\n",
-        "logreg = LogisticRegression()\n",
-        "\n",
-        "# Fit the model\n",
-        "logreg.fit(X_train, y_train)\n",
-        "\n",
-        "# Predict the output\n",
-        "y_pred = logreg.predict(X_test)"
-      ]
+    "id": "cq4zAghD-z0A",
+    "outputId": "7855a22c-e161-464c-b39c-61ca46f71804"
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.datasets import make_classification\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "# Generate classification data\n",
+    "X, y = make_classification(n_samples=1000, n_features=2, n_informative=2, n_redundant=0, random_state=42)\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1)\n",
+    "\n",
+    "# Create an instance of LogisticRegression\n",
+    "logreg = LogisticRegression()\n",
+    "\n",
+    "# Fit the model\n",
+    "logreg.fit(X_train, y_train)\n",
+    "\n",
+    "# Predict the output\n",
+    "y_pred = logreg.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
     },
+    "id": "TOaFPsXEBUD-",
+    "outputId": "66e3fc78-8e46-41e5-a31e-3d7ae711b90e"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "TOaFPsXEBUD-",
-        "outputId": "66e3fc78-8e46-41e5-a31e-3d7ae711b90e"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "0.92\n",
-            "[[39  4]\n",
-            " [ 4 53]]\n"
-          ]
-        }
-      ],
-      "source": [
-        "# Evaluate the output\n",
-        "from sklearn.metrics import accuracy_score, confusion_matrix\n",
-        "print(accuracy_score(y_test,y_pred))\n",
-        "print(confusion_matrix(y_test,y_pred))"
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.92\n",
+      "[[39  4]\n",
+      " [ 4 53]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Evaluate the output\n",
+    "from sklearn.metrics import accuracy_score, confusion_matrix\n",
+    "print(accuracy_score(y_test,y_pred))\n",
+    "print(confusion_matrix(y_test,y_pred))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 448
     },
+    "id": "vjyfqDL_CDgB",
+    "outputId": "3e6a9b73-62d4-45f9-e92c-d6c979bb7b00"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 448
-        },
-        "id": "vjyfqDL_CDgB",
-        "outputId": "3e6a9b73-62d4-45f9-e92c-d6c979bb7b00"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "<matplotlib.collections.PathCollection at 0x7f037bbc5420>"
-            ]
-          },
-          "execution_count": 16,
-          "metadata": {},
-          "output_type": "execute_result"
-        },
-        {
-          "data": {
-            "image/png": 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\n",
-            "text/plain": [
-              "<Figure size 640x480 with 2 Axes>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "plt.subplot(1,2,1)\n",
-        "plt.scatter(X_test[:,0],X_test[:,1],c=y_test)\n",
-        "plt.subplot(1,2,2)\n",
-        "plt.scatter(X_test[:,0],X_test[:,1],c=y_pred)"
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x7f037bbc5420>"
       ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
     },
     {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "background_save": true,
-          "base_uri": "https://localhost:8080/",
-          "height": 472
-        },
-        "id": "qi9xSQYxF0b-",
-        "outputId": "4a01b401-fb01-4e33-e6cd-4a84ff1c0450"
-      },
-      "outputs": [
-        {
-          "data": {
-            "image/png": 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\n",
-            "text/plain": [
-              "<Figure size 640x480 with 1 Axes>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "plt.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap='bwr')\n",
-        "\n",
-        "# Plot the decision boundary\n",
-        "x1_min, x1_max = X_train[:, 0].min() - 1, X_train[:, 0].max() + 1\n",
-        "x2_min, x2_max = X_train[:, 1].min() - 1, X_train[:, 1].max() + 1\n",
-        "xx1, xx2 = np.meshgrid(np.arange(x1_min, x1_max, 0.1), np.arange(x2_min, x2_max, 0.1))\n",
-        "Z = logreg.predict(np.c_[xx1.ravel(), xx2.ravel()])\n",
-        "Z = Z.reshape(xx1.shape)\n",
-        "plt.contour(xx1, xx2, Z, levels=[0.5], colors='black')\n",
-        "\n",
-        "plt.xlabel('Feature 1')\n",
-        "plt.ylabel('Feature 2')\n",
-        "plt.title('Logistic Regression Decision Boundary')\n",
-        "plt.show()\n"
+     "data": {
+      "image/png": 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s2us8r2hfrIp7oeopmj9kzAD3vimtGZOJn8NMjFmIWGirHL16z+Di5ObWEypU8Zso14Bkhwak+RoRIcQG2qqG6pcIfdKpBZ4p6MCyZIYlhEh3NRODIyGhNjUY6OrxSQyo5SQRESKVfD8H6z+Eo8EzIynhCCEyg/Z8SegbGLALX36epGhaR07fFWlDaw21k9HVL4JvLnYp++Go3NNRroGpDi9BQpfibswfuYkQ7ZQOrERXvwDV74AuA6MTKudYyDmp7a6r074o2kTbv6SWJCIiLWit0eXXQ80b2AN1FuAHzzS0ZyoUjEPlHJHiKONL+35BVz4eXWMpyS1Es7R/IXrtscGKscFfvNYKdOVDUDMJSl5DGcUpjTGetPZD1bP2aGpYBjhTsz4kVjI1I9KDZ0owCYHGc54BQKPLr0EHlqYgsMTQ3h/Qa48D348RWpr2djxn6PLmQrRX9knllzZOQupZEFiMLr81FaElhNYWev1YdOV9QE2E1hYq9+RkhNVqkoiItKCrXiTSj6OufiPs85lCawtddhV2xxnubBADjGJU4X1JikyIDOP/Ffy/E/aA0NqP0IE1yYwqcTzT7Ju2sGtDgv1ozingHpWMqFpNpmZEevD9SvhfylYUQ5EZwvu9Xd8hkuzjUHkXoMySxMckRCbyzY2iUQD888HsmPBwEk1Xv0b4k9mxzyErvB3ce2VM7RgZERHpQTkjNQBcyYgk8QL/RtVMZQ2XJESIsCL1G0ER+5cM4f+biAvcjUJU1qiMSUJAEhGRLtwjaXo+RmMqa2RyYkk0FeUq/mjbCdFeuXen6XlcG1F5bWextxFFcb4M7DckERFpQeWeRuh5TxNUB8hqI+dkuIcD7vBtjM7g3CEZ0QiRsZTZDbIOJOyvspxTUSoraTElkso6mPCJl8rI84QkERFpQTm3QxXeg71sqe7HMviBUwWo4ufbzHkrysiH3DPDt8m7qN0ddidES6iCW8C1S/A7s/GfWYej8sakIqzEyBkNRgnNjx6b9g1MduaVOZDFqmlKW5Xg/QZ0LTj6tYvtmyr7IHDtDDVvor2/gHKi3MMg62CUkZvq8OJK5V2IxgNVzwUfMbDnfh2o/LGonKNTGJ3IZNr3B/j/tA+7c+3Wdgt6BSkjF4rGg/dbdM27YK0Fc1NU9lEpO2clUZRRBMUvo0vPDa41c2CPJAfA3AxV9AQqmumbNCOJSJrROoCufACqxgOeDY87tkMV3oFy9ktVaEmhzC6Qd2GkWd+Mp5SByv8fOudkqP0Qba1FGV0h+0C7sxEiRtq/AL3+SntLaz03OvcUVN6lbXqETSkD3Luj3LunOpSEU44+0HEKeGegvTMBhXLtAq4hGbVAtSFJRNKMLr+xQWGvBvy/o9cdCyUTUY6eyQ5LJIgyN4Hc09t84iUSS/uXBKuLVm30jAeqnkZbpajC21ISm4g/O/HaA+XeI9WhxIWsEUkj2r+g+SQEgADo6uhLggsh2g1d+XgwCWlua6eGmrfs/kWINCSJSBrRNZMIv4U1ALXvo7U3WSEJIdKc1j6ofZ/w9SVMe/2EEGlIEpF0YkVThtiLXnsCunaKfVqtEKJ901U0XE/WvABUPY9VfivaH0VVXyGSSBKRdGJ0jq6d/xf0+ovQ5bdKMiJEe6dyiViXBgAfVL+CXnsI2vd7oqMSImqSiKQRlX0YEcv3AvWFv2peAs/0BEYkWkNrH9rzObr6DXTtNJlSEwmhlBOyDyNSZWJbAHSNfSOjw53tJFJJB5aha95BV09oF2t7ZNdMGlGO3ujsk+wEIyomuvpFVNaeCY1LxE7XfICuuBWsdRseVIWQfyUq56jUBSbaJJV3Hrr2E9DlRL6ZCUBgMXi/DZZIF+lCW5XosmvA8zENK01r5y6oDvfYu+zaIBkRSTOq4P9QeReByomidQB8vyQ8JhEbXTsFXTa2cRICoMvQ5degq99OTWCizVJmN1TJ6+DcMcpXmFGeXCuSRWs/uvR08HxCk+MufD+i1x6LtspSEluiSSKSZpQy7KPfO80Ax5ZRvKKNnCrZRmhtocvHhW9TcZdM04i4U47eGCWvQodHomit286JtG2FZxr45gDNTZkFwFoB1aHKO2Q2SUTSlDJyUNmHE/6AIxPcMi2TVnyzwVoevo0uBe+M5MQj2h3l3hOMjhFaWeAelpR4RHR0zTuE/5VsoWsmJCucpJJEJJ1lHxE80rm5fyY7QVG5pyQ1JBGBtTa6doF1kdsI0QJKOVC5Z4VpYYJrGMqxedJiElEIrKb50ZAGou1fMowkImlMGR1Qxc8HkxHFhtERA3CgOjyAcm6VugAFAFp70N4f0Z7v0ER5OF8bXXQm0kTOqZB9UvCbjU6kdW6H6nBvCoISG9P+/9CeGWjfXDA2JfzOJ9Vm+w3ZNZPmlHM76DQdaiehPV+D9qNcAyH7KJTZKdXhtWtaB6DqMXTVeNAVwUcdQC6w8ZkfdRQYXcA1OCkxivZJKYUqvA6dcyS65i3wLwajCJV1ILiHtekD8DKB9i9Al98M3u82PGiUEGnHk8o+JrGBpYgkIhlAGXmQcwIq54RUhyKCtNbosquhdhKNV7j7CT28GpxOK7hefhGIpFDO/ijnDakOQzSg/f+g1x4DurrxE2GnXUxwbA7ZbXPrv0zNCNESvjlQ+y5NttkBdiKiQJU0ftjsierwBCprVMLDE0KkJ11xTzAJCTX6YQKuxt9nHYAqfhllRFPWIfPIiIgQLaBr3sLuMEJ1JhqMHFSHZyCwEsyO4NgOpcLtghJCtGXaWmdv0232BqaOBflXoxx9AD84tkWZkXZBZTZJRIRoicB/RKxgGViBcm4Dzm2SEpIQIs0FVhI+CQEwwVqHcp+cjIjSgkzNCNESRgkRz/YwCpMSihAiQxhFUTQKoKJq13ZIIiJEC6isQwg/ImLadWCEECJImZuAcxDhf/UqyNo/WSGlBUlEhGgJ97Bgh9LcqIgJRiEqp/0MrQohoqPyx9K4LtRGck5DmZ2TGVLKSSIiRAsoZaKKngb3KDZ0KsGOxbEFqvjVdteZCCEiU66dUUVPNijDX/dr2AW556Lyr0hVaCkji1XbOK194PkUXfMh6PVg9kXlHI1y9k91aBlPGXmooofR/iXg/Qa0D5zbg3N72R0jMp4OLEVXvxk8iM2Fcg+H7MPsukaiVZR7GHT6ArxfB4vN5YF7L1Q7XVcmiUgbpgNr0KWngv9P7KzbAmaha15F55yKyr9afmHGgXL0AMexqQ5DiLjRNe+gy64JfmfXxdHeL6HyISgeLzcycaCUA9wjwJ3qSFJPpmbaML3+AvD/HfyurtpncIFl9XioeS0FUQkh0pn2zrGrBmOxod/Q9pcuR687DW1Vpi5A0ea0+xER7f8HXf0KeGYAGty7onJOzPiTKbXvF/tI+nBtKp+C7GNRSvJRIWKhtQ9qP7YL2wWWgtERlX04ZB+CUtmpDq9VdNWz2Peoze0Ks0CXQu17kHN8kiMTbVW7TkR0zQfossuxFxnWjRQsQle/DoXj7I4lU3m+InzlT8BaBoFF4OidrKiEyHha16DXnQW+H6if8gwsQftmQ/ULUPwyyihOdZgt5/mS8FvTFdrzJUoSEREn7fZWWPv/CSYhFo0/dAHAQpddjfb9mZrg4kBrPyG3hzVq6E94LEK0Jbr8TvDNCn7XcOoC8C9Er/9fKsKKowgVg9H2wmwh4iShici4cePYeeedyc/Pp3Pnzhx22GHMnz8/kZeMmq5+lfC/qBW6+uVkhRN3yrk99kmw4RrlgaNnUuIRIlpp3W9Y5VAzgdAnLAfA+yXa/28So4oz57aE/9Vg2LvDhIiThCYiX3zxBWPGjOG7775j6tSp+Hw+9tlnH6qqqhJ52eh4ZhA+8w/YWzIzlXsYGF0J/U9sQM6xKCVLtkV6Set+w/cr4I3czjsz4aEkiso9hdCJFoBC5RyTrHBEO5DQNSJTpkxp9P348ePp3LkzP/74I8OGDUvkpaMQ6eChaNukJ6VMKHoEve5k0LVsSLqCo0DOHVB5F6YqvIyi/YsgsASMAvskTFncm1CZ32/E0i4NufeD7OOCu+rqtv2DveZMowrvskuVi7C09oLvF9AecPSTAodhJHWxallZGQDFxc0v5PJ4PHg8nvrvy8vLExeMewhU/0voURETXLsl7vpJoJzbQcn76OoXoeY90JVg9kTlHAc5R8toSATaNw9dfnOD9QCA0Q3yL0dlH5S6wNqZSP0GJLHvcG4HOIEIayRcOyXm+kmglIKCG8E9BF31oj0KpBzgHonKPTU47StC0dqCqmfQVU+DLgs+aqDde6EKbpCEpBlKa52U1N2yLA455BDWr1/P119/3WybG2+8kZtuuqnJ42VlZRQUFMQ1Hu1fgF5zEKGHIBWqZBLKuVVcrysyg/b9iV432r6baeZnRBXc0m6Gp8vLyyksLEzI5zCSaPoNSG7fYZVdBzVv0XzfYYJrCEbxc3G9psgcVtktUPNSM8+YYHRBdXwns3dVRSmWfiNpY8xjxoxh7ty5vP766yHbXH311ZSVldV/LVmyJGHxKMfmqMI7sf8XNDy4zAQUquA2SULaMV1xB2gvoRJVXXE72kqDNQttXDT9BiS578i/Gpw7BL+r60KDZw2ZPVGFdyXs2iK9ad9fIZIQgABYK4N1WkRDSZmaueCCC/jggw/48ssv6d69e8h2brcbtzt50wUq+1Bw9LcLmnm/BjS4dkPlnCBJSDumAyuDC5XDDBbqGvB8DNlHJC2u9ibafgOS23coIweKX4Taj9DVb2woaJZzJGQdZj8v2iVd8w7h6zcFoPpNdN7lcrxGAwlNRLTWXHjhhUycOJHPP/+c3r3Tr3CWcvZDFd6Y6jBEOrFWEnmxoQMCy5MRTbuTEf2GckH2ofbNjBB1rGVE7Dt0GeABspIQUGZIaCIyZswYXn31VSZNmkR+fj4rVqwAoLCwkOzszC6DLNowVRRFowC0g3neVJB+Q2Qso4jIhSSzkJPuGkvoGpHHH3+csrIyRowYQdeuXeu/3njjjUReVohWUY4e4BxA+I+HCVn7JiukdkX6DZGpVNahhK9PZUL24TIts5GET80IkSm0DgT3/ZdB9vHguxr77qaZn+Pcc9rFyvdUkH5DZBodWAv+39HaANew4JrDjRe6m6ByULlnpCLEtNauD71LZ9r3G7pqfPDwOgtcg1A5p6Dcu6Y6tDZJ10xCV9wL1ooND5p9wCq1TxutT0jcqLxzIff8FEUqRGhae6HmHXT1axD4D4wOqOzDIOcESZwTQFvr0eW3QO2HbBgJyQJzcwj8hd1nBPsOsw+qw70oOVajCUlE0pCueRdddiWNjuL2fI72TIO8S1B5kX8Jat+v6JoPQVegHJvZq/nNjokMO2Pp6tfR5dc3fSLwL+CC/OvtWV+jwC7qZOQnN0AhoqCtanTpqeCbQ/0vv0AFuvJRqH4Nil9DOXpFfA9qJ6N9v4JyotxDwTXUrtQsGtFWFXrdieD/m8bTMbV2EuIaCu59UcoLjq3BuaNMyYQgiUia0f7F6LKrsDPpjU8FBl35ADgHhhwZ0VY1uuwS8HxOXU0UjQUV90L+NajckxIZfsbRVhW6fFyIZy3AB55PUcXjkxiVELHTlffYU4v2dw2escAqRa+/EEomhfxlqD3foNdfBLqCul8Nuvole2Sw+FmUuWlC4884NW+Cv27UY2MavF+ick9HuTO7QncyyKEZaSbyqcCmXbI91OvL/geeL4PfBbBP4LWAALriFnTtR3GLtU3wfALUhGkQAO8MdGBFmDZCpJa2KqE6wqnA/nnB0ZJmXu9fgC49xz4GArD7jeDp3YFF6HUn29M+op6ujrR42kTXTEhKLJlOEpF045tF5FOBZzX7jPYvCP5iDV22Xlc8nNGLAbUOoGs+wFp7HNbKnbFWjcCquBvd0poegRVENTAYWNmy9xciGfx/AbURGhngm93sM7rqeex+p7m+IWAf+pjhNzE6sByr4m67z1i5M9ba49E1H9hnw7SEtYLwNUMCdrE7EZFMzaSdKOZiQ83X1k6j8WmZG9MQWGB3KlEumNLaHmLUVa+A/zdQWZC1n1191uwW1XvEi9Z+9PqLwTOV+r+nLoOq5+zFecUv2Af9xcIoInzi17CdEOkq2nvKUH3HFMJ/Dgx07ScxFXDT1jqofgNdMwl0ub1YM+c4u/9I8poT7f0ZXXqaXRG57u/pm40umwW1H0OH+1Eqxl+Hqgh0dZgGBhidWhpyuyIjImlGuYcSsX6Fq/mj0LWuifDauobhpiIavp9Gl9+ALj0LvF+BtdpOYqqeRa/ZH+39Mar3iZvq8eD5NPhNw2QrALoaXXouWkc4FXVjWfsSPh83wLmDrHQX6c25NahIh/tZ4Nq9+ad0pNEUC3T0Zytp/z/oNQeiKx+EwD9grQHfLHTZpejS85I6zaO1F73+vGDS0DDZCvYhnk+g+oWY31flHEn4/taydyyJiCQRSTfZxwAuQq8T0ajcU5p9Rjk2p35eNyQXRLvorOYdqKk7bGyjD7D2oEvPsVfZJ4HWAXs7c8ihUMtOlOoTlegooyjMLiT730DlXx7TewqRbEq5gv1CqH7DtM/RcvZr/mlH3zCvDb7esUVUsWhtoUvPA2s9jW8Ygv/t/QJd+URU7xUXtZ/YiVCYkWJd9ULsUzQ5J4DRmeZHmUxwDgT3iNjes52SRCTNKLMTquhJ7DLADf95TMBAFd6JcvZv/sVZ+4AqJGxnlH0YysiLKhZd/VyY97Ls4dbayVG9V6tZq+yvsBxo70+xv3fu+ai8scBG5cONLqiip1CuXWJ/TyGSLfc8yDog+E3dL8dgH+LYHNXhvpAvVTknRHjzACrn2Oji8H4LgYWEnurRUP1y0kZFtG8OEVchWCui6F8aU0YRquS1YBXmRs+Ae29U0TOxT/e0U/J/KQ0p9xDo9CnUvIn2fAUEwLUzKvvYsFMESrmgwz323UiT7b8mmN1R+WOjikFb1cEFcOGYaN+PKEZH9Z6tE23OHHturZSCvHMh50R7x5EuB7MHuHaV+gkiYyjlgML7IHs0uuYt8C8Co9he15G1r90/hJJ9pL3GzPsFjUcd7bVYKu9ylKNPdIH4ZhP+BFpAr7fjCzVCE1etXD8ThjI3RZW8jvbNA9/PgAPcu8pW5xhJIpKmlNkJ8sag8sbE9jr3cCh+DV31WLCWiAaVC9lHo/LORUW76DLqwjtJKtBjdAZzMwgsIvT0jL9Ve/aVkQfZB0RuKESaUkqBe7eYPwdKOaDoUah+AV314oYKw45tUHlno2I6VynKPiFJxb2Uezd09fhwLcDsDUbLCz4q51bg3KrFr2/vJBFpg5RrAMr1pD2qoavBKEQpZ2zvobLRjm3A/wfhahMoV3JKziulIPdMdPm1IVqYYPYE1x5JiUeItkYpJ+SeCTmn20cbKCfKiLQAthmuwcBD4dsYHe0bi2RwDQ3exCyh+VEajco9S6qeppCsEWnDlJGDMjvGnITUvz73DEInIabdmWTt1+L4YpY9GnJO3XB9oP7uy+iMKnoapeRHWojWUMpAmSUtS0IAnDvZJc3DTHWonFOTtn5CKRNV9HSDrbR1CUcwvpzTIfuIpMQimicjIiK0rAPB/ydUPUHjOV8FKj+4GMudtHCUUqiCa9BZB6CrXwf/fDDyUVkHQNbBKCM3abEIIZqnlIKix+xzWALLgo9q6vuQrIMhySfQKkcv6PgR1L6Prp0MViU4tkTlHIdybbzYVCSbJCIiJKUUKn8s2r0nuvoV8P8OKhvl3hdyjkzoaZ46sDq4WHc6aD+4dkRlH49y9kO5dkC5dgj9Wv9CdM179nZeozMq+zCpAyJEEilzUyj5AGonoWvetwsPmn3snTeu3RM2DWIXYPzWLr8e+AdUB1T2QZB9iH2jknNsyN0/WvvAMw3t/c5eWucaFHmRr4gLpdO43nd5eTmFhYWUlZVRUNDCYUKRcbR3Jrr0TNAeNkwNmYCFKrg+5FZDrQP2kdw1r7JhWNjCvhtzgbEpKnc0ZB8jJ+jGIBM/h5kYs2gdrQPosquh9l02jOAGTyE2e6KKX0aZmzT/Wt98u3CjVXfkg27w+lx7J0zOSfaORhGVWD6DMqEu0oq2Su0OoVESAnXnYOjym9Demc2/tvLBYBJS177h2RlesBaiK+5Grz0cHYitZoAQIs1VPRdMQmDDNHLw8x9Yii4d0+w5W9oqRa872R5BBeyikA1fXwmez9Clp6ArH09U9O2aJCIivdS8EyxBH3qRrH1AV2PaqoRmHm9K251S2dWtiVIIkUa09gcLMIYSAP+vzZ8+XPO2Xdck7Fk7dn+kK+9He75teaCiWZKIiLSiPTOIeKKld0bTh73fAJ4orxIA71do/6LYAxRCpJ/Av2CtjdDItKu+bkTXfET4Pqfxe+jql2IMTkQiiYhIM1Gc99DcmRBRHuTXiK8F5eCFEOkn6nNimus7oj/Mz76JmRVDexENSUREWlGuQUQ+fXhg04cdLSkVLZvGhGgTHJtFcfpwAJw7Nn3YGb7mSVPyazPe5P+oSC/Zo7E7hVDb+wKo3FMbPaKtSvDOBGKpaWKAa+cWhSiESC9KuezTcEP+SjPtMu6uxqXvte/X4CnB4daHbPQ+7qEtD1Q0SxIRkVaU2QXV4QHsZKThXUrwv3PHoBocra0DK9BrD0FXjCP6NSIKsg5CmV3iEbIQIg2ovDENEo2Gv9oM+5iLosca1S/RVS+j1x7Z7LqR0KwmN0Ki9WRsWqQdlbU3dHwPXfUyeD4DfODcsdl9/Hr9pRBYTvSLzQBMyA91Zo0QIhMp5YKip6B2Mrr61eDpw/mo7MMg+1iUWVLfVnt/RlfcHPwu2vUlQNaBKOc2cY1bSCIi0pRybI4qvBG4MWQb7fsDfD+24N39KP8cMEe0KDYhRHpSygHZh6KyDw3bTle/SONjK6Lk+7XFsYnQZGpGZC7vLKI+crwRBf4l8Y5GCJEpvN8TcxICEFga91CEjIi0Odr3F7r2XQisBrMTKvtwlGPzVIeVIC09r0JDS08WFaIN0toLtVPsc1YA5RwI2QeiVHaKI0uUFvYdSo6GSARJRNoIrf3o8hug5i0aLvLUVU+js4+zz2hRsWxRywCuwcS2NqT+heDeM97RCJGRtO8P+2wnazV1fYeumQAVd0LRUyhXM1teM51rd6idRGyjIiZEmPIRLSNTM22ErnwAaiYEvws0+AJqXkNXPpKawBJIOfuBawix1QAAlXeOHHonBHXnrJwC1rrgIw36DV2BLj0NHVieqvASRuWeTEyLVDFB5ciOmQSRRCSDaWsduvJprLUnQdXThB0dqH4ObVUnLbZkUYX3grlZiGfrfrwd2EOxDsgdA7kXJCU2IdKR1hrtnYW1/ir0miPDnLNigfagq19JcoSJp5z9UQXjsPuIhr8GG07ZGNTf5JibBk/v7Za0GNsTmZrJUNr7A7r07GBp8yimJ3SNvUAra2TCY0smZXZE518D688DvBs964KcU1BGNhjFkLUPyihORZhCpAWtA/aBj7XvEt2ukQDUToH8yxMfXLJlHwK1U8E7relzjp0gaxgKDc7twTUEpeS+PVEkEclAOrAave4s7AJesayRiLbgV+bQgeWwfgz20d0b80L1C9DpY5TZNdmhCZF+qh4PJiEQ9foIXZuoaFJKVz4C3s82ftT+wz8bApuhCm9PelztkSQimajmTeykIpY5Tlp4HkvztLUOat5Be38CTJR7N8g6GGXkxu0aUcVR/Rr2SEhz/y8swIuufg2VPzapcQmRbrT2oqvGx/gqExxbxTEGDb7Z6JqJ9mm5Rhd7Z59rQNyuEVUcVjVUjyf0jZwFNRPReZeizE5JjKx9kkQkA2jf73alQN9PgDN43HWMC62cO6IcfeMTT+109PqL2DAVotCeKVBxPxQ/i3JuG5frRKV2KuH/X1hQ+ylIIiLaGa09UPPBhl/6qgB0eYzvEkDlnhineLzo9ZeA51M2TAuZ6JpX0VkHowrvtAuSJYPvJ9CR1swFwDtDdsokgSQiaU5XPYeuuIMWVQEE+3WqIG5DjNq/AL1+TDCWuruJ4J+6DL3uNOg0FWV0iMv1Qsahtd2R1K/2D6ftTUkJEY621qHXnQT+v7AXYGpi25sQfE32MeAaHp+Yym8HT916jEDjP2s/QJtdUUlYi6K1Hx1YFWXjjdediUSQRCSNac+MYBICLUtC3JB9hL1dNU6rvXXVS9idWnNDmpZ9x1XzDuSeHpfrNbm+tqDmDXuIObAwileY4NwhIbEIka70+svB/0/dd8E/YxhFNfuick+D7KMaHRTX4nisdcEp5VBTIRqqXkTnnpew6V3tX4yuehJq3iPqmxM5VyYpJBFJY7rqeVo8EtLhMZR7RPyHOj1TI8Sj0bXTUAlIRLS20GVXQO37RF8ZMYDKOSHusQiRrrR/AXi/bsErTTD7QPHrGGac6+x4ZtD8gvKGasE3C9zxGYFpSPvmo9cdH5yOibI/dWyJcvaPeyyiKUlE0pn3W2Ku/EcAci/AyBqVmJi0L4pG8ZsK0To4Txv4D+37O5iEQNS7hVQHlGtg3OIRIu15f2jBiwwwClFFj6DinYQAEE2/QVynQnRgFXi/RlteqH4OdBWxnbR7SNxiEeFJIpLpVEegFrDAOQiVeyrKPTRx13NuE+HAKNPedx8H2vM5uuw6sFa2/E2kbohodzQb1oWEYXQFqxSMQsg+HJVzUuJ2iDiimeJQ4Ni61ZfSuhZddhPUTiTmnYUNozE7tjoWEZ2EVmj58ssvOfjgg+nWrRtKKd59991EXq7tce1E+PLlBir3FIwuszG6zMEofjaxSQigck4m/CiNhco5vtXX0Z7v0KXnghXlorJQ3Lu3OhaRfNJ3tIJzEJFHDF2ojh9gbPILRuevMPLHJnSbqnJuAc6BhO7PTHANRTm6t+o6Wmt7Z04rkxAAnDu37vUiaglNRKqqqhgwYACPPvpoIi/TZqmcUwj9S18BTsgZncSIAPdIyK7bztfwx8fuYFT+NfWn/Wr/IqyKu7DWnY5VegG6ZqK9pTAKuuKuuv9qXbxZ+7fu9SIlpO9oOeXcCpzhbmIMyDkq6ectqcK7wOhA07gMMDqjCm8Fgrtaaj/GWn8x1rrTsMpvRvvmRXcR34/g+YxWJyHGJq1OikT0Ejo1s//++7P//vKLoKVU1kh07hioepTGi1ZNwLDnc5M89aCUgoLrwDUQXf0C+H4BDLsEcu4ZqOAIhK4aj66oO8shABhozydQ8SAUv4hy9Ax5De3/F/xz4xSvHG6XiaTvaB3V4T57cWZgafCRuu27ll1TKO9/yY/J0RNKJtmL8GsmgC4DVQw5o1G5p6GMYnRgLbr0NPDP2xCv9zt09cvo3LNQeZeH3cWjaybR8lIHDZih+ycRf2m1RsTj8eDxbLhjLi+PtfhO22PkX4x2D7G3zfrmgHKCey9Uzgkox2Zxv54OrLG3x9Z+CFY1OLe0p1pcQ+s7AKUUZB+Eyj7IrudR91jde9ROR1fU1S2p6xCCdyjWSnTp6dBxSugdPVHVBomGAqmK2C5I39GYMjeBkklQMwFd8479mTK7o3KOgayDUMoV1+tprYMJwyvg+w1UFmTth8o5FmV2aRBXZ1TBlVBwJVpbTc5v0esvDNY+gQ2jGsE+pOppMHtAzrGhA7HW0erREEwIc6Mk4i+tEpFx48Zx0003pTqMtKNcu6BcuyT8Otr3u30kuK6g/sPsWYH2fAbZo6Hg1iZ3I83dneiqp6i/m2kiAIHF4JkOWXs3H4i5SWv+GnVvAu7hcshdOyF9R1PKyIfc0+x6IAmktbZHP6vH02g0oupxdPV4KHoe5dqhaXwbJyG+X+ztuyEpuw5I9tGhD6Azu7JhFLalAqjsI1rxehGrtDpO8Oqrr6asrKz+a8mSJakOqd3Q2hs8zbdBEgLUf6Br3oKaN6J4nxp7njbsXYkD7fki5LPK7AauwYT/8XSBGWqPvwHKhcqTsu7thfQdKVT7QTAJgcYJgAW6Bl16tt0vROL5ivCL87U91RRYHLKFnUCES0IMu99QOTRfi0iBe//ggl+RLGmViLjdbgoKChp9iSSp/TS4QyVUAqHQVc/WT8WEpKO5E9ER65Go/KsBJ6F+RFXB9Rid3kUV3AxGSeMnnduhil+zV+qLdkH6jtTRVc8SusCgBXo91EyO/D7aF+Z9GjYM3XcoZ3/IPjrEsyYYXVElz6E6fgruvTa6XhbknonqcE9cqsmK6KXV1IxIHe2bif3jEKr6oYbAouBcc0mINoDKtedxA/8R7mRL5Qpfa0Q5+0PJa+jym8D3c4MniiHvIlSO3dmonGMh+0jwzrZHc8xeKGf8ThkWQoSmdS34f4/QykR7f0DlHBW2lXJuh45UfVXlRly/oQpuAnMTdNVzoCs3POEcCIV310/XqqLH0IHl4PvdXnvnHIgy8iL8XUQiJHREpLKykjlz5jBnzhwAFi5cyJw5c1i8OPTQmkiVKO8AItwpKKWC247DyYKs0Cdaau1F++baoysdxkPuGFCFwSfXQcXNWKXn250IoJQT5R6MyholSUgbIX1HO+QeDkYXQvdFCrKPQSl3yLfQgRX2Tr6sA6Hk3WAtkOCvOd9MWHcUuurFDYvsza6orL1Q7mGShKRQQkdEZs2axciRI+u/HzvWnrM/5ZRTGD9+fCIvLWKkXLugq18O1wLMXqCKIr6Xzj4WKh8FXdp8AyOX5qaAtPZD1RPoqhfsrX1A86M0Fnimo9f+AiXvoMzOEWMSmUX6jsygVBbasW1wVCTUtG4A5RocxXs50HkXQ/k1IVroYB2SZp7x/WUvmPV+w4aRWEcwpgZxWWvQFbeCtRqVf1nEmERyJDQRGTFiROQ1BSI9uPcCYxOwVtP8Yi9t1wmJYu5Ueb9Bh0pCAKy1UP0a5J2z4d21tk8M9XxE4ymdUEO1AbDWoqseQxXcGDEmkVmk78gcKvd0dFmoheGGPZqZfWB0b1bzDmHL01c+gs45oVExNu37E73uaNAeous7gKon0dmjw9YzEsmTVotVReoo5UQVPQ2qgOYqppJ9XJhFYI3pmolEWv2ua95q/JB3Bng+JLZKqgGonoiO40FZQogYZR0IuWcGv2n4uTdA5aKKnkKprIhvowPLgtt3w/UBPqid0vh1FbcFk5DYDgjVNe/E0F4kkixWFfWUc0vo9BFUv2UXNNNV9lHYOceDa7foV5JbK4nYKVhrGn2rq9+gZRURa8AqBzmgSoiUUEqh8v+Hdo9EV70Cfrugmcra117TEW1RwcDqKBqZwVFbmw4sDZ5S3gKB/1r2OhF3koikiPb9Bt6fQJl2efQEVEltCWUUQ945qAbTJjEzuhAxqTA26pwC/4ZvH/qNQBaZiXZC6xqo/cye3jS7gHtE2MWbyaRcO6NcrTgoLqqExQ9GgzVh9SXsW0CKHaYNSUSSTPuXoMsuDZ7RUjfCoNHuEajCu1AhFmNlEpVzBNozJVwLVPZGh/UZxYSuxhqKCe59ohr2FSLT6epX0BX32COVdesoVD7kX4PKOTLV4bWaMruhnTtHKIjohqz9GryosIVXC6CyD27ha0W8yRqRJNJWqX0Qle+3ukeonw/1fIVed1qwqE+Gcw0D1x40/+NlgtEDrdxYaw7BWtEfa+WOoL3EloQYgAOVd35cQhYinenq1+2aOrqq7pHgHxXo8qvRNe+nLLZ4Uvn/o+5Qz2blnoquvAdr5U5YK/qjSy8CVULU5QfAfm/33ijndq0PWMSFJCLJVP1amF0pAXtutXZqsqOKO6UMVNFjwcWtDQfdFLiGgqMHVNwG/vmA3+5cfbODbSL9SAafNzZBFb9gr2sRog3T2ouuuDd8m4q70FFVNU5vyjUAVfwimH02eqID5JwB1S9C9RugywE/WP+CXkvkRe4GdrJiQNZhqA73JSB60VIyNZNE9irtcHf9BrrmXVT2AckKKWGUykIV3ozOvwS8s4AAOLYDz8foiruCrRp2Hhv/fzE2PO7YEgpuQvn/Al0Ljs3tdTWhDr4Soi3xfNOgrk4I1ko7mW/NGo00oVyDoONk8P8K/iVgFKKdO8KafYK7Yxr2FXV9iMI+EsKLPaJi2X/mnAXuwXbfodz2mpq4HKop4kkSkWSywtTWsBs02U2SDFp7oHYyumay3eGZfezjwp0DW33mgjKKIWuf4HU0uupFwt+9WJB7frBOgAbnjhvicA1sVSxCZKSI/UZdu3WJjaMZ2r8QXf0a+ObYB026R0D2kSgjcuHDcJRS4Nze/gKo/RhthdtVowG3fUK4tcIufJa174Y43Lu1Kh6RWJKIJJPZDfx/EvoXsWmf05JEOrASve5kCCykfrGo7zd07buQfYw9EhGvkQe9HqzlERo5glUPL4nPNYXIdGbXKNttmtg4NmKvW7kBu9+wp4W0dyZUPg5Fz6JcO8TvWr5fCH8WFkCFPbXjOCxu1xXJIWPbSaRyjo3QIlB/mFsyaK3RpWMaHKtdN+QZnGuuecOek40bZxRtLCQ/FqIB12AwuhJ6QaYBZj9wbJO0kLR3VjAJ0TRe86ZBV6FLz0Bb5XG8oovoih1G08eIdCOJSDJlHxnsLJqrOqrAvQ+4kjiE6JsD/l8IV79DVz0bt0VwysgDR/8IrSzwfoMOrLWvrzXa8wVW6blYq0ZirTkQXflo/fNCtHVKGajCm7ETkY2TEQMwUIU3JPXoel31HKF/fVj2qbc1E+N2PeUeSjR1hrRn2ob/ttahKx/HWnOw3XesOwtdO12ODkhDkogkkVJZ9orw7CNplLmrXMg9G9Xh/qR2JvYBUeFKsWMvggssisvldGAFBJZFbhhYii49B8sKoMuvRZeeBZ4vwFoK/r/QlQ+j1+yP9v0Rl7iESHfKPRxV9Cw4Njpd2tHf3j3m2iW5AXm+JlJioD3fxO1y2vtzdA0rbkPXfoz2L0Cv3h9d+aC9O89aCt6v0evPQZddgdaxlAoQiSZj4EmmjDxU4a3o/CvA94ddWdW5LUplJz0We6QjisQnTiMiuvxm0BVRtAzYIzWVd0L9mTQNY7Ds+gmlZ0Onz1BKhmNF26fcu4PrffD/ZZcBMLugHJunKJpIv8g14ddzRE/7F0HlHVG2VuiKh+2SALqcxnEG+5Da98G5LeSeGpf4ROvJiEiKKKMQ5d7VLoucgiQECC4mi9BZqAJw9Gr1tXRgFXg+I/oy7ibUvE3oRClgj9Y0GIoVoq1TSqGcW6Dcu6cwCQGcOxB+NNVAxWmXm655g+h/VWkI/GmPgITsazS66nkZFUkjkoi0Z66hwV06oToUA3KORylX66/l/5vYKqcSHD0JN5/rQHt/aEVQQoiWULmnEPoXvQJM2PgYh5byzQtzrVAiTTkvj2IHn0gWSUQyiPb/bW+Zq34N7fur1e+nlIHq8CioPBr/KAT/27ULKu+CVl/Hvlisoz7RdjxJXFMjRAbS2ouu/RRd9RK6ZjLaqm79m7pH2ZVOgca/9O3y7KrDfSizS+uvA6ByiO1XlYPo+gXpO9KFrBHJANpah15/OXi/bvy4cxdUh3tb9YFXzq2g4wfo6legZpK92t3shco5HrIPjd/6C+e2YJTYp4ZGjgpw2qeLBv4j9KiIP/mL9ITIILpmcnBtVikbDsrLgbxLIOeUFi+OV0pB/v/AvatdpNA3B3BC1l6onJPjevSCyhqF9nwSZWsT3MMjT9ka3cCQCqvpQhKRNKd1LXrdieBf2PRJ34/2cyUT7a2xLaTMLqj8sZA/thWRRriGckDuueiK2yK0NAGNKrwLdDm6/PoQ7QwwuoB7rzhHKkTboGunosvGsiGRrzsorxpdcTsKBbmntPj9lVLgHo5yD291rGFl7Q+VD0JgBeFHSg1w9IWCcbDuyOAOvebbq9zT5YiINCL/Eumu5j3wLyDkQXmBxVDzTqsvU1vtYfanv/D95B9ZtThcKeVWyDkZcs+hfg65/iCqOgpce6CKX7bP28k+BrLrisCZjdupQlTR03aCI4RoRGuNrrgzfJvKB9C6pvXX8i+y63N4vkdrb6vfb2NKuVFFL4DZPfiIgya/ulQR5J6LKn4dw+yAKnrKLvPeqH8J9iFZR0DOiXGPU7Sc9OJpTtdMpH5INWSbd1C5J7fo/S3L4pVb32bCfe9TXR7slBTscsBALnn8bDp1L2nR+zZHKYXKvwydPdr+ewWWglEEWYegHL3ssyoaLIxVSkHBTZC1L7r6VbsegMpDZe0POaPtc2yEEE355zaomByCrgLP5/aIQwto/z/osuvB12DBuOoAeWMg5+S41kRSjp7Q8SPwfI72fAH4UM7t0O4D7JsRldtohEM5+trtayagaz6wp5wd/VA5x4FraHLrNYmIJBFJd1akI651lOsumvfQ+U8z+elPG19Cw6wpc7hoyDU89uNdFHUubPH7179lYLW9r9/ognL0ROVfHNXr7OHf3e0aCkKI6CT4oDztX4xee7SdzDR6Yr09/Wqtj/ozHvY62hucYjHB7I7KGoXKGlX/fLh0QhkdIPdMVO6ZrY5DJJYkIunO7B68swm19dVoMGQZm39+WcTkpz5t9jkrYLFuxXom3PMeZ911UtTvqbUfPNPRtVPAKgeVBYEl4P892MKJzjoYlX+JHMctRKKY3aJrZ0TZbiO6Mlg0LNSajarH0TnHxrSQXgdWQM1baO9cUA7QfvD+CATPrDF7Qu7ZkD1aRjTaGFkjkubsQ/DC1d+wUDnHtOi9Pxk/HdMR+kfAClhMfubTqM9m0IG16LVHoNePgdoPwfsFeD5ukIQA+KB2EnrtkXbHI4SIO+XYHBzbEraLN0rAvUfM762taqidTMQt9jWTon/Pmkno1SPRlY+Cdzp4ptp/1iUhAIEl9pEPlffEHLNIb5KIpDv33sGD8Jr7pzLAOQiyDmjRWy//dxWBQPgiY1Xrq/F5fBHfS2ttJyD+uvom4TqpAFjr0BV3Rx+sECImquA6NiwKb/QMoFAFN7Rse74uJXL5dgNtRXejob0/ocv+h91nhOuPgjdEVU+jfb+HaScyjSQiaU4pE1X0JOScBGQ1eMYF2ceiip6NufJpVVkVD573FN+9/2PEk7Wzct043VF0Vr5fwDeb6AuRBaD2I7RVFmV7IUQslGtHVPErwZGRBszNUB2eQGXtF/N7as836NLzommJMqJb6K6rniW24mImuvrNGNqLdCdrRDKAUm5Uwf+h8y4G31xAg3MblFEQ83tVV9Rw6bDrWfT7f1gRRkNMh8G+p46Maj5W134WcyzgD+6caf1iWCFEU8q1A6rjBLR/gV2HwygGx9YtWmOhaz9Br78wytYWZB0cXVPPZ8R2/EMAAn/H0F6kO0lEMogy8sC9a6veY9IjU/j3tyVoK/xQiGEa5ORnc/QVh0T3xoEWlpxXLS/EJoSIjr1mpOWH5GntRZddW/dd5BdkH29vuY34vi05pdewD+MUbYZMzbQzHzzxScQkBKDvgF7c//WtdO7ZKbo3DiyJMRIFji2Dh+4JIdKaZzro9UROQpyQezaq4NoI7YL8C1oQjGXXEhJthoyItCNaa1YtWROx3XbDtua+z2+O7c2tqshtGkeDyrtEtuEJkQkC/2Hft0aYQunwGEZWDCXfA//FGIgJZi/I2ifG14l0JiMi7YhSipyC8KfgKkPRIYYCZtq/GKvirugLKAHgRhWMQ2XJOTFCZARVQFTrOIzOUb2d1n57zUnVs7HF4eiPKn4h5gX6Ir1JItLO7HXCsLC1Q7Sl+frt73jishewrPAdj65+E71mH6h6HohhRKToZVTOkdG3F0KkVtYoohpAX38m2vtz2CbaKkevOw69/gLwzYw+BscAVMmEVp02LtKTJCLtzOjLDsaV7cIwwyQjGt5+4ANeuP6N0G28s9Dl12HfJUW7ZdemDJkRFCKTKKMIoimVbq1Bl56K9odeM6bLrgzu/oOoFr7WB+GSqdw2ShKRdqZrny7c89mNdOwe4cA4DW/dO4nK9c2PdOiq52jZj4/LnuMVQmQUlXeJXWI97Odeg65GVz/f/LP+ReCZRqw3L2CCc5sYXyMyhSQi7dAWg/py1YuR6wH4PAG+n/xD8096vqJFnUn24fY2ZCFERlHKwMi/HFxDIrTUUP128095vyG24mV1LFTOsS14ncgEkoi0UzWVtVG00lSv+zHEc7EmIQaYvVH5l8f4OiFEWgmsjKJRjX0A5saaeyws+1eUyv8/lKNPjK8VmUISkXZq077hd8/YFJtuFuK8COd2RP7xMe0/jBLIPR9V8iZKqqgKkdlUlGu8mjvU0jmA6NeFKHDtiip6DpV7crTRiQwkqwbbqW6bd2H73SqZ+30uVqDpUKkyNF26+9h+aPMVDFXOKeiyS0K8uwIc0PEzlFkkW+2EaEscW4N/XuR2ymz6mHN7cPQH/3yaH1U1IeswVOHNgIFq7j1EmyMjIu2VKuLCu0yycyxMs/EdimFqTFNz2f2LMbNCHBOetT9kn1j3igZPmIBCdbgHw9FFkhAh2hiVHcWxD0YPMDZp+lqlUB0eAKOIxv2GfSIwjn6ogqtRyilJSDsiiUg7pZSi145n8tCHf7L7Aesx6pIRpRk4tIL73/uH7fcoAXfzVRKVUqiC61AdHgHnzqBy7KJHWQejSt6REsxCtFWuIWD0JdyiU5V3VsittsqxGarkPcg9z05WVHZw/djVqOLXW3SYp8hsStunDqWl8vJyCgsLKSsro6BAfjjjTWuNrrwXqp6isszJulUGhcUBCkv8YHRBFb+EcmyW6jBFimXi5zATY84k2v8fuvQk+/TsesES8DmnoPKvkZof7Vwsn8GkjIg8+uijbLbZZmRlZTF48GB++CHEllCRFIt+X8Jnr33N1xN/oDpwHqpkAnldDqXn1ltQ2HVnVMFNqI4fSRIiUkr6jfSitRft+Rxd8x5Yq6H4A1TBreDaDRzb2Ws7it/EKPg/SUJETBK+WPWNN95g7NixPPHEEwwePJgHHniAfffdl/nz59O5c3TnEoj4WPb3Cu4+/THmfvVH/WNOt4NDzt+PM++4GYdT1i6L9CD9RvrQWkP1y+jKB0GXb3jC3AwKb8MoHp+q0EQbkfCpmcGDB7PzzjvzyCOPAGBZFj169ODCCy/kqquuCvtaGV6NnzXL1nHewP9RvrYCK9D4DBmlFCOO2Y1rXr0kNcGJtJaKz2Fr+g2QviOedNWz6Io7m3nGAAxU8Sso147JDkukubSZmvF6vfz444+MGjVqwwUNg1GjRvHtt982ae/xeCgvL2/0JeLjrbvfazYJAfuOZ/rr3zB/5oIURCZEY7H2GyB9R6JoqwJdcX+IZy3AQlfcncyQRBuU0ERkzZo1BAIBunRpfFpily5dWLGiabGbcePGUVhYWP/Vo0ePRIbXbmitmfL8Z80mIXVMh8nUF79IYlRCNC/WfgOk70iY2o8Bb5gGFvhmof3/JSsi0Qal1fbdq6++mrKysvqvJUtCn+Aoouf3+akurwnbxgpYrFtRmqSIhIgv6TsSxFpNVEsJrTUJD0W0XQldndixY0dM02TlysZnE6xcuZJNNmla7MbtduN2uxMZUrvkcDrILcyhqqw6ZBvDNCjpGuFEXiGSINZ+A6TvSBijMxDF+TBmp4SHItquhI6IuFwuBg0axLRp0+ofsyyLadOmMWRIpBMcRbwopdjv9D0xzND/3AF/gH1OHRHze+vAGnTl41jrTsJadyJWxYPo5s6YECJK0m+kkax9gXAJngHOXVDmpjG9rdaaOdPncsdJD3HJHtdy45F389U73xPwx3qYpmgLEr5fc+zYsZxyyinstNNO7LLLLjzwwANUVVVx2mmnJfrSooHRlx/C9Ne+Zv3q8mZ3zex1wlD6DYztdEvt+QZdeh72HHLwPb2z0FVPQYcHUFl7xyd40e5Iv5EelJEH+ZehK25v5lkDMFH5V8T0ngF/gHEnPsQXb87AdBgE/BaGafDNxB/Yetd+jPvo/8gtzI1L/CIzJHyNyDHHHMM999zD9ddfzw477MCcOXOYMmVKk4VoIrFKuhbx4Izb2H54/0aPu7NdHH3FIVz+3PkxvZ8OrECXngt4qE9CIPjffvT6i9H+v1sbtminpN9IHyr3VFTBzaCKGj/h6IsqfhHlGhDT+71445t8+Za9+yngt/uOupuj+TP/5u7THmt90CKjSIn3dmjJ/KUsmPMvWTluth/en9yCnLDttVUNtVPQgUX2ORBZ+6Gr34KqJ2ichDRkQs6xGAU3xD1+kVyZ+DnMxJjTndZeAjXfY1CGcvYEx3YRK6gunLuYb9+bhbfGS58Bvdhx1Pac2Os8qivCLJ5X8OJfj9C1jySdmSyWz6CU0mxH5s9cwJt3T2LGpJn4fQE23aIrh12wPweds3fIqqq65j10+fWgqwEHGgsq7gJVSOgkBCAAtdNBEhEhMprP6+P9xz9h0qNTWLZgBQ6Xg90P34VjrsgLOZ1bVV7NuBMe5PvJszFMA2UoAr4AeR1ywychABpmf/oLB54tU7vthSQi7cTXE7/nlqPvQ6kNw6FL/1rOYxc/zw8fzubmSVc2SUa053N02RVA3aBZg9Xzen0UV03twjOtveCbC9oLjs1RZseUxiNEpvF6fPzfgbfz8/Tf0MF+wO/18/Xb3/H1299z4ztXsOtBgxq9RmvNDYffxa9f2kdJWAGrvisIt3OvjlKqvo9KldX/reW/P5eRnZdFv0F9ME0zpfG0dZKItAMVpZWMO/EhLMvakFMAaNBoZn38M+8+/BFHjT240et0xQOtuKoJzkGRmyWAfTbGc+jKJxskTAbavS+q4DpJSISI0tv3fcDPn//GxjP4Ab+FUnDbcffzxrKnycnPrn/uly9/5+fpvzX7ftGsBNBas9XgzVsXeAst/2clj1z8HD98OLu+r+y4aTEn3XA0B5y5V0piag/SqqCZSIypL36Br9bXOAlpQGvNxIc/atRJaP8S8P9OyBdFFEDlntTC17aOrhhnn43RaNTGAs8n6HVHoy0p3CZEJJZl8e4jH6Gt5vsAraG22sO0V75q9PgXb8zAdLRsBMF0GGyxU1+2GNS3Ra9vjVWLV3PhkGv48eM5jbq9NUvXcf/ZT/D6ne8mPab2QhKRdmDBnIUoM/yislWLVjeeu9UVUbxz3Y9Pw07H/m+VNxblSv6IiPb9BdXjQzwbgMAydNXzyQxJiIxUvraCdcvDJ+2mabJg9j+NHqsqr45q5AOABt2SYRgUdirk2tcvjTXUuHjxxjepKK0MOS00/rrXKF25PrlBtROSiLQDLpcTRfhEBMDpajBTZ3ajcYLRHAtyx4B7FKh8UHngHoYqegGVd26rYm4pXfM24eO2oPr1ZIUjRMZyup0tarfp5l0jvqagJJ9z7zuFHltuSlaum849O3LCtUfy5Jy7U7Jbprbaw7RXv8YKszZFW5pPX/oyiVG1H7JGpB3Y5cCBTH7605DPG6bB9sP648py1T+mjA7orP2gdgrNLzpVoHJReeegVFb8g26pwDLC7+YB9Hq09qKUK3w7Idqx3IIc+u+2JfO++xMrxPRMwB9g14N3avTYfqeP5OVbJoR8X8M0OPjcfTjykoM48pKD4hpzS5WtLsfvDV/K3jANVi5anaSI2peMHhHRWqO9s7DKrsUqPQ+r7Ea09+dUh5V2Bh84kB5bbYrpaP6f2wpYHHvVYU0eV/mXg9GBpiMM9vuoglvSKwkBMIqI/GOdDUR3tyfaJm1VoKtewiq9EKv0AnTVC2irPNVhpZ3jrz48ZBJiOgx6b9eTgaO2a/R4556dOP324+1vNhqINUyDHlt2Y/TljRfGp1pehxyUEX7U2LI0hR2lJk0iZGwiorUXvf589LrjoeZt8EyDmjfQ60Zjrb8craM4qKmdME2TO6b8H5v0toc8DdMAZf9pGIpLnjibQXs3rY6ozE1RJW9D1v40GjxzbIsqehaVfWCS/gbRU9kHE37bsAnZh0UsxCTaLu2dhV49Al1xK3g+Ac9UdMXt6NXD0J7vUh1eWhl84CDGPHQ6ylCN+g2ATft15fYPr8Ewmv4aOfbKw7jyxQvp3m/DNI0728WBZ43i/q9uSbsS7rmFuQw+cGDY87isgMXI4/dIYlTtR8ZWVrXKboCa12l+V4eC3LMx8i9LSpyZwu/zM2PSTGZMmomnxkvvbXuy/5l70al7ScTXaqsMAivAyEeZ3ZIQbctordGl54D3S5pO0ZigslEl76IcPVMRXkbKxCqloWLWgZXoNfuA3vhoArBv392oTh/FfIhbW7dy0Wo+emYai/74D3eOiz0OH8yQg3eKuDtGa82yv1fgrfWxyWadyM7LDts+lRb8tJCLdrsGvy/QZKeQUor9ztiTsU+lZu1bJoql38jIRERb69Cr9iDs8dQqG9VpBspIr8xbJJ7Wteiy66F2Enaiquw/zT6oDvejnFunOMLM0pYSEaviQah6nLBHE+SejhHjQW6ibfjly9+58+SHWbV4DcpQaEtjOkwOOX9fzrnn5BZvS26P2n6Jd88MwiYhALoGfD+Ce1hSQhLpQ6ksVIe70IFLwfOlfffr3BqcOzWZkvHWevlywnfM+/4vTIfJoH0GsNO+A5odbhZtgOdTIh9NMBUkEWmXth/Wn5f+eZSfpv3K4j+Wkp2XxeCDBlHUubBJ2//+Ws5nr3zF+lVldOxewqiThtG5hxRLbInMTETwRtdMR9mundBaM/frefW/dAfuvT2bbdMj1WEljDK7Qs4xIZ+f+/UfXH/YXVSsq8R02nc67zw4mR5bdeO2ydfQtbccutXmRNMnSL/RROX6Kr6e+APrV5XRqXsJux22M9m5abZQPU4Mw2DQ3gOaXTcH9k6hhy98lslPTq1fZ2dZmvHXvc7x1xzBKTcfI2vQYpSZiYhjmygaKXBslfBQMsXieUu5efS9LPptCYZp2GspLM2gfQZwzSsXU1CSn+oQk2rpguVcte+teD0+AAK+QIPnVnDFnjfxzG/3k5XjTlWIIhGc20NgMaEXNJt2GwHYNy9v3DWJF296E5/Hh2EYWAGL7Lwszr3v1HZZ9vzZa17lw6emAvYCVqvBj9Irt71NQUk+R1ySfgv501lGjj8r55bg3JHQhatMcA1DObonM6y0tW5FKWOHXceSeUsB+8NTtxjrp2m/cuU+t+D3ta9dRhMf/BCfz99s+WrLb7Fy0Wo+f2NGCiITiaRyTiD8rqoAKvfEZIWT9ibc9wHPXv1K/RERVsCe1qqprOX+s59oUt69rasorWTiQx8SbmXlK7e9jc/rS15QbUBGJiIAqvCu0DUujM6owltSEFV6evfhj6gorarvRBqyAhYLflrIjEkzUxBZ6nzx5oywVRSVofjq7W+TGJFIBuXaAZV3SfC7hn1HsCvMPQ/l2iXJUaWnmqpaXrrpzbBtnr3mFQKB1J6ynUwzP/opYuGz8rUV/P7tn0mKqG3IzKkZQDl6Qcm76KrxUDMBdBmoYsg5GpV7KsooTnWIKfHfX8tZtWg1BSX5dO7VkWkvf8U7D05uNgmpY5gG0179imFHDUlipKlVW+0J+7y2NNXlNY0f05rfv/2T32fMRxmKHfbcls136J3IMEUCqLzzwbE1uvo58M4CNDgHonJPQ2XtnerwUsJb6+XPWX/j8/rZbNserPx3NS/fMoGaytqwr1u9ZC3zvl/ANrttmaRIU6u2Kny/Ud9uo/9vVeXVfDPxB9YtL6W4axG7H74LuQU5iQgxI2VsIgKgzC6ogiuh4Eq0tlAqYwd4Wu3PH//m0Yuf5/cZ8zc8WLdeKsIGbStgUbaqfVWV7Ll1d/6a/U/Ik0VNh0HvbTfUGln+z0puOuoe/p7zr130SGssS7Pt0K257o1LKd6kKFmhizhQWSNRWSPrD2drr4sLLcvitXETmXDv+1Sur2r0XKRKo3XK10ZzQGbb0LN/dNP9PbbaUIfmnQcn89w1r+Kp8WI6DAJ+i4fOf5rTbz+eIy6WtSSQwVMzG2vPSchfs//h0mHXM+/7vxo/oYmYhID9S7fb5pskJLZ0dcj5+4ZMQgACfosd9rJLV5evreDSYdfx79zFQHCBWvC1f3w7n8v3vAlPTXR3SiK9KKXabRIC8MC5TzH++tebJCFA2M9HQ5v07hzvsNLWNrttSY+tuoWswGoYir4DelHYya6b8cGTU3n80vF4auydWHUn+3pqvDx+6Xg+eHJqcgJPc+33t3cb8vil4/F7/WGnX8IJ+C32PyOzVr/7vD6+/3A2U1/8gp8//w3Liu3vPurEYex60KCwv4RuGX0vY3a+kvHXv8G6FeubPR484LdYMm8p01+Xha0is8yf9TcfPTMtqpuV5himwRaD+jQaOcwEK/5dxWevfsVnr33N6v/WxvRapRRXvnAhTrez2WTEsjR//7yIozc5k0cveo7nrn017Ps9f+1rsrCVDJ+aEfaUwa9f/dHyN1Cw98nD2XaPzNnq/OEz03j26lcaDQl37tWJix87i1323zGq9zAdJje8fTnvPDCZiQ99yJql65ptt2DOv/w5+5+wnbUyFNNe/pL9ThsZ099DiFSa8uw0TIdJwB/7YlPDNDCdJhc+emYCIkuM9avLuO/MJ/j2g1n1n2dlKIaPHsIlT5wd9fk3W+68OY98P46Xb3mLr97+vtkbQG+tj3cf/Shikle+toI5039j5313iPFv07bIiEiGW7V4TYtfW1CSz2m3HMdlz5yXMcPT7z/xCfef/USTeenVi9dw7cHj+HFq9KcvO5wOjr7iUJ6eex+ubFezbayAFbEz0ZZm/eqyqK8rRDpYtXhNi5IQgG332IoHvrqFrXbpF+eoEqOmqpbLR97I9x/NbvR51pbmywnfceU+t8Y0MrHZNj249vWxnHpz6IKJ0Y40VayrjPq6bZWMiGS4go4tK0S298nDufSpc3C6nHGOKHE8NR6euerlZp/TWqNQPHHZCzz1870xJVYz3p2Jt6bl1TRNh8GmDU4ZFSITFHYqwDCNmKZ0laF4dOYd9NuxTwIji79Pxn/Ooj/+azY5sAIW82cu4KsJ37Hn8UNjet+pL35Rf5RVS7WnNTahyIhIhttsmx4tqoo674e/Iu6HTzfffTC7yZbahrTW/Dt3Sf2i0mitWbquVYdZBfwWB541qsWvFyIVdt5/x5jXlWlL8+fMvxMUUeJMef4zwt2aGIZiyvPTY37fNUvXtTgJUYaix1bd2HpwZowqJZIkIhlu7fJSytfFvn3uvz+X88ETmbViu3TF+qi2FK5bsT6m9+3QuTCqokzNXVspxbCjdmWndj7HKzLPX7NallA8ful4qitC3xCko3XLSsNWQ7UszdoQ68TC6dDMYXgNKcPelWVs1HcYhsI0DS554pyMmRZPJElEMtznr3/Toh9kbWk+eCqzEpGSTYuj2lLYcdPYitkNPXIwTlfoWUplKLbcZXOOuPhAsnI3nD2T1yGXE687imtevUQ6E5FxPn7h8xa9zlPr5cu3MqvqcMfuJWE/o4Zp0Kln7Cfn7nvayCZJRkPa0pxx+/FsPWSLRo9vPWQL7pl+E9sP6x/zNdsiWSOS4cpWl2OaBn4r9kVnqxavTkBEiTP4gB3J65DbbM0DsBOGvgM2o1f/2E4Uzi/K46Trj+a5/2u61U4Z9t3MOXefzHZDt+aUm49h0W9LUIZB7+164nJnzhobIRqqbOEiScM0WPHvqjhHk1j7n7EXf/4YegTICljsf/qeMb/vweftw4dPf8raZeuabO83TINtdtuSoy4/mGOuPIzlC1eybvl6irt2kJO9NyIjIhmuY/eSZutbRMPvC/D8ta9lzIF3riwX5953SrPP1SUMoZ6P5NirDuPsu04iO7/x0eadenTktsnXsN3QrQHIzs1iq136seVOfROahCz8dREPnPsUp/S7kFO3vJCHzn+af39bkrDrifanqGvLqgFbfotPXvicud/Mi3NEibP3ycPoO2CzZmt/GKbBdkO3ZvfDYz9jqKA4n/u/uqVJ+QNlKIYfPYRbP7ga07TXn3Xt3YVtdtsyoUmIz+tj6otfcOnw6zmx9/lctNv/MfmpqRGPtEg1pXW4mbPUKi8vp7CwkLKyMgoKClIdTloqX1vBMZue3eKFp0ophh45mGvfGJsx0wvTXvmKp698ibXLSusf67HVplz06JnsMHLbVr13bbWHmVPmULGukq59OjNgxDYYRnLz9Y/HT+feMx7HMFV9kmk6DCxLc+ULF7LXCbGt7G+tTPwcZmLMyfbSzW/x8s0TYi4GCIACh8PkzqnXZ8z0QuX6Kh46/2m+eOvb+kW6psNk75OHcf6Dp5OdmxXhHcJb9PsS/vh+AabDYIeR29Kpe0k8wo5adUUNV+17C3989xfKUGhLo5RCa81m2/bg3uk3tWhjQ0vF8hmURKQNmHDf+zx5+Yuteo87P7mOgaO2j1NEiRcIBJj79TzKVpfTuWdHttx584xJpMJZ+OsiztnhCkJ9LA1D8fTc++nZ4CyLRMvEz2EmxpxsleurGLPLVaz4d1XYk6hDUYai59bdefqX2LbLp9ra5aXM+/4vlFL0320LOnQKv+A0U9xzxmNMffGLZndCGabB4AMGcvOkK5MWTyyfQZmaaQOOGnswlz55DkWbdGjR602HwUfPTYtvUAlmmiYDhm/DsKOGsNUu/TKqIwxn0qMfY5ih/y7KULz36JQkRiTaqrwOuTzw9a3sfuguUR9w15C2NIt+W8KCnxYmILrEKelaxO6H7cJuh+7cZpKQ9avL+PSlL0Nux7YCFt9+MIvlC1cmObLoyGLVNuKAs0ax72kjmTHpB24efV9Mrw34LZb/nZ4/oKmw7O8VTH3xi/oju0edNIxNN09OwbLZn/4Sds1PwG8xe9ovSYlFtH1FnQu5/q3LWLNsHQ+PeYZv358V9WF3dVb8u5p+AzOrwFkiBPwBZrw3izmf/Yq2NP1325JhR+2KK6v5qs3xNO/7BZGr5GqY+/W8tFwoK4lIG2I6TIYeOYTTbzu+2R0goRiGirgfvj2wLIsnxr7AxIc+xDCN+vnVl2+ZwKEX7Mf5D5yW9PUizWsboz8ifXTsVszYp8/lgsFXs3LR6piSkcIWVnduSxb98R//d8DtrFy02i6OqOzjKJ4Y+wK3vH9VwouWZfqAcDr0qiJOSleV8dz/vcq7D3+ICjO8vzHL0ow6cVgCI8sMr9z6NhMf+hCwhzID/kD9UOekR6bw0k1vJTyGgaO2x3SE/liaDoNBGbSWR2SGH6f+zB0nP8zaZetCrk9qTkm3IrbZfcsERpb+KtdXccWeN9af5BvwBwj47NGJinWVXLnPzQkvlbDV4H6Rq0Mr0vZwU0lE2ojlC1dy7o5X8MZdk1i3Yj06EF1nogzF5jv2btHWtbakpqqWN+95L2ybCfe9T01lYitKHnrBflhh7ka1hoPP3zehMYj25fU73+WqfW9l9tRf8Hn8MZUsP/OOE+u3p7ZXn7zwOetXlTe7PsOyLDzVXt577OOExlDYsYC9Tx7W7PZksBerDjl457SclgFJRNqMO096mPWry2I+O2Ln/XbgzqnXZdThd4nw8/TfqK2sDdumtsrDT5/NTWgcvbftyRXPjUEZqtHIiOEwMEyDK1+4IKk7ZkTbNu+Hv3j26lcAQvYdzS0EzynI5rJnzpORVOCrt78LO4pkBSw+f3NGwuM4/4HT2Co4BVRX7bXu326zbXpw+bPnJTyGlpI1Im3AwrmL+W3G/IjtNt+xN1bAomP3ErbbYyv2OGIw3bfoloQI019tVfgkZEO7xBcG2vvk4WyxUx8mPfoxs6f9glKKQaO255Ax+0kSIuLqvcc+xnQYIRdIG4aiuFsxHYIn9fYdsBk7jtqO3Q7ZCXe2u9nXtDfRnLuTjH4jOy+bez67gS/f+o6Pnv2UlYvWUNK1iH1PG8mex++R1v9ekoi0AfOjPA3z5BuPZsjBOyU4mszUa5voysJvFmW71urVvwcXPXpmUq4l2q/fZ8wPu0vLsjQOh8njP96VxKgyS98Bm7HotyWhkznTPg4iGZwuJ3udMDTpRQ9bSxKRNiDcgW0NOaJslwxaa+ZMn8vHz09nxcJVFG1SyF4nDGPIwTtFXnSVAL237cnWg/sxf9bfIQsC9RvYhz7b90p6bEIkiiOKYwoc7vTpNwDK11XwyfjP+eGjn/B7/Ww9uB8HnrM33fpukpJ4Djpnb6a++EXI562AxSHn75fEiDJPwtaI3Hbbbey2227k5OTQoUOHRF1GADvsuW3IRUp13DluttktPVa3+31+bjv2fv436mamv/4Nv82Yz4xJs7jpyHsYO/x6qsqaP9Qu0S579jyy87Oa7FoxHAbZeVlc/tz5KYmrvZG+I3mGHDQobN9hmAa7HbJzEiMKb94Pf3FKvwt56oqX+Gnar/z61R9MuP8DTtvyIqY891lKYuo/ZEuOvuJQgMaF4YL/udcJQ9n9sPT5f5iOEpaIeL1eRo8ezXnnpe8CmbaipGsRgw8YGLbNoWP2Iyc/O0kRhffijW/y5YTvgA0L5Or+nPfDAu454/GUxNWrfw8en3UXo04cVj965HCajDphGI/OvCNp0zLtnfQdyXPQuXuHLUtjOAwOPm+f5AUURlVZFVfvfxvV5TWNFodaAQvL0tx31hP8/m3ktXKJcOYdJ/C/Fy6gV//u9Y917d2FCx4+g/+9cEGa1B9KXwkbc7vpppsAGD9+fKIuIYJKV67n16//CPm80+3gqLEHJTGi0Gqqann34Y9CrjK3AhZfT/yeFf+uYpPNOic5OujapwuXPzeGix47i8r1VeR1yE1KZUSxgfQdyfPFm9+GPWdm2JG7puRz2JxPXviCqvXVIfsOZcDb939A/yHJH/lVSrH3ScMZdeIwKkor0ZamoCS/zRw9kWjpNfknWuSDJ6dSXVYd8nm/L8CnL33J6MsPSWJUzftz1t/URNgmi4bZU3/hgLNGJSeoZriyXBRv0jQBWfTHf7z/2Mf8NmM+DqfJLgcM5ICzRlHSwiPVhUgVb62X18ZNDNvmm4k/UFVeTW5BTpKiCm3WJ3MibJPVfP/RT0mMqCmlFAXFTSvN+rw+vprwHZ+88DmlK8vo0qsT+52xJ4MPHNju67BAmiUiHo8Hj2fDNqfy8vIURpMZaqs9TH5qavgiWJZm+hvfxCURWbeilMlPfsrnb86gprKG3tv34pBz92GXAwZGlf2HW6Hf0Npl61obatx98ORUHjr/aZSp6u8i589cwCu3vc0+Jw9n14N2sjuWFCy2be+k74jdp698ReX68OuxPDVeZk/9haFH7tqqa2mt+Wnar7z32Mf8+eM/uHNcDD1iMAefty+dupdE9R511UrD8dZ4WxVnIlSUVnLVPrfw54//oAyFtjQL5y7m2/dnsWm/rux/5l4MPWJwyhbbpoOYJq6uuuoqlFJhv+bNm9fiYMaNG0dhYWH9V48eMicfzpzpczm2+9msXVYasW00e90jWfDTQs7ofykv3zKBxX/8x+ola5k1ZQ7XHnwH9575OJYVOcnoO6BXVEelLF2wotXxxtNvM+bz4PlPobVuNJSttd1BfvTsZ9xw+F0c3/Nc5kxPbNGzTCR9R/rw+/zcffqj3H/WE1G1jziCGYHWmsfHjufKfW7huw9msXrJGv6bv4w37prEGf0viaoGEkDPBusvQl7L0qxasqZV8cbbvWc8zoI5/wLUn+FT9+fSv5bzzFUvc8oWF3LrsfdTE2U9o7YmphGRyy67jFNPPTVsmz59Wn4K49VXX83YsWPrvy8vL5cOJYQl85dyzYG34/f6I7Y1HQZ9tmvdtlOf18f/HTSO6oqaRglH3SLTj5+fzpY79eXg88KXHy/sWIA7y4Unwp3Lv78taVW88fbOAx9gmqELP9UpXVXGNQfcxkMzbmfzHXsnKbr0J31H+njqfy8x9YXQ20031nPr1hXRm/bKV0x80D7DqeHnxwpY1FZ7uPbgcby6+Amyc7PCxxFlMb8/vv2Tzj06tjzgOFq+cCXfTPohfNn84HNfvf0dNZW13Pr+Ve1ubUlMiUinTp3o1KlTomLB7Xbjdqdv9bd08vb9k7H8gahOyQz4LQ46t3Ur32e8O5N1y8OMvCj7LJaDzt0n4ocot0NuxEQk3T6Isz75OappJW1pAn6LV257mxsmXJ6EyDKD9B3poWxNOe89+nFUB9sZpkGv/t3ZcufNW3XNCfe9Xz8lsTFtaSpLq5j+2jcccOZeYd+nsGNBdBdMo77j589/j/rsHitg8cOHs5n3w4KEn9abbhK2p2jx4sXMmTOHxYsXEwgEmDNnDnPmzKGysjJRl2xXvpzwbdTrLQ46Z2923HPbVl3vly9/x3SGWfugYdnfK1m/OvzcfFVZFdm54X9hGKbBoL3T64TZWI5FtwIWM979AU9N4ss6t0XSdyTODx/9RMAfea2FYRi4spxc8fyYVt0UeGu9/D3n37CfH8M0+PWr3yO+l88XefTXMI20OmFWRzFd3ZDpMJn+2tcJiiZ9JWyx6vXXX88LL7xQ//2OO+4IwPTp0xkxYkSiLttueKoj/5JzZbs4775TOfDsUa0eYYj29eGa1VZ7uGzkjSz/Z2XY9zAM1eoRnHjbdujW/PjJz1EfKmhZmuqK2rQ+3yFdSd+RONGeebLNHlty8eNn02vryOsy4iFS//Lt+7O444SHwrYxTIMRx+yWVjvY+g/ZIqb2WmsqSttfwp2wEZHx48ejtW7yJR1JfPTq371xFb+NKFOx/xl7ctA5e8dlmmPAiG3CrlpXCrpv2S3s8OlHz0zjn58Xhd3hY5gG1711WdrULqhz+EUHxHSycXZeFvlFuQmMqO2SviNxoi3Kd+ULF8YlCXFludhip771p8E2xwpYbD98m5DPBwIBHjr/aSLNcWyxUx8ueuysloaaEL3692DAyG0wHNH/qu3au0sCI0pPUu4tQx0yZv+ww506oDk4jqMKQw7ZiU49SkKWg9YaRl92SNikZ/JTU9FhOhOlFIMPHJhWJaXr7LzvDpx0/WgADDN8YmeYBvudvicOZ1rtjheCbffYiu5bdA35OTZMg53224EuveK3nmf0ZQeHvPkwTEVhxwJGHrtbyNfP+Wwua5auI9KylgsePiMt6p1s7KoXL6RLz05hbxzraEuz72kjkxBVepFEJEPtfZJ9QNzGv/jrfthPveVYevWP364Bh9PBbZOvIb8ot9EHqu5cloPP24f9z9gz7HusWrwm7E2N1pr1q9K3/sPJNx7N3dNuYMjBO5Od1/wKf8Nh0Kl7Ccf/3xFJjk6IyJRSXPXSRTjdzibJiOEwKCjJj/upz8OP3o1jrzocoNE5TspQZOdnc9uH14Sdwly5KLrtuNGUMUiFjpuW8PiPd3LWnSfZSWBzCUnwoVNuOiauSWCmkFu2DGU6TG54+3LeefBDJj40mdVL1gKw+Q69OebKwxg+ekjcr9l7254898eDfPTsZ3zx1gyqK2ros10vDj5vH3YYuW3EKaCCkvywNQkM06CoS2G8w46rHUZuyw4j7YW/n7zwOS/d/BYrFq4C7H+TEcfuxtl3nUSHTun99xDt15Y7b86jP4zj5Vsn8NWE7wj4LVxZLvY+eTgnXHtk1AXGoqWU4ozbj2fwgQN579Ep/PXTQtzZLoYesSsHnD2Kos7hPyuFHZtWKm2+XZS7alIgtzCX0ZcdzOjLDmb96jKeuuIlpr/2Nf7gdHeXXp048dqj2O/08DdzbZXS0ezjSpHy8nIKCwspKyujoCB9f8hSzbIsytdW4HA6yOuQvusSXrzxTV659e2whc+uf+uyVldxTCbLslj8x1I81R669u3SbHnnTJeJn8NMjDkVPDUeqsqqySvKw+V2pjqcZnlrvRzd9Syqwhxj0blnR17659GMOlyuorSSZQtW4Mp20at/94yKPRqxfAZlRKQNMAwj5B14bbWHKc9+xuSnp7Jq8RoKOxaw72kjOfjcfSgoSe4vzUPG7Mvkpz+lbHVZk63HhmnQb2Afdjs0/daHhGMYhpzKKzKWO9sdclrk75//ZeJDH/Ld+7MIBCy23nULDr9wf3beb8ekxujKcnHqLcfy6EXPhWxz1p0nZtwv8vyivFbXaGkrZESkDatcX8VlI25g4a+LGxUwUoaipFsxD3x1S9zmI3VgDdS8ia79FPCCcwAq53iUs/Fq+GV/r+C24x7gz1l/o5SyF69qezHs/8ZfkNYjOu1VJn4OMzHmdPLFmzO4/YQHAY0VsPsOwzSwAhbH/O9QzrzjxLhcR2vNnOlzmfzkVBb98R+5hTmMOGZ39j55eJOFpxMf+pDnr32Nmsra+gJpeR1yOf+B09j75OFxiUfETyyfQUlE2rC7Tn2ET1/+stndNYap2HrwFjzw9a2tvo72/oQuPR10DVA30mECAVTe5ai8s5u8Zv6sv/njuz8xHSYDR23Hppt3bXUcIjEy8XOYiTGni9X/reXEPuc3OlNpY7e8dxW7HjSoVdexLIsHzn2Kj56ZhukIHp8QXGbWcdMS7p1+Y5OD4Gqqavnu/R8pXbmeTt1LGHzQoLSdUmrvZGpGULamPGQSAvaR2b/NmM/CXxfRuxXn0GirEl165kZJCIC9CEtX3gPOrVDuYY1et+VOfdlyp74tvq4QIjHeeXBy2CREKcU7D05udSIy6ZEpfPTMNKDBGTTB7mrd8lKuO+QOnv71vkZTLtm5WYw8dvdWXVekn8yaVBNR++Gjn6IqS/77t3+27kI174KupHES0pCJrgo9tyuESC9fvDEj7PNaa36b0fKTksEeDXnr3vdCPx+wF4H/NO3XVl1HZAZJRNqoWZ/8HFW7UIWNoqW930VoEQDv91EdsiWESC1vrZe14Q63DNKxHaHSxMpFq+tLDoRiOkzmfDa3dRcSGUGmZjJc6aoy1q9cT4fOhRR16VD/eG2Yeh0NbT+8fysjiCbBCN1m7fJSVv67ivziPLpv0S3tTt0Voi0KBAIsW7ACK2DRte8m9ess1q1YH9VRBp16tLLWSDTdhiLkDUwgEODfuUvw1vrosWU3WeSe4SQRyVD//LKIZ695hR8++sn+UCsYNGp7Tr/9eLYY1JfcDjn2rpQwIxHKUE0Wg8VKuQahPZ+GaWGAc8cmCcaS+Ut54vIX+eHD2fWdUu/tenL6bce3eu5ZCNE8y7KY+OCHvHXve/WVSPOLcjlkzH6ccO2RZEU4GbvOtrtv2ao4OvfsSFGXQkpXloVsE/AF2HaPrRs9prXmvcc+5vU7JrJm6ToAHC4He50wlLPvOinpJQlEfMjUTAaaP+tvLhpyDbM+/nnDnYWGnz6byyV7XMtvM+Yz7MghEadDhh4xuPUjENlHAFnUL3dvwkLlntrokSXzl3LhkGuYNWVOozujf+cu4bpD7+CzdngMthCJprXmgXOf4onLXmhUDr2itIpXb3+H6w65k/yiPLbZfcuI56IcfvGBrYrFdJgccfGBIfsfwzToslkndt5/h0aPP3fNqzxy4bP1SQiA3+tn6otfcMke11JVVtWquERqSCKSgR4450l8Xn+TIVQrYBHwBbj3zMfZab8d6DugV/OnPipwOE2Ou6b156EoowOq6FHAib1lt07wv3PPAnfjw/eevPxFaipqm8SvtV1T5KHzn8ZTE91x5UKI6Pzy5e/1u1Q2pi3Nj5/8zLRXvuKk60eHvIkxDMUuB+zI5jv0bnU8oy8/hN0P38V+3wZr1QzTIK9DLrdMuhLT3NCnLJm/lNfvfLfZ97ICFksXrGDCfR+0Oi6RfJKIZJgFcxay4KeFIedxLUuzZN5S/pz1N+OmXMsWA/sA9h2Iw2l/qHMLcrj5vavi0pkAKPceqI4fQs5JYGwKRidwj0QVjcfIv6LRXc/a5aV8/+HssPPQVWXVfPPuzLjEJoSwTX7q00aHzm1MGYr3n/iEQXsP4KoXL8KV7UIphcNp1r9u0L478H+vXRqXeEyHyXVvjuW6N8ey3bCtKdqkA9236MaJ1x3FM3Pva1JWYMqzn4WN3wpYfPDk1LjEJpJL1ohkmKV/rYiy3XL677oFD317O3O/nsd3H/yIt9bL5jv2ZvjRu5GVE91ccLSUoyeq4BoouCZsu0gn8ILdQS3/Z2UcoxNCLJm3tMnRCg1pS7P0r+UA7HXCUHY9aCDTXvmaJfOWkpWXxbCjdqVf8MYmXgzDYNhRQxh2VORDOpf/u6q+ymso61eV4fX4pMhZhpFEJMPkdciJ3AjqV5Erpdhu6NZsN3TrCK9IjvzivIhtAv4AAX8gCdEI0X7kF+VGXMCeW5jT4L9zOeT8fZMRWlQKivIwTCNs32A6jLCjJiI9yb9Yhtl+eH9yCrLDtsnOz2LgqO2SFFFsNt18E/ps3yviYrh3H/6INcvWhW0jhIjeiGP3CL+LTin2OmFoEiOKzYhjd494gxLwWzxz5StJikjEiyQiGcYwjYhFyDbbpkfIEzVTTSnFGeNOiFj1taqsmokPTE5SVEK0fe7s8NMVWmt22neH5ATTAgNGbMMOe24bsd3bD3wgNzEZRhKRDDPr45+pLA2/RW3BTwuprqhJUkSx22X/HdluWPipIitgMWX89CRFJETbN/mpT8OORCpD8c3EH5IYUWyUUlz65DmR2wGfv/5N4gMScSOJSIZZMHshpsMM28bn8dcvOktX0SyWrVxXmYRIhGgf/vppYdiRSG1p5s9akMSIYuet9UVsY5gG61eFLpQm0o8kIhnG4XJEVYJ58lNT+fCZaWlb4Kdzj44RF5WVdCtOUjRCtH112/fDWTJ/Ga/e/g6/fvVHWp4PVdy1Q8T1ZQG/ReeenZIUkYgHSUQyzOADB0bVQXz0zDTuP+cJRnc9i7fvT78iP/uevmfYrYSGoTjw7L3jci2/z8+Kf1exZtm6tOxchUiG3Q/dOWKb9SvLeOGGNxg7/HrO2eFylv0dXbmAZCkozmf3w3YJu07O4TIZedzucbleRWkly/9ZSU1VdGd3iZaRRCTDbLZND4o26RCxnWXZVUp9tT6euOwFJj+VXoV+ttplc/Y+eTjNVXg2TIOufTfh0Av2a9U1PDUeHhrzNEd2PJ2T+ozhuO7ncPaAy6SEvGiXhh+9W1Tt6kZcF/3+H2OHX0/5uopEhhWz0287jqxcd8hk5IzbTyC/KHKZgHB+/fp3Ltz1ao4oOY2TN7+AI0pO4+7THmXV4tWtel/RPElEMlBxg1N2ozX++tfx+/zxD6aFlFJc9ux5nHDtUY22IxumwdAjd+WBr29p1Ymav3zxG6O7nMn7j3/SaOHuot+WMO6EB3nltrdbFb8QmSY7Lyum9lbAYt2K9Ux59rMERdQyPbbclAe/uY1t99iq0eMdNy3msmfO48hLD2rxewf8Ae446SHGDruBeT9sWC/j9/r59OUvOX/nq1i+UIotxpvSaTxWXV5eTmFhIWVlZRQUFKQ6nLRxzYG38+PHc+xRjxjc9en17Lhn+tUX8dR4mPf9AnxeP32270nxJkWter+538zjshE3RFxL8/z8h+jer2urrtUeZOLnMBNjTrTF85ZyRv9LYn5d72178tQv98Y/oDhY9vcKlv61nNzCHLbcZfNGZ9PESmvN7cc9wOdvzgjZxjANBh84kJvfvbLF12kvYvkMyohIBtr7pOExJyFAxG2/qeLOdjNgxDbstM+AVichWmseueCZiEmIYRp89PSnrbqWEJmkx5bd7GKCMZ64XVGavrvXuvXdhJ3325H+Q7ZsVRIC8MsXv4dNQsAeJfru/R+lTkmcSSKSgfY4Yhc237F3xMJmG+vat0uCIkofC39dzN8/L4rYzgpYLPrjvyREJER6UEpx5p0nBr+J7jWGodi03yaJCyqNTHnus4g7csC+2Vm2IL0W8WY6SUQykNPl5M6p17HzfjtE1d4wFH0H9IrbabvpbOWi6BaTKUNFLJUvRFuz8747cMPbl9OhU6H9QITfu5alOfDsfRIfWBpY/s/KiBWf6+TkS98RT3LoXYYqKM7n1vev5r+/lvPTtF9ZMn8p7z36MdqyGk3bGKaB6TS5+InIFQnTXU1VLV+/8z2rFq2hoCSPPY7claLOhY3aFHbMj+q9tKWjOvFTiLZm98N2YfCBA5k5ZQ7L/17JZ699xZ+z/mmytV0Zip32GcCw0bumKNL4WfT7En748Cd8Xj/9BvZm0D4DMIzG9+EduhSiDBUxGencqxN9BvRKZLjtjiQiGa57v671Cy5HHLM7z179Cr988Xv98wNGbMNZd54Y9+O7k+2TFz7nkQufpaayFtNpYvktHr34OUZfdgin3XZcfaey1eB+dO7ViVURRkZ6bt2dIQfvlIzQhUg7Dqej/uf/0Av247VxE5n40IeUr7W36uYW5nDomP048fqjWr32IpUqSiu5/YQHmTVlDoZpoJRd8KzLZp24/q3L2GJQ3/q2e50wLKoS96fceHSTJEa0juyaaYNWLVlD6coySrp2oOOmJakOp9W+evs7bh4detX+8dccwWm3Hlf//RdvzuDWY+8P2b5Tj4488v3trV4Y215k4ucwE2NONZ/Xx+I/lqK1pudWm+LKcqU6pFYJBAJcOvQ65s/8u8nidcM0cOe4ePKne+jax147F/AHuGTotcyf+XfIUZGTbzyak64fnfDY2wLZNdPOde7RkS136tsmkhCtNc9e82rYuew373mvUdGl4Ufvxv9euID8omAdkuBrHU6Twy7Yn5cXPipJiBAbcbqc9B2wGZvv0DvjkxCwDwj947u/mt1BZwUsPDVeJtz3fv1jpsNk3EfXsutBTUdKO3Yv4eHvbpckJEFkakaktYW/Lo54gJ/f62fGpFnsd9rI+sf2Pmk4w4/eje8/+JHV/62lQ+dChhyyE9m5sRV1EkJkpumvf43hMLBCHCVh+S0+ffkrLnzkzPrH8jrkcvO7/2PpguXM/vRXAv4AWw/ux5Y7b56ssNslSUREWoumhoFhGlQ2087ldjL0yMxfaCeEiF3luqqQSUidmsoatNZNaqtsunlXNt1cih0mi0zNiLTWtXfniG2sgFU/zyuEEGDXTYp0wnfnHh1jLvAm4k8SEZHWOvfsxMBR24Uu3qbsLbuDDxyY3MCEEGntgDP3CnvCtzIUB53bPmqkpDtJRETaO/+B03DnuJokI8pQKKW49KlzcThlllEIsUHv7XqFPADPMA36bNeTQ8fsm+SoRHMkERFpr1f/Hjz83Th22ndAo90z/Qb24Y4p17L7YbukLjghRNo6556TGfPQ6XTctLj+MVeWkwPPGsW9n99Edp5USE0HUkdEZJS1y0tZvWQNBSX5dOvbPs7ASLVM/BxmYswicQKBAIt//w+vx0+PLbtJifYkSIs6Iv/++y9nnHEGvXv3Jjs7m759+3LDDTfg9XoTdcl2Z/V/a3n+2tc4Z4fLOb3/xdxzxmP8NfufVIeVUCVdi9hql36ShLRR0m8kntaa7z+czfWH3cmpW13EhbtezcSHPqSqvDrVoSWMaZr03q4XW+7UV5KQNJSwifV58+ZhWRZPPvkkm2++OXPnzuWss86iqqqKe+65J1GXbTfmTJ/LtQeNw+f11xfsWbZgBR8/P52z7zqJ0ZcfkuIIhYid9BuJFQgEuPOkh5n++jcYpmH3HQrmz/ybt+59j3s/v4muvWUHmkiupE7N3H333Tz++OP88090d+0yvNq88rUVnLDZeXhqvCFLEd859XoG7rVdkiMTbVGqP4ex9huQ+pjT1et3TOS5/3uV5np9w2HQq393nvzpHtnSKlotLaZmmlNWVkZxcXHI5z0eD+Xl5Y2+RFMfPz89bBJiOgzevv/9Zp8TItNE6jdA+o5oBPwB3nlwcrNJCNiVRhf+sphfv/ojuYGJdi9piciCBQt4+OGHOeec0MfRjxs3jsLCwvqvHj16JCu8jDLn87lhj6oO+C3mfDY3iREJkRjR9BsgfUc0/vtrOaUry8K2MR0GP0//LUkRCWGLORG56qqrUEqF/Zo3b16j1yxdupT99tuP0aNHc9ZZZ4V876uvvpqysrL6ryVLlsT+N2rjvB4fa5eWRmyXtluhRLuUyH4DpO+IxqpFq6NopUjjjZSijYp5sepll13GqaeeGrZNnz596v972bJljBw5kt12242nnnoq7OvcbjdutzvWkNqNmVN+YtwJD0U8f8UwDbbbY+skRSVEZInsN0D6jnC8Hh/3n/0En770ZcS2AX+AbYdK3yGSK+ZEpFOnTnTq1CmqtkuXLmXkyJEMGjSI559/HsOQ+mktNX/mAq475E4sK/whTmCfvRKqoqAQqSD9Rurcd+bjfPba1xHbGabBpv02Ycc9t01CVEJskLDtu0uXLmXEiBH06tWLe+65h9WrNwwLbrKJ1ICI1cu3TkBrHXZtSN12vFNuOoZd9t8xidEJER/Sb8TXf38tZ9orX0VspwxFYacCbn73StkxI5IuYYnI1KlTWbBgAQsWLKB79+6NnpM5yNjUVnv4fvLssEkIQLe+m3DZM+eyrUzLiAwl/UZ8ffnWtxvqhYRx7FWHc9SlB1FQkp+kyITYIGFjnqeeeqp9B9/Ml4jN0gXLIyYhpsNk2z22kiREZDTpN+JHa82C2f9ENZ2790nDJAkRKSOTr2nu92/nc+ke10VsZ1kWm/brmoSIhBDpzrIs7jv7Cb565/uIW+gcLgcl3cLXaREikSQRSWO11R6uPfgOPNWeiG0NQ7HPKcOTEJUQIt1NfupTpjz7WcR2hsNgrxOGyvkrIqUStkZEtN70176mYl34rbp1zrnnFIo3KUpwREKIdKe1ZsJ974Mi7GiIMhTFXTpw6i3HJi02IZojiUga+/XrPzAdBgF/+DneK1+8kFEnDktSVEKIdFa+toJlC1ZEbLfp5l25+7Mb6CjTMiLFZGomjUW7jW7okYMTHIkQIlNE02+YDpMd99pOkhCRFiQRSWMDhm8TdjREGYp+A/vgzpaKkkIIW35xHj226ka4fCTgDzBgxDbJC0qIMCQRSWPDjx5Ch04FGGbz/0za0oy+/JAkRyWESGdKKUZfdkjIU3YN06Bj9xJ2P2zn5AYmRAiSiKQxd7ab2z68hpz8bJSx4fbGdNj/bMf871BGHLNbqsITQqSp/U7fk0Mv2A/Y0F+APYqaX5TL7ZOvxuGUJYIiPchPYprbYlBfnpv3IB89M42v3vkOT5WHzXfszSHn7yvFy4QQzVJKMebB0xl6xK68//jH/P3zIrLzsxg+ejf2P2NPKV4m0orSaVyysLy8nMLCQsrKyigoKEh1OEK0S5n4OczEmIVoS2L5DMrUjBBCCCFSRhIRIYQQQqSMJCJCCCGESBlJRIQQQgiRMpKICCGEECJlJBERQgghRMpIIiKEEEKIlJFERAghhBApI4mIEEIIIVJGSrxngKqyKmZ/+iu1VR56bdOdLQb1TXVIQogMsGDOQhb+shhXtotBe29PXofcVIckRBOSiKSxQCDA+Ove4J0HPsBb66t/fPMde/O/8WPovV2vFEYnhEhXi35fwl2nPsqfs/6uf8zpdnLoBftx5rgTMB1mCqMTojGZmkljj1z4HK/fObFREgLwzy+LuGTodfz31/IURSaESFfLF67kkqHXseCnhY0e93l8vH3fB9x39hMpikyI5kkikqaWzF/KB098As0cSWgFLDzVHl697e3kByaESGtv3PEuNRU1WAGryXNaaz4Z/zkL5y5OQWRCNE8SkTQ17eWvMMzQ/zwBv8X0177GW+tNYlRCiHQW8AeY+vKXBPxNk5A6psPg0xe/SGJUQoQniUiaWrdiPUqpsG38vgBVZdVJikgIke5qq2rx1oS/OdEa1q4oTVJEQkQmiUiaKulWhNbNzMs04HQ7yJVV8EKIoKy8LNw57rBtlIKO3YqTFJEQkUkikqb2Pnl4s3O8dUyHwV4nDMPldiYxKiFEOjNNk31OGYHpCD+tu8+pI5MYlRDhSSKSprr13YQjLzmw2ecM0yAnP4cTrj0yyVEJIdLdcVcfTl5RXsg1Zgeftw89t9o0yVEJEZokImns7HtO5rRbjyOnILvR49vstiUPfXsbm2zWOUWRCSHSVafuJTw04za2G7p1o8ez87M4+YajueDhM1IUmRDNUzrSQoQUKi8vp7CwkLKyMgoKClIdTsp4ajz88uUfeKo99OrfnR5byt2MSJ5M/BxmYsyJ8N9fy/l37mLc2S62G9afrAjrR4SIl1g+g1JZNQO4s93svO8OqQ5DCJFhuvfrSvd+XVMdhhBhydSMEEIIIVJGEhEhhBBCpIwkIkIIIYRIGUlEhBBCCJEykogIIYQQImUkERFCCCFEykgiIoQQQoiUkURECCGEECkjiYgQQgghUiatK6vWVZ8vLy9PcSRCtF91n780Pg2iCek7hEitWPqNtE5EKioqAOjRo0eKIxFCVFRUUFhYmOowoiJ9hxDpIZp+I60PvbMsi2XLlpGfn49SKtXhxEV5eTk9evRgyZIlbeowLvl7ZZ5o/25aayoqKujWrRuGkRmzudJ3ZA75e2WWRPQbaT0iYhgG3bt3T3UYCVFQUNCmfjjryN8r80Tzd8uUkZA60ndkHvl7ZZZ49huZcXsjhBBCiDZJEhEhhBBCpIwkIknmdru54YYbcLvdqQ4lruTvlXna8t+tLWqr/17y98osifh7pfViVSGEEEK0bTIiIoQQQoiUkURECCGEECkjiYgQQgghUkYSESGEEEKkjCQiKfLvv/9yxhln0Lt3b7Kzs+nbty833HADXq831aG1yKOPPspmm21GVlYWgwcP5ocffkh1SK0ybtw4dt55Z/Lz8+ncuTOHHXYY8+fPT3VYcXfHHXeglOKSSy5JdSgiSm2p72hr/Qa0j74j3v2GJCIpMm/ePCzL4sknn+S3337j/vvv54knnuCaa65JdWgxe+ONNxg7diw33HADs2fPZsCAAey7776sWrUq1aG12BdffMGYMWP47rvvmDp1Kj6fj3322YeqqqpUhxY3M2fO5Mknn2T77bdPdSgiBm2l72iL/Qa0/b4jIf2GFmnjrrvu0r179051GDHbZZdd9JgxY+q/DwQCulu3bnrcuHEpjCq+Vq1apQH9xRdfpDqUuKioqND9+vXTU6dO1cOHD9cXX3xxqkMSrZCJfUd76De0blt9R6L6DRkRSSNlZWUUFxenOoyYeL1efvzxR0aNGlX/mGEYjBo1im+//TaFkcVXWVkZQMb9+4QyZswYDjzwwEb/biJzZVrf0V76DWhbfUei+o20PvSuPVmwYAEPP/ww99xzT6pDicmaNWsIBAJ06dKl0eNdunRh3rx5KYoqvizL4pJLLmH33Xdn2223TXU4rfb6668ze/ZsZs6cmepQRBxkYt/RHvoNaFt9RyL7DRkRibOrrroKpVTYr40/aEuXLmW//fZj9OjRnHXWWSmKXIQyZswY5s6dy+uvv57qUFptyZIlXHzxxbzyyitkZWWlOhzRgPQdbU9b6TsS3W9Iifc4W716NWvXrg3bpk+fPrhcLgCWLVvGiBEj2HXXXRk/fjyGkVm5odfrJScnhwkTJnDYYYfVP37KKaewfv16Jk2alLrg4uCCCy5g0qRJfPnll/Tu3TvV4bTau+++y+GHH45pmvWPBQIBlFIYhoHH42n0nEie9tR3tPV+A9pW35HofkOmZuKsU6dOdOrUKaq2S5cuZeTIkQwaNIjnn38+ozqSOi6Xi0GDBjFt2rT6DsWyLKZNm8YFF1yQ2uBaQWvNhRdeyMSJE/n8888zviOps9dee/Hrr782euy0005jq6224sorr5QkJIXaU9/RVvsNaJt9R6L7DUlEUmTp0qWMGDGCXr16cc8997B69er65zbZZJMURha7sWPHcsopp7DTTjuxyy678MADD1BVVcVpp52W6tBabMyYMbz66qtMmjSJ/Px8VqxYAUBhYSHZ2dkpjq7l8vPzm8xV5+bmUlJSkvFz2O1FW+k72mK/AW2z70h0vyGJSIpMnTqVBQsWsGDBArp3797ouUybLTvmmGNYvXo1119/PStWrGCHHXZgypQpTRaiZZLHH38cgBEjRjR6/Pnnn+fUU09NfkBCBLWVvqMt9hsgfUdLyBoRIYQQQqRMZk0sCiGEEKJNkURECCGEECkjiYgQQgghUkYSESGEEEKkjCQiQgghhEgZSUSEEEIIkTKSiAghhBAiZSQREUIIIUTKSCIihBBCiJSRREQIIYQQKSOJiBBCCCFSRhIRIYQQQqTM/wNTSGv4Txue1wAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
-  ],
-  "metadata": {
+   ],
+   "source": [
+    "plt.subplot(1,2,1)\n",
+    "plt.scatter(X_test[:,0],X_test[:,1],c=y_test)\n",
+    "plt.subplot(1,2,2)\n",
+    "plt.scatter(X_test[:,0],X_test[:,1],c=y_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
     "colab": {
-      "provenance": []
-    },
-    "kernelspec": {
-      "display_name": "Python 3",
-      "name": "python3"
+     "background_save": true,
+     "base_uri": "https://localhost:8080/",
+     "height": 472
     },
-    "language_info": {
-      "name": "python"
+    "id": "qi9xSQYxF0b-",
+    "outputId": "4a01b401-fb01-4e33-e6cd-4a84ff1c0450"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
+   ],
+   "source": [
+    "plt.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap='bwr')\n",
+    "\n",
+    "# Plot the decision boundary\n",
+    "x1_min, x1_max = X_train[:, 0].min() - 1, X_train[:, 0].max() + 1\n",
+    "x2_min, x2_max = X_train[:, 1].min() - 1, X_train[:, 1].max() + 1\n",
+    "xx1, xx2 = np.meshgrid(np.arange(x1_min, x1_max, 0.1), np.arange(x2_min, x2_max, 0.1))\n",
+    "Z = logreg.predict(np.c_[xx1.ravel(), xx2.ravel()])\n",
+    "Z = Z.reshape(xx1.shape)\n",
+    "plt.contour(xx1, xx2, Z, levels=[0.5], colors='black')\n",
+    "\n",
+    "plt.xlabel('Feature 1')\n",
+    "plt.ylabel('Feature 2')\n",
+    "plt.title('Logistic Regression Decision Boundary')\n",
+    "plt.show()\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
   },
-  "nbformat": 4,
-  "nbformat_minor": 0
-}
\ No newline at end of file
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Week 1/Python Assignment/.ipynb_checkpoints/Chained Comparison Operators-checkpoint.ipynb b/Week 1/Python Assignment/.ipynb_checkpoints/Chained Comparison Operators-checkpoint.ipynb
new file mode 100644
index 0000000..7a567d9
--- /dev/null
+++ b/Week 1/Python Assignment/.ipynb_checkpoints/Chained Comparison Operators-checkpoint.ipynb	
@@ -0,0 +1,202 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Chained Comparison Operators\n",
+    "\n",
+    "An interesting feature of Python is the ability to *chain* multiple comparisons to perform a more complex test. You can use these chained comparisons as shorthand for larger Boolean Expressions.\n",
+    "\n",
+    "In this lecture we will learn how to chain comparison operators and we will also introduce two other important statements in Python: **and** and **or**.\n",
+    "\n",
+    "Let's look at a few examples of using chains:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1 < 2 < 3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The above statement checks if 1 was less than 2 **and** if 2 was less than 3. We could have written this using an **and** statement in Python:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1<2 and 2<3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The **and** is used to make sure two checks have to be true in order for the total check to be true. Let's see another example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1 < 3 > 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The above checks if 3 is larger than both of the other numbers, so you could use **and** to rewrite it as:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1<3 and 3>2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It's important to note that Python is checking both instances of the comparisons. We can also use **or** to write comparisons in Python. For example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1==2 or 2<3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note how it was true; this is because with the **or** operator, we only need one *or* the other to be true. Let's see one more example to drive this home:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1==1 or 100==1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Great! For an overview of this quick lesson: You should have a comfortable understanding of using **and** and **or** statements as well as reading chained comparison code.\n",
+    "\n",
+    "Go ahead and go to the quiz for this section to check your understanding!"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/Week 1/Python Assignment/.ipynb_checkpoints/Functions and Methods Homework-checkpoint.ipynb b/Week 1/Python Assignment/.ipynb_checkpoints/Functions and Methods Homework-checkpoint.ipynb
new file mode 100644
index 0000000..994610b
--- /dev/null
+++ b/Week 1/Python Assignment/.ipynb_checkpoints/Functions and Methods Homework-checkpoint.ipynb	
@@ -0,0 +1,392 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Functions and Methods Homework \n",
+    "\n",
+    "Complete the following questions:\n",
+    "____\n",
+    "**Write a function that computes the volume of a sphere given its radius.**\n",
+    "<p>The volume of a sphere is given as $$\\frac{4}{3} πr^3$$</p>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import math\n",
+    "def vol(rad):\n",
+    "    a=(4.0/3.0)*(math.pi)*(rad**3)\n",
+    "    return a\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "33.510321638291124"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Check\n",
+    "vol(2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "___\n",
+    "**Write a function that checks whether a number is in a given range (inclusive of high and low)**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def ran_check(num,low,high):\n",
+    "    if low<=num<=high:\n",
+    "        print(f'{num} is in the range {low} to {high}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "5 is in the range 2 to 7\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Check\n",
+    "ran_check(5,2,7)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you only wanted to return a boolean:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def ran_bool(num,low,high):\n",
+    "    return low<=num<=high"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ran_bool(3,1,10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "____\n",
+    "**Write a Python function that accepts a string and calculates the number of upper case letters and lower case letters.**\n",
+    "\n",
+    "    Sample String : 'Hello Mr. Rogers, how are you this fine Tuesday?'\n",
+    "    Expected Output : \n",
+    "    No. of Upper case characters : 4\n",
+    "    No. of Lower case Characters : 33\n",
+    "\n",
+    "HINT: Two string methods that might prove useful: **.isupper()** and **.islower()**\n",
+    "\n",
+    "If you feel ambitious, explore the Collections module to solve this problem!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def up_low(s):\n",
+    "    pass"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Original String :  Hello Mr. Rogers, how are you this fine Tuesday?\n",
+      "No. of Upper case characters :  4\n",
+      "No. of Lower case Characters :  33\n"
+     ]
+    }
+   ],
+   "source": [
+    "s = 'Hello Mr. Rogers, how are you this fine Tuesday?'\n",
+    "up_low(s)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "____\n",
+    "**Write a Python function that takes a list and returns a new list with unique elements of the first list.**\n",
+    "\n",
+    "    Sample List : [1,1,1,1,2,2,3,3,3,3,4,5]\n",
+    "    Unique List : [1, 2, 3, 4, 5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def unique_list(lst):\n",
+    "    pass"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[1, 2, 3, 4, 5]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "unique_list([1,1,1,1,2,2,3,3,3,3,4,5])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "____\n",
+    "**Write a Python function to multiply all the numbers in a list.**\n",
+    "\n",
+    "    Sample List : [1, 2, 3, -4]\n",
+    "    Expected Output : -24"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def multiply(numbers):  \n",
+    "    pass"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-24"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "multiply([1,2,3,-4])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "____\n",
+    "**Write a Python function that checks whether a passed in string is palindrome or not.**\n",
+    "\n",
+    "Note: A palindrome is word, phrase, or sequence that reads the same backward as forward, e.g., madam or nurses run."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def palindrome(s):\n",
+    "    pass"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "palindrome('helleh')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "____\n",
+    "#### Hard:\n",
+    "\n",
+    "**Write a Python function to check whether a string is pangram or not.**\n",
+    "\n",
+    "    Note : Pangrams are words or sentences containing every letter of the alphabet at least once.\n",
+    "    For example : \"The quick brown fox jumps over the lazy dog\"\n",
+    "\n",
+    "Hint: Look at the string module"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import string\n",
+    "\n",
+    "def ispangram(str1, alphabet=string.ascii_lowercase):\n",
+    "    pass"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ispangram(\"The quick brown fox jumps over the lazy dog\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'abcdefghijklmnopqrstuvwxyz'"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "string.ascii_lowercase"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    }
+   },
+   "source": [
+    "#### Great Job!"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Week 1/Python Assignment/.ipynb_checkpoints/Objects and Data Structures Assessment Test-checkpoint.ipynb b/Week 1/Python Assignment/.ipynb_checkpoints/Objects and Data Structures Assessment Test-checkpoint.ipynb
new file mode 100644
index 0000000..cf83972
--- /dev/null
+++ b/Week 1/Python Assignment/.ipynb_checkpoints/Objects and Data Structures Assessment Test-checkpoint.ipynb	
@@ -0,0 +1,800 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Objects and Data Structures Assessment Test"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    }
+   },
+   "source": [
+    "## Test your knowledge. \n",
+    "\n",
+    "** Answer the following questions **"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    }
+   },
+   "source": [
+    "Write a brief description of all the following Object Types and Data Structures we've learned about: "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Numbers: Can be of different Data Type int(+/-) float(decimal) complex(real + (imaginary)j). type() can be used to determine the type of the number. \n",
+    "\n",
+    "Strings:Its a combination of character inside quotes. Can use negative indexing to access elements from the back of string.\n",
+    "\n",
+    "Lists:Order  of collection of data.Can be of different type. Use []. to acess element use index and negative again means from back so -1 means last -2 means second last.\n",
+    "\n",
+    "Tuples:Immutable ie cant be modified. Its like coordinates.Inside () separated by comma and any data type is allowed.\n",
+    "\n",
+    "Dictionaries:Unordered collection of data stored by mapping. Uses key-value pair. Each key-value pair in a Dictionary is separated by a colon : , whereas each key is separated by a ‘comma’. Use {}\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Numbers\n",
+    "\n",
+    "Write an equation that uses multiplication, division, an exponent, addition, and subtraction that is equal to 100.25.\n",
+    "\n",
+    "Hint: This is just to test your memory of the basic arithmetic commands, work backwards from 100.25"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Answer=100.25\n"
+     ]
+    }
+   ],
+   "source": [
+    "a=((5*(40/2))+(0.50-(0.50*0.50)))**1\n",
+    "print(f\"Answer={a}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Answer these 3 questions without typing code. Then type code to check your answer.\n",
+    "\n",
+    "    What is the value of the expression 4 * (6 + 5) = 44\n",
+    "    \n",
+    "    What is the value of the expression 4 * 6 + 5   = 29\n",
+    "    \n",
+    "    What is the value of the expression 4 + 6 * 5   = 34"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Answer 1 = 44\n",
+      "Answer 2 = 29\n",
+      "Answer 3 = 34\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Answer 1 =\", 4*(6+5))\n",
+    "print(\"Answer 2 =\", 4*6+5)\n",
+    "print(\"Answer 3 =\", 4+6*5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "What is the *type* of the result of the expression 3 + 1.5 + 4?<br>float<br>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'float'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(type(3 + 1.5 + 4))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "What would you use to find a number’s square root, as well as its square? "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "sqr root =  7.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Square root:\n",
+    "a=49\n",
+    "print(\"sqr root = \", a**0.5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "square =  49\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Square:\n",
+    "a=7\n",
+    "print(\"square = \", a**2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Strings"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Given the string 'hello' give an index command that returns 'e'. Enter your code in the cell below:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "e\n"
+     ]
+    }
+   ],
+   "source": [
+    "s = 'hello'\n",
+    "# Print out 'e' using indexing\n",
+    "print(s[1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Reverse the string 'hello' using slicing:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "olleh\n"
+     ]
+    }
+   ],
+   "source": [
+    "s ='hello'\n",
+    "# Reverse the string using slicing\n",
+    "print(s[::-1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Given the string hello, give two methods of producing the letter 'o' using indexing."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "o\n"
+     ]
+    }
+   ],
+   "source": [
+    "s ='hello'\n",
+    "# Print out the 'o'\n",
+    "\n",
+    "# Method 1:\n",
+    "print(s[4])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "index = 4 and letter = o\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Method 2:\n",
+    "for i in range(len(s)):\n",
+    "    if s[i]==\"o\":\n",
+    "        print(f\"index = {i} and letter =\",s[i])\n",
+    "    \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Lists"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Build this list [0,0,0] two separate ways."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0, 0, 0]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Method 1:\n",
+    "lst1=[0,0,0]\n",
+    "print(lst1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0, 0, 0]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Method 2:\n",
+    "lst =[0]*3 \n",
+    "print(lst)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Reassign 'hello' in this nested list to say 'goodbye' instead:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1, 2, [3, 4, 'goodbye']]\n"
+     ]
+    }
+   ],
+   "source": [
+    "list3 = [1, 2, [3, 4, 'hello']]\n",
+    "list3[2][2] = 'goodbye'\n",
+    "print(list3)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Sort the list below:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1, 3, 4, 5, 6]\n"
+     ]
+    }
+   ],
+   "source": [
+    "list4 = [5,3,4,6,1]\n",
+    "list4.sort()\n",
+    "print(list4)\n",
+    "     \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Dictionaries"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Using keys and indexing, grab the 'hello' from the following dictionaries:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "hello\n"
+     ]
+    }
+   ],
+   "source": [
+    "d = {'simple_key':'hello'}\n",
+    "# Grab 'hello'\n",
+    "print(d['simple_key'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "hello\n"
+     ]
+    }
+   ],
+   "source": [
+    "d = {'k1':{'k2':'hello'}}\n",
+    "# Grab 'hello'\n",
+    "print(d['k1']['k2'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "hello\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Getting a little tricker\n",
+    "d = {'k1':[{'nest_key':['this is deep',['hello']]}]}\n",
+    "\n",
+    "#Grab hello\n",
+    "print(d['k1'][0]['nest_key'][1][0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "hello\n"
+     ]
+    }
+   ],
+   "source": [
+    "# This will be hard and annoying!\n",
+    "d = {'k1':[1,2,{'k2':['this is tricky',{'tough':[1,2,['hello']]}]}]}\n",
+    "print(d['k1'][2]['k2'][1]['tough'][2][0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Can you sort a dictionary? Why or why not?<br> As its unordered collection so we acess by keys <br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Tuples"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "What is the major difference between tuples and lists?<br>tupple are immutable<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "How do you create a tuple?<br>using () and fill in data separated by comma<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Sets "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "What is unique about a set?<br>Unordered,unchangeable,unindexed but we can add/remove \n",
+    "and we use { }  and duplicates not allowed \n",
+    "True and 1 are considered the same value in sets<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Use a set to find the unique values of the list below:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{1, 2, 33, 4, 3, 11, 22}\n"
+     ]
+    }
+   ],
+   "source": [
+    "list5 = [1,2,2,33,4,4,11,22,3,3,2]\n",
+    "\n",
+    "set1=set(list5)\n",
+    "print(set1)\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Booleans"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For the following quiz questions, we will get a preview of comparison operators. In the table below, a=3 and b=4.\n",
+    "\n",
+    "<table class=\"table table-bordered\">\n",
+    "<tr>\n",
+    "<th style=\"width:10%\">Operator</th><th style=\"width:45%\">Description</th><th>Example</th>\n",
+    "</tr>\n",
+    "<tr>\n",
+    "<td>==</td>\n",
+    "<td>If the values of two operands are equal, then the condition becomes true.</td>\n",
+    "<td> (a == b) is not true.</td>\n",
+    "</tr>\n",
+    "<tr>\n",
+    "<td>!=</td>\n",
+    "<td>If values of two operands are not equal, then condition becomes true.</td>\n",
+    "<td> (a != b) is true.</td>\n",
+    "</tr>\n",
+    "<tr>\n",
+    "<td>&gt;</td>\n",
+    "<td>If the value of left operand is greater than the value of right operand, then condition becomes true.</td>\n",
+    "<td> (a &gt; b) is not true.</td>\n",
+    "</tr>\n",
+    "<tr>\n",
+    "<td>&lt;</td>\n",
+    "<td>If the value of left operand is less than the value of right operand, then condition becomes true.</td>\n",
+    "<td> (a &lt; b) is true.</td>\n",
+    "</tr>\n",
+    "<tr>\n",
+    "<td>&gt;=</td>\n",
+    "<td>If the value of left operand is greater than or equal to the value of right operand, then condition becomes true.</td>\n",
+    "<td> (a &gt;= b) is not true. </td>\n",
+    "</tr>\n",
+    "<tr>\n",
+    "<td>&lt;=</td>\n",
+    "<td>If the value of left operand is less than or equal to the value of right operand, then condition becomes true.</td>\n",
+    "<td> (a &lt;= b) is true. </td>\n",
+    "</tr>\n",
+    "</table>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "What will be the resulting Boolean of the following pieces of code (answer fist then check by typing it in!)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Answer before running cell\n",
+    "2 > 3\n",
+    "#false"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Answer before running cell\n",
+    "3 <= 2\n",
+    "#false"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Answer before running cell\n",
+    "3 == 2.0\n",
+    "#false"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Answer before running cell\n",
+    "3.0 == 3\n",
+    "#true"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Answer before running cell\n",
+    "4**0.5 != 2\n",
+    "#false"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Final Question: What is the boolean output of the cell block below?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# two nested lists\n",
+    "l_one = [1,2,[3,4]]\n",
+    "l_two = [1,2,{'k1':4}]\n",
+    "\n",
+    "# True or False?\n",
+    "l_one[2][0] >= l_two[2]['k1']\n",
+    "#false"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Great Job on your first assessment! "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Week 1/Python Assignment/.ipynb_checkpoints/Statements Assessment Test-checkpoint.ipynb b/Week 1/Python Assignment/.ipynb_checkpoints/Statements Assessment Test-checkpoint.ipynb
new file mode 100644
index 0000000..d3b80e2
--- /dev/null
+++ b/Week 1/Python Assignment/.ipynb_checkpoints/Statements Assessment Test-checkpoint.ipynb	
@@ -0,0 +1,358 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    }
+   },
+   "source": [
+    "# Statements Assessment Test\n",
+    "Let's test your knowledge!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "_____\n",
+    "**Use <code>for</code>, .split(), and <code>if</code> to create a Statement that will print out words that start with 's':**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "st = 'Print only the words that start with s in this sentence'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "start\n",
+      "s\n",
+      "sentence\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Code here\n",
+    "list1=st.split(' ')\n",
+    "for i in range(len(list1)):\n",
+    "    if list1[i][0] == 's':\n",
+    "        print(list1[i])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "______\n",
+    "**Use range() to print all the even numbers from 0 to 10.**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n",
+      "2\n",
+      "4\n",
+      "6\n",
+      "8\n",
+      "10\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Code Here\n",
+    "for i in range(11):\n",
+    "    if i%2 ==0:\n",
+    "       print(i)\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "___\n",
+    "**Use a List Comprehension to create a list of all numbers between 1 and 50 that are divisible by 3.**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[3, 6, 9, 12, 15, 18, 21, 24, 27, 30, 33, 36, 39, 42, 45, 48]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Code in this cell\n",
+    "ans=[i+1 for i in range(50) if (i+1)%3 == 0]\n",
+    "print(ans)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "_____\n",
+    "**Go through the string below and if the length of a word is even print \"even!\"**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "st = 'Print every word in this sentence that has an even number of letters'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Lenght of only = \"even!\"\n",
+      "Lenght of that = \"even!\"\n",
+      "Lenght of with = \"even!\"\n",
+      "Lenght of in = \"even!\"\n",
+      "Lenght of this = \"even!\"\n",
+      "Lenght of sentence = \"even!\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Code in this cell\n",
+    "l=st.split(\" \")\n",
+    "for i in range(len(l)):\n",
+    "    if(len(l[i])%2==0):\n",
+    "        print(f\"Lenght of {l[i]} = \\\"even!\\\"\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "____\n",
+    "**Write a program that prints the integers from 1 to 100. But for multiples of three print \"Fizz\" instead of the number, and for the multiples of five print \"Buzz\". For numbers which are multiples of both three and five print \"FizzBuzz\".**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1\n",
+      "2\n",
+      "Fizz\n",
+      "4\n",
+      "Buzz\n",
+      "Fizz\n",
+      "7\n",
+      "8\n",
+      "Fizz\n",
+      "Buzz\n",
+      "11\n",
+      "Fizz\n",
+      "13\n",
+      "14\n",
+      "FizzBuzz\n",
+      "16\n",
+      "17\n",
+      "Fizz\n",
+      "19\n",
+      "Buzz\n",
+      "Fizz\n",
+      "22\n",
+      "23\n",
+      "Fizz\n",
+      "Buzz\n",
+      "26\n",
+      "Fizz\n",
+      "28\n",
+      "29\n",
+      "FizzBuzz\n",
+      "31\n",
+      "32\n",
+      "Fizz\n",
+      "34\n",
+      "Buzz\n",
+      "Fizz\n",
+      "37\n",
+      "38\n",
+      "Fizz\n",
+      "Buzz\n",
+      "41\n",
+      "Fizz\n",
+      "43\n",
+      "44\n",
+      "FizzBuzz\n",
+      "46\n",
+      "47\n",
+      "Fizz\n",
+      "49\n",
+      "Buzz\n",
+      "Fizz\n",
+      "52\n",
+      "53\n",
+      "Fizz\n",
+      "Buzz\n",
+      "56\n",
+      "Fizz\n",
+      "58\n",
+      "59\n",
+      "FizzBuzz\n",
+      "61\n",
+      "62\n",
+      "Fizz\n",
+      "64\n",
+      "Buzz\n",
+      "Fizz\n",
+      "67\n",
+      "68\n",
+      "Fizz\n",
+      "Buzz\n",
+      "71\n",
+      "Fizz\n",
+      "73\n",
+      "74\n",
+      "FizzBuzz\n",
+      "76\n",
+      "77\n",
+      "Fizz\n",
+      "79\n",
+      "Buzz\n",
+      "Fizz\n",
+      "82\n",
+      "83\n",
+      "Fizz\n",
+      "Buzz\n",
+      "86\n",
+      "Fizz\n",
+      "88\n",
+      "89\n",
+      "FizzBuzz\n",
+      "91\n",
+      "92\n",
+      "Fizz\n",
+      "94\n",
+      "Buzz\n",
+      "Fizz\n",
+      "97\n",
+      "98\n",
+      "Fizz\n",
+      "Buzz\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Code in this cell\n",
+    "for i in range(1,101):\n",
+    "    if i % 3 == 0 and i % 5 == 0:\n",
+    "        print(\"FizzBuzz\")\n",
+    "    elif i % 3 == 0:\n",
+    "        print(\"Fizz\")\n",
+    "    elif i % 5 == 0:\n",
+    "        print(\"Buzz\")\n",
+    "    else:\n",
+    "        print(i)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "____\n",
+    "**Use List Comprehension to create a list of the first letters of every word in the string below:**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "st = 'Create a list of the first letters of every word in this string'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['C', 'a', 'l', 'o', 't', 'f', 'l', 'o', 'e', 'w', 'i', 't', 's']\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Code in this cell\n",
+    "l=[x[0] for x in (st.split())]\n",
+    "print(l)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Great Job!"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Week 1/Python Assignment/Chained Comparison Operators.ipynb b/Week 1/Python Assignment/Chained Comparison Operators.ipynb
index 7a567d9..6f77769 100644
--- a/Week 1/Python Assignment/Chained Comparison Operators.ipynb	
+++ b/Week 1/Python Assignment/Chained Comparison Operators.ipynb	
@@ -176,11 +176,18 @@
     "\n",
     "Go ahead and go to the quiz for this section to check your understanding!"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -194,9 +201,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.2"
+   "version": "3.12.1"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }
diff --git a/Week 1/Python Assignment/Functions and Methods Homework.ipynb b/Week 1/Python Assignment/Functions and Methods Homework.ipynb
index 95d6109..994610b 100644
--- a/Week 1/Python Assignment/Functions and Methods Homework.ipynb	
+++ b/Week 1/Python Assignment/Functions and Methods Homework.ipynb	
@@ -14,26 +14,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
+    "import math\n",
     "def vol(rad):\n",
-    "    pass"
+    "    a=(4.0/3.0)*(math.pi)*(rad**3)\n",
+    "    return a\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "33.49333333333333"
+       "33.510321638291124"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -53,24 +55,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
     "def ran_check(num,low,high):\n",
-    "    pass"
+    "    if low<=num<=high:\n",
+    "        print(f'{num} is in the range {low} to {high}')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "5 is in the range between 2 and 7\n"
+      "5 is in the range 2 to 7\n"
      ]
     }
    ],
@@ -88,17 +91,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
     "def ran_bool(num,low,high):\n",
-    "    pass"
+    "    return low<=num<=high"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -107,7 +110,7 @@
        "True"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -355,7 +358,10 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    }
    },
    "source": [
     "#### Great Job!"
@@ -364,7 +370,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -378,9 +384,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.2"
+   "version": "3.12.1"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }
diff --git a/Week 1/Python Assignment/Objects and Data Structures Assessment Test.ipynb b/Week 1/Python Assignment/Objects and Data Structures Assessment Test.ipynb
index 149e61a..cf83972 100644
--- a/Week 1/Python Assignment/Objects and Data Structures Assessment Test.ipynb	
+++ b/Week 1/Python Assignment/Objects and Data Structures Assessment Test.ipynb	
@@ -10,7 +10,10 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    }
    },
    "source": [
     "## Test your knowledge. \n",
@@ -21,7 +24,10 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    }
    },
    "source": [
     "Write a brief description of all the following Object Types and Data Structures we've learned about: "
@@ -31,15 +37,15 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Numbers:\n",
+    "Numbers: Can be of different Data Type int(+/-) float(decimal) complex(real + (imaginary)j). type() can be used to determine the type of the number. \n",
     "\n",
-    "Strings:\n",
+    "Strings:Its a combination of character inside quotes. Can use negative indexing to access elements from the back of string.\n",
     "\n",
-    "Lists:\n",
+    "Lists:Order  of collection of data.Can be of different type. Use []. to acess element use index and negative again means from back so -1 means last -2 means second last.\n",
     "\n",
-    "Tuples:\n",
+    "Tuples:Immutable ie cant be modified. Its like coordinates.Inside () separated by comma and any data type is allowed.\n",
     "\n",
-    "Dictionaries:\n"
+    "Dictionaries:Unordered collection of data stored by mapping. Uses key-value pair. Each key-value pair in a Dictionary is separated by a colon : , whereas each key is separated by a ‘comma’. Use {}\n"
    ]
   },
   {
@@ -55,10 +61,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Answer=100.25\n"
+     ]
+    }
+   ],
+   "source": [
+    "a=((5*(40/2))+(0.50-(0.50*0.50)))**1\n",
+    "print(f\"Answer={a}\")"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -66,25 +83,56 @@
    "source": [
     "Answer these 3 questions without typing code. Then type code to check your answer.\n",
     "\n",
-    "    What is the value of the expression 4 * (6 + 5)\n",
+    "    What is the value of the expression 4 * (6 + 5) = 44\n",
     "    \n",
-    "    What is the value of the expression 4 * 6 + 5 \n",
+    "    What is the value of the expression 4 * 6 + 5   = 29\n",
     "    \n",
-    "    What is the value of the expression 4 + 6 * 5 "
+    "    What is the value of the expression 4 + 6 * 5   = 34"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Answer 1 = 44\n",
+      "Answer 2 = 29\n",
+      "Answer 3 = 34\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Answer 1 =\", 4*(6+5))\n",
+    "print(\"Answer 2 =\", 4*6+5)\n",
+    "print(\"Answer 3 =\", 4+6*5)"
+   ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "What is the *type* of the result of the expression 3 + 1.5 + 4?<br><br>"
+    "What is the *type* of the result of the expression 3 + 1.5 + 4?<br>float<br>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'float'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(type(3 + 1.5 + 4))"
    ]
   },
   {
@@ -96,20 +144,40 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "sqr root =  7.0\n"
+     ]
+    }
+   ],
    "source": [
-    "# Square root:\n"
+    "# Square root:\n",
+    "a=49\n",
+    "print(\"sqr root = \", a**0.5)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "square =  49\n"
+     ]
+    }
+   ],
    "source": [
-    "# Square:\n"
+    "# Square:\n",
+    "a=7\n",
+    "print(\"square = \", a**2)"
    ]
   },
   {
@@ -128,13 +196,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "e\n"
+     ]
+    }
+   ],
    "source": [
     "s = 'hello'\n",
     "# Print out 'e' using indexing\n",
-    "\n"
+    "print(s[1])"
    ]
   },
   {
@@ -146,13 +222,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "olleh\n"
+     ]
+    }
+   ],
    "source": [
     "s ='hello'\n",
     "# Reverse the string using slicing\n",
-    "\n"
+    "print(s[::-1])"
    ]
   },
   {
@@ -164,25 +248,44 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "o\n"
+     ]
+    }
+   ],
    "source": [
     "s ='hello'\n",
     "# Print out the 'o'\n",
     "\n",
     "# Method 1:\n",
-    "\n"
+    "print(s[4])\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "index = 4 and letter = o\n"
+     ]
+    }
+   ],
    "source": [
     "# Method 2:\n",
-    "\n"
+    "for i in range(len(s)):\n",
+    "    if s[i]==\"o\":\n",
+    "        print(f\"index = {i} and letter =\",s[i])\n",
+    "    \n"
    ]
   },
   {
@@ -201,20 +304,42 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0, 0, 0]\n"
+     ]
+    }
+   ],
    "source": [
-    "# Method 1:\n"
+    "# Method 1:\n",
+    "lst1=[0,0,0]\n",
+    "print(lst1)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 33,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0, 0, 0]\n"
+     ]
+    }
+   ],
    "source": [
-    "# Method 2:\n"
+    "# Method 2:\n",
+    "lst =[0]*3 \n",
+    "print(lst)"
    ]
   },
   {
@@ -226,12 +351,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1, 2, [3, 4, 'goodbye']]\n"
+     ]
+    }
+   ],
    "source": [
-    "list3 = [1,2,[3,4,'hello']]\n",
-    "\n"
+    "list3 = [1, 2, [3, 4, 'hello']]\n",
+    "list3[2][2] = 'goodbye'\n",
+    "print(list3)\n"
    ]
   },
   {
@@ -243,12 +377,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1, 3, 4, 5, 6]\n"
+     ]
+    }
+   ],
    "source": [
     "list4 = [5,3,4,6,1]\n",
-    "\n"
+    "list4.sort()\n",
+    "print(list4)\n",
+    "     \n"
    ]
   },
   {
@@ -267,51 +411,87 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "hello\n"
+     ]
+    }
+   ],
    "source": [
     "d = {'simple_key':'hello'}\n",
-    "# Grab 'hello'\n"
+    "# Grab 'hello'\n",
+    "print(d['simple_key'])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "hello\n"
+     ]
+    }
+   ],
    "source": [
     "d = {'k1':{'k2':'hello'}}\n",
-    "# Grab 'hello'\n"
+    "# Grab 'hello'\n",
+    "print(d['k1']['k2'])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "hello\n"
+     ]
+    }
+   ],
    "source": [
     "# Getting a little tricker\n",
     "d = {'k1':[{'nest_key':['this is deep',['hello']]}]}\n",
     "\n",
-    "#Grab hello\n"
+    "#Grab hello\n",
+    "print(d['k1'][0]['nest_key'][1][0])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "hello\n"
+     ]
+    }
+   ],
    "source": [
     "# This will be hard and annoying!\n",
-    "d = {'k1':[1,2,{'k2':['this is tricky',{'tough':[1,2,['hello']]}]}]}"
+    "d = {'k1':[1,2,{'k2':['this is tricky',{'tough':[1,2,['hello']]}]}]}\n",
+    "print(d['k1'][2]['k2'][1]['tough'][2][0])"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Can you sort a dictionary? Why or why not?<br><br>"
+    "Can you sort a dictionary? Why or why not?<br> As its unordered collection so we acess by keys <br>"
    ]
   },
   {
@@ -325,14 +505,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "What is the major difference between tuples and lists?<br><br>"
+    "What is the major difference between tuples and lists?<br>tupple are immutable<br>"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "How do you create a tuple?<br><br>"
+    "How do you create a tuple?<br>using () and fill in data separated by comma<br>"
    ]
   },
   {
@@ -346,7 +526,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "What is unique about a set?<br><br>"
+    "What is unique about a set?<br>Unordered,unchangeable,unindexed but we can add/remove \n",
+    "and we use { }  and duplicates not allowed \n",
+    "True and 1 are considered the same value in sets<br>"
    ]
   },
   {
@@ -358,12 +540,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{1, 2, 33, 4, 3, 11, 22}\n"
+     ]
+    }
+   ],
    "source": [
     "list5 = [1,2,2,33,4,4,11,22,3,3,2]\n",
     "\n",
+    "set1=set(list5)\n",
+    "print(set1)\n",
+    "\n",
     "\n"
    ]
   },
@@ -426,52 +619,112 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Answer before running cell\n",
-    "2 > 3"
+    "2 > 3\n",
+    "#false"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Answer before running cell\n",
-    "3 <= 2"
+    "3 <= 2\n",
+    "#false"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Answer before running cell\n",
-    "3 == 2.0"
+    "3 == 2.0\n",
+    "#false"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Answer before running cell\n",
-    "3.0 == 3"
+    "3.0 == 3\n",
+    "#true"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Answer before running cell\n",
-    "4**0.5 != 2"
+    "4**0.5 != 2\n",
+    "#false"
    ]
   },
   {
@@ -483,16 +736,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# two nested lists\n",
     "l_one = [1,2,[3,4]]\n",
     "l_two = [1,2,{'k1':4}]\n",
     "\n",
     "# True or False?\n",
-    "l_one[2][0] >= l_two[2]['k1']"
+    "l_one[2][0] >= l_two[2]['k1']\n",
+    "#false"
    ]
   },
   {
@@ -501,12 +766,19 @@
    "source": [
     "## Great Job on your first assessment! "
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -520,9 +792,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.2"
+   "version": "3.12.1"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }
diff --git a/Week 1/Python Assignment/Statements Assessment Test.ipynb b/Week 1/Python Assignment/Statements Assessment Test.ipynb
index b9e5454..d3b80e2 100644
--- a/Week 1/Python Assignment/Statements Assessment Test.ipynb	
+++ b/Week 1/Python Assignment/Statements Assessment Test.ipynb	
@@ -3,7 +3,10 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    }
    },
    "source": [
     "# Statements Assessment Test\n",
@@ -20,7 +23,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -29,11 +32,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "start\n",
+      "s\n",
+      "sentence\n"
+     ]
+    }
+   ],
    "source": [
-    "#Code here"
+    "#Code here\n",
+    "list1=st.split(' ')\n",
+    "for i in range(len(list1)):\n",
+    "    if list1[i][0] == 's':\n",
+    "        print(list1[i])"
    ]
   },
   {
@@ -46,11 +63,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n",
+      "2\n",
+      "4\n",
+      "6\n",
+      "8\n",
+      "10\n"
+     ]
+    }
+   ],
    "source": [
-    "#Code Here"
+    "#Code Here\n",
+    "for i in range(11):\n",
+    "    if i%2 ==0:\n",
+    "       print(i)\n",
+    "    "
    ]
   },
   {
@@ -63,12 +97,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[3, 6, 9, 12, 15, 18, 21, 24, 27, 30, 33, 36, 39, 42, 45, 48]\n"
+     ]
+    }
+   ],
    "source": [
     "#Code in this cell\n",
-    "[]"
+    "ans=[i+1 for i in range(50) if (i+1)%3 == 0]\n",
+    "print(ans)"
    ]
   },
   {
@@ -90,11 +133,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Lenght of only = \"even!\"\n",
+      "Lenght of that = \"even!\"\n",
+      "Lenght of with = \"even!\"\n",
+      "Lenght of in = \"even!\"\n",
+      "Lenght of this = \"even!\"\n",
+      "Lenght of sentence = \"even!\"\n"
+     ]
+    }
+   ],
    "source": [
-    "#Code in this cell"
+    "#Code in this cell\n",
+    "l=st.split(\" \")\n",
+    "for i in range(len(l)):\n",
+    "    if(len(l[i])%2==0):\n",
+    "        print(f\"Lenght of {l[i]} = \\\"even!\\\"\")"
    ]
   },
   {
@@ -107,11 +167,127 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1\n",
+      "2\n",
+      "Fizz\n",
+      "4\n",
+      "Buzz\n",
+      "Fizz\n",
+      "7\n",
+      "8\n",
+      "Fizz\n",
+      "Buzz\n",
+      "11\n",
+      "Fizz\n",
+      "13\n",
+      "14\n",
+      "FizzBuzz\n",
+      "16\n",
+      "17\n",
+      "Fizz\n",
+      "19\n",
+      "Buzz\n",
+      "Fizz\n",
+      "22\n",
+      "23\n",
+      "Fizz\n",
+      "Buzz\n",
+      "26\n",
+      "Fizz\n",
+      "28\n",
+      "29\n",
+      "FizzBuzz\n",
+      "31\n",
+      "32\n",
+      "Fizz\n",
+      "34\n",
+      "Buzz\n",
+      "Fizz\n",
+      "37\n",
+      "38\n",
+      "Fizz\n",
+      "Buzz\n",
+      "41\n",
+      "Fizz\n",
+      "43\n",
+      "44\n",
+      "FizzBuzz\n",
+      "46\n",
+      "47\n",
+      "Fizz\n",
+      "49\n",
+      "Buzz\n",
+      "Fizz\n",
+      "52\n",
+      "53\n",
+      "Fizz\n",
+      "Buzz\n",
+      "56\n",
+      "Fizz\n",
+      "58\n",
+      "59\n",
+      "FizzBuzz\n",
+      "61\n",
+      "62\n",
+      "Fizz\n",
+      "64\n",
+      "Buzz\n",
+      "Fizz\n",
+      "67\n",
+      "68\n",
+      "Fizz\n",
+      "Buzz\n",
+      "71\n",
+      "Fizz\n",
+      "73\n",
+      "74\n",
+      "FizzBuzz\n",
+      "76\n",
+      "77\n",
+      "Fizz\n",
+      "79\n",
+      "Buzz\n",
+      "Fizz\n",
+      "82\n",
+      "83\n",
+      "Fizz\n",
+      "Buzz\n",
+      "86\n",
+      "Fizz\n",
+      "88\n",
+      "89\n",
+      "FizzBuzz\n",
+      "91\n",
+      "92\n",
+      "Fizz\n",
+      "94\n",
+      "Buzz\n",
+      "Fizz\n",
+      "97\n",
+      "98\n",
+      "Fizz\n",
+      "Buzz\n"
+     ]
+    }
+   ],
    "source": [
-    "#Code in this cell"
+    "#Code in this cell\n",
+    "for i in range(1,101):\n",
+    "    if i % 3 == 0 and i % 5 == 0:\n",
+    "        print(\"FizzBuzz\")\n",
+    "    elif i % 3 == 0:\n",
+    "        print(\"Fizz\")\n",
+    "    elif i % 5 == 0:\n",
+    "        print(\"Buzz\")\n",
+    "    else:\n",
+    "        print(i)\n"
    ]
   },
   {
@@ -124,7 +300,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -133,11 +309,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 26,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['C', 'a', 'l', 'o', 't', 'f', 'l', 'o', 'e', 'w', 'i', 't', 's']\n"
+     ]
+    }
+   ],
    "source": [
-    "#Code in this cell"
+    "#Code in this cell\n",
+    "l=[x[0] for x in (st.split())]\n",
+    "print(l)"
    ]
   },
   {
@@ -150,7 +336,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -164,9 +350,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.2"
+   "version": "3.12.1"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }
diff --git a/Week 1/SVM-Assignment/.ipynb_checkpoints/Assignment_SVM-checkpoint.ipynb b/Week 1/SVM-Assignment/.ipynb_checkpoints/Assignment_SVM-checkpoint.ipynb
new file mode 100644
index 0000000..f734b95
--- /dev/null
+++ b/Week 1/SVM-Assignment/.ipynb_checkpoints/Assignment_SVM-checkpoint.ipynb	
@@ -0,0 +1,380 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "sqGpps43M-Oa"
+   },
+   "source": [
+    "This assignment is based on Support Vector Machines.\n",
+    "\n",
+    "**Instructions for this assignment:**\n",
+    "\n",
+    "\n",
+    "1.   Certain sections of code are missing are have being replaced by 'pass'. You need to replace 'pass' with your block of code by following the instructions provided.  \n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "0I5S6wA_OjSj"
+   },
+   "source": [
+    "# Linear SVM"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "id": "084lXnMMJQ8-"
+   },
+   "outputs": [],
+   "source": [
+    "#Importing Libraries\n",
+    "\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.svm import SVC # A module of scikit-learn library used for implementing SVM. SVC stands for Support Vector Classifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 430
+    },
+    "id": "JuLdsSfRMWNg",
+    "outputId": "9a2e57a7-a229-4bf7-a722-6f6e4fc701f4"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Preparation of dataset\n",
+    "\n",
+    "x = np.array([[1,2],[4,6],[8,9], [3,4], [2,6], [4,9]])\n",
+    "y = np.array([0, 1, 1, 0, 0, 1])\n",
+    "for i in range(y.shape[0]):\n",
+    "  if (y[i]==0):\n",
+    "      plt.scatter(x[i][0], x[i][1],color='red', marker='x')\n",
+    "  else:\n",
+    "      plt.scatter(x[i][0], x[i][1],color='green', marker='o')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "4CP_8mgsT5WS"
+   },
+   "source": [
+    "**Task 1**: Read about kernel argument of SVC and replace kernal_used by the kernel required for linear SVM."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "id": "Aryh-j-nMWxm"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(gamma=&#x27;auto&#x27;, kernel=&#x27;linear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(gamma=&#x27;auto&#x27;, kernel=&#x27;linear&#x27;)</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "SVC(gamma='auto', kernel='linear')"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "Classifier = SVC(gamma = 'auto', kernel = 'linear')\n",
+    "Classifier.fit(x, y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "2UxJ_6MTWNi-"
+   },
+   "source": [
+    "**Task 1:** Find the equation of boundary by using the parameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "id": "66Jzs5BJMeqX"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "weight_matrix = Classifier.coef_[0]\n",
+    "slope = -weight_matrix[0] / weight_matrix[1]\n",
+    "bias = -Classifier.intercept_[0] / weight_matrix[1]\n",
+    "val = np.linspace(0, 14)\n",
+    "boundary = slope * val + bias\n",
+    "\n",
+    "# Equation of the decision boundary\n",
+    "equation = f\"y = {slope}x + {bias}\"\n",
+    "\n",
+    "plt.plot(val, boundary, 'k', label=f\"Decision Boundary ({equation})\")\n",
+    "\n",
+    "for i in range(y.shape[0]):\n",
+    "    if y[i] == 0:\n",
+    "        plt.scatter(x[i][0], x[i][1], marker='x')\n",
+    "    else:\n",
+    "        plt.scatter(x[i][0], x[i][1], marker='o')\n",
+    "\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "1ZIrqz3TUXs5"
+   },
+   "source": [
+    "# Non- Linear SVM"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 430
+    },
+    "id": "pt5ZDqEZPwaD",
+    "outputId": "ad023c42-ab42-4711-f719-248a0164f686"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "weight_matrix = Classifier.coef_[0]\n",
+    "slope = -weight_matrix[0] / weight_matrix[1]\n",
+    "bias = -Classifier.intercept_[0] / weight_matrix[1]\n",
+    "val = np.linspace(0, 14)\n",
+    "boundary = slope * val + bias\n",
+    "\n",
+    "# Equation of the decision boundary\n",
+    "equation = f\"y = {slope:.2f}x + {bias:.2f}\"\n",
+    "\n",
+    "plt.plot(val, boundary, 'k', label=f\"Decision Boundary ({equation})\")\n",
+    "\n",
+    "for i in range(y.shape[0]):\n",
+    "    if y[i] == 0:\n",
+    "        plt.scatter(x[i][0], x[i][1], color='red', marker='x')\n",
+    "    else:\n",
+    "        plt.scatter(x[i][0], x[i][1], color='green', marker='o')\n",
+    "\n",
+    "plt.legend()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "6xfDKUFsTZmM"
+   },
+   "source": [
+    "**Answer the following questions** \\\\\n",
+    "Q: Is the above dataset linearly separable? yes\n",
+    "\n",
+    "Q. How many classes are there in the above dataset 2\n",
+    "\n",
+    "Q. How many features are used in the above dataset? 2\n",
+    "\n",
+    "Q. What will be dimension of hyperplane used for this dataset?  1d line"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "QH9QWWi_RA0J"
+   },
+   "source": [
+    "#Splitting the Dataset\n",
+    "**Task**: Split the given dataset into training and testing data. The code snippet to illustrate the data has been given."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "id": "bBr0AVWC4H_t"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "X_train, X_test, Y_train, Y_test = train_test_split(x, y, test_size=0.2, random_state=42)\n",
+    "\n",
+    "for i in range (X_train.T[0].size):\n",
+    "  if(Y_train[i]==1):\n",
+    "    plt.scatter(X_train[i][0], X_train[i][1], color=\"green\")\n",
+    "  else:\n",
+    "    plt.scatter(X_train[i][0], X_train[i][1], color=\"red\")\n",
+    "plt.show()\n",
+    "\n",
+    "for i in range (X_test.T[0].size):\n",
+    "  if(Y_test[i]==1):\n",
+    "    plt.scatter(X_test[i][0], X_test[i][1], color=\"green\")\n",
+    "  else:\n",
+    "    plt.scatter(X_test[i][0], X_test[i][1], color=\"red\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Tk-kkt4tRSzO"
+   },
+   "source": [
+    "# Non Linear Classifier\n",
+    "**Task 1**: Generate a classifier for the above dataset using suitable kernel function. Also, provide an explanation for your choice of kernel. \\\\\n",
+    "**Task 2**: Explain and illustrate, using plots, how the kernel used, enables the data to be classified using a SVC."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "id": "YmQb8dw2QVHR"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "classifier = SVC(kernel='rbf', gamma='scale')  \n",
+    "classifier.fit(x, y)\n",
+    "def plot_decision_boundary(clf, X, y):\n",
+    "    plt.figure(figsize=(10, 6))\n",
+    "    x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
+    "    y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
+    "    xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.01),\n",
+    "                         np.arange(y_min, y_max, 0.01))\n",
+    "    \n",
+    "    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
+    "    Z = Z.reshape(xx.shape)\n",
+    "    plt.contourf(xx, yy, Z, alpha=0.3)\n",
+    "    for i in range(y.shape[0]):\n",
+    "        if y[i] == 0:\n",
+    "            plt.scatter(X[i][0], X[i][1], color='red', marker='x')\n",
+    "        else:\n",
+    "            plt.scatter(X[i][0], X[i][1], color='green', marker='o')\n",
+    "\n",
+    "    plt.scatter(clf.support_vectors_[:, 0], clf.support_vectors_[:, 1], \n",
+    "                s=100, facecolors='none', edgecolors='k')\n",
+    "    plt.xlabel('Feature 1')\n",
+    "    plt.ylabel('Feature 2')\n",
+    "    plt.title('Decision Boundary with RBF Kernel')\n",
+    "    plt.show()\n",
+    "\n",
+    "plot_decision_boundary(classifier, x, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Week 1/SVM-Assignment/Assignment_SVM.ipynb b/Week 1/SVM-Assignment/Assignment_SVM.ipynb
index 3300031..f734b95 100644
--- a/Week 1/SVM-Assignment/Assignment_SVM.ipynb	
+++ b/Week 1/SVM-Assignment/Assignment_SVM.ipynb	
@@ -1,278 +1,380 @@
 {
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "sqGpps43M-Oa"
-      },
-      "source": [
-        "This assignment is based on Support Vector Machines.\n",
-        "\n",
-        "**Instructions for this assignment:**\n",
-        "\n",
-        "\n",
-        "1.   Certain sections of code are missing are have being replaced by 'pass'. You need to replace 'pass' with your block of code by following the instructions provided.  \n",
-        "\n",
-        "\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "0I5S6wA_OjSj"
-      },
-      "source": [
-        "# Linear SVM"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "084lXnMMJQ8-"
-      },
-      "outputs": [],
-      "source": [
-        "#Importing Libraries\n",
-        "\n",
-        "import numpy as np\n",
-        "import matplotlib.pyplot as plt\n",
-        "from sklearn.svm import SVC # A module of scikit-learn library used for implementing SVM. SVC stands for Support Vector Classifier"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 430
-        },
-        "id": "JuLdsSfRMWNg",
-        "outputId": "9a2e57a7-a229-4bf7-a722-6f6e4fc701f4"
-      },
-      "outputs": [
-        {
-          "data": {
-            "image/png": 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",
-            "text/plain": [
-              "<Figure size 640x480 with 1 Axes>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "#Preparation of dataset\n",
-        "\n",
-        "x = np.array([[1,2],[4,6],[8,9], [3,4], [2,6], [4,9]])\n",
-        "y = np.array([0, 1, 1, 0, 0, 1])\n",
-        "for i in range(y.shape[0]):\n",
-        "  if (y[i]==0):\n",
-        "      plt.scatter(x[i][0], x[i][1],color='red', marker='x')\n",
-        "  else:\n",
-        "      plt.scatter(x[i][0], x[i][1],color='green', marker='o')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "4CP_8mgsT5WS"
-      },
-      "source": [
-        "**Task 1**: Read about kernel argument of SVC and replace kernal_used by the kernel required for linear SVM."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "Aryh-j-nMWxm"
-      },
-      "outputs": [],
-      "source": [
-        "kernel_used = 'pass'\n",
-        "Classifier = SVC(gamma = 'auto', kernel = kernel_used)\n",
-        "Classifier.fit(x, y)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "2UxJ_6MTWNi-"
-      },
-      "source": [
-        "**Task 1:** Find the equation of boundary by using the parameters"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "66Jzs5BJMeqX"
-      },
-      "outputs": [],
-      "source": [
-        "#####\n",
-        "weight_matrix = Classifier.coef_[0]\n",
-        "slope = -weight_matrix[0]/weight_matrix[1]\n",
-        "bias = - Classifier.intercept_[0]/weight_matrix[1]\n",
-        "val = np.linspace(0, 14)\n",
-        "boundary = slope * val + bias\n",
-        "#####\n",
-        "\n",
-        "plt.plot(val, boundary, 'k', label = f\"Decision Boundary ( y = pass\") #write the equation for the decision boundary using the variables used above\n",
-        "\n",
-        "for i in range(y.shape[0]):\n",
-        "  if (y[i]==0):\n",
-        "      plt.scatter(x[i][0], x[i][1],color='red', marker='x')\n",
-        "  else:\n",
-        "      plt.scatter(x[i][0], x[i][1],color='green', marker='o')\n",
-        "\n",
-        "plt.legend()\n",
-        "plt.show()"
-      ]
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "sqGpps43M-Oa"
+   },
+   "source": [
+    "This assignment is based on Support Vector Machines.\n",
+    "\n",
+    "**Instructions for this assignment:**\n",
+    "\n",
+    "\n",
+    "1.   Certain sections of code are missing are have being replaced by 'pass'. You need to replace 'pass' with your block of code by following the instructions provided.  \n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "0I5S6wA_OjSj"
+   },
+   "source": [
+    "# Linear SVM"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "id": "084lXnMMJQ8-"
+   },
+   "outputs": [],
+   "source": [
+    "#Importing Libraries\n",
+    "\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.svm import SVC # A module of scikit-learn library used for implementing SVM. SVC stands for Support Vector Classifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 430
     },
+    "id": "JuLdsSfRMWNg",
+    "outputId": "9a2e57a7-a229-4bf7-a722-6f6e4fc701f4"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "1ZIrqz3TUXs5"
-      },
-      "source": [
-        "# Non- Linear SVM"
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
       ]
-    },
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Preparation of dataset\n",
+    "\n",
+    "x = np.array([[1,2],[4,6],[8,9], [3,4], [2,6], [4,9]])\n",
+    "y = np.array([0, 1, 1, 0, 0, 1])\n",
+    "for i in range(y.shape[0]):\n",
+    "  if (y[i]==0):\n",
+    "      plt.scatter(x[i][0], x[i][1],color='red', marker='x')\n",
+    "  else:\n",
+    "      plt.scatter(x[i][0], x[i][1],color='green', marker='o')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "4CP_8mgsT5WS"
+   },
+   "source": [
+    "**Task 1**: Read about kernel argument of SVC and replace kernal_used by the kernel required for linear SVM."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "id": "Aryh-j-nMWxm"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 430
-        },
-        "id": "pt5ZDqEZPwaD",
-        "outputId": "ad023c42-ab42-4711-f719-248a0164f686"
-      },
-      "outputs": [
-        {
-          "data": {
-            "image/png": 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ppD1NmXk5Qa4MPREBBQAgSbJH2lVbuEaSOoSUi/t1hat5QBZBQUABAHiMXZmrfUs2qdE+wKu9wZ6qfUs2aezK3BBVhp7G52nGVsA0YwAILFaSRSD4cv72+1L3AIDwZ4+0a0TBuFCXgR6MWzwAAMByCCgAAMByCCgAAMByCCgAAMByCCgAAMByCCgAAMByCCgAAMByCCgAAMByCCgAAMByCCgAAMByCCgAAMByCCgAAMByCCgAAMByCCgAAMByCCgAAMByCCgAAMByCCgAAMByCCgAAMByeoW6AADoidxtblWXlqulpkHRGcnKzMuRPdIe6rIAyyCgAECQVRaVKb1koUa4T3ja6henqrZwjcauzA1hZYB1cIsHAIKosqhMo1fNlOOScCJJDvdJjV41U5VFZSGqDLAWAgoABIm7za30koWSTIc/vhEykqS0kgK529xBrw2wGgIKAARJdWm5UtwnLvuHN0JGA9x1qi4tD2pdgBURUAAgSFpqGvzaD+jOCCgAECTRGcl+7Qd0Zz4HlN27d2v69OlKSUmRzWbT5s2bPcfOnz+vJ554QpmZmbrhhhuUkpKi73znO6qvr/f6jEGDBslms3ltK1asuO4vAwBWlpmXo3p7qtpl6/R4u2w6aU9TZl5OkCsDrMfngNLc3Kzhw4dr7dq1HY61tLTo0KFDevrpp3Xo0CGVlZXp6NGjuueeezr0fe6559TQ0ODZFixYcG3fAADChD3SrtrCNZLUIaRc3K8rXM16KICuYR2UKVOmaMqUKZ0ei4uL07Zt27zafvGLX2j06NGqra1Venq6pz0mJkYOh8PXXw8AYW3sylxVapPSSxYq5ZKpxg32VNUVrmYdFODvAv4MitPplM1mU3x8vFf7ihUr1L9/f2VlZWnVqlW6cOFCoEsBAEsYuzJXSS1/UdVP39G78zeo6qfvyNFynHACXCKgK8l+9tlneuKJJzRr1izFxsZ62h9//HHdfvvtSkhI0LvvvqulS5eqoaFBJSUlnX5Oa2urWltbPfsulyuQZQNAwNkj7RpRMC7UZQCWFbCAcv78ef3rv/6rjDF64YUXvI4VFhZ6/j1s2DBFRkbq+9//voqLixUVFdXhs4qLi7V8+fJAlQoAACwmILd4LoaTTz75RNu2bfO6etKZMWPG6MKFC/rLX/7S6fGlS5fK6XR6trq6ugBUDQAArMLvV1AuhpOPP/5Y77zzjvr373/Vn6mqqlJERIQSExM7PR4VFdXplRUAANA9+RxQzp07p2PHjnn2jx8/rqqqKiUkJCg5OVkzZ87UoUOHtGXLFrndbjU2NkqSEhISFBkZqYqKCu3du1fjx49XTEyMKioqtGjRIj3wwAO68cYb/ffNAABA2LIZY4wvP7Bz506NHz++Q/ucOXP07LPPavDgwZ3+3DvvvKNx48bp0KFDysvL00cffaTW1lYNHjxYDz74oAoLC7t8lcTlcikuLk5Op/Oqt48AAIA1+HL+9jmgWAEBBQCA8OPL+Zt38QAAAMshoAAAAMshoAAAAMshoAAAAMshoAAAAMshoAAAAMshoAAAAMshoAAAAMshoAAAAMshoAAAAMshoAAAAMshoAAAAMvpFeoCAMDf3G1uVZeWq6WmQdEZycrMy5E90h7qsgD4gIACoFupLCpTeslCjXCf8LTVL05VbeEajV2ZG8LKAPiCWzwAuo3KojKNXjVTjkvCiSQ53Cc1etVMVRaVhagyAL4ioADoFtxtbqWXLJRkOvxhi5CRJKWVFMjd5g56bQB8R0AB0C1Ul5YrxX3isn/UImQ0wF2n6tLyoNYF4NoQUAB0Cy01DX7tByC0CCgAuoXojGS/9gMQWgQUAN1CZl6O6u2papet0+PtsumkPU2ZeTlBrgzAtSCgAOgW7JF21RaukaQOIeXifl3hatZDAcIEAQVAtzF2Za72LdmkRvsAr/YGe6r2LdnEOihAGLEZY0yoi/CVy+VSXFycnE6nYmNjQ10OAIthJVnAmnw5f7OSLIBuxx5p14iCcaEuA8B14BYPAACwHAIKAACwHAIKAACwHAIKAACwHAIKAACwHAIKAACwHAIKAACwHJ8Dyu7duzV9+nSlpKTIZrNp8+bNXseNMVq2bJmSk5PVt29fTZw4UR9//LFXn9OnT2v27NmKjY1VfHy85s6dq3Pnzl3XFwEAAN2HzwGlublZw4cP19q1azs9vnLlSv3sZz/Tiy++qL179+qGG27QpEmT9Nlnn3n6zJ49W++//762bdumLVu2aPfu3Zo3b961fwsAANCtXNdS9zabTW+88YbuvfdeSZ9fPUlJSdEPfvADLV68WJLkdDqVlJSk9evX67777tOHH36ooUOHav/+/Ro1apQkaevWrZo6dapOnDihlJSUq/5elroHACD8+HL+9uszKMePH1djY6MmTpzoaYuLi9OYMWNUUVEhSaqoqFB8fLwnnEjSxIkTFRERob179/qzHAAAEKb8+i6exsZGSVJSUpJXe1JSkudYY2OjEhMTvYvo1UsJCQmePl/U2tqq1tZWz77L5fJn2QAAwGLC4mWBxcXFWr58eajLAOCDtvNulf62XDVNDcpISlbetBxF9uaNwgC6xq+3eBwOhySpqanJq72pqclzzOFw6NSpU17HL1y4oNOnT3v6fNHSpUvldDo9W11dnT/LBuBnRevKFP3kIC3643j9ovF+LfrjeEU/OUhF68pCXRqAMOHXgDJ48GA5HA5t377d0+ZyubR3715lZ2dLkrKzs3XmzBkdPHjQ02fHjh1qb2/XmDFjOv3cqKgoxcbGem0ArKloXZlWfTJT7htOeLW7bzipVZ/MJKQA6BKfA8q5c+dUVVWlqqoqSZ8/GFtVVaXa2lrZbDYVFBToxz/+sX7zm9+ourpa3/nOd5SSkuKZ6XPrrbdq8uTJeuSRR7Rv3z794Q9/0Pz583Xfffd1aQYPAOtqO+9WyQcLJRnJ9oWDts8nDJZ8UKC28+6g1wYgvPgcUA4cOKCsrCxlZWVJkgoLC5WVlaVly5ZJkoqKirRgwQLNmzdPd9xxh86dO6etW7eqT58+ns949dVXNWTIEE2YMEFTp07VXXfdpZdeeslPXwlAqJT+tlzufic6hpOLbEbufnUq/W15UOsCEH6uax2UUGEdFMCaFvzXa/pF4/1X7TffsUE///6sIFQEwEp8OX+HxSweANZyuRk6GUnJUuerBXjJSEoOfJEAwhoBBYBPitaVqeSDhZ/fypGkRmnxH1JVOHSNfvzAN7X4D6ly33DS88yJF2OTvTlVedNygls0gLDD24wBdNnVZug89as3VTh0zeeN5gsPovx9v3DoatZDAXBVBBQAXdLVGTo/fuCbWjJwk+zNA7y62JtTtWTgJq18ODc4BQMIa9ziAXBFF5832XRo+z9u63Tmkhk6Kx/O1Y/Pf5OVZAFcMwIKgMvyet6ki9mipqlBkhTZ266Ce8cFrjgA3RoBBUCnLj5voht8W4mAGToA/IFnUAB0cMXnTS7H2GQ/l8YMHQB+wRUUAB14VoTtKmboAPAzrqAA6ODicyRdxQwdAP7GFRQAHXR1Rdg725/SzKwJzNAB4He8iwdAB23n3Yp+ctBVV4Rt+clxggmALvPl/M0tHgAdRPa2syIsgJAioADo1MqHc1kRFkDIcIsH6EEu9xZif/8MAHTGl/M3AQXoITq8hViS/dznbyHmagiAYOAZFABervYW4qJ1ZSGqDAA6R0ABurmuvoW47bw76LUBwOUQUIBuzrMq7OWWrL/kLcQAYBUEFKCb6+qqsL6uHgsAgURAAbq5rr5dmLcQA7ASAgrQzeVNy5H9XGrHBdcu4i3EACyIgAJ0c6wKCyAcEVCAHoBVYQGEGxZqA8LQta7uyqqwAEKJlWSBbowVYQGEK1aSBbopVoQF0FMQUIAwwYqwAHoSAgoQJlgRFkBPQkABwgQrwgLoSQgoQJhgRVgAPYnfA8qgQYNks9k6bPn5+ZKkcePGdTj26KOP+rsMIGy1nXdr9eadWvBfr2n15p2eZ0pYERZAT9LL3x+4f/9+ud3/eEjvyJEj+pd/+Rd9+9vf9rQ98sgjeu655zz70dHR/i4DCEsdphA3Sov/8I8pxIVD12jVJzM/Dym2S1YIYEVYAN2M3wPKzTff7LW/YsUKZWRk6Bvf+IanLTo6Wg6Hw9+/GghrF6cQ6wbvpYkuTiHWur+v+LpuU8d1UJpTVTh0NeugAOg2ArpQW1tbm1JSUlRYWKgnn3xS0ue3eN5//30ZY+RwODR9+nQ9/fTTPl1FYaE2dBcXV3Y92nBS//WXRTJ9/6/zWTrGJntzqlp+clyRve2sCAsgLPly/vb7FZRLbd68WWfOnNFDDz3kabv//vs1cOBApaSk6PDhw3riiSd09OhRlZVdfoGp1tZWtba2evZdLlcgywaCosPtnCtl9EumEBfcO06Rve0quHdcMMoEgJAIaEB5+eWXNWXKFKWkpHja5s2b5/l3ZmamkpOTNWHCBNXU1CgjI6PTzykuLtby5csDWSoQVJe7nXM1TCEG0FMEbJrxJ598orffflvf+973rthvzJgxkqRjx45dts/SpUvldDo9W11dnV9rBYLpiivCXgVTiAH0FAG7grJu3TolJiZq2rRpV+xXVVUlSUpOvvwf3qioKEVFRfmzPCBkPCvC+uLvz6AwhRhATxGQgNLe3q5169Zpzpw56tXrH7+ipqZGGzZs0NSpU9W/f38dPnxYixYt0te//nUNGzYsEKUAluPzbRqmEAPogQISUN5++23V1tbqu9/9rld7ZGSk3n77ba1evVrNzc1KS0vTjBkz9NRTTwWiDMCSMpKSpcau92cKMYCeKKDTjAOFacYIZ23n3Yp+cpDcN5z0XmztIiPZ/nazHh30U305eQBTiAF0G5aZZgygo8je9quuCLt4yItcMQHQo/GyQCAEVj6cqyUDN8nePMCr3d6cqiUDNxFOAPR43OIB/ORaVndlRVgAPYkv528CCuAHHVaFlWQ/94+X/AEAfDt/c4sHuE4XV4V13+C9tsnFl/wVrbv8axwAAJ0joADX4Yqrwv794deSDwrUdt4d9NoAIJwRUIDr4FkV9nJL1l/ykj8AQNcRUIDr0NVVYXnJHwD4hoACXIeuvryPl/wBgG8IKMB1yJuWI/u5VM8Cax0Ym+zn0njJHwD4iIACXIeLq8JK6hhSeMkfAFwzAgpwnVgVFgD8j4XaAD9hVVgAuDJeFgiEQGRvuwruHRfqMgCgW+AWDwAAsBwCCgAAsBwCCgAAsBwCCgAAsBwCCgAAsBxm8aBHcbe5VV1arpaaBkVnJCszL0f2SKYCA4DVEFDQY1QWlSm9ZKFGuE942uoXp6q2cI3GrmQxNQCwEm7xoEeoLCrT6FUz5bgknEiSw31So1fNVGVRWYgqAwB0hoCCbs/d5lZ6yUJJpsN/8BH6fCHltJICudvcQa8NANA5Agq6verScqW4T1z2P/YIGQ1w16m6tDyodQEALo+Agm6vpabBr/0AAIFHQEG3F52R7Nd+AIDAI6Cg28vMy1G9PVXtsnV6vF02nbSnKTMvJ8iVAQAuh4CCbs8eaVdt4RpJ6hBSLu7XFa5mPRQAsBACCnqEsStztW/JJjXaB3i1N9hTtW/JJtZBAQCLsRljTKiL8JXL5VJcXJycTqdiY2NDXQ7CCCvJAkDo+HL+ZiVZ9Cj2SLtGFIwLdRkAgKvw+y2eZ599VjabzWsbMmSI5/hnn32m/Px89e/fX/369dOMGTPU1NTk7zIAAEAYC8gzKF/96lfV0NDg2fbs2eM5tmjRIr311lvauHGjdu3apfr6euXmcv8fAAD8Q0Bu8fTq1UsOh6NDu9Pp1Msvv6wNGzbon//5nyVJ69at06233qrKykqNHTs2EOUAAIAwE5ArKB9//LFSUlJ0yy23aPbs2aqtrZUkHTx4UOfPn9fEiRM9fYcMGaL09HRVVFQEohQAABCG/H4FZcyYMVq/fr2+8pWvqKGhQcuXL1dOTo6OHDmixsZGRUZGKj4+3utnkpKS1NjYeNnPbG1tVWtrq2ff5XL5u2wAAGAhfg8oU6ZM8fx72LBhGjNmjAYOHKhf//rX6tu37zV9ZnFxsZYvX+6vEgEAgMUFfKG2+Ph4ffnLX9axY8fkcDjU1tamM2fOePVpamrq9JmVi5YuXSqn0+nZ6urqAlw1AAAIpYAHlHPnzqmmpkbJyckaOXKkevfure3bt3uOHz16VLW1tcrOzr7sZ0RFRSk2NtZrAwAA3Zffb/EsXrxY06dP18CBA1VfX69nnnlGdrtds2bNUlxcnObOnavCwkIlJCQoNjZWCxYsUHZ2NjN4AACAh98DyokTJzRr1ix9+umnuvnmm3XXXXepsrJSN998syTppz/9qSIiIjRjxgy1trZq0qRJKi0t9XcZAAAgjPEuHgAAEBS+nL95mzEAALAcAgoAALAcAgoAALAcAgoAALAcAgoAALAcAgoAALAcAgoAALAcvy/Uhp7N3eZWdWm5WmoaFJ2RrMy8HNkj7aEuCwAQZggo8JvKojKllyzUCPcJT1v94lTVFq7R2JW5IawMABBuuMUDv6gsKtPoVTPluCScSJLDfVKjV81UZVFZiCoDAIQjAgqum7vNrfSShZJMh/+gIvT5mxTSSgrkbnMHvTYAQHgioOC6VZeWK8V94rL/MUXIaIC7TtWl5UGtCwAQvggouG4tNQ1+7QcAAAEF1y06I9mv/QAAIKDgumXm5ajenqp22To93i6bTtrTlJmXE+TKAADhioCC62aPtKu2cI0kdQgpF/frClezHgoAoMsIKPCLsStztW/JJjXaB3i1N9hTtW/JJtZBAQD4xGaMMaEuwlcul0txcXFyOp2KjY0NdTm4BCvJAgAux5fzNyvJwq/skXaNKBgX6jIAAGGOWzwAAMByCCgAAMByCCgAAMByCCgAAMByCCgAAMByCCgAAMByCCgAAMByCCgAAMByCCgAAMByCCgAAMByCCgAAMByCCgAAMBy/B5QiouLdccddygmJkaJiYm69957dfToUa8+48aNk81m89oeffRRf5cCAADClN8Dyq5du5Sfn6/Kykpt27ZN58+f1913363m5mavfo888ogaGho828qVK/1dCgAACFO9/P2BW7du9dpfv369EhMTdfDgQX3961/3tEdHR8vhcPj71wMAgG4g4M+gOJ1OSVJCQoJX+6uvvqqbbrpJt912m5YuXaqWlpZAlwIAAMKE36+gXKq9vV0FBQW68847ddttt3na77//fg0cOFApKSk6fPiwnnjiCR09elRlZWWdfk5ra6taW1s9+y6XK5BlAwCAEAtoQMnPz9eRI0e0Z88er/Z58+Z5/p2Zmank5GRNmDBBNTU1ysjI6PA5xcXFWr58eSBLBQAAFmIzxphAfPD8+fP15ptvavfu3Ro8ePAV+zY3N6tfv37aunWrJk2a1OF4Z1dQ0tLS5HQ6FRsb6/farcrd5lZ1ablaahoUnZGszLwc2SPtoS4LAIAucblciouL69L52+9XUIwxWrBggd544w3t3LnzquFEkqqqqiRJycnJnR6PiopSVFSUP8sMO5VFZUovWagR7hOetvrFqaotXKOxK3NDWBkAAP7n94CSn5+vDRs26M0331RMTIwaGxslSXFxcerbt69qamq0YcMGTZ06Vf3799fhw4e1aNEiff3rX9ewYcP8XU63UFlUptGrZkryvtjlcJ+UY9VMVWoTIQUA0K34/RaPzWbrtH3dunV66KGHVFdXpwceeEBHjhxRc3Oz0tLS9K1vfUtPPfVUl2/X+HKJKNy529xqih4kh/tEp1Ou2mVTgz1Vjpbj3O4BAFhayG/xXElaWpp27drl71/bbVWXlnvd1vmiCBkNcNepqrRcIwrGBa8wAAACiHfxWFxLTYNf+wEAEA4IKBYXndH5g8PX2g8AgHBAQLG4zLwc1dtT1a7On+1pl00n7WnKzMsJcmUAAAQOAcXi7JF21RaukaQOIeXifl3hah6QBQB0KwSUMDB2Za72LdmkRvsAr/YGe6r2LWGKMQCg+wnYSrKB1JOmGV+KlWQBAOEspNOMETj2SDtTiQEAPQK3eAAAgOUQUAAAgOUQUAAAgOUQUAAAgOUQUAAAgOUQUAAAgOUQUAAAgOUQUAAAgOUQUAAAgOUQUAAAgOUQUAAAgOUQUAAAgOUQUAAAgOUQUAAAgOUQUAAAgOUQUAAAgOUQUAAAgOUQUAAAgOX0CnUBVuJuc6u6tFwtNQ2KzkhWZl6O7JH2UJcFAECPQ0D5u8qiMqWXLNQI9wlPW/3iVNUWrtHYlbkhrAwAgJ6HWzz6PJyMXjVTjkvCiSQ53Cc1etVMVRaVhagyAAB6ph4fUNxtbqWXLJRkOgxGhIwkKa2kQO42d9BrAwCgp+rxAaW6tFwp7hOXHYgIGQ1w16m6tDyodQEA0JP1+IDSUtPg134AAOD6hTSgrF27VoMGDVKfPn00ZswY7du3L+g1RGck+7UfAAC4fiELKP/zP/+jwsJCPfPMMzp06JCGDx+uSZMm6dSpU0GtIzMvR/X2VLXL1unxdtl00p6mzLycoNYFAEBPFrKAUlJSokceeUQPP/ywhg4dqhdffFHR0dF65ZVXglqHPdKu2sI1ktQhpFzcrytczXooAAAEUUgCSltbmw4ePKiJEyf+o5CICE2cOFEVFRVBr2fsylztW7JJjfYBXu0N9lTtW7KJdVAAAAiykCzU9te//lVut1tJSUle7UlJSfroo4869G9tbVVra6tn3+Vy+b2msStz5f7xN1X1hZVkB3DlBACAoAuLlWSLi4u1fPnygP8ee6RdIwrGBfz3AACAKwvJLZ6bbrpJdrtdTU1NXu1NTU1yOBwd+i9dulROp9Oz1dXVBatUAAAQAiEJKJGRkRo5cqS2b9/uaWtvb9f27duVnZ3doX9UVJRiY2O9NgAA0H2F7BZPYWGh5syZo1GjRmn06NFavXq1mpub9fDDD4eqJAAAYBEhCyj/9m//pv/7v//TsmXL1NjYqBEjRmjr1q0dHpwFAAA9j80YY0JdhK9cLpfi4uLkdDq53QMAQJjw5fzd49/FAwAArIeAAgAALIeAAgAALIeAAgAALCcsVpL9oovP9QZiyXsAABAYF8/bXZmfE5YB5ezZs5KktLS0EFcCAAB8dfbsWcXFxV2xT1hOM25vb1d9fb1iYmJks9n8+tkul0tpaWmqq6tjCnMAMc7BwTgHB+McHIxz8ARqrI0xOnv2rFJSUhQRceWnTMLyCkpERIRSU1MD+jtYUj84GOfgYJyDg3EODsY5eAIx1le7cnIRD8kCAADLIaAAAADLIaB8QVRUlJ555hlFRUWFupRujXEODsY5OBjn4GCcg8cKYx2WD8kCAIDujSsoAADAcggoAADAcggoAADAcggoAADAcggol1i7dq0GDRqkPn36aMyYMdq3b1+oSwprxcXFuuOOOxQTE6PExETde++9Onr0qFefzz77TPn5+erfv7/69eunGTNmqKmpKUQVdw8rVqyQzWZTQUGBp41x9o+TJ0/qgQceUP/+/dW3b19lZmbqwIEDnuPGGC1btkzJycnq27evJk6cqI8//jiEFYcnt9utp59+WoMHD1bfvn2VkZGhf//3f/d6fwtj7bvdu3dr+vTpSklJkc1m0+bNm72Od2VMT58+rdmzZys2Nlbx8fGaO3euzp07F5iCDYwxxrz++usmMjLSvPLKK+b99983jzzyiImPjzdNTU2hLi1sTZo0yaxbt84cOXLEVFVVmalTp5r09HRz7tw5T59HH33UpKWlme3bt5sDBw6YsWPHmq997WshrDq87du3zwwaNMgMGzbMLFy40NPOOF+/06dPm4EDB5qHHnrI7N271/z5z382v//9782xY8c8fVasWGHi4uLM5s2bzR//+Edzzz33mMGDB5u//e1vIaw8/Dz//POmf//+ZsuWLeb48eNm48aNpl+/fmbNmjWePoy17373u9+ZH/3oR6asrMxIMm+88YbX8a6M6eTJk83w4cNNZWWlKS8vN//0T/9kZs2aFZB6CSh/N3r0aJOfn+/Zd7vdJiUlxRQXF4ewqu7l1KlTRpLZtWuXMcaYM2fOmN69e5uNGzd6+nz44YdGkqmoqAhVmWHr7Nmz5ktf+pLZtm2b+cY3vuEJKIyzfzzxxBPmrrvuuuzx9vZ243A4zKpVqzxtZ86cMVFRUea1114LRondxrRp08x3v/tdr7bc3Fwze/ZsYwxj7Q9fDChdGdMPPvjASDL79+/39Pnf//1fY7PZzMmTJ/1eI7d4JLW1tengwYOaOHGipy0iIkITJ05URUVFCCvrXpxOpyQpISFBknTw4EGdP3/ea9yHDBmi9PR0xv0a5Ofna9q0aV7jKTHO/vKb3/xGo0aN0re//W0lJiYqKytL//3f/+05fvz4cTU2NnqNc1xcnMaMGcM4++hrX/uatm/frj/96U+SpD/+8Y/as2ePpkyZIomxDoSujGlFRYXi4+M1atQoT5+JEycqIiJCe/fu9XtNYfmyQH/761//KrfbraSkJK/2pKQkffTRRyGqqntpb29XQUGB7rzzTt12222SpMbGRkVGRio+Pt6rb1JSkhobG0NQZfh6/fXXdejQIe3fv7/DMcbZP/785z/rhRdeUGFhoZ588knt379fjz/+uCIjIzVnzhzPWHb2d4Rx9s0Pf/hDuVwuDRkyRHa7XW63W88//7xmz54tSYx1AHRlTBsbG5WYmOh1vFevXkpISAjIuBNQEBT5+fk6cuSI9uzZE+pSup26ujotXLhQ27ZtU58+fUJdTrfV3t6uUaNG6Sc/+YkkKSsrS0eOHNGLL76oOXPmhLi67uXXv/61Xn31VW3YsEFf/epXVVVVpYKCAqWkpDDWPQi3eCTddNNNstvtHWY1NDU1yeFwhKiq7mP+/PnasmWL3nnnHaWmpnraHQ6H2tradObMGa/+jLtvDh48qFOnTun2229Xr1691KtXL+3atUs/+9nP1KtXLyUlJTHOfpCcnKyhQ4d6td16662qra2VJM9Y8nfk+i1ZskQ//OEPdd999ykzM1MPPvigFi1apOLiYkmMdSB0ZUwdDodOnTrldfzChQs6ffp0QMadgCIpMjJSI0eO1Pbt2z1t7e3t2r59u7Kzs0NYWXgzxmj+/Pl64403tGPHDg0ePNjr+MiRI9W7d2+vcT969Khqa2sZdx9MmDBB1dXVqqqq8myjRo3S7NmzPf9mnK/fnXfe2WGa/J/+9CcNHDhQkjR48GA5HA6vcXa5XNq7dy/j7KOWlhZFRHifnux2u9rb2yUx1oHQlTHNzs7WmTNndPDgQU+fHTt2qL29XWPGjPF/UX5/7DZMvf766yYqKsqsX7/efPDBB2bevHkmPj7eNDY2hrq0sPXYY4+ZuLg4s3PnTtPQ0ODZWlpaPH0effRRk56ebnbs2GEOHDhgsrOzTXZ2dgir7h4uncVjDOPsD/v27TO9evUyzz//vPn444/Nq6++aqKjo82vfvUrT58VK1aY+Ph48+abb5rDhw+bb37zm0x9vQZz5swxAwYM8EwzLisrMzfddJMpKiry9GGsfXf27Fnz3nvvmffee89IMiUlJea9994zn3zyiTGma2M6efJkk5WVZfbu3Wv27NljvvSlLzHNOBh+/vOfm/T0dBMZGWlGjx5tKisrQ11SWJPU6bZu3TpPn7/97W8mLy/P3HjjjSY6Otp861vfMg0NDaErupv4YkBhnP3jrbfeMrfddpuJiooyQ4YMMS+99JLX8fb2dvP000+bpKQkExUVZSZMmGCOHj0aomrDl8vlMgsXLjTp6emmT58+5pZbbjE/+tGPTGtrq6cPY+27d955p9O/yXPmzDHGdG1MP/30UzNr1izTr18/Exsbax5++GFz9uzZgNRrM+aSpfkAAAAsgGdQAACA5RBQAACA5RBQAACA5RBQAACA5RBQAACA5RBQAACA5RBQAACA5RBQAACA5RBQAACA5RBQAACA5RBQAACA5RBQAACA5fx/vtH2hDcXuyIAAAAASUVORK5CYII=",
-            "text/plain": [
-              "<Figure size 640x480 with 1 Axes>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(gamma=&#x27;auto&#x27;, kernel=&#x27;linear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(gamma=&#x27;auto&#x27;, kernel=&#x27;linear&#x27;)</pre></div></div></div></div></div>"
       ],
-      "source": [
-        "#Preparation of dataset\n",
-        "x1 = np.linspace(0, 100, 12, dtype=int)\n",
-        "x1 = np.append(x1, np.linspace(30, 50, 8, dtype=int))\n",
-        "x1 = np.sort(x1)\n",
-        "x1 = np.delete(x1, np.where(x1 == 30))\n",
-        "x1 = np.delete(x1, np.where(x1 == 50))\n",
-        "n = x1.size\n",
-        "x2 = 2*x1+3\n",
-        "coordinates = np.column_stack((x1, x2))\n",
-        "label = np.zeros(n, dtype=int)\n",
-        "plt.scatter(x1, x2, color=\"blue\")\n",
-        "for i in range(n):\n",
-        "  if(30<x1[i]<50):\n",
-        "    label[i]=1\n",
-        "    plt.scatter(x1[i],x2[i],color=\"green\")\n",
-        "  else:\n",
-        "    label[i]=0\n",
-        "    plt.scatter(x1[i],x2[i],color=\"red\")"
+      "text/plain": [
+       "SVC(gamma='auto', kernel='linear')"
       ]
-    },
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "Classifier = SVC(gamma = 'auto', kernel = 'linear')\n",
+    "Classifier.fit(x, y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "2UxJ_6MTWNi-"
+   },
+   "source": [
+    "**Task 1:** Find the equation of boundary by using the parameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "id": "66Jzs5BJMeqX"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "6xfDKUFsTZmM"
-      },
-      "source": [
-        "**Answer the following questions** \\\\\n",
-        "Q: Is the above dataset linearly separable?\n",
-        "\n",
-        "Q. How many classes are there in the above dataset?\n",
-        "\n",
-        "Q. How many features are used in the above dataset?\n",
-        "\n",
-        "Q. What will be dimension of hyperplane used for this dataset?  "
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "weight_matrix = Classifier.coef_[0]\n",
+    "slope = -weight_matrix[0] / weight_matrix[1]\n",
+    "bias = -Classifier.intercept_[0] / weight_matrix[1]\n",
+    "val = np.linspace(0, 14)\n",
+    "boundary = slope * val + bias\n",
+    "\n",
+    "# Equation of the decision boundary\n",
+    "equation = f\"y = {slope}x + {bias}\"\n",
+    "\n",
+    "plt.plot(val, boundary, 'k', label=f\"Decision Boundary ({equation})\")\n",
+    "\n",
+    "for i in range(y.shape[0]):\n",
+    "    if y[i] == 0:\n",
+    "        plt.scatter(x[i][0], x[i][1], marker='x')\n",
+    "    else:\n",
+    "        plt.scatter(x[i][0], x[i][1], marker='o')\n",
+    "\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "1ZIrqz3TUXs5"
+   },
+   "source": [
+    "# Non- Linear SVM"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 430
     },
+    "id": "pt5ZDqEZPwaD",
+    "outputId": "ad023c42-ab42-4711-f719-248a0164f686"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "QH9QWWi_RA0J"
-      },
-      "source": [
-        "#Splitting the Dataset\n",
-        "**Task**: Split the given dataset into training and testing data. The code snippet to illustrate the data has been given."
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
       ]
-    },
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "weight_matrix = Classifier.coef_[0]\n",
+    "slope = -weight_matrix[0] / weight_matrix[1]\n",
+    "bias = -Classifier.intercept_[0] / weight_matrix[1]\n",
+    "val = np.linspace(0, 14)\n",
+    "boundary = slope * val + bias\n",
+    "\n",
+    "# Equation of the decision boundary\n",
+    "equation = f\"y = {slope:.2f}x + {bias:.2f}\"\n",
+    "\n",
+    "plt.plot(val, boundary, 'k', label=f\"Decision Boundary ({equation})\")\n",
+    "\n",
+    "for i in range(y.shape[0]):\n",
+    "    if y[i] == 0:\n",
+    "        plt.scatter(x[i][0], x[i][1], color='red', marker='x')\n",
+    "    else:\n",
+    "        plt.scatter(x[i][0], x[i][1], color='green', marker='o')\n",
+    "\n",
+    "plt.legend()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "6xfDKUFsTZmM"
+   },
+   "source": [
+    "**Answer the following questions** \\\\\n",
+    "Q: Is the above dataset linearly separable? yes\n",
+    "\n",
+    "Q. How many classes are there in the above dataset 2\n",
+    "\n",
+    "Q. How many features are used in the above dataset? 2\n",
+    "\n",
+    "Q. What will be dimension of hyperplane used for this dataset?  1d line"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "QH9QWWi_RA0J"
+   },
+   "source": [
+    "#Splitting the Dataset\n",
+    "**Task**: Split the given dataset into training and testing data. The code snippet to illustrate the data has been given."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "id": "bBr0AVWC4H_t"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "bBr0AVWC4H_t"
-      },
-      "outputs": [],
-      "source": [
-        "X_train, X_test, Y_train, Y_test = #pass\n",
-        "\n",
-        "for i in range (X_train.T[0].size):\n",
-        "  if(Y_train[i]==1):\n",
-        "    plt.scatter(X_train[i][0], X_train[i][1], color=\"green\")\n",
-        "  else:\n",
-        "    plt.scatter(X_train[i][0], X_train[i][1], color=\"red\")\n",
-        "plt.show()\n",
-        "\n",
-        "for i in range (X_test.T[0].size):\n",
-        "  if(Y_test[i]==1):\n",
-        "    plt.scatter(X_test[i][0], X_test[i][1], color=\"green\")\n",
-        "  else:\n",
-        "    plt.scatter(X_test[i][0], X_test[i][1], color=\"red\")\n",
-        "plt.show()"
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "Tk-kkt4tRSzO"
-      },
-      "source": [
-        "# Non Linear Classifier\n",
-        "**Task 1**: Generate a classifier for the above dataset using suitable kernel function. Also, provide an explanation for your choice of kernel. \\\\\n",
-        "**Task 2**: Explain and illustrate, using plots, how the kernel used, enables the data to be classified using a SVC."
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
       ]
-    },
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "X_train, X_test, Y_train, Y_test = train_test_split(x, y, test_size=0.2, random_state=42)\n",
+    "\n",
+    "for i in range (X_train.T[0].size):\n",
+    "  if(Y_train[i]==1):\n",
+    "    plt.scatter(X_train[i][0], X_train[i][1], color=\"green\")\n",
+    "  else:\n",
+    "    plt.scatter(X_train[i][0], X_train[i][1], color=\"red\")\n",
+    "plt.show()\n",
+    "\n",
+    "for i in range (X_test.T[0].size):\n",
+    "  if(Y_test[i]==1):\n",
+    "    plt.scatter(X_test[i][0], X_test[i][1], color=\"green\")\n",
+    "  else:\n",
+    "    plt.scatter(X_test[i][0], X_test[i][1], color=\"red\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Tk-kkt4tRSzO"
+   },
+   "source": [
+    "# Non Linear Classifier\n",
+    "**Task 1**: Generate a classifier for the above dataset using suitable kernel function. Also, provide an explanation for your choice of kernel. \\\\\n",
+    "**Task 2**: Explain and illustrate, using plots, how the kernel used, enables the data to be classified using a SVC."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "id": "YmQb8dw2QVHR"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "YmQb8dw2QVHR"
-      },
-      "outputs": [],
-      "source": [
-        "Classifier = #pass"
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
-  ],
-  "metadata": {
-    "colab": {
-      "provenance": []
-    },
-    "kernelspec": {
-      "display_name": "Python 3",
-      "name": "python3"
-    },
-    "language_info": {
-      "name": "python"
-    }
+   ],
+   "source": [
+    "classifier = SVC(kernel='rbf', gamma='scale')  \n",
+    "classifier.fit(x, y)\n",
+    "def plot_decision_boundary(clf, X, y):\n",
+    "    plt.figure(figsize=(10, 6))\n",
+    "    x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
+    "    y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
+    "    xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.01),\n",
+    "                         np.arange(y_min, y_max, 0.01))\n",
+    "    \n",
+    "    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
+    "    Z = Z.reshape(xx.shape)\n",
+    "    plt.contourf(xx, yy, Z, alpha=0.3)\n",
+    "    for i in range(y.shape[0]):\n",
+    "        if y[i] == 0:\n",
+    "            plt.scatter(X[i][0], X[i][1], color='red', marker='x')\n",
+    "        else:\n",
+    "            plt.scatter(X[i][0], X[i][1], color='green', marker='o')\n",
+    "\n",
+    "    plt.scatter(clf.support_vectors_[:, 0], clf.support_vectors_[:, 1], \n",
+    "                s=100, facecolors='none', edgecolors='k')\n",
+    "    plt.xlabel('Feature 1')\n",
+    "    plt.ylabel('Feature 2')\n",
+    "    plt.title('Decision Boundary with RBF Kernel')\n",
+    "    plt.show()\n",
+    "\n",
+    "plot_decision_boundary(classifier, x, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
   },
-  "nbformat": 4,
-  "nbformat_minor": 0
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
 }
diff --git a/Week 1/SVM-Example/.ipynb_checkpoints/LinearSVM-checkpoint.ipynb b/Week 1/SVM-Example/.ipynb_checkpoints/LinearSVM-checkpoint.ipynb
new file mode 100644
index 0000000..6bbb228
--- /dev/null
+++ b/Week 1/SVM-Example/.ipynb_checkpoints/LinearSVM-checkpoint.ipynb	
@@ -0,0 +1,272 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "id": "yJ2qCHbR6afK"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "from sklearn.datasets import make_blobs\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.metrics import accuracy_score"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 38,
+      "metadata": {
+        "id": "fRpU6rQE6evG"
+      },
+      "outputs": [],
+      "source": [
+        "class LinearSVM:\n",
+        "    def __init__(self, learning_rate=0.01, lambda_param=0.01, num_iterations=1000):\n",
+        "        self.learning_rate = learning_rate\n",
+        "        self.lambda_param = lambda_param\n",
+        "        self.num_iterations = num_iterations\n",
+        "\n",
+        "    def fit(self, X, y):\n",
+        "        y = np.where(y <= 0, -1, 1)\n",
+        "        num_samples, num_features = X.shape\n",
+        "        self.w = np.zeros(num_features)\n",
+        "        self.b = 0\n",
+        "\n",
+        "        for _ in range(self.num_iterations):\n",
+        "            for idx, x_i in enumerate(X):\n",
+        "                condition = y[idx] * (np.dot(x_i, self.w) - self.b) >= 1\n",
+        "                if condition:\n",
+        "                    self.w -= self.learning_rate * (2 * self.lambda_param * self.w)\n",
+        "                else:\n",
+        "                    self.w -= self.learning_rate * (2 * self.lambda_param * self.w - np.dot(x_i, y[idx]))\n",
+        "                    self.b -= self.learning_rate * y[idx]\n",
+        "         \n",
+        "      #Vanilla gd\n",
+        "        for _ in range(self.num_iterations):\n",
+        "            scores = np.dot(X, self.w) - self.b\n",
+        "            margins = 1 - scores * y\n",
+        "\n",
+        "            if np.any(margins > 0):\n",
+        "                gradients = -np.dot(X.T, y * (margins > 0)) / num_samples\n",
+        "                self.w -= self.learning_rate * (gradients + 2 * self.lambda_param * self.w)\n",
+        "                self.b -= self.learning_rate * np.mean(-y * (margins > 0))           \n",
+        "        return (self.w,self.b)\n",
+        "        \n",
+        "    def predict(self, X):\n",
+        "        prediction = np.dot(X, self.w) - self.b\n",
+        "        return np.where(prediction<=0,0,1)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "class NonlinearSVM:\n",
+        "    def __init__(self, learning_rate=0.01, lambda_param=0.01, num_iterations=1000, degree=3):\n",
+        "        self.learning_rate = learning_rate\n",
+        "        self.lambda_param = lambda_param\n",
+        "        self.num_iterations = num_iterations\n",
+        "        self.degree = degree\n",
+        "        self.w = None\n",
+        "        self.b = None\n",
+        "\n",
+        "    def fit(self, X, y):\n",
+        "        y = np.where(y <= 0, -1, 1)\n",
+        "        num_samples, num_features = X.shape\n",
+        "\n",
+        "        # Initialize weights and bias\n",
+        "        self.w = np.zeros(num_samples)\n",
+        "        self.b = 0\n",
+        "\n",
+        "        # Gradient Descent\n",
+        "        for _ in range(self.num_iterations):\n",
+        "            scores = np.dot(self.w, self.kernel(X)) - self.b\n",
+        "            margins = 1 - scores * y\n",
+        "\n",
+        "            # Compute gradients\n",
+        "            dw = -np.dot(y * (margins > 0), self.kernel(X)) / num_samples + 2 * self.lambda_param * self.w\n",
+        "            db = -np.mean(y * (margins > 0))\n",
+        "\n",
+        "            # Update weights and bias\n",
+        "            self.w -= self.learning_rate * dw\n",
+        "            self.b -= self.learning_rate * db\n",
+        "\n",
+        "        return self.w, self.b\n",
+        "\n",
+        "    def predict(self, X):\n",
+        "        prediction = np.dot(self.w, self.kernel(X)) - self.b\n",
+        "        return np.where(prediction <= 0, 0, 1)\n",
+        "\n",
+        "    def kernel(self, X):\n",
+        "        return np.power(np.dot(X, X.T) + 1, self.degree)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 39,
+      "metadata": {
+        "id": "-0OXjMNm6sR5"
+      },
+      "outputs": [],
+      "source": [
+        "X, y = make_blobs(n_samples=1000, n_features=2, centers=2, random_state=42)\n",
+        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 40,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 472
+        },
+        "id": "8nQW96-yJRUI",
+        "outputId": "bcc5cc1b-b4bf-4590-8e02-b89355ecdabf"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "plt.scatter(X[:, 0], X[:, 1], c=y)\n",
+        "plt.xlabel('Feature 1')\n",
+        "plt.ylabel('Feature 2')\n",
+        "plt.title('Generated Data')\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 41,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "DBPM-4ChBAy7",
+        "outputId": "075d9c0e-7be1-4b84-e66b-a93b8b78ec5c"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Accuracy: 1.0\n"
+          ]
+        }
+      ],
+      "source": [
+        "svm = LinearSVM()\n",
+        "w,b = svm.fit(X_train, y_train)\n",
+        "\n",
+        "# Make predictions on the test set\n",
+        "y_pred = svm.predict(X_test)\n",
+        "\n",
+        "# Calculate the accuracy\n",
+        "accuracy = accuracy_score(y_test, y_pred)\n",
+        "print(\"Accuracy:\", accuracy)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 42,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "9xRJtVo8SoiC",
+        "outputId": "a7507743-e293-453c-bca3-2c6035b8f3b6"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "[[ 0.35026469]\n",
+            " [-0.29642149]] [[-1.225325]]\n"
+          ]
+        }
+      ],
+      "source": [
+        "w=w.reshape(-1,1)\n",
+        "b=b.reshape(-1,1)\n",
+        "print(w,b)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 44,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 490
+        },
+        "id": "Jqf3nUwwKb87",
+        "outputId": "efce2b66-5381-439c-f17a-fa45433f084a"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "Text(0.5, 1.0, 'Decision Boundary')"
+            ]
+          },
+          "execution_count": 44,
+          "metadata": {},
+          "output_type": "execute_result"
+        },
+        {
+          "data": {
+            "image/png": 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ygiAIgiAkWyKREQRBEAQh2RKJjCAIgiAIyZZIZARBEARBSLZEIiMIgiAIQrIlEhlBEARBEJKtRE1kDh06RIMGDciQIQOSJLFp0ybjsbCwMIYMGULhwoVxcnIiQ4YMtG3blqdPnyZewIIgCIIgJCmJmsgEBARQtGhRZs+eHelYYGAgZ8+eZeTIkZw9e5a///6bGzdu0LBhw0SIVBAEQRCEpEhSVVVN7CAAJEli48aNNG7cONpzTp06RenSpXnw4AFZsmSx6L6+vr64urri4+NDihQp4ilaQRAEQRASkqXvb/0njCnOfHx8kCQJNze3aM8JCQkhJCTEuO3r6/sJIhMEQRAEITEkm8G+wcHBDBkyhFatWpnNzCZNmoSrq6vxV+bMmT9hlIIgCIIgfErJIpEJCwujefPmqKrKnDlzzJ47bNgwfHx8jL8ePXr0iaIUBEEQBOFTS/JdSxFJzIMHD9i3b1+M41zs7Oyws7P7RNEJCe3xrWec3H6WsJBwcn6VjeLVCyPLySL/FgRBED6BJJ3IRCQxt27dYv/+/aRMmTKxQxI+kUC/IKa0n8WRjSeRZAlJklAMCulzpOXH1f3JWzJnYocoCIIgJAGJ+qOtv78/58+f5/z58wDcu3eP8+fP8/DhQ8LCwmjWrBmnT59mxYoVGAwGnj9/zvPnzwkNDU3MsAVAVVXePH/Ly8evMRgM8X7vUY0mc2zLaW1bUVEMCgBeD14yuNoYntx+Fq/PFARBEJKnRJ1+feDAAapUqRJpf7t27RgzZgzZs2eP8rr9+/dTuXJli54hpl/Hv39XHGbVzxt5cEUbf+Se1pVGverQfHBDbGxt4nz/c/su8UP1cdEe1+llanesRr8/u8T5WYIgCELSZOn7O8nUkUkoIpGJX0tGr2H5+PVIksSH3zqSLFG8ehEmbB2K3iZuPZbTOs1hz9IDGMKVaM+xc7Rjq98yJEmK07MEQRCEpMnS97cYNSlY7N7lhywfvx6Aj/NfVVE5s+cCuxcfiPNz/N76G7uSohMSGBLjOYIgCMLnTyQygsX+mbsHnT76bxkJic1/7Izzc9JnT4usM/+t6Z7ODZ1eF+dnCYIgCMlbkp61JCQtD64+Ntvdo6oqj27EfVHP2t9XZf2vW6M9LssyDbrWjPLYs3te7F16iJePX+ORzo1q31Ukc96MJuf4vfXn9K4LBAcEk7VgZvKXyS26qARBEJIpkcgIFnNM4YAkS6hK9MOq7B3jXsMna/5MfDuwAeumRU5mZJ1MxtzpadKvrsl+VVWZ/8My1v26FVmWjXGumLiBup2r0eePzqDCgmEr2DRzB+Gh4e+fVzAzQ5f2JlexqAeXC4IgCEmXSGQEi1Vs+jVHN5+K9rhOL1O5Rfl4eVbnKW1IkyU1q37eyJtnbwHQ2+qp1roiXaa2wcnVyeT8NVM2GxMfxaDABzPCdyz4Fxd3Z3xe+7Fr4T4+Ht7+6PoTBniOYtbJn8mSz7T1RhAEQUjaxKwlwWKhIWF0KTKQ5/e8InUxybKEjZ0Nf57/hUy508fbMw3hBu5dfkhocBiZ82bAxd05clzBoTRP35kAn8Bo76O31Zu0wnxM1slUaVmeocv6xEvcgiAIQtyIWUtCvLO1s2HK3lFkLaAtxKmz0aGz0QbcOrs78/OuEfGaxADo9DpyfZWdAl/niTKJAbh0+JrZJAYgPDQcWY5+HIxiUDiw9iihwaLYoiAIQnIiupYEq6TJnIo/z03l3L7LnN55jvAwA/lK56JC06+xtYt7MbzYCA4Iseg8NYbxvIYwAwE+gdja20Y69tbLmyObThHgE0imPOkpU694nOvlCIIgCHEn/iUWrCZJEsWrFaZ4tcKJHQoAWQtksuzEGDpRbextcHY3HXtjCDcwd9BSNv+xE8WgIMsyikHBLY0rg/7qTpl6JWIZtSAIghAfRNeSkOxlypOBIp4Foq09I+tkMufLaHa2lU4vU7ONZ6QlFmb3XcjGmdtRwhVQMRbh83npy6jGU7h0+Fr8fRBBEATBaiKRET4L/ed2xcnVEfmjgn2yXsbO0ZbhK/vSfHCjKK+VdTJOrk7878cmJvv3rz7C1jm7o2zJUVUVVJVFI1fF22cQBEEQrCcSGeGzkClPBv44PZla7SpjY6+1quhtdFRpWZ4/Tk0m11fZ6fRza7pMaYPLR91HRSsX5PdjE0mTJbVx38VDV5n03W9mn6koKpcOXeP1u+nhgiAIwqcnpl8L8SIoIJj9q45wevd5lHADeUvlpnbHKrindfvksYSGhOH/1h9nN6coB+4GB4Wwd+kh/L0DKOKZnwJf5410Trfig7lz/r5Fz5t74RdyFM4a17AFQRCED1j6/haDfYU4u3vxAUNqjsf7hY+xou7RLadZOnYtw1f2o2KTMp80Hls7GzzSuUd5bOei/Swbu5YXD18B2qrd5RqVosf09sYWmXuXH1qcxAA8vvFUJDKCIAiJRHQtCXES4BvIDzXG4fvaD8A4oFZVVMLDwpnY8ldun7+XmCEarZ26mWnf/2FMYkCL89iW0/T+ejivnrwG4NWTN5bfVIIzuy/Ed6iCIAiChUQiI8TJ3mWH8Hnla5zNY+Jdp+XfM/75tEFF4a2XNwt/XBnlMcWg4P3Kl2Vj1wHgkc7NqnsHBQTHNTxBEAQhlkQiI8TJ8W1nMFdnzhCucGzL6U8WT3T2Ljtkdvq1Eq6wZ/khQoNDyVEkK1nyW7jmkgp28bBQpiAIghA7YoyMECdhIWGRFmGMdE5oWKR9oSFhKAYFOwdbJCmGkrvx4Nm9F8g6GUUxRHtOWHAY3i99SZM5Fd2mtePHepOwZCz83QsP2LP0IK+fvsE1dQpU4NDao7x6+pbUmVNSu0NVKnxTGp1eF4+fSBAE4fOlquDtbdm5IpER4iRvyZxcOnwt6q4ltBotuYvnAMBgMLDxt+3sWryf+5cfAZAxT3qa9KlHva7V0ekS7kWfIqUzigVJSZciAylRowhN+tVn7KYfmPTdbwT5me86unn6DlPaz9ISpXdfB0nS/kd8dP0Jp3eep3DF/EzcPhwHJ/t4+TyCIAifq+Bg6NQJzp+37Hwx/foL5vPKl50L93Ph4BUAinoWpHbHKrimsvzr9PTOc9rn7WO222bEmgGEBocyq9dfBPoFRT5BAs9vyzJ8ZT9kOWF6Ox9ef8L3BfpZdK5OL2MIV+g9qxNP7zxj4+/bUQxx+99E1snUbF+ZgfO7x+k+giAIn7Pnz6FxYzhxAmTZF0WJ+f0tEpkv1Nm9FxnVeDKhwWHGJESSJWztbRi78QdK1Chq8b3+mbeHGd3mIetlrZQ/IMsSiqKS/+s8PLz+mABv86tTA/ywuBc12npa9TnePH/L9nn/cvbfC8iyTImaRanTqRpuqV0jnTu57Uz+XXnYbNJlQoL/DWvCqkkbLepiiolOr2P1k7lRxmapiOTzxPYzhIWEU+DrPNTvVoPMeS0c0yMIgpBEnT0LjRrB48fg7g5LlvjSsKFIZEQiEwWvBy/pmL8fYaFhUb7U9bZ6Fl6bQcoMHhxef5xLh68hSVoF3PLflI60HhHAhYNXWD9tK6d2nUcxKOQokpW3Xj68sbDqrSRL5CmRg1knfrb4cxzdcopxzX7BEG7araW31TPxn+GRFrUMCw1jWO2JXDhwxaL76/Qy1dt4sm/lYcJCwi2Oy5yxG3+gXKNSsbr26vGbDK8zkUC/IOPfm6yTURWV3rM70aBbTavv+eDaY3Ys+Jend57j7OZE5RblKVmraIK1jAmCIERlwwZo0waCgiBfPti6FdKksez9LRKZJERVVd4890ZVVTzSuSXYy2TB0OWsm7Y12nEtAGmzpiIkKBTvF77GQaqGcAOpMnow8Z/h5CgSdQG4iG+nMU2mcnzbGbPP+JitvQ3/BEY9Rfpjj248oWOBftGuaC3rZZbdmU2azKlM9v/WfR7bF/xrcVxZC2YmffY0HN92xqLzYzJ6wyAqfGN9gcAAnwBaZ+9BkG8QSlQtShL8emAchSvmt+h+qqqyaMQqVk3aaOxKi/g9/9e5mfjPcFzcna2OUxAEwRqqCuPHw+jR2natWrB6Nbi5Wf7+Fj92JQGqqrJ1zi7a5elNy4xdaJWpK21z9tLGZiiWJwKWsiTB8HrwCp+XvoCWwBjCtdk+b557M7jaWHxe+UZ5nSRJvHz0imNbTluVxADG5QTCQsM4vu0MO/76l1O7zhuf/aHZfRdFm8SANp166Zh1kfbLOtmqWVJ6vQ57J7t4mVklyRL5yuSO1bV7lh4i0CeaJAbQ6WTW/7rV4vttn7+XVZM2AhhbtCJ+v3HqDhNbzYhVnIIgCJYKDISWLd8nMf36wbZtWhJjDZHIJDJVVfmtx3x+77mAZ3e9jPu9Hr7kj36LmNp+dryMz/iQpd0kUT1WMSj4vfVnx1/7or3u2onbVscs6yQqNCnDzoX7aJGhCyMb/syvnf9keJ2JtMrSjUPrj5mcf/FgzN1Du5fs5/GtZyb7StQoGmViFHVMMqXrFsPeyR5ZF7f/VWSdTKVmZUmVwSNW15/efR7VTOZmCFc4s+eiRfdSFIWV75KYKI8bFM7svsC9Sw+sjlMQBMEST55ApUqwdi3Y2MD8+TB9OuhjMZdaJDKJ7Ny+y/wzd4+28eF76t2f9y4/xIl/zsbrMwuUyxOnbitVUSMlFh+KzUtflmXSZk3NtE5z8Hvjb3Ls7XNvxrf4lf82njDuCw+NORlTFZXBVceYzJQqU6846XOkjTFGSZLQ2+io37UGlZp9bXHyE/k+2q9sBTPTd07nWN0DtOTCXAuUdo5lMT668ZQXD16aPUfWyfH+fScIggBw8iSUKgVnzkCqVLB3rzbdOrZEIpPItv25C50++r8GWSez5Y+d8frMhj1qx7nLKtg/GFVVeXj9CTfP3MHv7fvko1CFfGY/08dsbPWMXDeAv38zv5TB3EFLjXHroxhwHJVXT9+wd9khDOEGdi85QN/yP/L23Tik6EiyhI29DWM3DSFNltSUqFmUPCVzRpn8SJLEx6WN02ZLTYZc6XBP60quYtnpPbszvx2dGKcxJwXK5kWWo+/eknUy+b/OY9G9LEkCJUmKtwHOgiAIEVatAk9PePYMChXSkppKleJ2T1EQL5Hdu/Qw0qybDykGhftXHpm9h88rX3Yt2s+tc/ewsdPzdb0SlGtUCr1N1H+9+cvk5ttBDVj3i+VjKj6k08s4uzvRIV9fnrzrutHb6PBsUY4uU9rgkc6dat9VYu+yQzGOkylVpxg/ruzL5f+uR2qJMaHC83svuH7yNgW+zsPX9UtweMPxGGOVgANrjnBq5zltOYV3q3OD9rJWUZFlCUmScEzhSLrsaSherTAqsO6XzaydupmilQsyeFEPZnSbz5Uj19HpdUgShIcZcEzhwI+r+5M5bwZeP32LRzo30udIa+mX0mJ1OlVjxcQNKGHhUbbMKAaFJn3rWXSvDLnSYedoR0hgSLTnGMIN5CqePbbhCoIgmFAUGDUKJk7Uths0gBUrwMUl7vcWs5YSWc/SQ7l5+o7Zc7Lkz8RfV6Ybt1VV5ezei+xctJ9bZ+7w5PZzQHtpyzpt5kn6HGmZvHuk2Zdq9xKDuX3ufuyDlzB5qcp6mdQZUzLzxCTsnewYUX8SFw9e1RKGj77N7Bxt6fdnF6p/p9WN2blwH9M6zYnxkRWalKHP7E7cPn+f4XUmWhRmyvTuxtlg5kTM2tHb6DAYFJP6Oja2ekatH4SLhzPHtpwmLDiUHEWz4dm8LHYOn2atpcN/n2Biy1+B9wNzI6oJNxvYgC5T2lg8KHl2n4VsmbMrykRT1sl4pHNj+f0/ErTasiAIXwZ/f2jbFja+G5o3ZIiW0MT0z4ul72+RyCSydb9sYf7Q5dEWaZNliTajm/PdyGaANqNnQovpHN18yqQk/sd0epnUmVOx8NqMKOu+gFZPpk+5H/F+4WN6Hwls7WwIDQ4zltoHjAlJVImJMV6dTINuNek183sM4QaObzvDrkX7eX7/BQDZCmWhdJ1iVGxaBjsHO1RV5dLha6z/datFi0vKsoRbWjcKV8rPoXXHYixuJ+sk9LY2hAaFxnhvcyRJq0/z19UZpM8e/y0ulrp3+SEbf9/OsS2nCQ8LJ1/p3HzTpy6lan9l1cyqAN9ABlYezd2LD0y+hjq9jI2dDVP2jiZ/LGdYCYIgRHj4EBo2hAsXwNYWFizQ6sVYQiQy7yT1RMb3jR/fF+iP72u/SEmJrJNxcnXkryvTcU/rBsC8wUtZP32bxdVpf1zVj8otykd7/K2XN7P7LuTQ+uMm93RwsefreiW4de4ej288BSBrgUxkLZiZ/zYcj3YaMIC9kx1/v14UbQIFEBocyv7VR1g+fj3PIxZ0tHC6tjXnxidZlmjavz5dprb95M9OCEEBwWyeuYOtc3bz4tErHJztqdqqAs0GNSRT7vSJHZ4gCMnc0aPwzTfw4gWkTau1yJQta/n1lr6/xRiZRJbCw4Vf9o/hx3o/4XX/JTqbd8Xnwgx4pHdnwtahxiQm0C+ILX/ssjiJkXUyRzafJFOeDHg9eIlrKhfyl81j0l3w+OYz/vv7RKRrg/yC2b/6CF1/aUvN9pWRJAlnNyd+6z4fSSeDmVWkgwNC8H3tT8r07lEe373kAH/0W0SAz/tlC6xJTKw518nN0aLlESx6rqKya/EB8pfNS/FqhXBydTIeMxgMqIqKTq9DUZRk0SXj4GRPy6Hf0HLoNyiKIqr5CoIQb5Yuhc6dITQUvvoKNm+GLFkS5lkikUkCsubPxJJbMzmx7SwXDlxBVVUKVypAuYYljVV1Aa4dv0mIFV0kikHh5PZzHFh91LgvVaaUdJ78HVVbVQC0Fh5FUaNNjhaPXE2dTtVwSuEIgIu7U9QFZj4gyRKOLlGv8nxgzRGmdpht8WeIqwDvQNJmS82LB6/ipR6P72s/xjX7Bb2dnia961Ky9les/3Urp3aeR1VUY1dcqkweNOxem8Z96iT6itfP7nrxz7w93Dh9Bxs7G8rULU71NpWMf6eASGIEQYgXBgMMHw5Tpmjb33wDy5aBk5P56+JCdC0lI6d2nmN43Z+su+ijAbkRBi/qScHyeWmfp0+Mtxi8qCc121UG4M6F+3QrNjjacyOKyI3fPDTSMUVRaJOzV4w1TOJb6TrFOLnjXILdP2KBzEgkyPVVdqYdGIuji0OCPd+c7Qv+5bduc0GSUAyKlmgBrilTMHnPSHIWzZYocQmC8Pnx9YXWrbXqvAAjR8KYMRDbn5PEEgWfoVzFcyBbUZ8FiLaI2pz+i3n56HWMl8s6mTfPvY3bOYtmo0KT0khR1DSRZAlZlvhuRLMo73Xj1J1PnsSANpU4qnjjS7TjhVS4fe4eI+pPMqmz86lcOnyN6V3/RFFUY3ecqmpx+b31Z0jN8QQFBH/yuARB+PzcuwflymlJjL29Vi9m3LjYJzHWEIlMMuKexpXKzcvFuVw+gL93AA+vP47xPMWgkCqjaVn9ocv6UKVleZC05CWi+ytFShcmbBtG3lK5oryXbzTrMyW0M3suWjyuKCFcOnyNdrl7c/vcvU/63HXTtkT7vaIYFHxe+rJ/5X+fNCZBED4/hw5plXqvXIH06bXtli0/3fNFIpPM9Py9I1nyZ4z7jSQIDzVQsLz5irH2TnaUb1zKZJ+dgx3Dlvdl6a1ZdJnShu9GNmPUuoGsfjyXEjWKRrqHwWDg6JZTHFwXcwE7Y3iyRKY8GQCMVYJjncBZ2Bhj62CDrb1lFYOtFeAdwNBaE/B944fBwqUE4urUzvMoZootSpLE6d3nP0ksgiB8nhYsgGrV4PVrKFECTp3SkppPSQz2TWZSeLjw+9GJdCo0gBcPX8X+Rip4pHOj6y/tGOA5CsINUXaRdJ7cBgfnqMd3pM+RlmYDGph9zL3LDxnZ4Ge8HryfkWUJWzsbMuZJR62OVXh84ymvHr9GUVTO/XvJ4nsYWdAYkyV/RkZvGMypHef4c+AS658RA0VR8XnlS9NUHZFkiZK1vqLFD40o6lkw3p8FWtHEmGZ3qapKeOinSaoEQfi8hIfD4MEwY4a23aIFLFwIjo5mL0sQokUmGXJwdqDO99XiPO6jiGcB8pfJzdR/R5M5fyaTY+5pXRm4oDsNe9SK9f29X/owqMoYXj7WxuIYwix8aUoQEhTKqR3n+WvoCrzuv2TkuoGEh4QlyFiXnF9lY8Hl6WTJl5Em/erR87eOOKZIuMG5qqJyZvcFBlUdw67F+xPkGZIkkbtYdszVyJNkibylo+4GTGyKonBq13kmt5vJj/V+4veeCz5515wgCFHz9ob69d8nMWPHamNiEiOJATFryWovH79m42//sHf5Ifx9AkmXLTUNutWibudqn6xUPWiF7Nrl7k1IYIjZ4nTm5CuTi18PjsPG1gZVVbl19i7P770gRUoXClfMbzL1OzZWTNzAktFrLBufEs3sKtBeyjb2ekKDwuIUT1RknUzzQQ35flJr4z5FUZjQ4lcOb4hcXye+6fQyKx78GW3NndgKCgimR4kfeHzzWbTn6G10rHgwB4908fvsuArwDWRkw5+5dOgasl5GCVeMy0fU71aT3rO+F9PFBSGR3LqlrZN044aWuCxdCk2bJsyzREG8BHDv0gMGVB5NoG+Qsdn+8Y1nzOm/mH9XHGbqv6Oi7YaJb+5p3fhp+3CG15tkXIkatJ+yVVVFlqKZEvyB6yduc3j9car+ryKSJJGnRE7ylMhpcs7tc/f4+7d/OPHPWQwGA1nyZcLG3oa75+9jCDeQr4xWIv/r+iUilcg/sPqI5YNszZymqmqCJDHvbk7dLtVNdh3ecNziJMYxhQOBvkGxfrxiUNj51z5aj4jffwkWDFnO0zteZs8ZtqKvSRLz7J4Xl/+7jiRJFKqQj3TZ0sRrTJb6peMfXDlyA8A4xidibaltf+4mXbY0tPihUaLEJghfsr17oXlzePsWMmWCLVugWLHEjkokMhZTVZWxzaaZJDER+wFunb3LwuGr6Pl7x08WU6EK+Vl+bza7Fx/g7L+XUA0KBcvnI1vhzIxt8kuM18uyxI6F+6j6v4pRHt+36j9+bvM7siwZXyTXjt80OefCgSuc+/cSzQZopfs/TGYC4vCCTwgfrnwt67REr/+8bpHWTpo/ZIVF92vcuy6dJrfm+JbTHNt2mpun7vDo3XIOllJVuH7yZswnRiMsNIx/V/zHP/P28OyuFyk8nPFsXp4dC/fFOEYmXXYtUfF97ccv38/h2NZT7xNKCco1LMXAv7qTwiMelqe10NM7z/lv4wmzie26XzbTpF9ds0tgCIIQv/74A/r00QrelSkDmzZBunSJHZUmUdtnDx06RIMGDciQIQOSJLFp0yaT46qqMmrUKNKnT4+DgwPVq1fn1q1biRLruX2XeXLrWbQvB8WgsGPhPoL8P+3LO4WHC80GNOCnf4YzaecIvhvZjAqNy9Dnj84xXqsoKq8ev4ny2IuHL5nSbiaqohqTmCjv8e7rsf7XbRzbarroY7aCmeJlqnh8sbF7n7crBhUHZwcWDFtBh/x9WTB0OV4PXhIcGILXgxcW3c8tTQrs7G3xbF6OoUv78NfVGTToXhOwbobV3YsPzR4PDgzh4LpjbJq1gyObThIaorVOhQSFMKTmeKZ9/wc3Tt3G56Uvj248ZfmEdYQFm2/BkmSJK0duEBIUwsAqoznxzxnT5EGF49vO8EO1sYQGx23BTWuc2nkeKYZpZj6v/OK2arsgCBYLC4MePaBnTy2J+e47OHAg6SQxkMiJTEBAAEWLFmX27KhL1k+ZMoXff/+dP//8kxMnTuDk5EStWrUIDv70RbxunLwdYzG6kMAQq38iTygNutUke+HMZs+RZYlUmbQaMaHBoZzZc4Ejm07y6MYTts//N6aVCEzvpZPZ+Pt20xi610qUxR2jExoUZjIVO8gvCJ+Xvjy+8ZR107byfYF+bJm906JZToC2+vYHXyRJkug9qxNjN/5AEc8COLk6WlTA8OXj17x+9jbSft83fkxsNYNvPNozocWvzO6zkDFNptIyQ2f2Lj/EwuGruPLfdQDTLjxL4le1ZGbvskPcv/Ioyr8nxaBw58ID9q06YsEN40d4aLhF0+XDQ8MTPhhB+MK9eQO1a8OcOSBJMGmSNibGPnFXXYkkUbuW6tSpQ506daI8pqoqM2bMYMSIETRqpPWHL126lLRp07Jp0yZafspqO7yrZWLBO1lvk3R66xr1rMOM7vOifbEpikrtDlVYO3UzK3/622QRRydXR6sXcrx+wrS1rEy94lT9XwX2rfrP4uQgwUX3tTAohIaEsWT0GotvdffiA7b9uZsG3d/P7JIkiXKNSlGuUSnCQsPo+tUgHl03n9yqisrtc/eMA35VVWXN5E0sGrk6yr8Dv7cBTG47ExtbfawHequqSrGqhfjl+zlISKjRfGEkWWLnwn3U7lAlVs+xVu4SOWIcV6W30ZG1YCaz5wiCEDfXrkHDhnD7trZO0ooV0CiJDk1LOu3+H7l37x7Pnz+nevX3AzFdXV0pU6YMx44d++TxlKr9FYpi/sXuns6NrAWSzj+w1dtUImeRrFF2c8g6mfxf5+bioWvMH7LcJIkBIm1b4uPnyLLMoEU9qNupOg7RLCKZlKiKauy2sYgEa6Zujvb7Yu7ApRa30H04Q2zjb9v5a/jKGBPJsFi2Ssh6mWLVC5O1QGbePHtrdjFNVVF5E0VrUUIpXDE/WfJnjLZrTtbJVP1fxU86bkcQvjQ7d8LXX2tJTNascPRo0k1iIAknMs+fPwcgbVrTgZhp06Y1HotKSEgIvr6+Jr/iQ/bCWSlWrbDZsQ/NBtSP85Tl+GTnYMfUfWOo0KSMSf0VWSdTtVUFWg1vwvb5e+PlWTq9tljkh57eeU6XIoPYPn8vIYGfbpxFnFjTwKGC1/2X3L/yiO0L/uWvYStYM2Uzz+554fPKl3/m7bHofnYOthQomwfQuviWjl0bu9ijEfE9G/E9kCVvRoYt7wtAmiypzNbmkWWJNFlSxWs85kiSxIjV/XFK4RCpW06SJTLnzUDXaW0/WTyC8CVRVa02TL162gKQFSpolXqLFEnsyMxLOv0g8WTSpEmMHTs2Qe7946p+DKk5njvn70d5/OjmU9TvWjPRVjr+kKIoeL/wMb4YXj99w9VjN5EkiYLl8+KRzp3W2brH3/MMKk371TduB/oFMbDKGONP80lprEx8611mGKEhYej1OhRFZcGw5RT1LEi4hQUAHZztObD6CDXaeXLu30uxag2Ljns6V/KXycPTO89xS+NKzbaV8WxeFlt7WwBqd6zK5XfjbKKiKCp1vq8Wb/FYInvhrPx5biobpv/D7qUHCPAJJHXGlNTrWoNGvWrjlCKRqm4JwmcsNFQb0LtggbbdsaM2NsbWNnHjskSSTWTSvRsS7eXlRfr06Y37vby8+Oqrr6K9btiwYQwYMMC47evrS+bM5ge9Wso1VQqKVSnEnQv3o/xJ+9rxW0zvOpcfV/aLl+fFhqIobJm9i/XTt+J1X1tpOlOe9Hw7sCF1OlVDkrQ6M5tm77R8iQMzxeoiVGhahnylcxu39y47xKsnr5PO2JgEFPpuhtCHicuFg1csvt77pS/Tu87lwNqj0U6Fj623z31oM+pbchXLHuXxKq0qsG3ubm6evhsp2ZR1MvnK5KbSt1/Ha0yWSJMlNd2nt6f79Paf/NmC8KV5+VIranf4sLZa9S+/QL9+mK0MnpQk2a6l7Nmzky5dOv7991/jPl9fX06cOEHZsmWjvc7Ozo4UKVKY/IovgX5BbJ0bfXeBYlA4uOaosST/p6aqKlPazWJ234V4PXhp3P/41jOmd53L7L4LCQkKoU+5H5nd+y+L7inJEu3HtaRAuTxmuyAOrz/OT61/Y9PMHbz18ubguqOWrtWYvEX3IWORwJ3fd5nLh6/GKZyoRDUjKoKtnQ2Td4+i2ncVTbpFdXodNdp68vPOH0W9FkH4jF2+DKVLa0lMihSwbRv07598khhI5BYZf39/bt++bdy+d+8e58+fx8PDgyxZstCvXz8mTJhA7ty5yZ49OyNHjiRDhgw0btw4UeK9fuIWIYEhZs9RVZWzey9Sq/2nmeXxoWNbTvPvisPvAvkwKO23zbN28vDqk0izi8yRJImvqhRix1//xjibZP/q/ziw5gh/DlyMg4uDVdO3k60YPmNEC5hFt1JVjm45Taa86Xly67nlVZFjkDKD+SUIHF0c+GFRLzpPbsP1E7eQJIl8ZXLhlto1Xp4vCELStHUr/O9/4O8POXJo2wUKJHZU1kvUROb06dNUqfL+hR/RJdSuXTsWL17MDz/8QEBAAF26dMHb25sKFSqwc+dO7BNpErsh3LIxD+YKyCWkLX/sRNbJ0Y5HkXUS5/ZZt3q0YlDoX2mkZS9VVXsZGxQV/7cBVj3nc2XtUmZ+b/zpNq0tv3w/J16eL8kSY5pMpfp3lWjUszbuad2iPTfIL4jL/13n9rm72NrbUrZBSar8rwIOTkl/xpkgCJZTVa37aMgQ7c+VK8P69ZAyZWJHFjti0UgrvPXypmWmrjEOXJ13cRrZC2WJ07Nio2WmLrx++ummygoJI3eJ7Nw+dz/eWmRAG+/i4u7EtIPjyJo/comArXN2MbP3X0iShGJQjC1JHundmbJ3VJTXCIKQ/ISEQNeusGSJtt21K8ycCTZJsAfZ0vd3kh0jkxS5p3XD89uy0U7B1ullCpbPlyhJDCStYnxC7Nja23DrzL14TWJAa1nzexvAmCZTI7USnd17kd97LkBVVGOSHnGO9wsfhtYcb119HUEQkiQvL6hSRUtidDotgZkzJ2kmMdYQiYyVes38nsx5M0Qa+CrLEu7p3Bm2vM8nj+nB1Uf0rzTSZICvkDzFJRmVZCnSCuQfUgwKj2885cIB0xlVa6dujjY5VwwKr5684fD647GOSxCExHfhgjao99gxcHODHTugV6/kNag3OiKRsVKKlC78fuwnOk9uQ6Y8GbB3siNt1tR8N+pb/jw7hbRZU3/SeB7fekbf8iO4eiz2KygLSYOTqwOBfrFbdFSSJWRZjnFMjixLXDp8zbitKApn/71ktrtU1smc2nkuVnEJgpD4Nm6EcuXg4UPIkwdOnIAaNRI7qvgjEplYcHRx4NuBDVh0/Te2+i1n+b0/aDPqW1xTxd9Ub0stGb2aIP/gz7rg3Ofo45+CUqR05vdjP2FjF7s2XscUDvSdY9mK5xumb+PykfeLTcbUjaWqKuFhYpFGQUhuVBUmToQmTSAwUEtejh/XkpnPiUhkkrFAvyAOrz8ukphkSFW15QHyl81D9+ntWXp7FlnyZaJSs6+1BUqtpNPJ1GjriVuamKdMB/oF8UONcdy79ACdXke2QpnNdkkB5C2V2+xxQRCSlqAgaN0aRozQtvv0ge3bwd18NYZkSSQyyZjPS99Em+r9KdTuUIWqrSuaXd8quZIkCccUDvz23wSa9K2Hk6sTAM0HN0KS5RgTi4/5vvYn0D8Yj/RuMZ6rKipKuIEVEzcA0KRvvei7pCSwsdVTq31lq+IRBCHxPH0Knp6wahXo9TB3Lvz2m/bnz9Hn94b4gqRI6Wy22q61dHpdjMXTPqX63WqSJnMq5Hj8jEmFqqrcv/yIB1cfm+zPUSQrE7YOxdFVW69Lb6OzKKnR2ehYOGwF9y49tOj5hnCFwxtOEBIUQq0OVajWWlsa4cOvtU4vo9PpGL6yHylSitWmBSE5OHMGSpXSFnv08IA9e6BLl8SOKmGJRCYZc3J1omyDkjG3WEhY9DKcdXISOb/KFj/BxYOpHf9g9c8bLV58MTnyeRl5dfYSNYqy5sk8Bi/qSYPutfBsUc7sPXR6mbINSrJj4T6rpm0rBoVAv2BkWeaHJb0YuqwPeUrmxMbeBscUDlT9X0VmnZxE+calrf5cgiB8euvWQcWKWotMgQJw8qRW7O5z95k2NH052o9rwZndFwgLDUMxRH6JpcuehmwFM1PEsyAOzvbM7LUASXpffVinl1EMKv3mdsXeyZ6T25PO7JQHVx4ldggJ7p/5ezm18xyezcuRu3gO4347BztqtqtMzXaVUVUVby8fLh66GnlhR1lC1uvIUTQr//19wqpn2zvZ4eLu9O4+MtVaVzS2zAiCkHwoCowbB2PHatt16mjdSq5fyCojIpFJ5rIXzsov+8cwpf1sHl1/YtxvY6enaqsKdP21HS5uzsb9hSrkY8vsnZzefQFVVfmqSkFqd6xK3lK52LP0YGJ8hC/aofXHQIU1UzZTpl5xflzdP9KSAJIkMXLdACa3ncnJ7eeQdTKSLGEIM+CWxpUfV/fn7oUHVq3rJOtkaneoKoooCkIyFxgI7dtrrTEAAwfC5MlawbsvhViiIInx9w5g16L9HN18ipDgUPKUyEmDbjXIXjir2etUVeXK0eusmbJFa6F5V4lV1stUbVmeXrM64ZTC0Xh+oF8Q66dtZcucXfi89EXWyeQokpXb5+4l6OcToifrZMo2LMmYDYON+26fu8eqn//myMaTGMIVUqR0IVex7OQtlZM8JXNStkFJdHodFw5cYVDVMRY/xyO9O3+c+tns2kuCICRtjx9Do0Zw9qxWnXfuXOjQIbGjij+Wvr9FIpOE3D5/jyE1xuP3xt/4k7VOL2MIV+gypQ3fDmoY7bWqqjKq8RSObz0d5fF02dMw/9Kv2DvaEeAbyEDPUdy7/Mikq0LSSahRdE8Jn9aCK9PJmj8Tp3dfYGSDSdpCnB/MTpNliexFsvLrwXE4umiDglVVpX3ePjy/5xVlF+PHchbLzriNg0mTJfYFHBVF4e6FBwT6BZEhZ1pSZUymK84JQjJ04gQ0bgzPn0Pq1PD331ChQmJHFb/EWkvJTEhQCMNqT8TfO8CkeyDiBTbvh2Wc3BH9+JVTu85Hm8QAPL/3gqVj1gKwbOy6SEkMIJKYJEDWyRzddIrQ4FAmtpqOwaBEmmKvKCp3zt+nefrODKk5jv82nkBVVYYt74ONrY1FdWjuX3rAgMqjCfANjFWce5cfom2uXnQv8QMDK4+mVZZu/NhgEk/vPI/V/QRBsNyKFdr06ufPoXBhbVDv55bEWEMkMknEgTVH8X7hE21xO1kns3bq5mivXzp6bYzP2DZ3N6HBoWxfsFcU0UsE3/SuE+M5siwR5B/EofXH8X8bYHYWUkhgCGf/vcTYpr/wc5uZ5C6Rg9mnfsazeTl0NuY7yA3hCi8evGLXwv1Wf45NM3cwue1MvO5/sLaXCqd3nqdHqaFsnLmdPUsPcu/SA6vvLQhC9BQFhg+H777TVrFu2BCOHIFs2RI7ssQlEpkk4uzei2anUSsGhYsHr2AwRD0V+fHNpzE+I8gvmI0zdxDkF2z2PJ1epni1wmTJnxF7J3tj94UQe6kyetB2XAts7M0vQRAeZiDAJ4iDa49aVuH3XZ6zf/V/bJ61k6wFMjNseV+2+C4jd4kc5i9VVdZM3czbFz6Wfgx83/gxd/DSKI8pBoUA7wD+6LuIKe1n0aXoIPqU+5Ent59ZfH9BEKLm768tNTBpkrY9dKi2hpKLKPEkEpmkQjEoMc44UVWiraliaQvLgiHLLTqvQLm8/HVlBlv9lrHZZylj/h6Mk5s2WDg2JfS/dH3ndMbZ1YlqrWJu/93yx06ObztjXdVmFTbM+AdF0a6xtbMh0CfmbqM3z97yXfYenNp13qLHHFh9FIMVdX1unLpN3/IjePX0jcXXCIJg6sEDKF8eNm8GOztYtkxLaGTxTzEgEhmrqKrKtRO32L/6CKff1W6JL/m/zmP86dqc1lm7s3TMWoIDQ4z7ggKCCAkKteg5kixh72xntiKwIVyhXKNSJvvKNy7N2qfzGbaiLxW/LWvRswSNrJPJXSInACkzeSTYc148eMlbr/etK+lzprNoeYew4DBGN57Ms3teMZ7rdf+FVYmsYlDwe+PP+mlbLb5GEIT3jhzRKvVevAhp08L+/VrXkvCeSGQsdPHQVb4v2I8+ZYfz0/9mMKz2BFpl6so/8/bEy/1rtPXENoZuB9Aqwa6YsJ7BVccQHBjCgTVHaJ21h8UtMqqiEuwfEu3YC1knU6xaYZPibBH0tnruXXzAwTVHLXqWoCWOFb4pTcr02tIPm2ftTNjnfZCf1utS3aLvi4hZUdvm7I7xXJeULihWVA8GLZnZtcj6sTiC8KVbvBiqVIGXL+Grr7RBvWXFz5GRiETGAleP3WBIjXE8vmna1+/zyo8Z3eaxaeaOOD/j7N5L6G0tK06mKCo3Tt/h5za/M7HVDPze+Fv9vA4TWmlVYWUZWSej02uDQwuUzcPItQOivGb5+PWsnrzJqjL4X7oUKV3oNfN7AO5feYT/24AEe5Z7OleTujBlG5akdL3iFq3HpRgUjm07E+N5lVuUM3ZfWcPfOyDa8V2CIJgyGGDQIK0mTFgYNG0K//0HWbIkdmRJkyjraYG5g5dpY1iieYEvGLaCmu0rx3pQ7NEtp5jQ8leLupYiqIrK0U2nYvU8tzSutBzamJrtPNm16ACPbj7B0cURz2/LUsSzgMm6TIF+QWyetZMtc3by6rEY52AtRxcHepQaQqY8GcheKGH/FfJ56Yf3Cx9jMqPT6RizYRBLR69lzdTNMSag4aHhMT4jXbY0NOhWk21/7saaClQpUrqg+5JKjQpCLPn6QqtWsH27tj1qFIweLcbDmCMSmRg8u+vF1aM3zJ4TEhjCkY0nqdHW0+r7q6rKvMHLkLAqjzFeay1JlmjYoxayLJMqY0paj2ga7bl+b/3pX2kUD689Fq0wsfTsrjbu5M0zb87vuxz7G1nwDaKqKrsW7afl0G+M+2xsbfh+UmtePHrFgTVHo+1q0ullCpbPa1EoPX/riI2dDZtn7cQQblkrS2z+3xCEL83du9CgAVy9Cvb2WtdSixaJHVXSJ3K8GLx+9jbGc3R62aLzonL73D2e3Hpm1U+3sSXLEvnK5DZbIfhD839YxqPrT0QSEw/iWrfH1i7m8VOoKrcv3I/yUJO+9czGYAhXaNSztkWx6PQ6uv/anlWP59JtWjssWFidtNliX0FYEL4EBw9C6dJaEpMhAxw+LJIYS4lEJgYe6dxiPMcQrhgHc1rL+6VvrK6TLHl7fMA9nRttx7Rg6t5R2DvaxXi+v3cAe5YdEoXzEpkkSUiSRGhwzDPkVBVO7zrP/tVHIh3LWyoXnSdrUx0+nMkUMQOp08/fka90bqtic0/jStHKBWNMwnV6Hb6v/Ky6tyB8SebNg+rV4fVrbYbSqVNQsmRiR5V8iK6lGGTImY78ZfNw48StaGdr2DnaUf6b0rG6f5rMsVifRtK6EewcbM1Ou3ZL48rvxybi4u6MYwoHZCs6WR9cfWzRmAkh9nR6GUVVkQDFoJI2ayq8Hr4ydiHp9DoUJfqxWVEJ8Ankp//NwOv+C5MuJoDmgxuRq3gONv72D+cPXAGgqGcBmvSrT/FqhWP1GVw8nGM8R1EUi84ThC9NeDgMGAAzZ2rbLVvCwoXgIGqQWkUkMhboOrUtAyuPRiLql8r3P/0v1gN9sxbITO4SObhz7p7Zaa3arCIVg0FBp9fx/cT/Yetgy6zef0U6V2uskfhhSS/SZ09rcSzhYeEcWHOU7fP3WlQpWIi9b/rWxc7eltfP3pIyvTvV23qSNX8mvB685Map2+j0Os7tvcjWuXtQrRoFrv32148rqfRtWTLkTGdyuHi1wrFKWsLDwvF55Ye9k53JKupps6YmX+lc3Dh9x2zCVUnUHhIEE2/fal1He95V8JgwQVt+wMrGdgGx+rXFLhy8woyuc02mYKdI6UL78S1p0K1mnGK8euwGA6uMQQk3mCQzkqR1FzQf1JDDf58wDhyNULRyAUrUKMraqVvw934/rTdVppT0md2Jsg0sb5sMCghmeJ2JXP7vOrIsWV0rRLBOswEN6PpLW7PndCrUnwdXH8fq/rJOpvmghnw/qXWsro8Q4BPAyol/88/8vQS8qxRcrFphvhvZjCKVCgBw9t9LDK01Xht8/tG3jSRB4z516TG9Q5ziEITPyc2b2qDemzfB0RGWL4dvvon5ui+Npe9v0SJjoaKeBVl47TeuHb/J83svcPFw5quqhbCxtWAQZgwKlM3LL/vGMLvvQm6duWvcnyFXejpO/B+rf96I14OXka67/N91vB68YtH1GVw9fgufl76kzZqaolUKRjvV9cntZxxYfRTf136kz5GWqv+rQIqULswbtJSrR28CiCQmgUmShKOLA1eP3WDjzO1cOnQNSZYoWfMrGveuQ86i2QCsW6LgI4qi8CiOrWr+3gH0qzCCRzeemoyVunDgCuf3X2bE6v5UalaW4tUKM3r9IKZ1moPfG3+ty8ygIuskGveuS+cpogypIETYsweaNwdvb8icGbZs0YrdCbEnWmSSmAdXH/Hi4StcU6cgd/Ec7F99hEmtf4v2fEmW6Dy5Dd8ObGD2vuFh4UzvOpfdiw8g62RkWTJ2U7Uf14Ilo9cQFiLGxHwqTQfUZ8Ov25B1sjFJ0OllFEVlyJLeVGtdkWmd5rBn6YFYJTQ6vUzV/1Xkh8W9Yh3jnP6L2TRrR9QDviWwd7BjzbP5xm7VsNAwjm89w5Pbz3F2c6L8N6VxT+Ma6+cLwudEVWH2bOjXTyt4V7astuhjWst7/784lr6/RSKTxA2rPYEzey+aHX+QtUAmFlyeHml/kH8Qu5ccZPeSAzy89pjggJAorhY+JUmWyFsyJ9dP3o72HFkn89fVGQT7B9O9xA+xftb4LUP5un6JWF0bGhJGszQdza+ULkG/OV2o16VGLCMUhC9DWBj06QN//qltt22rzVSyi3kC6RdNdC0lE4ZwAyf+Ocve5Qd56+VDuuxpqN2xKkUqaRV23zz3jnHWivcLn0j7Xj97y8DKo3ly+5n1lfaEBKMqKrfO3YvxvG1/7qbbtHb0/K0js/suRJIli2cvyTqZHEWyUqrOV7GO882zt+aTGECv1xnH8Nw+f4/9K//D97Uf6bKnpUY7T9JkThXr5wvC5+L1a/j2W22xR0mCyZO15QfEoN74IxKZRBTgE8Dwuj9x9dhNYxfDteM32bvsEJVblGPosj6kzZaa+1ceRV/PRQKDQaFXmaG4pnalWuuKVGxahknf/aYNDo7HJMaal6kQPUOY+Wq4ikHh4sGrAGQpkAl7JzurWtPyl8nNmI2D47QkgL1TzD8qqqqKjZ2e8S1+5dC6Y8b1ulRVZcnoNbQZ/S3fjWxmdc0jQfhcXLumDeq9cwecnWHlSm1biF+iaykRjWkylWNbT0eZpEiSxP+GNyFv6VyMajTZ7H0iEoyI2UapMrrz6on1lYYjZkl9TNbLFKlUgBcPX/H09nOr7ytYz8nNkVxfZePCgatWXzv/8q9kK5A5zjH0Kf+j2fpJoC1MeXzbmWgT3H5/iq4n4cu0Y4dWF8bXF7Jlg61boVChxI4qebH0/S0q+yaSZ3e9OLL5ZLQtLaqqsnHmdopWKUjJWl+ZXcE44iUS8cKJVRIjSxQolw94X/k14pmZ82QgT4mcPL0jkphPJcA7MFZJDMD2uXviJYY2I5uhRPNzjqyTKVwpv9kkBmD5hA1i1Wvhi6KqMH061K+vJTEVK8LJkyKJSUgikUkkZ/dejLHbJ9A3iNtn7zF242Aa96qDrX3cp3pHRaeXKd+4NNP2j2HE6v58VaUgGXKmo8DXeeg/rxvTDo5j29zdYqxNMnHTgjE4lihVuxiD/uqB3laPJEnobHTG7qOvqhSkfOPSMX5PvHr8mrsXHsRLPIKQ1IWGQqdOWrVeRdH+vHcvpBZLjSUoMUYmkRjClWi7ckzPM2Brb0uPGR1oN7Y510/e5v6VR/w5YEm8xCHrZFw8nOk2rR06vQ7P5uXwbF7O5Jyz/14i0DcoXp5nMQlc3JzwexsQ87kJLH2ONNja28a6ON2n5mDB+BZL1WpfhbINS7J32SEeXX+Cg7M9lb4tS95SuVj/6zatW9Ng/ps4JFDMlhM+fy9fQtOm2mKPsgy//qrNVBJDxBKeSGQSSd7SuWJMYvQ2OmNxNAAnVydK1CiKnYNtvMWhGLRlF078c5YG3WtGOTAzLDj69ZwSSr7SubkdTy0LcfXs7gtSZvBINoOda7arHOtrQ4JCuHrsJmEh4WQvnIXUmVKSwsOFJn3rRTo3W6HMMS4qKutkMubJEOt4BCE5uHRJG8T74AG4usKaNVCrVmJH9eUQiUw8u3/lEce3nSEsOIwcRbPydf0Sxub4D+UtmVNbY+n8/ShfBrJOpsq7qrsfy14kKzb2NoRZsCKyJXxe+TGz1wK8X/jQdkzzyM8rnAUkPmnX0q0zdzAkoZW33zx7S3IYF++aOgUVm35t9XWKorBiwgbW/7rVpPWtePXCDF7ci1QZPCJdU6JGEdJkScWrx6+jHBAc0WUpiuIJn7MtW6B1a/D3h1y5tO38+RM7qi+LGCMTTwJ8AhjRYBKdCw9g0YhVrJi4gTFNpvK/LN24dPhalNeMWN0f19QpjINrAZC0GUvZCmaOdn0apxSO1G5fxfS6eLB8/HpePHoVaX/KjB64RpFQJSRDuGJR4mRuEHR8Sg5JjKOrI7NP/ozexvqfT37rMZ+lY9dG6kI8u/cSbXL05OiWU5GukWWZYSv6orfVo9Obfi/KOhn3dO50n97e6lgEITlQVa0mTOPGWhJTtSqcOCGSmMQgEpl4oKoqIxtN5tTO84DWXWMI12ZqvH3hw9Ba47l/5VGk6zLkTMfc87/wv+FNSJ05JXaOtmTKnYGuv7RlxpEJOLs5RfvMTpO/I3fxHEhS/PXBSrLEnqUHI+0/uvk0Pq/8YnXPRr1rk6No1riGFq3E6OpJ7D5vO0dbqrQsT+rMKXFwtidNllS0Hd2cDS8Xkjar9aMK71y4z/Z5e6NNHMNDwxnzzVRO7Tof6Vih8vmYdWISFZp+bUxm7J3saNi9FrNPTiJVxpRWxyMISV1wMLRrB0OHaglN9+6wcyd4RG64FD4BUUcmHpzff5nB1cZGe1ynl6nSqgJDlvSO1+eGBoeya9F+ts3bg9f9lzi5ORIeGs6bZ96xup+sk6ndoQr953Uz2T+87kTO7LkY43iIj5WsVZQJW4chyRI/t53J/pX/xSqumNjY6gkL/XTrREUM0tbb6ggP/XRTi23tbajRtjLtx7fALXX8ddf80W8RW/7YZUy+o5MqowfL7/8RbaG90OBQAv2CcHZzilWrkCAkB8+fQ5MmcOwY6HTw++/Qo0diR/V5EksUfEIHVh9Bp9dF+yIwhCscWHOUwYt6Isvx1whma29Lg+61aND9/aiykKAQlo1dz5opm6y+n2JQcHR1jLTf6/5Lq5MYWS8zZGlvdHodu5ccSLAkBvikSQy8n2lmSRLTZWobPNK5Yedox6ZZO7iw/4pVz7Kxt2HuualIskyqjB7YO8b/4iyvnryOMYnRznvDuX8vU7Jm0SiP29rbYmsffwPRBSGpOXcOGjWCR4/A3R3WrYNq1RI7KkF0LcUDf58AVMX8iz48NJzwT/DCtXOwo9PPrbUp1LHoAjm14xwhQabTZd3TuVk9FkUJV3h88xmqqrJq0t+xiiW502aASVRrXYkK35Qhe8EsVt+jbIOSZM6bkUy505skMT6vfHnx8CVhoXEf8O2aKoVFCbYkwTNRFFH4Qm3YABUqaElM3rzaeBiRxCQNX0wi8/zeiwS7d4ac6WIcOOGWxhUbO/MF7RRF4b+NJxhaazz/y9qNLkUGsvrnjfi+tn58ypClvWjYo5bV4zkeXH3MzoX7TfbVaOsZq7EoNrZ6Xj56xeObX+7CldeO3zD+WSssZ8XFktZ99aGTO87Rp/yPNEvzPa2z9aBZmk7MG7yUAN/AWMdYvY0nSgyJOGgtUU5mxm0JwudIVWH8eGjWDAIDtWnVx49D7tyJHZkQIUknMgaDgZEjR5I9e3YcHBzImTMn48ePj9UMkq7FBjGmyVQCfOK/wFqd76uZfRHIOpkG3aKu0RLBEG5gQotfGdv0F87tu8zLR6+5d/khC0es4vuC/Xl044lVMdnY2tB7ZidWP51Pte8qYudoeZP/P/NMS9xXaVme7IWzWDVLyi2NKzm/yvbJu32SEhUVnV5HUEAwUzvMZsOMbTHWDvroBlw+fN24uXPhPn6s9xM3Ttwy7gv0DWTDjH/oX3FktMnMi0evuHHqdpQz0gAKlM1D2YYlYwzH1t6GMvWKW/EBBCF5CwqC//0PRo3Stvv2hW3bwM0tUcMSPpKkE5nJkyczZ84cZs2axbVr15g8eTJTpkxh5syZ1t9MhWNbTzOszkSLxgNYI32OtLQb0yLKY7JOJnO+jDQdUN/sPdZO3cJ/f58AMBmPoioqvq/9GNlwskU/NX/MI60bQ5f2YeNbyysBez14abJta2/LL/vGULJW1GMjotJ8UEP0Nno80rtjlwDjOpIFFYpWKcSoRpPZu+xQrFq1Irp8fF758lv3eQCRarYoBoUHVx+z+udNJvtvnL7DoKpjaJ21O73KDKN11u4MrDKaax8kQqB1gY1Y3Z8ingXMxtJqWBOcUkQeQyUIn6MnT6BSJVi9GvR6mDcPZszQ/iwkLUk6kTl69CiNGjWiXr16ZMuWjWbNmlGzZk1OnjwZq/spBoVrx29xfNuZeI4UvhvZjEELe5A+Z1rjPlt7G+p1rs6Mw+PNvgAM4QY2/v5PtD+tKwaFJ7eecXbvpdgHaEVTgIuHc6R9KVK6MHHbcBbd+J3+c7vSfXp78pTMCWgDewHj9NtGvWrTbGADQoJCGNVw8hdbot7FwwXXlM6c33c5VkmoTi8bk8c9Sw+aLRCoGBT+mbvbmKRfOXqD/hVHRKphdPm/6wzwHMXl/0z329rbMm3/WDr93Bq9rfYvtayTtTWW9Dpa/9iU1iOaWv0ZBCE5OnUKSpeG06chZUptvaTOnRM7KiE6STq3LFeuHPPmzePmzZvkyZOHCxcu8N9///Hrr79Ge01ISAghIe9fnL6+vibHZZ3MvysOaQvexbNa7atQs11lHt98SmhwGOlzpMXRxSHG657d9eKtl4/Zc3R6HZcOXY12xkhMbGxtyFYos1bPxkxOI0kStdpVifZ4ptzpyZQ7PQCNe9fh3L+X2LfqP3xf+5EuWxrqfF+NHEW0ujFLRq3h4kHrZukAyWYpgJh0m9aWA2uPIetkq2d9gZZ7NupVB4BH158gyzIGJfrWRL+3Afi+8cctdQpmdJtLeJgh0tdRMSioqsq0zn+y8OqMSN2dLX5oTIPutTi0/jgvH77CNXUKKn37dbxO9xaEpGz1aujQQasVU7CgVqk3R47EjkowJ0knMkOHDsXX15d8+fKh0+kwGAxMnDiR1q1bR3vNpEmTGDs2+pouikGJMWmIC0mSyJw3o7UXWXzvuGjStx6/dv7T7DmuqV1o0KMmAC8fv2bbn7s5uuUUYSHh5P86NxW+KUNYSBiSLFOoQj5K1ChKiRqRk6vgwBC2zdsTZel6cyRZwiO9G6+fvLXquiRHgll9/iJz3gwWJTEfJjtaSwgMWdqHbAUzA+DgbG/RM+0dbbl55i73L0cuwBhBVVQe33jKtRO3KPB1nkjHHV0cqN0h+mRWED5HigKjR8OECdp2vXqwciUkUPkxIR4l6URm7dq1rFixgpUrV1KwYEHOnz9Pv379yJAhA+3atYvymmHDhjFgwADjtq+vL5kzZzZu6/Qy6bKlSfDYrZE+Rxo80rvz5ln0L29DuIEilQvG6Tm1OlThwoEr/LvicDRxpOXnXSNwS+3KhYNX+LHeJMJCwowv2Ce3n7F32SHj+bJOpkrL8vT5o7Ox5Sk0OJTDG05wZvd5gvyCrY5Rp5NRwpPOGksRrG4lUiEkMJTggJAYW2TSZE1N1ZblObP3Iqqi8lWVQtTvVoOMudIbz6nQ9Gs2zPjHbHzFqxfBwdnB4inSz+96RZnICMKXJiAA2raFv//WtgcPhkmTtIJ3QtKXpBOZwYMHM3ToUFq2bAlA4cKFefDgAZMmTYo2kbGzs8POLvrBpYZwhdrfV02QeGNLp9PRtF895g9dHmW3j6yTyZQnPUUrF+DMngsc33qG0OBQchTNRvXvKuLkatmUWFmW+WFJL0rXLc6mmTu4c/4eSBLZC2WmUrOyFK9RBPe0rvi99Wdkg58JDQ41fXl/FJtiUNi/+gjP7nox7cBYzu27zE//m4H/24BIa+9YKjzcgGJInG4lWS+DqiJJkrbW0zuSLOGWOoXVLXmKQcHfO8BsEiPJEg2716LFD434flL0LY0Fy+WliGcBLh2+FmVCpSoqb569JcA3EGf3yGOcoiKmUgsCPHyoFbk7fx5sbWHuXGjfPrGjEqyRpBOZwMDASIW6dDpdrAZOAiBB5eblKFLJ/OyMxNB0QH3uXLjPvpX/mfwEL0kSHuncGLigO71KD+P2uXvG1bQVg8L8IcsYuqwPFb4pY9FzZFmmaqsKVG1VAVVV2TZ3D6t/3sj8IcthCOhsdHikdSMoINii2i+KQeHqsZus/3UrS0atMQ5INcSyVcXO3pY0WVPh89r3k9eeUcIVGvWqQ6BvIAfWHCUsJIw0WVLRoHstnt55zo4F/1p/T4NC6XrFObXjXKQERNbJZMiZlvpdq8d4H0mSGLvxB7qX+CHamkgPrj5maofZ/LiqHylSupitP+Ts5kSxaoWt+zCC8Jk5dgy++Qa8vCBNGti4EcqVS+yoBGsl6bWW2rdvz969e5k7dy4FCxbk3LlzdOnShY4dOzJ58mSL7hGxVkOdFC1o3qcxbUZ/a0wEkhpVVTm18zz/zNvDg6uPcXZzpGqrilRrU5Efqo3jwdVHkRMESUtOZvw3gfxlrKvQ9NewFayevCleYrd3tic0KDRWg1o/NGB+d0KDQ5nV+694ictaels9a57Ow8XdGcWgGL9XDq4/xoTm0Q8yj06ekjmZfng88wYvZfv8vYSFaHV1JEmibKOS9PuzK+5pLBtIGxoSRov0nfH3NlMLSYKlt2Zxaud5ZvZaEO1pPWZ04Js+da36LILwOVm2DDp1gtBQKFoUNm+GrAm3vq0QC5/FWkszZ85k5MiR9OjRgxcvXpAhQwa6du3KqIjqRFZYfGMmadJZvzLwpyRJEqXrFKN0nWIm+49vO8Pdiw+ivkgFJFgzeRNj/h5s9v4Gg4HHN59pyyWEG+ItiQEI9rd+PExUarb35N7Fh/Fyr9gwhBk4tO449bvWMEl4KzQujb2zvdWfM0/JHNja2dDr9+9pN7YFV47cwBBuIHeJHKTJnMrstaqqcuPUbZ7dfYGLhzN2DjbmkxgAVVtmokGPWoQGh7JwxCrCQsKMg+VtbPW0G9uSxr3rWPU5BOFzoSgwfDhE/CzcuLGW1Dhb1iMrJEFJOpFxcXFhxowZzJgxI873SojF9j6VI5tOotPL0XbXKOEKx7acwmAwRLkysaqqbJ2zm9WTN/Ly0WtAm84tSVKsqiQnBEmWyF08BzqdjtCQuK8fFFuyXo5y0LVOr2PC1qEMrjbWqkG/H3Zjurg783X9EhZdd/HQVX7rPo+H195XdLa1N7/ERYRdS/ZTv3tNmg1oQJ3vq3Jo/XFeP32LR3p3KjX7GmcxNkb4Qvn5QevWsHWrtj18uLb8QDyu5SskgiSdyAia4MCQGF+eiqJiCIs6kZn/wzLWTdtqsi++qxvHlaqoFKlUgJ/b/p6g0+NjYgg3kCqjR5THinoWZO75X5je+c9I1XGjIsmSMXEJDgzB/60/zu7OMSbVV4/dYEiNcYR/9HcUGmxZgnfz9F22z/+X+l1r4OTqRJ3vxcp2gnDvHjRsCJcvg50dLFyoLT8gJH8ikUkGshXIzKEYzkmTJRW29pHXU7p78UGkJCYpck2dgvW/bn0/0FkiURaatLGzodK3ZaM9nr1QFn4/9hPBgcE0T9/Z7BTzKq0q8Oa5N8vGzufAmqMYwg3o9Do8m5elzejmZMqdnvCwcHxe+WHvZGes/jx38DIt0YzD5182fi31ulSPc+0hQfgcHD4MTZrAq1eQLh1s2gRlLJsfISQDIpFJBmp/X5WlY9cS3ZtNkiUa9awd5bEdC/412y1lCVknkzKDO6hakbz45uBsb5xhYxwsHMNLPG221Hjdf2n+pFjoOKGVRV0v9o72zDg8gb7lfyQ4IPISDEU9C9Dih0b0LDWU4IBg49ffEG7g4NqjHNt6mopNv+bIxpME+GiLPRavXpg6napx9eiNSPez1pun3gT6BYm1kYQv3l9/QffuEBYGxYtrg3ozZUrsqIT4JBKZZCBlenf6/tGZ6V3nRiquJskShSrki3bw5pPbz+KUxEiyRNsxzWn9o7bOzsSWv3Jg7bFY3y8qQbEYKBzfSYyLuxMdf2pN/a41Ih27cfoOW2bv5MqR6+hsdJSpW5wGPWqRo0hWVj2ay5Y/drJr0QGCA4JJlyMt7cc2p1i1IvT+ehhB/sGRZnIZwhWC/ILZvfiAyf7z+69wbt/lePtMT+94kbtY9ni7nyAkJwaDVthu+nRt+9tvYfFicBS5/WdHJDLJRN3O1UmdJRWrJv3NpUPagn/u6dxo1KM23w5qEGW3EoCzu1Os1/qRdTLZC2ehSd/303Trd68V74mM1eKx2ylX8ew0H9SQik2/Rm8T+X+H9b9uZe6gpSatWg+vP2H9r1v5ukFJ2o9ryf+GN+V/w00XVLx36QHXT962KhbFoFi6WoXF9xOEL5GPD7RsCTt3atujR8OoUWJQ7+dKJDLJSKlaX1Gq1lcE+gURFhKGi4dzpIKBH6vcojz7Vx2J9risk6nU7GsMBoXz+y7j98YfAHsnO2p3rEr78S1xcNaWH/D3DuDepYfYO9oRHI8rWks6CdWaar7xlMTIOhmPdG5UaVkhyuMXDlxh7qClwEcF/lQthGNbTnNsy2ma9q9P11/amoxHeXD1caxiiq9JZBHF9gThS3PnDjRoANeugYMDLFmitcYIny+RyCRDji4OYMGq2gBl6hUnT4kc3D5/P9JP6LJOxs7Rlo4T/0f6HGlRVZWnd54bV+7+cHbN3uWHmN7lT8JCwpHk+Gk20OllUqRMwVsv73i5n7UUg8LTO16oqsqzu17cv/IIe0c7ClXIh629LRtmbEPWx7z204bp20iXPQ2Ne73v3rN3smCRx2jEdUyTJEtUaVUeFwuXKhCEz8X+/dCsGbx5AxkzaitXFy+e2FEJCS1JV/aND5ZWBvyc+b72Y0LL6Zz799K7lZUlDOEG0mRJxah1A8lbKpfZ68/sucDQ2hPipSVEkiQkWUIxKOQrk5sfV/WjT9nh+Lz0tXql7Phi52hHyActTE6ujrT4oTGrfv7b4oUvU2VKyfJ7s43T34MCgmmRvnOsxv/o9DKeLcpxbPPpKK+XdTLpsqXm+f2XqKpqMjU/YmD2zOOTSJne3epnC0JyNXcu9OoF4eFQurQ2Myl9+hgvE5IwS9/fIpH5gty5cJ+T288RHhpOnpI5KFn7qyjrznysf6WRXD16M1ZrXDm5OpKvdC5K1S5G5vwZuXvhAZIk8VWVgsYE6vKR6wytOZ7wsPA4tUREkCQJva0eSbK89kpUdDY6DGGW19uZd3Ea2QtlMW6vmLCBxaNWx+rZf5yeTPbCWZjeZS4H1h4lNCgU0JZQqNGmEl2ntePR9ScsH7+eE9vPgqolZLXaV+a7kc1wT+sWq+cKQnITHg79+8OsWdp269Ywf77WrSQkbyKReUckMnHj+9qPpqk7Wn2dJEk4uzvRflwLarSrjEMMXS2Pbjxh3bSt7F/1H8EBIXikc8PnlS+qSqRZWpZU1h2xuj+l6hSje4kf8Lr/Il4SpJj8cXoyuYvnMG4risIf/RaxedZOi+8h62SKVy/MpB0jjPtCgkK4deYuhnCFHEWzRuoyCvANJMgviBSpUmBrZ1n1X0H4HLx9C82bw9692vZPP8HQocTroHkh8Vj6/hZjuAWzggNi7hqJ6h8NVVUJ9A1kZq+/aJGhC8e3nTF7j8x5MzJgXje2+i1nt2Eta57OZ86ZKXh+WxadjdZq5OzmRMWmX1sQj4T3S18cXRyYfmgcRasUivGaKO9jxVggO0c7MuUxbceWZZmQwJAY76Oz0aHTa/8rflW1ECPWDDC9t4MdhSrkp2jlglGOe3FK4UiqjClFEiN8UW7c0Ira7d0LTk7aytXDhokk5kskBvsKZrmldcPBxd7sWBEVaDe+BbsW7sfrwUtji0lEK0iwfxBjmkzlt6MTyVsyZ6TrQ0PCuHz4GsEBIWTJn5FMeTIAkL1wVoav7McPYeGEBIXi4GzPoxtPObTO/PRvVVVJk0VbkNEjnTuTd43k0Y0n9Co9jEC/IIs/uyRJqBYMDJJkidodqhhnd0XwfunDnqWHzLcgSVClZXk80rpRsdnX5C2VK8lV43397C1b/9jFvysPE+ATSOa8GajfrSZVW1VIsivJC5+33bu1lhgfH8iSRRvUW7RoYkclJBaRyAhm2drZULdTdTb+vj3auiR29rZkzZ+Z5/deRHlc67xUWTN5E6PWDfxgv8r6aVtZ+dPfJqs6F66Un/5zu5I5b0YA9DZ6Y42XrPkzabOwzt2LdnCwa+oUlKr9lcm+THkyYO9sZ2UiAylSOuP3JsDs4po5imal40+RF225evRmzGtaqVCmbnEqtyhvcVyf0r1LDxhYeTQBvkHGv//rJ25x9dhN9q/6j7GbfsDGVrQECZ+GqsLMmdqYGEWB8uXh778hTZrEjkxITKJrSYjRdyObkTlvBmSd6bdLxAyogX/14PTOc2Z/OjeEKxzZdBKD4f2LfeGPq5j3wzKTJAbgypEb9C33I8/uekV5r54zv0fW65A/6rKRJECC3rM6mRS3Cw0OZWjtCbx55m3hJ34fc+cpbZB1UrTdQzXaejL90HhtSvxHLB0cnViztT6kqirXT97ij36L+LnN7yz8cSWPbjxhZKPJJkkMvI/39O4LrJ60KZEiFr40oaHQtSv07aslMe3bw7//iiRGEIN9BQv5ewewYsIGti/YS6Cv1qpRtHJBWo9oSrGqhfmp9QwOrjka40t5q/9y7B3tePHwJd9l7xltS4esl6neuhKDF/WM8vjVYzf4o99ibpx6Xz03U570dJ7ShmwFM7N9/l4eXn+Crb0NFw9d4+1zb6s/c422nvywuBcXDl5hdp+F3Lv00HgsY+70dJvWzri6dVRePX3D/7J0i7Fradmd2aTLlnj/GocEhfBT6984uukUOr3O+HdiSWXgFCldWPN0XpRVkQUhvrx6pdWHOXhQ+4Fl6lQYMECMh/ncWfr+Fv/6CJGEBIVw6fB1QoNCyVYoMxlypsPZzYmuv7Sl40+t8H7hi72TncnA08x5M777VyX6l3bKDO7YOWhLKexZdkibgRRNRV8lXGHfysP0+aMTdg52kY4XKJuXWScm8eDaY148fIV7GldyfpWNdb9sYfQ3U5Dl2C3LANqYlyZ969F58ncAFPUsyNzzv3Dv0kNePn6Ne1pXchfPEeNYllQZPKjUrCyHNxyPMhZZJ1OmbvFETWIAfu+xgGNbTgPE3BX2Ed/Xfjy940WWfBkTIjRB4MoVrVLvvXvg4gKrVkG9eokdlZCUiERGMFIUhVU/bWTtL5uNrS4AxaoVpv+8rqTPnhYbWxtSZ0oZ6do631dl+fj10d5bliUa9qhtfPm/fvIGWZZQzLw3w8MM+L72J3WmyIlMhKz5M5E1v7aU7aH1x5g/ZLn2WWKRxEgSpMuRll/2jSFN5lQfHZPIUSQrOYpkteqeff7oxKPrT7h36QHwbrzQu/wnU570DFjQDUVROL//Ck9vP8fZzZHSdYtH2VWVEF4+fs2epQfNjgGKycddjoIQX/75B1q1Aj8/yJFDG9RbsGBiRyUkNVYlMkFBQZw5cwYPDw8KFChgciw4OJi1a9fStm3beA1Q+HSiq3ly4cAV+pT9kT9OT44yiQFIlTEl3ae3Z3afhVqC8lG12dzFc9Ck3/sfo9zTusXYDaXTyzi7O1kc/6pJGy2uM/MxtzSuNB/UkAY9apkszRBXKTxc+O3oRPYsPcj2BXt5/eQNHundqd2xKrXaV+baidtM6zSHFw/er+Zt52BLq2FN+N+PTRJ8BtOJf85aNDMrOqkzpyR9DjFIQYhfqgrTpsEPP2h/9vSE9eshVaqYrxW+PBb/KHXz5k3y589PpUqVKFy4MJ6enjx79sx43MfHhw4dOiRIkELCe3j9SbSF2xSDgu8bP1b/vNHsPRr3qsPYTT+Qu8T7KdYu7k60+KERU/eNNkkQqn1X0WyriayTqdD06xgL6UXwfunD7XP3YpXEgFbM7ttBDeM1iYlg72hHg241mXN6CmufLeDPs1Np3KsOdy8+ZHidibx89Mrk/JCgUBaPWs2SUWviPZaPhQaFxilZ+nZgQ4uqQwuCpUJCoGNHGDxYS2I6d9amW4skRoiOxYnMkCFDKFSoEC9evODGjRu4uLhQvnx5Hj58GPPFQpIRGhJGUEBwpK6EPUsOmO0iUMIVdi0+YDLrKCrlGpZi1olJrPNawIoHc1j7fAEdJ/4vUkKSIWc6GvaoZexm+ZCsk7G1t6HNKMuXrA0LCbf43I+VbVgy2pamhPTX8BWR1kr60OrJm3j7widBY8hRNKvFyV9EwhNRvK9u5+o06lU7wWITvjwvXkC1arB4Mcgy/PabtoaSrW1iRyYkZRZ3LR09epS9e/eSKlUqUqVKxdatW+nRowcVK1Zk//79ODlZ3gUgfHqnd19gzeRNnN9/GYD0OdPSpE89GnSviU6v49XTNzHeIyQwhCC/YJzdYv67dkvtGuM5PX7rgJOrIxumbzNZEylL/oz8sLiXceyLJTzSueGaOgU+L30tviZCz9/i1pL47K4X/8zbw+3z97FzsKVco1JUblEuykHKEV4+fs2lQ9fM3lcxKBxad4xGPRMuWShauSAZcqXj+b0XUQ9I1suUbVCSMnWLs3/Vf/i9DSBrgUzU7VydwhXzJ7nifULydfGiNqj34UNwdYW1a6FmzcSOSkgOLE5kgoKC0Ovfny5JEnPmzKFXr154enqycuXKBAlQiLutc3bxe88FJi0uz+++YHa/hZzbf4lR6wbinsY1xqmMtvY2ODhb1tVjCZ1OR8eJ/6PFD404vfsiQf7BZC2QiXylra9uq9PraNSjNsvGr7Oqe8nWwYY0WVJbG7rRplk7+KPfIiRJW9FbkiWObj7FktFrmLJ3NJlyR738riUJl6yX8U7gFhlJkhi+sh+DqowmLCTMZE0qWSeTKoMHvWd1ImV6d+p8Xy1BYxG+XJs3a4s9BgRArlywdSvky5fYUQnJhcVdS/ny5eP06dOR9s+aNYtGjRrRsGHDeA1MiB/P7nkxs/dfgOlMHlVVQYWjm06xe/EBqrfxNLuwok4vU611xQQpSe/k6oTnt2Wp3aEK+cvkNklint9/wYl/znB+/2VCQ8yvZN1iSKP3rQQW5EGyTqJWuyqxblU4tes8s/ssRFVU49c2Iol6/fQtQ2tpK3pHJWUG9xhjNIQbSJ054QcG5C2Zkz9OT6b6d5XQ22o/rDi5OtK0Xz1mn/qZlOndEzwG4cukqjBpEjRurCUx1arBiRMiiRGsY3Ei880337Bq1aooj82aNYtWrVrFaQqnkDC2z//X7ItakiU2ztxOjiJZqd6mUpTnyjoZe2d7Wg1rkpChmnh2z4thtSfQJkdPRjT4mcHVxtIyQ2fWTNkc7feZrb0tk3aOoNuv7ciYMx3wbuHHaMbh2DvZ8+2g2Cfga6dsjnZckWJQ8Lr/kiObTkV53D2tG6XrFDM7LsnGzgbPb2NeJDM+ZM6bkUELe7LNfzmbvJfw9+tFdJna1qIuQkGIjeBgaNMGhg/Xtnv1gh07wMMjceMSkh9R2fcz92P9SZzcftbsObJOZlfYGsLDwlkwdAWbZ+8kPPR9S0KuYtn5YUkvshfKktDhAvDqyWt6lByCzyu/KMdtfDuwAV2mxjzNX1VVggND+LXTHA6sPYqE1pWiKCqZ8qTnx9X9yfVV9ljFGBYaRl37yOsrfUinl6n+nSeDFvaI8vi9yw/pU3Y4ocFhUX7Onr91pHHvOrGKTxCSsmfPtFaYkydBp4NZs6Bbt8SOSkhqRGVfAQA7R9sYa6vYvOtO0Nvo6TatHf/7sQln91wkJCiU7IWzkKdE5BWr41tYaNi7WGxY+dNGfF5HncQArJu2lfrdapLhXatLdCRJwsHJnh9X9ef7Sa05teMcocFh5CqWnSKeBeI0UNVcN1wEVSXariWA7IWyMOO/CfzeYz5Xj9007k+ZwZ0OE1pRq32VWMcnCEnV2bPQqBE8fgzu7lp9mKpVEzsqITkTicxnrlzDUhxefzza4zq9TIUmZUz2pfBw+WSrMR/++wTrpm3h2rsXea5i2bl/+SGKmURB1snsXnKA9uNaWvycdNnS0KB7rTjHG8HOwZZMedLz+NazaFdlUBWVPCXNJ4E5i2bjtyMTeXTjCU/veOHs5kS+MrlEbRbhs7RuHbRrB0FB2jiYrVu1wb2CEBeitvhnrlKzr0mbLbWx9ocJSftPswENPnVYACwetZpxzX7hxolbxn13LtwnPMx8rRpJ0qYvJyZJkvimT/QLvkgS2NjbUKOtp0X3y5w3I2XqFqdgubwiiRE+O6oKY8dC8+ZaElO7Nhw/LpIYIX6IROYzZ2tvy9S9o0mbVZtirNPLyDoZSZKwtbdl1LqB5CoWu3EicXHtxC1WTNgAYLJUgaVTp5PCINR6XatTqak2GFeS33dTaV9jHSNW9zdZWFMQvkSBgdCyJYwZo2337w/btmm1YgQhPoiupS9A+hxp+evqDI5tPcPJf84QFhZOnhI5qdHWM9FetFvn7EKnly0aa/IxQ7hC9e8qJkBU1tHpdAxf1Y/SdQ+xedYO7l1+hI2dngrflKFp//rkLJotsUMUhET15Ik2HubMGbCxgTlz4PvvEzsq4XMTq1lLy5Yt488//+TevXscO3aMrFmzMmPGDLJnz06jRo0SIs5Y+9JnLSVV3xfqz8Orj62+TpIkqrWuyJClvRMgKkEQ4svJk9rMpGfPtHWS/v4bKib+zx9CMmLp+9vqrqU5c+YwYMAA6tati7e3t3HtHTc3N2bMmBHrgIUvi51DzIunRBRnM27b6Gjcuw4D/+qeUGEJghAPVq3SVqx+9gwKFdKSGpHECAnF6q6lmTNnMn/+fBo3bszPP/9s3F+yZEkGDRoUr8EJn68K35Qxu1q1rJNp2q8e5RqX5v7lR9g72lKy1lekSOnyiSMVBMFSigKjRsHEidp2gwawYgW4iP9thQRkdSJz7949ihUrFmm/nZ0dAQEB8RKU8Pmr06kaa6duJsg/CMVgmszIsoSNnQ0NutcibdbUFPg6TyJFKQiCpfz9oW1b2LhR2x4yREtoxCQ8IaFZ3bWUPXt2zp8/H2n/zp07yZ8/f3zEJHwB3NO4MnnPKJzdtMHGsk6bTYUEDi4O/LR9uHGmlSAISdvDh1ChgpbE2NrC0qXw888iiRE+DatbZAYMGEDPnj0JDg5GVVVOnjzJqlWrmDRpEgsWLEiIGIXPVN6SOVnxYA4HVh/h/IHLoEKhCvmp1roCDs4OiR2eIAgWOHoUvvkGXryANGlg0yYoWzaxoxK+JLGatbRixQrGjBnDnTt3AMiQIQNjx47l+yQ4r07MWhIEQUgYS5dC584QGgpffQWbN0OWT7Mkm/AFSJC1lsLDw1m5ciW1atWidevWBAYG4u/vT5o0aeIcsCAIgpA8GAzaqtVTpmjbTZpoSY2TU+LGJXyZrBojo9fr6datG8HBwQA4OjqKJEYQBOEL4uur1YeJSGJGjNDWUBJJjJBYrB7sW7p0ac6dO5cQsQiCIAhJ2N27UK6ctsSAvb1WL2b8eJDFYjdCIrJ6sG+PHj0YOHAgjx8/pkSJEjh9lIYXKVIk3oITBEEQkoaDB6FpU3j9GtKn18bDlCqV2FEJQiwG+8pRpN6SJKGqKpIkGSv9JhVisK8gCELczJ8PPXpAeDiULKnNTMqYMbGjEj53CTLYF7SCeJ/SkydPGDJkCDt27CAwMJBcuXKxaNEiSpYs+UnjEARB+NKEh8PAgfD779p2ixawcCE4OiZuXILwIasTmaxZsyZEHFF6+/Yt5cuXp0qVKuzYsYPUqVNz69Yt3N3dP1kMgiAIXyJvby1x2b1b2x43ThvYK0mJGpYgRGJ1IrN06VKzx9u2bRvrYD42efJkMmfOzKJFi4z7smfPHm/3FwRBECK7dUtbJ+nGDXBw0KZWN2uW2FEJQtSsHiPzcWtIWFgYgYGB2Nra4ujoyJs3b+ItuAIFClCrVi0eP37MwYMHyZgxIz169KBz584W30OMkREEQbDcv//Ct9/C27eQKRNs2QJRLK8nCAnO0ve31ZPm3r59a/LL39+fGzduUKFCBVatWhWnoD929+5d5syZQ+7cudm1axfdu3enT58+LFmyJNprQkJC8PX1NfklCIIgxOyPP6BWLS2JKVMGTp0SSYyQ9MVqiYKonD59mu+++47r16/Hx+0AsLW1pWTJkhw9etS4r0+fPpw6dYpjx45Fec2YMWMYO3ZspP2iRUYQBCFqYWHQty/MmaNtf/edNlPJ3j5x4xK+bAnWIhMdvV7P06dP4+t2AKRPn54CBQqY7MufPz8PHz6M9pphw4bh4+Nj/PXo0aN4jUkQBOFz8uYN1K6tJTGSBJMmaWNiRBIjJBdWD/bdsmWLybaqqjx79oxZs2ZRvnz5eAsMoHz58ty4ccNk382bN83OnLKzs8POzi5e4xAEQfgcXbumDeq9c0dbYmDFCmjUKLGjEgTrWJ3ING7c2GRbkiRSp05N1apVmTZtWnzFBUD//v0pV64cP/30E82bN+fkyZPMmzePefPmxetzBEEQvjQ7d2rTq319IWtWbVCvKMwuJEfxNkYmoWzbto1hw4Zx69YtsmfPzoABA8SsJUEQhFhSVfjtN63QnaJAhQrw99+QOnViRyYIphJsjMy4ceMIDAyMtD8oKIhx48ZZe7sY1a9fn0uXLhEcHMy1a9esSmIEQRCE90JDoUsX6N9fS2I6dIC9e0USIyRvVrfI6HQ6nj17Rpo0aUz2v379mjRp0oi1lgRBEJKgV6+0RR8PHdJWq/7lF+jXT1TqFZKuBFtrKWJxyI9duHABDw8Pa28nCIIgJLDLl7VBvffvQ4oUsHo11KmT2FEJQvywOJFxd3dHkiQkSSJPnjwmyYzBYMDf359u3bolSJCCIAhC7GzdCv/7H/j7Q86c2qDej6paCEKyZnEiM2PGDFRVpWPHjowdOxZXV1fjMVtbW7Jly0bZsmUTJEhBEATBOqoKU6fC0KHanytXhvXrIWXKxI5MEOKXxYlMu3btAG3RxnLlymFjY5NgQQmCIAixFxysDepdtkzb7toVZs4E8c+28DmyeoyMp6en8c/BwcGEhoaaHBcDagVBEBKPlxd88w0cOwY6HcyYAT17ikG9wufL6kQmMDCQH374gbVr1/L69etIx5ParCVBEIQvxfnz0LAhPHoErq6wbh3UqJHYUQlCwrK6jszgwYPZt28fc+bMwc7OjgULFjB27FgyZMjA0qVLEyJGQRAEIQZ//w3ly2tJTJ48cOKESGKEL4PViczWrVv5448/aNq0KXq9nooVKzJixAh++uknVqxYkRAxCoIgCNFQVZg4UasRExioJS/Hj0PevIkdmSB8GlYnMm/evCFHjhyANh7mzZs3AFSoUIFDhw7Fb3SCIAhCtIKCoHVrGDFC2+7dG7ZvB3f3xI1LED4lqxOZHDlycO/ePQDy5cvH2rVrAa2lxs3NLV6DEwRBEKL29Cl4esKqVaDXw59/wu+/a38WhC+J1d/yHTp04MKFC3h6ejJ06FAaNGjArFmzCAsL49dff02IGAVBEIQPnD4NjRppyYyHB2zYoNWJEaKmGp6hBiyCoI2g+oKcFsmxJTi2QZJdEjs8IY7ivPr1gwcPOHPmDLly5aJIElwDXqy1JAjC52TtWmjfXutWyp9fZcuGi+TMfBhQwaYU2JaJchmZL5Uadgv1zf9A9Qc+nFUrgy4bUspVSLLoi0uKEmytpQ8FBweTNWtWsmbNGpfbCIIgCDFQFBg7FsaN07br1gli+az2uDqeQ/XXvTvLALrc4P4Hkl78u6yqKqpPvyiSGAAFDA9QfX9CcpuaCNEJ8cXqMTIGg4Hx48eTMWNGnJ2duXv3LgAjR47kr7/+ivcABUEQvnQBAdC8+fskZsCAUDb9VRdXx4vvzjBgfFEb7qK+aY2q+CRGqElL2DkIv0XkJCaCAYL/QVXefMqohHhmdSIzceJEFi9ezJQpU7C1tTXuL1SoEAsWLIjX4ARBEL50jx5BxYraOBgbG1i4EH4Ztwqd9JSoX9AGUF5C0PpPHWrSE3YZiKmbLRzCbn2KaIQEYnUis3TpUubNm0fr1q3R6XTG/UWLFuX69evxGpwgCMKX7MQJKFUKzp2DVKlg3z7o0AHUoC2AueGNKmrQ5k8VZtIlWbi4lKXnCUmS1YnMkydPyJUrV6T9iqIQFhYWL0EJgiB86Vas0KZXe3lB4cJw6hRUqPDuoGpBt5Him6DxJQu2FWM+R3IFm0IJH4uQYKxOZAoUKMDhw4cj7V+/fj3FihWLl6AEQRC+VIoCw4bBd99BSIi2dtKRI5At2wcn6XIAumjuACCDPnvCBpoMSPpMYFcbc686yakjkmQb7XEh6bN61tKoUaNo164dT548QVEU/v77b27cuMHSpUvZtm1bQsQoCILwRfDz0xKYLVu07aFDteUH5I/ew5JjK9TQA2bupGh1UgQk14mo3m8g9ARa8md4/7tDC3DqmrgBCnEWqzoyhw8fZty4cVy4cAF/f3+KFy/OqFGjqFmzZkLEGCeijowgCMnB/fta68ulS2BnBwsWaElNVFRVQfXuDyE7iTxWRgK7akhus5AkqxvdP0uqqkDoMW1skfIG9BmRHJohiS6lJM3S97fFiczdu3fJnj17siu0JBIZQRCSuv/+gyZN4OVLSJsWNm2Cr782f42qhkPAfNTAJdrLGUByQ3JqB05dkSSxVoGQvFn6/rY4Xc+dOzcvX740brdo0QIvL6+4RSkIgvCFW7QIqlbVkphixbRBvTElMQCSpEdy7o6U+jBSqu1IKf9BSvMfknNPkcQIXxSLE5mPG262b99OQEBAvAckCILwJTAYYOBA6NgRwsKgaVM4fBgyZ7buPpJkg6TPhWSTWwxaFb5IogNVEAThE/Px0cbDRKyzO2qUtoaSk1PixiUIyZHF7Y+SJEUaH5PcxssIgiAktjt3oEEDuHYN7O1h8WJo0SKxoxKE5MviREZVVdq3b4+dnR2gLRjZrVs3nD76EeLvv/+O3wgFQRA+EwcOaF1Ib95AhgyweTOULJnYUQmWUg3PtYHVclokXcrEDkd4x+JEpl27dibb30U3L1AQBEGIZN486NkTwsO1ZQc2bdKSGcF6qqoA4bEeE6SqwRC8HTX0Akgykm0FsKuMJEVdZFANPYPqNw3CTr/bI6HaeiK5DEayyR27DxEHqhIIwZtQA9eD8gp06ZEcmoNDgy9ynFSs6sgkJ2L6tSAIiSk8HAYMgJkzte2WLbWFHx0cEjeu5EgNu4rqPw9CdgPhIKdDcmwNTu2QJHvL7hF6CvVtj3fLPET8LB8OuixI7guQ9NlMzw85gvq2M6C8+xVBB5IdksdqJJt8cf5sllKVN6ivW4PhbsQetIUxVdAXQfJYjCQ7f7J4ElK8T78WBEEQrPP2LdSt+z6JmTABVq4USUxsqCGHUV83g5BdQLi2U3mO6v8r6pu2qGpQzPcIf4j65ntQ/d7tCX9/L8MT1DdtUJX3s3FVVUH1GY5WDVj56G4GUINRfUahhhxFDfkPNaKeTwJSvYeC4T5aAhPRDvHu9/DLqL4TEjyGpEYkMoIgCAng5k2tHsyePeDoCBs2wI8/gpgjYT1VDUL17oeWUBg+PgphF1H958R8n8BlQBiRkxK0+ypeELz1/a7QY6A8I/qVxhUIP4/6tj3q246oLyqgeA9BVfyiOT9u1PCHEHqAyF+DD+IJ3vJJEqqkRCQygiAI8WzvXihTRktmMmfWFn1s0iSxo0rGgne8a0Uxk1AErkJVw2K4z3aiTwIAJNTgne83DQ+ti5NwLZF400YbhxNHquE5it80lJe1UF5URvXua1kMoRfi/OzkRCQygiAI8URVYfZsqF0bvL2hbFmtUu9XXyV2ZMmbGnadGOemqD6gvIjhnJi6n1RQPyj0KrlYEt5HDBB+DYI2xOLaDyIJPYP6qhYEzAfDPVCeQvhVyy7+wpr9RCIjCIIQD8LCoEcP6NVLq9rbpg3s26etnSTEkWRj4Yl25g/r82D+tacDff4Pblc55ntGQw1cHavrAFQlAPVtF1BDMO0Gs2Rujg3YFI31s5MjkcgIgiDE0evXUKsW/Pmn9sPw5MmwZIlW8E6IO8muKsZBuVGSQV8ASZfK/H0cWxP1+JgIBiTHlu/Pl52RnLtaE+o7KhiexeK6d4K3vutKMxdrVGRwaIIku8f+2cmQWFlMEAQhDq5d0yr13rkDzs7arKQGDay7hxr+EDVwOQTvAULBphCS43dgW0FUUAewKQ42xSDsIlGPcVGQnLvHfB/7etrXOGQXpq0b76YvO/VEsilgeo1TT1BDtS4eFECH+aTqHTn2BfPU0NNo7QwxJTIR57z73aY4ksuwWD83uRKJjCAIQizt2KHVhfH1hWzZYMsWKFzYunuoIUdR33ZFezm+e0mHHEIN2Q+O7cFl2BeZzKjhD1ADV0DIfsAA+kKgy6KNF0GH9gLXEhDJZSiSfa0Y7ylJMrhNh8DlqIGLwfBEO6DPi+TUBcmhfhTXSEguA1Ad22lF9JQ3WhG6oDXmnoTk0NTqz/zxPWJkU/xdQbwMSA7fgn1NJIu74SJT1RAIPQcEgz4Pki55VGwUiYwgCIKVVBVmzIBBg0BRoGJFbXp16tRW3kfxRfXuAYRi2kLwLqEJXKyNd3CoFy9xJxdq8H5U755oX5N3XwvDM+3PDs20/Wog6HIgOX5r1QtXknTg1A4c24L6FtAhya4xX6dLCU5ttNRJDUMNuwbhV4jcQqQDOS04xmEBLX1+YIuZE2SwKYyccmXsn/EBVVUgYB5qwAJQfd/tlVBtKyG5jk3yCY0YIyMIgmCF0FDo1Emr1qso8P332nRra5MYAII2vZtJE90gTllrOfhCqKqKavBC9e5N5Jox7/4ctB7JvjGy22/ILn1j/ZKVJAlJ9rAoiYl8rQ2Sx0Kwq0qklhM5NTg0Mp39ZAU1eC/4T4vhLAXJ6ftY3T/KZ/r9hOr/6wdJDIAKof+hvv4W1RDDbLBEJhIZQRAEC718CdWra0sMyDJMnw7z54NtLJe3UcPOxHCGAmEXUFVztU+SN9XwGsXvFxSvr1G98qG+qolWtC665E4Xb8mdaniF4vcbysuqKF4lUV41QQ1ch6qGxnitJKdAdp+NlGov2H+DsYNDeQUBc1FfVkHxGY2qWjCeJiKe8Nuo3n0wX+sGcOqOZF/b4vuaf+YdCFwazVEDKG+0lpokTHQtCYIgWODSJW0Q74MHkCIFrFmj1YuJGxnjQNNoSVg0XiIZUg1PUF+30F7+EQNbY6z1YoDQE+bvqxrA8Fi7py5TlONG1PDb2ppFqs/7Z4dfRfX9EYK2gMcCJMmCqdfhVyF444c73v8xaDUqOiTXUTHfB7QB3yZLD0TBoRmyS3+L7mfRM4P+RhtzFF3yZICgdaguQ7UxRkmQSGQEQRBisGULtG4N/v6QMyds3Qr588d8XUwk2zKowf+YOUMGm5JJ9gUSV6r3UFBeY/0046gTO1VVIHAJasBCbbkBAMkDnNqAUxdjQqOqKurbnu+6Uj589rs/h51C9f8dyWWw+fhVFdVvOtEnoyoErUCRXbUqwZItkl1lsKuGJEXx+g3eR4ytMWE3zB+3lsGLGOvTqAHamCQpaS5G+Xn+3yEIghAPVFWrCdO4sZbEVK0KJ07ETxIDgH0DkNyI/p/i+B0LkZSo4Xch7AQxvrgj0YHt15Hvp6qoPj+i+k16n8QAqG9Q/X9H9e79vosu9Ni72U9m1iwKXBnzMgOGO+9WoTaXCKgQ8AcE/wNBm7Q4XtVBDX8U1Q3NPw/Qut1iT1W83y1yeRxV8QfZg5hTATuQku5Kp8kqkfn555+RJIl+/foldiiCIHzmgoOhXTsYOlRLaLp1g507IWXsy4NEIslOSB4LQHLC9J9jnXbcuT+SfZX4e2BSEmZhuf1IDEhO7SPvDj0GwdEtC6BCyD5tzSaAsPNEfI2jpQZA+F3z5yj+5o9/+HwU3s/AeqwtNPnxWBybr2KISwf6YqiqJRV+P4pA8UfxGYH6ovy7RS7bor4o965FzNw4Hh04NNJmeyVRySaROXXqFHPnzqVIkSKJHYogCJ+558+hShVYtgx0Opg1C+bMAZvYl+iIlmRTBCn1HiTnfmBTRCujb/8NUsq/LSvyllxZXe/kXXLn8iOSbalIR7UlAcy9bOUPlg2QsaTcvxq0GdX/T631IqrkQZeJ2L1GDWB49K4A4nuSY1vMt8oYIHgdqlc+lFf1UQPXWjQQXFVDUd92hKD1mLboBEPwNm2mVZR0IDkiOXWJ8RmJKVkkMv7+/rRu3Zr58+fj7v5llV4WBOHTOncOSpeG48fBzU1rhenZM2GfKckeSM7dkFOuR061DdntJySbQgn70MRmWwaIKZmRQZcD5DQgpQI5E2rwXtSgjZFbMwx3MJ8EKO+6gQC78lg0LidwMar/b1rrxavaqOG3TQ5LulTvpmDHprVCQg3513SPXRkk597vtnQm52pktNYTFcJvofqOQPXuH3MyE7TpXStUVJ9ZBeUl2FYi0rBZfU4kjxVI+iwWfaLEkiwSmZ49e1KvXj2qV68e47khISH4+vqa/BIEQbDEhg1QoQI8egR588LJk9p0ayHuVFXVZgqFXkBV3iDJbuDQkuhnZEng8D/QpdFWtVZfgvJYG4jrMwT1dXNUxeeD01OYuVfEOdpq1pJNYW3JgxgTkA8L8j1Eff2/SDVVJJeh755tbTKjQshJbZzKh/dz7o3k/hfYln03LsXxg6NRLCAZsguC1pl/UuAazH9tdCDpkNL8h+Q6FSnFeCSPdUgptyLZ5LPiMyWOJJ/IrF69mrNnzzJp0iSLzp80aRKurq7GX5kzZ07gCAVBSO5UFcaPh2bNIDAQatbUWmRy507syD4PavBOrUXjVV3UN9+iviiH8ra3NpvILiJT1Jn+blcLCIPQk+/2K6a/h99A9Xm/rlBUywuYkpEcGr4/320m6LIaj5n+HhUDqL7vpkhrY07UgGWoPkO1AbNyuo+ut2DFUPUl6tsu2myrD0h2FZE9FiKnvfCue9F8gqYGRFcH5h3lKea70rTp6pLsgeTQCMmxBZJt0WSzNEaSTmQePXpE3759WbFiBfYWLiM7bNgwfHx8jL8ePYpqZLggCIImKAhatYJR70p99OkD//yjdSsJcacGrtaKvBnuf7BXgZC98KYVuAxH8lgJDk3A1hMcmiJ5rIYUYyHob6LvAjJAyF7U8Mfapn0jkNMTdcuIDiTXdy1AGkmXBinVZiTXyWBTFnQFQE6F+aRB0WYehd9FfVUL1W8ChJ3RurWU59px+yaQ6iBSiqGWfHUg7DSEHo7+jLArxDgrynDbfOG9GBewlN999uQpSdeROXPmDC9evKB48eLGfQaDgUOHDjFr1ixCQkLQ6Uy/ae3s7LCzs6CIkSAIX7wnT7Sp1adPg14Ps2dDl6Q9rjHRqWFX3s/6sS1ndvyEqvii+k6M2ProqAEUb/CfgeQ2Fcm2pOnhkIOoFkw1VoM2I7n0RJKdwWM5qnc3CL/J+9dbOOgyIrnN0dZL+lDYddTgfyHsuBYPtlHE+RHFB/VtZ1DefHTuuy6o4L+R7EqDQ3MIOaTNljJLhxq0FcnOM+rDki1am4O5cTA6zLVLSA5NUP0mE/1nU5AcvokhzqQrSScy1apV49KlSyb7OnToQL58+RgyZEikJEYQBMFSp05Bo0bw7Jk2pXrDBvCM5l0igBr+ENW7P4Rf4n0BOAnVrjqS689Iskvki4L/QVsQMzoGCP4HVRmtJSIfPk/xsyywkH/BRRuNLekzQcqtEHoSNfQ4oCDZlgDbCto9Q97Vj5GctQGyvsPefZaIJCGmpQkkrRvJYK6lX0L1X4Bk/w2S2yxUr0KYH1hseJcUvaeqoRDyn7ZfTk+MSYxdJfNFEx2+hcAVYHgaxb10oM8N9nXNPCNpS9KJjIuLC4UKmY7cd3JyImXKlJH2C4IgWGr1aujQQasVU6CAVqk3R47EjirpUpU3qG9affDCVd//HrIP9e334LEyUrVaNfwhWmuBuTol4VoBu48SGUKi724xvfwqqhpiXE5AkiSwK4NkV+Z9HKEnUX2GgOGJZfeMlgox1lPRunpQ3/6/vfOOk6o6//Bz7tSdbbCLKCj23gtC7BqNqLFgrwGJYkONwRjLL4IaFTV2rNhDidhRY8cWK9ZEjb3FRt1ep9z398eZbezULczM8j6fz7h77z3n3HfujpzvnPMWjFOBOMPATXVfTzyMO967aS5Sf3W8dEIbbSIlkSByMcUnprTIOKVQMRup+RNEFnS96N8ZM+gqjOlhwbA8IK+FjKIoSl/iujB1Klx6qT3+7W9hzhxbO0lJjjTOTFFKIGa3mlpfguBvulwxTjmSSZizKY+vQrwE0e8QfPHVnExwbfK6JHWRJPIRUjWB7DMIdzMSzGBbaiATRBBpheCe0DST5Ns6MUzRobZL0/1IXaK6TJ2foRM/tnW6TNllCfPqdLPesxqmchYS+QIi79v34x+N8a6d2fvJYwpOyLz88su5NkFRlAKksRHGjYNHHrHH55wD06bZhHdKGlI63QI41ldlOSFDcD9ouC5lP3zbQ/RjpOZckGrsCo5LJgnrADChePhzd9zYYqg+jd6m9QcD3s0g+nFmzZ3hSOM90DwHJE3236LDwbclIi1I/VVpxl0FfCOBMMa3GRQdhvGslplNcYxvQ/BtmFWffKfghIyiKEq2/O9/1h/mww/B74cZM2z5ASVDpCZNAzdewborxrsmUnQoND9Md2ESjw4KjkGqT+l0PcuVE1Nu+wd2haKD23113Nq/QPMD2Y2VFImLmHSVytts8kHTnaQUf6YcU/x7KD4ZYwzS8mp60eMuwZRMTJgsUUSs/1LkM7s65d+5u3PzAEWFjKIoA5o334SDD4ZFi2DoUHj0Udhxx1xbVWA4w+JFFpNN4l39PDpjyi62W0XNc+P94z4zZhCmfBrSdG+8Zfb1gwBwf4HwQiT8L2iYDhV3I83/7EMR05kMbPRuEXeITkHpRZjQYV39UtxlmVkQ+ahdyFjx8jESfg+aZi237eVBig7HlP2lV/4vEv4AabwPwm+BMeDfARMaj/Fv1eMx+xoVMoqiDFhmzoQTT4RwGLbaCubNg7XWSt9P6YoJHYnUX5GiRQxTdHjivsaHKb8YKTnV1haSBvCuC4E9wK23E2SviQsMqUeWTQAa+2DMHuAMtdkV2/1YEuGByPsYc8xyfVfN7B7hBRA6Ggm/g9ROtY7FCYlB81zErYJB03uU3E4aZyL1f7U2E7OPueVppOWfUHYRJnR01mP2B3mdEE9RFKUnxGK2avW4cVbEjB0Lr72mIqaniG9LUiaKC+wH/lEpxzCe1TDFv8OUnIoJjrGrBJJNCZnUuVIsLlBH7x17e4KDCR0D7vekDbeOftv9dGBnMpqSI/9Fwu8hVeM7akclRaD1OYj8J/24y/eMfBwXMdD1ecYAQeouQiKfZj1uf6BCRlGUAUV9vd1KuvJKe/x//2dzxJSUpO6nJEakOe4wm2JbJbh/0m/8Evkv0vwI0vxU19pIEK+6nG5jwEBgHwgdBcUTrXNvXxLYk7Q1mtLSljn4yKSOxx044JR3O2uM3+aoyQCpm4YVSxlEhOFBWuZlNG6XezTOIn018dlZj9sfqJBRFGXA8N131v/liScgEIDZs22otaP/0vWc5ifj0UTJhIwD7X4uHUjkS9ylByPLxiK15yG1ZyGLd8Ktu6I9nb5xSiD4W1JPmD5M+aWY0r9g/KNAehuB1HnokZhBN4A/G6eptsR/XtpFmGd1TOVs61wbPIB0ZQ6S1oUK/IbU07IHfJtD9D9kJmLs/Yj9iDTMwK27HGm8G4l1d8zuRuQdUq9sxTrVwcot6iOjKMqA4F//gkMOgaVLYbXVrD/MqNS7HUoGSPhNUvt8uLYitbjt2WUl+j+k6iiQpuXahqHpHlv9epANNTalk5Hwa7ZcQZeJ00YImbL/g8j7SO2FNnFeWjKMLAKIfITUnA3hNzJrD0C9XXXxrGFXh7xbQMkkjKcMiXxkq1GndIoekTSLrgkdizTPTXFvsaKr5Yks7BVofRlpfRXwIMSg/m9QcjoUn5bCdyYT9Z8fuQv0e4qiKAXPXXfBnntaEbPddrb8gIqYviITfxOhs9CRxhlxEZOor0DLY0jkcwCMZxim4sH4Fk+nKcmzFqb8evCsZcOr3cUZ2mtLJ2RGaxrhkewWdRD9r00s13wPLNkFt/5GZNlxEPs+eT/fZpiKmRiTuAiy8W2IKb8c+xw6iwTrH2TKr+xFtJCLzafjAjGk4QZk6QG4S/bDXXaMTcYnzR3NA7uRWqjY0gj5gK7IKIpSsMRiNrHddfGca4cfDvfeC6E+dqMYaLSF7bbnHAnsjEngnyGxZdhpInUyPLybtpcnEIlC8zzS1QeS5scwvnMBWyPJDL7J3i/2gy1X4FkPYwzu0rFt1mTzDrNom8UKTjfankszNN5E6pUroPxajCd1dJIpOgS8WyBNszpWivw7YYqPw3jXB0C8m0D089T3yoTYF/GfBom8C413Q8VMjGdVuzrUNIfEz8cABhM6tnf37yNUyCiKUpDU1sJRR8Ezz9jjiy+GCy+0qS6U5Ej0K7udEu0cceJFio7AlF2AMX5Ewkjd5fHcL+lWZFxM8fGdbtAEtKY3xF3S7ZTxVCJOma3f1HAzEltsVz76lZ6KmESkEhYeTMsTUGILXIpbCy0v2PpVnmEQ3Kt9pcb4NsCUX5x0JFN6HlI9gd6JsM7Ex4j9gNSciamci/GuA4OuR2rOoqtjsV0tMoNuTFn5fEWiQkZRlILjq6/gwAPh00+hqAjuu8+uxiipkeiPyLKjbG2iLkSh+X7EXYYZPB2pPS9e6yjVJBnPLVJ0XNzBNX4PMYCftJWkE6TWl9gSpPp4iH7ZMf6Awdj357bYlY/GW7DPKP4+60qg9C+Y0CFJRxC3EcKvglsHJZOhaQ64P/ehjTGIfIBEPsb4NscE94ZVXkCa7o+vDhnw74gJHQXOELsd1TQLot+BCULwt5ji460IWoEYEelLOZp31NXVUV5eTm1tLWVaGU5RCp4XX4TDDoPqalh9dXj8cdh221xbVRi4tRelX2XxjepeIbkbfvCPhqJDIbYkHuES95NpfQNoTtMfqHwKx7d++6GIIMsOi6/ADCQB05n0As8MugET3LfLORGBxtuRhlvp8myd1cG3JbQ+3Yc2OpjSP6WsqC0SRqpPhvDrdF0V8gA+TMVdGRWyTEem87euyCiKUjDceiuccYb1jRk1Ch57DIYNy7VVhYGIQPOjpBUJaUUMgAvFJ0LNpE6rO1l+J64+ESn+PYSOs9FOkffSp/YveNKsUgFS/zcIjMEYBxEXwv+yeWMSJb9zf4HWhTaKShrpMwGYbn2j8c5OkV6d28aT5VWfDkP/1avSCNmgUUuKouQ9kQicfjqcdpoVMcceCy+/rCImO1rJaKUkI6JQfUrcH0bokZ+G+zNSfymydG/clvlIy4vod2sg9iNEP7GrHjWnIdUTU2TwjVcJ94zolGSvzUnMoXv0Uya44N+u/UjcaqRxJm7935DGu3CjPyJNM0n+N3dt3qGWZ7O8b8/RT42iKHlNdbX1f5k/3x5ffrktP6BOvdkSAFOSvsJyRnixwqiXUTNgCx3WnAqmkr51vC1g3Bqk/jpofSmTxhD9BIY8jQm/g7S8BITBt6WtkdXyNNRfmeGNPeDdAHzbACCNdyH112BXWjwILtRfRfq/k9cWtyw6IE27vkGFjKIoectnn1mn3i+/hOJimDXL1k1SsscYgxQdAU330bstCE9cENX0kWVxpJrMhJEPQuNstedMoqMKEDGV1pE3C2FnpAUTOso64nbGt0Xmo5hSKL/RflaaHkC6CKBoxrbYsVacvNCtJUVR8pLnnoNf/cqKmDXXhNdfVxHTW0zxieAMoecZWT22HpBTmr5p1rSJmHRLbRFougso6gcbco0Dvq0xUkd224DGVt5OhG/beE2rDMaQGqg9HTf6M9JwQxb3X54oxr9LL/pnhwoZRVHyChG48UbYd1+bK2annWym3q16mtA0jxEJI81P4taej1tzDtI4E3H7YusnMcYzBFP5APh3oatgcEgvIAD/LpjKB+3k2C/p6dv8OjLZN6xhYE1hDuDDlE0luxUzx/5dPInFijFeTOmfMhgnvm4T/RqqxyfM85MZHvBuBP5f9bB/9ujWkqIoeUM4bKOSZsywx+PHw+232wKQAw2Jfo1U/d5GnsRFgbQ8Dg3XIGVXYJwQIODbImHW3Z5iSwLMQGI/QeQLMAEk9gvUnZ+iVwAqH8HxbWAPQ8f1qKJyehwoOgSaH8iwfR/46OQLvq0xZVMwvk0Rtxo7PWeyneOCU4GIJK2bZIoOBgkj9VckyCG0PLHUZRa60ZbvJ57V2DMCM/iOFDWc+h4VMoqi5AXLltn8MC+/bB15r7oKzj57YDr1ituIVI2zWV2BLt/ApQlqz+zk1+BFggdiyv5iq0W3NROxOVdiP4IzGHzbtpcJyATjWR08q7cNhkT+C80zk7T2YNptxdb7KTkLabiezBLXpUnd304UnCxC0UwlyLLM2+cznvUwvk0BMM5gJHgQtDxKRs+t5TEo2j917aOiA22Ry5ZXoTWdCM0wGWHZ5RD9zCYwNCWY4D4QHLPCwq7b0IR4iqLknE8+sU6933wDpaXwj3/Ab3+ba6v6D2m6H6mbkkUPB7ybYSrnYEwACb+L1F0E0S86NVnFJjIrOrhHNrluCyzdA9xEwiC+7TFkHsa7bsf7aHkJaboHwm/T+4gjA2ZQ5iUOADzbgXdoHyeEyxU+zKr/wZj46pzbgFT9Lp4gMN2z9YB/F5yKGd2uiAg03Y003JTBakwbXiuO3SoSCxoHPMMwQ+a3VzzvDzKdvwfSBqOiKAXIP/8JO+xgRcw668AbbwxsEQMgLc+TeYVmsCG2H0Hz40j4A7uaE/1quSZLkNpzbTr5HmBan04iYuL3J4o0/r1rn+AeOBV/B6d7uYHs8UBgJ7KKjom9B+F/gX8P7PMs5CktAlLffmScEkzlP+I+M+n2VmMQ+U/iS4132OijjEUMQBSCh9kVnG6+UPFaS+VX9quIyYb8sEJRlJUOEbjmGjjgAKivh912gwULYPPNc23ZCkCayX4FwyDNc5H6aXQt4rfc0PVX4saWII2zcGsm49acjTQ9iEjqKBhpeY7U4ioGzXPjieuWw12Y6ZtIgoHQ8RB+n6xDw6UBwi+Df1cIjAUKt/S5NN0f94+xGBPEhI4B3ybpOyfYzhG3Hmm4sQeWeDElJ2IqH4LAb+iQCvFaS5X/wPhH9WDc/kF9ZBRFWeG0tsKpp8I999jjiRPhppvAv2K31nOHb1OIfEB2k7ZA9EeQqjTNGmHJrxHCtAkTaXkC6q+Gijsxvi2S9GsgvbiKITWnIMWTcEr/0HHalILUZfpGEt0cE9wbaX6ox/0JvwKhU4CmXtiRYxquQxrvgYpZmDbHasAE9kQi/yGlv4xnNUTCXf1TWp4nk7II3Qjsg3FKwSnFDL7RVup2l4BT0aeO532FrsgoirJCWbwY9tzTihjHgRtusJFJA1nE2DDrebhVE3CXHhjfFso2KZ3JIn9LmPYijm2Tn9QiVRMQN4kQ8m5ExiHVjTcj4Xc6jovGZtA32WqPB7ybgm8r8G5Iz6clDzTd28O++YKA1CHVExHptMUWOhxMMSmfTeRDZNFI3OrTbDmB6HfxrcIehMkv9xkxTjnGu35eihhQIaMoygrkP/+B7be3ye3Ky+Hpp+HMMwdmZFIb4lYjyw5Das+B8Js2yiP8dqcWWbz54H6Z3jXBOdeuujQ9mLCHKTqSzMWVB2mc1dG3+PfxiTbRpOkB77ad8op0nnaMdVIeNN2GoZsgPQ+pjgEtPeybT8RsHaqayUj0BwCMU4EZfLfNqJySFmh9ARrvRpbujWS96hcn8gYS/V/2/XKEChlFUVYI8+bBjjvC//4HG2wAb78Ne++da6v6H6k5x4anAh2TdNvkYsBZtVNrP4mFjQc8a2GKJ4J3M3r+T7eLtD6f8IrxbYApOSvDcWIQ+bCjr2c4DL7XZv1dnsCvMRV3YgbfiSn/W0emWe8GmNJzMEOeAGlClh4I4dezfUOd6O/prD8SAKag9Vlk6V64dZfh1t+ONN4OBDPsHP98tb6A/UxliwMthRMJpj4yiqL0KyJwxRVwwQX2eM894YEHoCI/V6n7FIl+A+FXU7WwviVDF2BMwCYtqz0fWufTZVXFvwOm/CqbR6b0PKR6PFbw9CDkWZKvWpiS08CzNlJ7LmlDoDv5YkjsZ6idHM8G22aX/Wn822KcEhsGHNwPp+igruaIIDVHx6NqelMDqh+T4wX2h8DuUJdJhty+Iv63bbqvl+NEetDHQaQ+q7i6XKJCRlGUfqOlBU48EWbPtseTJsF114HPl1u7Vhjht0grOKQJE/0S498eTBFm8C12SyH8ju3n3xbjXae9uQmMhsF3InUXQuynjnFMmV3diX1DckHgAWcE0voWeIZjvGt2a2GK9kOiX0LjrSQXBx4I7GXNFxepmgixH9reUJefUn8l0vomRN4DaUTMIAgdiSmeYH0uIu9D7Ktud8geL3g2hth/U9jdQ1qfhMgn4N0eou+kb59X9CS/TxTj6f7ZyFdUyCiK0i/88ost8rhgAXg8NirplFNybdUKRjJcYZCuE6/xjgDviKTNTWBnGDIfIu/aSCZnsM3BEn4LqT4xxY1iEJ6PhOfb2/pG2ozB8Yyy7eOHjkIa78auyiwvCgzgtWHBYLeDYl+Sks6rUlIDjXcizU9C5VwrEHq6utQFF/wbQss3IK30bnUn0fDfgvtd346ZtxRl4Y+Ve9RHRlGUPuf9961T74IFMHiwrWS90okYAP92pJ+g/ZnlCVkOYxyMfxQmdAgmuIcNu/XvAkW/i7dYzqk2EZH3kWVH2fIEcUQiEPkIig4GfPG+bf0NEMQMvs2KLUCan8raduvQutBmJ+6zdPYutMzHVDwIgd36aMzlGdCJ8DsIHZZlAr3coisyiqL0KQ89BOPGQXMzbLIJPP44rL9+rq3KDca3KeLb2gqDZKneiw7GOH1TPsUYA2V/Af/WNh9J9OOO+yS8vwuEkbpLMZVzbMmBuvPj4bdt9ZEc8IwAz7qYwCgoOgTjDO4YoscOujFofQGJLabPBILUIrGFIGE6ihiuC7Fv++4eKwNNM5Gm2UhwX0zZxX3y+ZTYko5cNJ6+yATdga7IKIrSJ4jAJZfA4YdbEbPPPvDmmyuviGnDDLoOnKF0XRWJ/+7bAlN6Xt/f1LMa+DYD/17g243U2ywuRN7FbXocqTkV2jPLuh0/Y9+Dd21M8QldRIxEv+99Vt/oR73r34Ug1Jxgw9zb7I99h4qYnuBCyzNI1e8QybD2VQIk8hlu1YnIkp2RZWORJbviLvsdEv53n1mqKzKKovSapiaYMMFGIwFMnmyrV3tWcMRqPmI8q8OQx6FpLtL8sBUKntUxoaOgaKyNVuojRJqR6tNt/SE8ZOX02nhb2yiJrzfdhxT/HuPpFC4e6QsR0pcioy0iq7Nw68dopgFPDKKfQvPjNilflkjkE2TZ0djIqU5/58g7SNXRUHFvn5Q6UCGjKEqv+OknOOggeO89G410661wwgm5tiq/ME45lJyEKTmpX+8jtRd22urJ0tk1k8ihlmegeHzHsdEpJDlBBkaCPoM0P4jpiZCpnYLNMr28mHTj1y+AIc/bLdFeoFtLiqL0mAULrFPve+/BkCEwf76KmFwhsZ+h5QmyX4Ew4KyRQTunS0FDAPyjWOGJ4gqGgSBiAARi2W8fSuSL+LZhss+jC7H/2ci7XqJCRlGUHvGPf9iK1b/8ApttZkXNLrvk2qqVmNZX6UlFbQBKziL9dBDFeLoKHuNUQPDgLO+p9C+JhGVvpnoDnbcTMyX2fWbt+qAUggoZRVGywnXhL3+BY46xCe/23x/eeAPWWSd9X6UfkY5q1+mJ/9NvyjHl1+KEDoTAnqReXfEgkY+Rlue6FDQ05VOATItZKv2PAXzgrA7FZ8CQZ2HoR5jSc8m8xEFnBFN0WA/MyDDSKeNCqMnRDU5FUTKmocGGVj/6qD0+91y47DJ16s0LfJuQfkXGgdK/YIiCZzgEdrf5ZwBTeg4SfgukicT+NQLNDyDNc8AZBoNnYHwbYUwQ8awHsQ/79v0oPSQuMt2foPEmaH0OUzEbU3wCFB2JtDwHjXdA7OsMxwsigd90k8gS+xkiHwNe8I/sHqLt3xacyngF7iSYEPh3ztCO5OiKjKIoGfH997DzzlbE+P1w3322hpKKmDzBNxI865F8VcWAGQStz4FE7OTTKRmd8a6NqXwQ/DuSeGXHpWOSXGzDct0qe+zpyTd9pf8RiH6F1F0OgHFKcEKHYIY8CaXnk9kKXis0zugYMbYEt/oUZMkeSM3pSM0pyOIdcWv/iki4vZ0xPkzJH1KObIpPwzihnryxLqiQURQlLW+8AaNGwb//DUOHwssv25UZJX8wxticNSZEYjEjIFW2jEHD1ciSPZFw17pBxrsuTsVdmFVehqIjSD7RxWyxy6Z4vL13y757I0ofE4OWx7s4ahvjwSmeYIWrGZSmv0DzXERaELfOhk23vkLX1b8wNM9Cas60xUHb7hM6ClN6Ph1V3b3tP03JGVA8sU/eoQoZRVFSct99sMcesHgxbLUVvPMO7LBDrq1SEmF8G2MqH4PQkWCKUrQUkGakemLHqkrncTzD4jliUm1VuUjLP+O/Lu6F1Ur/E4Vo93pYxrclFB1DWikgjRD9HppmQ+xHkm49tr4YT0bY6R7FEzBD38CUXQLFJ2PKpmCGvoYpOaPXYddt5LWQmTZtGttvvz2lpaUMHTqUsWPH8vnnn+faLEVZKYjF4M9/huOPh3AYDj4YXn8d1iycorgrJcY7AqfsIszQDyE4luRbTS5ICzQ9mPiyNKS/WVs9ntbnsje0oAiSuSN1b2hbseivsbtjnEBm9zQ+pOlBUof3e5DmRxLcowwTOhKn9A+Y0LE22q0PyWsh88orrzBp0iTeeustnn/+eSKRCHvvvTeNjYVTzEpRCpG6Olu5+m9/s8cXXmhrKBUX59QsJQuMMfGQ7NTlCaT1lcSXvBuSLooJ74Z2K0Gaem5oQdDCChEyZVMxg2fY2lZ9iSmzJSsSEdiFtMkTnWGIMyKDlbcYxH5Ja45ICxL93tZf6gPyOmrpmWee6XJ87733MnToUN577z123XXXHFmlKAObb7+FAw6ATz6BYBDuuQeOOirXVik9I5PsvtEOMWJ8HVFMoWOQ1vmpxw6OwRiDOMPATT+BFTb9XerAQNNDiG8T8O8KnmHQeD/Ij70fuXhC0lIYxrc54tseIu+T9PPiXQeW/AqbpTcVnpQ5Z8StQRpuhKaHgWZ7zrcVpmQSJrB7ureRlLwWMstTW1sLQEVF8mWp1tZWWls7ClzV1dX1u12KMlB49VU45BBYtgyGDYN582zmXqVA8W0Tr7uUTNB4gCCydC+I/QCA+Edjik+yYbFFh0Pzg9jViAT+MrXn4ba+ZR2DG29M3EbJEIHovyH6Sfw4CqYv8vMEkNCJKdeTzODpSNXxEP2MjqrnHiAGpgTCb5OZKI5hihInSBS3Bll2RPxz1mmsyEdI9UlQdgUmdEhmb2k58nprqTOu63LWWWex0047sfnmmydtN23aNMrLy9tfI0b08RKdogxQ7rwT9tzTipiRI61Tr4qYwsYUjyP1BBSDyFtxB8444XeQ6hOgeQ6m7K+Ysovi1bsTIdAyz06+3o1ZMX4kWVCQ0VRR2sPcpb4PxmvFtNffSoxxKjCVj2AGTYfgGBuCHzoS/LuBNJN53S4v0vwCEv222xVpuKW7iAHa6y7VTUHc2gzvs5z90jlWKo859dRTefrpp3nttddYY43kdUESrciMGDGC2tpaysoyzDSoKCsR0Siccw5cf709PvJIuPtuCPU+vYOSB7j110HjrbR/w4blfk+GwQyZj/GugVt9JrQ+n7rP4DnQ8jg0zyUvVmY8G0H59VC1b64tyT2B/TDll1nnbmcQxqRP/iRuDbJ4R9pFVdb4bMLF4t+Dbwtk8ag0vlQGU/p/cfFtqauro7y8PO38XRArMqeffjpPPvkkL730UkoRAxAIBCgrK+vyUhQlMTU1tsRAm4i55BJbQ0lFzMDBKf0jZvCd9lu2KbaOn8F9wbMmqVdQHKR5LiKt6UUMHgi/iFN+CQy6hZ6lwu9jYp9D1X65tiI/aH0OWbwNsmQHZPGvcOuvRdw0UWnR7+i5iAGIQOuLSNUxSOPMDBzCPUis+0pOJuS1j4yIcMYZZ/Doo4/y8ssvs44Wc1GUPuPLL+HAA+Gzz6xw+fvf4dBDc23VyktbVtTO2Xb7ChPYFRPoGiDhLtyY1CsnMYh8EQ+xTrd6E7NbUtFvoGYy0Jqm/YoiD1aG8oJOgkRqoXEG0voyVMzBOCWJuyRxDs6O+Oem4W8ZtBUrtHtAXq/ITJo0iVmzZjFnzhxKS0tZuHAhCxcupLm5OdemKUpBM38+jB5tRcyIEfDaaypicoGIIM1P4i49FFm0ObJoc/t78z/p/13/dILJASdoHU5NBkt0kX8jtRdjRYwKiMTkiw+RC9HPkLorEbcWkZbuTbwb2ZpafYIBZzipJUcME+zZNmBeC5lbb72V2tpadt99d4YNG9b+mjt3bq5NU5SC5ZZbYMwYqK6GX/0KFiyAbbbJtVUrJ1J/FVI7uVOkChD9BKn9I9Jwdf/ePPgbUueJcTGBPTHGB0WHkdF0EXmL/g9TLmTyTOC1zEUWb48s2gq36kQk/G77JWMcTMlpfXSjGDiD2kZOcN0B/66YZLlu0pDXQkZEEr6OP/74XJumKAVHJAKnnQaTJtmsvccdBy+9BKutlmvLVk6k9S1ouit+1Hnyj//eeEe3Wkh9iSk+ATupJJpYPOBZHYL7xNuemqbkQRt5NlErGSIQfh2pOg63+amO1cCiIzAlZ2I/I55OL4BstoEMOEMwg67v9DnydYwV2A0z6IYeW5/XPjKKovQNVVVw+OHw4otgDEybZssP9FGpE6UHSNNsUkcPeZDG2Rh//8TAG9+mMGg6UnMWNtFZ2/famBUxg26FyCcIrt1mCI6B5kdRsTJQiX8Oa89CaicjvpGY4t9jSk6H4FhbeiD2g416Cu6PRH+AuskZj26Ce2CC+4B/F2h5Col+BSaECY7B+DbuleUqZBRlgPPppzZT79df2xIDc+ZYJ18lx0Q/IW2Ol+jH/WqCCe4JQ1+D5seQyMdgfHaiiX4CVUcgbbWUCIJ/a9KLmCA2nb9S2LgQeRepWQAlZ9gCj6Vndmlh/Fsh7vdIww10JNFLhCceKXeQ7ecUQ+jwPvUWUiGjKAOYZ56xeWHq6mCtteDxx2HLQswRNiDJIETZ9H8Ys3HKoXi8zd0rLlJzOrTOp6toaYlnd3Xi5xMJGgdC46DlBXC/6Xe7V2oC+2DDm1+mXQw7lfZ88+w+ukk8UV3DdPDvjPF3d6QzJZMg8Buk+X5ofRNibX93oT0btCnDVNyTPDqqD1AhoygDEBG44QY4+2xwXdh5Z3jkEVhllVxbprQT3BsavyH5N1kHAnv3y63FbbDZfE0IPCNsgUmA8KvQ+kKyXvGXE391Xk0y4NsGU3oGEjoalu7RL3YnxJSA+IGqFXfPXOMuwan8B+JWQfRrwA++TQEvEvkAop+TeTbedHiQptkJhQyA8W2I8U0BQGKLoPlBJPwfW7crsAsED7CrMP2IChlFGWCEw9ah98477fGECXDrrRDoi7QQSp9hQkchTffFU8AvL2Yc6z8Q6ttqnRJbhtRfDS1P0F4A0LM+lJ6JCe6DND1A6m0CwLue9ZlpeRaIgGd1TOg4CB1nCxM65SvWi2bQnVA3BWIrkZCJvIcb+QTHtxn4l6s9OHgGUj0Bol+SWQbndMQg8mFGLY1nVSg5fYUHmauQUZQBxNKlNh/Mq6+C48Df/gZ//KM69eYjxrMaDL7bFsyTWjqiQWJgSjGD78B4ktU46kCkBVpeBHcROEMg8OuE34DFrUKqjoDYz3SZ3GJfIzVnQumU+OSXJnw6uhRnSFuemxjGeBG3Bhr/jtvyBMRqMnr/fUJoHE5gW9zAztD0xYq7bz5QdRxu+d8wbo1NXhfYydZM8gyFynk2q27LsyAN4NZA5HMgXXbdJGSYHE9iC6HlWcStxXhHQGAMxun/NOEqZBRlgPDxx9ap97vvoKwM7r8f9tUyM3mN8W8Dq7wCLU8i4QXxc6OhaH9MBuHO0vQwUn+ZnazaV1KKoPSPEBrfsWVEW9G+5USMvWL/W385kEFJF8dOanZsLxL9Bqk6DtxlrPCIJt9o+7P4BGi6Z8XfP5dII9Sc1ukde5CiIzFlF9js0MG9McGOrUmRFohYB3NpeghaHsvwRum3OEViSP00aJqF/Rt4EKJgLoGyizBFB2X77rJChYyiDACefBKOPhoaGmC99axT76ab5toqJROME4LQEZjQEVn1k+YnkLrzO51pW0lpRuovx+BAvACfSBiaHyL1NoMLVKe/sdPhaCXiItUng1tNTkRE82zEuxrGuxHi/w2En1vxNuQNMWieg8SqMBU3drtqTBD828WPfEjGQsaPCR2Z9KpIFKn5E7Q+1elsW/XuRqT2HDAlNkKun8jrhHiKoqRGxG4fHXigFTG77w5vv60iZqBjvwGnrl8jDTfYgo9ghUbaon1tEUlp6LzdFX4dYt/Td46lWRJ+E1l2KLJo2+VETG/2UpMlCQTwgjOiD+7Rj4SfwQ3/O3Ub39ZkWtjTVNxlt0GXQ0SQxlnI4p2XEzHdRkAaruvXkhsqZBSlQGlpgeOPt4ntRODkk+G556CyMteWKf2NhN8Hd2GaRvXQ+i/7uykm/cQrQDp/Bsc6+rb1aH0rTfv+pm1yDCc4n+n0FgKzOh3Pp3N4uUO775KpgMAY+/JtT94KGYDGGSkvG2O6/B2T40uakFEapiP1l4Ckc7IWiH4BPaxsnQkqZBSlAFm0CH79a1ux2uOB6dNtZJLPl2vLlBVC06zM2rk1ADaHh39XUtdWikFwL1JPC4IpOqxTl68zsyMnZFrzqQnkJxKvRgXAWc/+KjV25aH5Toi8k8X4OSCSPpGiKUrnQOdAYJeEVyT2CzTenJ1Nbn127bNAhYyiFBgffgjbbw9vvgmDBsHTT8Ppp2tk0sqCuDXQ+nxmjT3D2381JaeTetsEaHmcjmRmnbHHpvQcjHeNjtPR/2VgRKdVjYKjGdy2aCiXXvsBmUG9tCdDElWzXp6iQ7ArcMk+Dy6m+PeJLzU/lqJfIkyXz2Jfo0JGUQqIRx6BnXaCH36ADTeEt96C3/wm11YpK5SW+bQ7U6bCGQL+X7UfGv9WmMG32HTxgI31SDQZdd5WaWu6Aab8OkzxiV2bussyMNifQZuVADMEM+RJzKofQ9k0rLjrpynYkzzzpYjYaLeq47Dh2MuLMw9gMKV/wfhHJR4j9hOZ2+6xKQFS2NRbNGpJUQoAEbjsMrjwQnv8m9/A3LkweHBu7VJygNSQNmkdQOh4jOk62ZjA7jD0dWh5Don8B5ruS3YT+6PoGEzJKeCs2iWUux2nFGJpIp2cIeD+nLrNgKcUKu5vzwtkQocigR2QprnQ+gZEP6JPt6qiXyENd2BKJnY5LSJI3YXQ/ABdRWxbOYFKCI7BhI7B+DZMPr5TQWarU46NWCo9L/v3kAW6IqMoeU5zMxxzTIeIOfNMeOopFTErLZ41yGTSM0W/TXze+DFF+2OcClJvDwi0PAXO0MQiBiB4AGmnkdAxae6zMlAP1b+3JQXiGM9wnNI/4gx5EPy79fH9XKThb0jT/V1Pt74cFzHQVYjEf5dlmNAhqUUMYIoOJKNItcBemMqHMd61MrS7Z6iQUZQ85uefYbfdbHI7rxduv93WUPLqWurKS2CPuK9FMnHgAf+OGM/qKYeR6LcpxmhrVGOjn5Y/7VYjjXdB+H3sNJJoHA941rDf7sumxs+txFOO+z9k2dFJrv2Y5WCl4GSQ9bnhRkQ6tiGlaSap/ZU8SOOctOMa7/rgTVV91sDgu3EG34Txrpl2vN6yEn+qFCW/efdd69T7zjtQUQHPPw8nnZRrq5QVhUgMaXkJt+ZPuFUTcev+ikQ+sysq5X+Nt1peQHjABDClF6S/gVOSoH+3RrBchmFpfQtZsgdSfxVE3sT66yQIV/ZuiKmYhXFszSgz6HbwreSl12Pf4ra81P28Sbc61qUxFP8OSv6Uvqm7FCKdcspEPyX1SkoMov9NO6zEFsW3w1JYGflPevv6CBUyipKHPPAA7LqrXZHZdFMrZnbfPddWKSsKcWuRqqORmpOh5Z8QfgWa5iDLDsStuxwCe2MG3wHezlsABvy/wlQ8mHZrAMAE9yG103DcSdN0OOtKbGG8NlQzSX0kio7BVMzGVD6GiUeqiAj4NsKUXwWrvAqetdPaN2CpOQVpmt3llN2qycTnxJaFoHE2NFyX2f2kodNBBknwMiiNQfMjpNuWlKZZ/ZoErzO6QK0oeYTrwsUXwyWX2OP99oN//MPWTlJWHqTmbIi0feONdf3ZdK/dsikeB/5dbKIxtwY8wxNmYE2Kb6StVRR5l+7f0m2Ytik5patdTf/AJp9LNkEZMN4uSdSk+cl4naev4k1C4FkvczvznhA4ZekTFLYjSN3FiNuMUxKPAis6ABrvhNj/SL1iIkAEqAW3NrPbdRaNwTFxB+9k9zBd6jMltSL6NWmFl7vUZpM23QuY9jW6IqMoeUJjIxx5ZIeIOftsWzNJRczKhUS+hPCrpJrQpHEGIjGMMRjvuhj/ttmJGGx2VzP4FvDvED/jof27rSmG4P5I86NIw63xcFug9SVSOxrHoPXFDjsb7kBqJ3dNnCdNKbYlOn/L95D5dsuKxIBvF6iYDUOewqz6Aabs4uyHabgace1qiTFBTMVM8G0Tv+jQPj171gFnfbKfrj3gG9XF0daEjsOGwycay7G+V0WHZjC2l/QrSA6YFRN6rysyipIH/PijrZf0wQc2O+/tt8OECbm2SskJ4VdJG17tLobol+DbuFe3Mk4ppuJuJPJfpOUFm0gt+qXdymp5HHAQXGi4HglNALc1/aBiywVI9Eek4eq2k5kZ5N0Uik/COOVIy9PW0TjyRVwI5Utla4HIaxjPFR25UYJ7QNklSN2ULMZxkaZHMCW2sKfxDMVUzkEi/4Xwm/Y+vm0RUwrLEkegJccDJoQpv6jLWeNdAyruRKpPiTtxx8Ou295XYBeQSAam/5K+jX9njFkxqcZVyChKjnn7bRg7FhYuhFVWsUnvdt4511YpOUPCZLYSkcGEkyHGtynGt6ldfQm/Ej/r0kVMNd1thYbrIflqkad9VUGaH8IKskwKShbBkKdxvJ0yEQd2tFZUndyxLZU3CMR+7pJ4zoSOQsIfQ8sDKfotR+vTEBcy7eP4NgVfp6qvTQ9lKeEc60NVehbGu063q8a/PTJ4BlSNp+tnSKDln0j4bah8AOMZlnB0141A+O30Znj6P1qpDd1aUpQcMmuWDa9euBC22AIWLFARs9Lj25z0k3/Abjn0IeI2IY23p24U/YZ0US+m+HfxX78l81WUZkyk++Qobj1Ihr4gKxpnULdTpuwCMq0qDUDkE9zoYiTyBRJLliU5y2navzum/JKEIgbijte1F9I12qyNGLhLkboUW2WZ1plyf8rQ4N6jQkZRcoDrwvnnw+9+B62tcNBB8MYbsPbaubZMyTn+neJJ75L98+yBokNtIci+JPy29V9JSUsnH4rO+Ujs76bkDxj/dvZURhW32/Aiy9VtErcKWXYoRD7McIze4ABF4N0CPOunaWvAu2nCJG/GCWEq7gECGd63BZbujCzbH1myI27ViXZrqTP+UWTlKxR+Gakah0iSbcDIB/EVrmRiJAatLyGxJM7LGZWlACSDMhp9hAoZRVnB1NfDIYfAFVfY4/PPt9tJJX08LymFiTEOZtB0G93TLXmZY+selWaQQyRb0ooYi/HvhBl8jxVcBAC/DfsefCemZFJHu+B+ZLatBOBiHOvVLm49buvbSNUJEPueFVNl2oXgftZvJO02lkDJWUmvGv92mFWe7UGBSIHw68iyI5FwR+4X410DAnuReeFN1+aLaX4y8eXoZ5nZEk38HIwzJDMz/Dtm1q4PUB8ZRVmBfPedder96CMIBOCuu+DYY3NtlZJvGN9mUDkPaboXmufZXCDOcEzoKAgdh3FCfX9Tb4Yh0d51rU9NYKfU7fw7gm8riHxMekEjiGctpGp83NG1P0jjQN3ycOZDNVyPG/0G4x0OgT0wpusKjPEMR8ouhdrTs7QxBsSQ6hOQQbfbaDRjMOXTkKrjIfpxxiNJ80OYUKIIpAy3vkySVSX/SFt4VOpSdPZAaMX9w2ZkRWWsyRF1dXWUl5dTW1tLmcaxKjnktdfg4INh6VJYdVV47DH41a/SdlOUFYa79FCIfkLiCd8B7yY4Qx7NeDxxa5DqMyCB/0sHxtYaCr9GRlW9s8ZmO850xSlz2gotlmLK/oIpOrjLVZEYsmQXm0+lpwQPxJRfgTFeRMJI/fXQdGdmfT0jcFaZ3+20xJZau1KJS1OOGfp6l2SIXcZommuLTyaj5GyckpMzszMFmc7furWkKCuAe+6BX//aiphttrGZelXEKPmGKb88yZaWB0wRpnxaduM5gzCDroayK8C3fZJWEl+FyXQbKisLbPSMk12OncxoK7RYj9SeizQ/1fXOxoMpObt3t2h5Amm4OT6eHxMYnWFHA07iqCPjGQJFR5Bq+jclJycVMQAmdCSm9P+wOWni9wPAiyk5G1O8Ymup6NaSovQjsRj8+c9w7bX2+NBD4b77oLj/k10qStYY30ZQ+TDSMB1ansaKCw8E98WUnI7xrpu0r7g10Pw4EvsR45Qh3o2haQ6EXyd99FIG+WnS4d8dfJtAyzPgLgFnFUzoCCg60kZjNd5Bf/rbSP1VENwHYzoEggkdClKL1F9N+7MkRubRXAJN9yIlJ2FMEfi2xoqHcNp+JnRY0qum7P/s36v1abqK1hiEJkDohLSWmeLx1vG75RlwF4EzxL5/pzxt375Gt5YUpZ+orYWjj4ann7bHU6bA1Kng6DqoUgCI22SrX5tyjJNaeUvTHKTuMuzWkIduOWj6HQd8I3EqZyW2L/YLsmQMVjD135RnKh7A+Lfufn+3CpqftBmSnTJouBNozHzg4FhM2f9hnHLc2qnQPJeUz9e3la13lSazrkQ+QZrngVsFnmGYokOShm2vSESi0PoKtcsWMHiNC9LO37oioyj9wNdfwwEHwKefQjAI995ryw8oSqFgHYrTOxVL81NI3UWdzqy4sNsOXIh+kfSq8QyDwbcjNaemLnjZazOqE9/fqYDice0bMGJCSH0W23Qt85DIB1A5F1N2PhL7FsJvJW4b2BdTfllaEQPWqdz4NsvcjhWARD5Bqk+z2YObMgs7VyGjKH3Myy/bLaSqKhg+HObNg5Ejc22VolhEBCL/tqHNpgwCO2BMFknclhtLGm6ga6r7HNEpykai3yBNf4eW522mZN+mmNDvYMiLmJZHkda3ALGRN5H/0GerR57VE54WEQi/gYRft/lVfFtC8enQeCuZ+QYJxH5E6i7DGXQNDL4bWucjTQ/aQpMmAP5fQej3ON7EvjGFgMR+Rqp+18kxOzNRrEJGUfqQGTNg0iSIRmH77W1k0vDhabspygpBwu8jtf/XtYijKYGSSRD6Pcak/wYs4kL4NaT1dbslEfu2Hy3OAncxbs25Njy47iKsOImLhPACJPwmFB0FZRfjFFsfEHFrkWVHQewbeifEHPBujPFt2O2KxH5BqifGV4zaptwoOBUwaDrUXmC38NISg5anEPf/7ApPcAwmOKYXNucf0nhffMUsO2GpQkZR+oBoFCZPhunT7fHRR9scMUVFubVLUdqQyMdI1Ti6fcuVBqT+Soy0WEGTAjfyHVSPA3chfV6Z2rMxxDJJ1pYMgZZ50PIo3VeI4oKm+X7wbwdFBwFYx9TKB5CaP8TDv3uCATyYsqndLZJWu8LQVj2887N3q6HmtCzvFbNFNAOJQx7FrYHIZ2A84NvcOggXEi1P0JPoNXU7VJReUl0N++3XIWIuuwxmz1YRo+QXUn8NdiJN/G1XGm62TqlJcGOLbRVmty11vdD77aT4FBTYBzPkkbRhwelpe2/J7ZKGGV2OjVMKxemjdJLjtY61/m26X2p5ym79JJyce/jsEvi+iFuPW3M+snhHpHocUnUssnhH3PrrrONsoeA29KibrsgoSi/44gvr1PvFFxAK2SKQBx+cvp+irEgktiQeBp2KmA25TpaRtXoifVZx21SA8YF3PUzoaAj8xoYte9fpf0+b2Je4kc9xfBt1nIt82osBI5DEYVaan6Fv/YfKwbc5IuF4CQGDeIZD1bj41lUnwSSN0HgbEv0OBl2f0bZhzvGuDdHPyfZ5qZBRlB7y/PNwxBFQUwMjRsDjj8PWW+faKkVJQEbZZT1IbEnCDSOJfm/r92RF5wncS3seleKTMSVnJp5Yg/tB/VUpxnTAsx7EvszSluWovRCGPNBxHHm/F4P5SDSVilsF0S/pUyfo0DFIw63QNLNTiYAg0JKkg9hcMeFjIONkernDhI5B6qZk3U+FjKJkiQjcfDOcdZZNeLfDDvDoo7bsgKLkJc4qpF8ZiGE8QxNekZYXenDPYbZys3dtDAJOpXVQdQYn7WI8w5Ci46B5VgJb48Kn6HBouDx7ezoT/RCJfoXxtlW6NvRs5cQDwb27iTJpfgKpPY8+W8ECW0088ilEXqGrnclETIeN0vxAFlmBc0jRIdD8T4i8QzYOv+ojoyhZEInAaafBGWdYETNuHLz0kooYJb8xniHg35nUFZS9ENw38aXmBxKfXx5nNTvhAriLrfNmw/VI5D8Q3D+liGm3tex8CB1Px/fsNpHggG878K0LlGZmTyo6VXc2/p7UC7HixxRP7HJWwu8gtX+iT0UMBoL7QORlshdbMYj90Ie29B/G+DEVd0LxyTY1QIaokFGUDFm2DPbeG267DYyBq66yie4CSYrEKko+YUrPxoqDxP/sm5IzEwoNiXySeYi1u2i5HCBxn43WV2xkUCZ2Gi8mdAwED4zb2zZxx+wWUPWJSfO1ZIOEFyCtryISg6KDbRh6NlOiKcYMvhXj27TruA0zshsn9U3sj8CvIfpzD8d1bPmAAsGYAE7pHzFD38BUZlaRXIWMomTAp5/C6NE22V1JifWHOeccK2gUpRAwvk0xlbPBu1yuE1OOKf0LJCn0Jy3zyWyq8JN8e8aF8L/sykwapPkpZOk+0PII3ROixYVR7HPwtUUJeTq9gMABQAbfLppmIdUnIkt2g/A7mMF3ZNYPbFXqVV7DBHbraruEIfwqPQkh7o7H5qYpuxwzaLpNYNijxH0uJh5uXkgY409Z26sz6iOjKGl4+mk46iioq4O114YnnoDNN8+1VYqSPca3JVTOs467se/BlIJ/VJp09q1YIZNuEk1XyNCDNN0PpWtinEEJW0j0G6T2bNILAYHo11D5FLT804aEOxWY4FiMbwPc+hug8eY0Y8RxlyA1p2IG3w2Dp9sVn5R4Md514yUcljcrQvqtn8x8cczgWzGB3TuGdgaB+3Pafl3xgHczCOyZZb/CQoWMoiRBBK67zq68uC7suis89BCsskquLVOUnmOMAd+m9pVJe+9GSNpU8akiZ9qIQfNDSPPDSGAPTMlZGN/GXVpI0+yMbLKN6zDSiCntvmVlSs5A3Lq403DbalIycWRFhdRfBUWZFESLgidJGQATsk7O7i9pxkgnZgLg61rXxBQdiNR/mqZf57ENBPbAlF+BMQN7qi+IraWbb76Ztddem2AwyOjRo1mwYEGuTVIGOOEwnHginH22FTEnnGDDrVXEKCsdwTFxx8tk+6gem8jOlGQ4oFifmWWHI+F/d73U+hrZbcskbmuMg1N+IWbI85iS06yTcDqbov+F+u7ZebsPXgSBxKUBjDGY4nGkznrsheD+JJ9+DYSOwTjLPc+iw6wzdUKHbQ84q8PgWZjSCzFll2CGPI8z+BaMk7nTbKGS90Jm7ty5TJ48malTp/L++++z1VZbMWbMGBYvXpxr05QBypIlsNdecPfd4Dhw/fVwxx3gT19MVlEGHMYEMIOuo4sfSjsOeNe3qyJFRyS4nowYEEFqL7AFFdvJJiIn2N3fZzmMd01MyRlxX5ZMbUuNKb0w8bZSG6HfgX803cWMBzCY8ssx5ZeBf6dO5zv9DPwm7pi93H2dMkzFLPBu0Kl9vI93I0zlLJzAKEzxcZjQURjvmj15ewWJka6forxj9OjRbL/99tx0000AuK7LiBEjOOOMMzjvvPPS9q+rq6O8vJza2lrKyga+MlV6x0cf2Uy9338P5eUwdy6MGVh12RSlR0jkE6ThNmh9AYjZoodFR2OKT8A4JfECjEekSMmfGFPxIMa/FQBu7YXQ/FAG/R0IHYNTllnyNGl+EqmdnLFNiQlgBl2FSRai3vl+EoamvyONM+PbTAb8O2NKTsb4R8XbuBB+E2l+zIaqe4Zhig4B3/Yps/Da6uXvQ3hBfNxR4NumMDL3Zkmm83deC5lwOEwoFOKhhx5i7Nix7efHjx9PTU0N8+bN69antbWV1tbW9uO6ujpGjBihQkZJy+OPw7HHQkMDrL++Pd5kk1xbpSj5hUgEpNWGHy+fCM6tQRpugKaHSe8zYzHlV2KKbF0PiXyOLDuQtCszvq0xg+9NvTLSxeZmZPFOID2r5RO3FDP0g4zvae8rNhzd+NI4VCuJyFTI5PXW0tKlS4nFYqy6XLaxVVddlYULFybsM23aNMrLy9tfI0aMWBGmKgWMCFx5JYwda0XMr38Nb7+tIkZREmGMD+OUJFwBMM4gnLKpmFXfhtC4jMaTWEehSuPbCFN2KW0VpbvhrGb9PypmZiUojCnClP5fkquZToMCUp3xPe19DcYpVhHTz+S1kOkJ559/PrW1te2vH34ojIyGSm5oaYHx4+G886ygOe00eOYZqKjItWWKUsC0PANNf8+sbcO1SPjd9kMTOhxT+SgEx9oIIGcYBA/BVD6GM/RV6/9hss9CaUKHYsqvBWd41wvezYFMHJUdMOVZ31fpf/I6JmvIkCF4PB4WLVrU5fyiRYtYbbXVEvYJBAIENNWqkgELF9pK1W+9BR4P3HijFTKKovQcceuR2mwK/8WQ2nNhyAvtqzzGtylm0LQ+t80U7W8LU0Y+sqsrnjUw3vVxG26HhmtSdw7s1T2SSMkL8npFxu/3s9122zF//vz2c67rMn/+fHbYYYccWqYUOh98AKNGWREzeDA8+6yKGEXpE1qeJH1yvM64thZQeMWk1TDGwfi3wgR2by8aaYpPAs/GKXoFMCVnrBD7lOzJayEDMHnyZO644w7uu+8+Pv30U0499VQaGxuZMGFCrk1TCpSHH4add4YffoCNNrL+MHsO7MSXirLCkOi39CjUOdN6Tv2AMQYz5FEIHkS3adGzFqZiFsa3UU5sU9KT11tLAEceeSRLlixhypQpLFy4kK233ppnnnmmmwOwoqRDBC69FKbEV73HjIH774dBg3JqlqIMKIxTimRdoZksEur1D8Z4MIP+hrgXQuurII3gXQ982w3I0OaBRF6HX/cFmkdGAWhuht//3goXgD/8Aa6+Grx5L+UVpbCQ6FfI0v2y7OXHDH0T45T2i01KYTIgwq8VpS/46SdbJ+n++61wmTHDZutVEaMofY/xrg+Bfclqeik+UUWM0mP0n3JlQPPOO3DQQfDLL1BZaf1jdtst11YpysDGDLoSqXVsZep2f5m2bL1tOWJcQCB0PKbkzFyYqQwQVMgoA5b774cJE2yumM02s5l6110311YpysDHmCBm0HVI9AxoeRaRRoxnXSQwGtMyH4n9gvFUQnB/jCdxKg1FyRQVMsqAw3Vh6lTr2Avw29/CnDmgLlKKsmIx3nWh5NT28okGoHhcytrQipItKmSUAUVjI4wbB488Yo//9Ce44gqb8E5RlMJGYsts0UqpB8+aENhd0/8rKmSUgcP//mf9YT78EPx+uP12OP74XFulKEpvEYkh9X+Llz2IYR2JY2AGQ/nlmKAmglqZ0aglZUDw5ps2U++HH8LQofDiiypiFGWgIPVXQNPdQBRbGTvuOCw1SM0kpPXNHFqn5BoVMkrBM3Mm7L47LFoEW24JCxbATjvl2ipFUfoCiS2CppnJrtr/Nly34gxS8g4VMkrBEovZqtXjxkE4DGPHwuuvw1pr5doyRVH6jJZn0jRwIfIhEvt5hZij5B8qZJSCpL7eVq6+8kp7fMEFNkdMiRanVZQBhbg1ZDRVuTX9bImSr6izr1JwfPcdHHggfPQRBAJw991wzDG5tkpRlP7AeNZAiKZp5YDmo1lpUSGjFBT/+hcccggsXQqrrQbz5lknX0VRBijBfaDuEqA5SQMPBPbEOBUr0qpeI24VNM9DYv8DU44J7ofxbZhrswoSFTJKwXDXXXDqqRCJwHbbWRGz+uq5tkpRlDZEYtDyONI4C2JfAUEI7ospHo/xrtOjMY1TDGUXInUXYFPqda5z7AFTjCn9Ux9Yv+KQxplI/TRs9JUHEKTxFiSwL2bQVRgTsO0kDOH3QJrBux7Gqw6AiVAho+Q9sRiccw5cFw9MOPxwuPdeCIVyapaiKJ0QiSI1Z0Hrc1ifFhdohub7kea5iO9X4N8UU3SwLSyZBSZ0GDilSP21EPu27Sz4d8aUXYDxrt2n76UvEYmCuxjwgTMEWp5C6v/aqUWnbbPWZ5FaP5RfBU13Iw23gdR2jOUfjSn7a16/31xgRETSNytcMi0DruQntbVw1FHwTDxw4eKL4cILwWiOc0XJK6Tx3vgqQ6opJS5wio7BlF2IMdml3BYRiH4JUgeeNfK6TpNIGBpvt6tTUm1PejYAqQF3Kcmfk4Gio6D5HwmuecCUYiofwXjX6B/D84hM529dkVHylq++ggMOgM8+g6IiuO8+uxqjKEp+ISJI472kFjFgV2mA5jmIU4Epza7qtTEGCsCPRCSMVJ0AkXdof88AsS8zGyChiAGIgdQjDbdgBl3eWzMHDBp+reQlL70Eo0dbEbP66vDaaypiFCVvkRpws8zj0nQ34jb1izk5p+kBiCygi4jJGBN/JSPuhyQtPbNtAKJCRsk7ZsyAvfeGqiobkfTOO7Dttrm2SlGU5PRgcV+a4isWAw9pmt2L3i6phQxAWPPmdGLAby21uQDV1dXl2BIlUxoaIBq1KzDTp9ttJf3zKUp+4zZvCNHPyWYVwjjLMMGB9z+3W/M17fWgssIAASBM6ufowQTBOAPv2XWmbd5O58o74J19f/zxR0aMGJFrMxRFURRF6QE//PADa6yR3Ll5wAsZ13X5+eefKS0ttY5iKzF1dXWMGDGCH374QSO4MkCfV3bo88oOfV7Zoc8rOwbC8xIR6uvrGT58OI6T3BNmwG8tOY6TUsmtjJSVlRXsBzsX6PPKDn1e2aHPKzv0eWVHoT+v8vLytG3U2VdRFEVRlIJFhYyiKIqiKAWLCpmViEAgwNSpUwkEArk2pSDQ55Ud+ryyQ59Xdujzyo6V6XkNeGdfRVEURVEGLroioyiKoihKwaJCRlEURVGUgkWFjKIoiqIoBYsKGUVRFEVRChYVMis5ra2tbL311hhj+PDDD3NtTl7y3XffccIJJ7DOOutQVFTEeuutx9SpUwmHw7k2LW+4+eabWXvttQkGg4wePZoFCxbk2qS8ZNq0aWy//faUlpYydOhQxo4dy+eff55rswqGK664AmMMZ511Vq5NyWt++uknjjvuOCorKykqKmKLLbbg3XffzbVZ/YYKmZWcP//5zwwfPjzXZuQ1n332Ga7rcvvtt/PJJ59w3XXXcdttt3HBBRfk2rS8YO7cuUyePJmpU6fy/vvvs9VWWzFmzBgWL16ca9PyjldeeYVJkybx1ltv8fzzzxOJRNh7771pbGzMtWl5zzvvvMPtt9/OlltumWtT8prq6mp22mknfD4fTz/9NP/973+55pprGDx4cK5N6z9EWWl56qmnZOONN5ZPPvlEAPnggw9ybVLBcNVVV8k666yTazPyglGjRsmkSZPaj2OxmAwfPlymTZuWQ6sKg8WLFwsgr7zySq5NyWvq6+tlgw02kOeff1522203+cMf/pBrk/KWc889V3beeedcm7FC0RWZlZRFixYxceJEZs6cSSgUyrU5BUdtbS0VFRW5NiPnhMNh3nvvPfbaa6/2c47jsNdee/Hmm2/m0LLCoLa2FkA/S2mYNGkSv/3tb7t8zpTEPP7444wcOZLDDz+coUOHss0223DHHXfk2qx+RYXMSoiIcPzxx3PKKacwcuTIXJtTcHz11VdMnz6dk08+Odem5JylS5cSi8VYddVVu5xfddVVWbhwYY6sKgxc1+Wss85ip512YvPNN8+1OXnL/fffz/vvv8+0adNybUpB8M0333DrrbeywQYb8Oyzz3Lqqady5plnct999+XatH5DhcwA4rzzzsMYk/L12WefMX36dOrr6zn//PNzbXJOyfR5deann35in3324fDDD2fixIk5slwZCEyaNImPP/6Y+++/P9em5C0//PADf/jDH5g9ezbBYDDX5hQEruuy7bbbcvnll7PNNttw0kknMXHiRG677bZcm9ZveHNtgNJ3nH322Rx//PEp26y77rq8+OKLvPnmm91qcIwcOZJjjz12QCv3zmT6vNr4+eef2WOPPdhxxx2ZMWNGP1tXGAwZMgSPx8OiRYu6nF+0aBGrrbZajqzKf04//XSefPJJXn31VdZYY41cm5O3vPfeeyxevJhtt922/VwsFuPVV1/lpptuorW1FY/Hk0ML849hw4ax6aabdjm3ySab8PDDD+fIov5HhcwAYpVVVmGVVVZJ2+7GG2/k0ksvbT/++eefGTNmDHPnzmX06NH9aWJekenzArsSs8cee7Dddttxzz334Di6mAng9/vZbrvtmD9/PmPHjgXsN8L58+dz+umn59a4PEREOOOMM3j00Ud5+eWXWWeddXJtUl6z55578tFHH3U5N2HCBDbeeGPOPfdcFTEJ2GmnnbqF9H/xxRestdZaObKo/1EhsxKy5pprdjkuKSkBYL311tNvhwn46aef2H333VlrrbW4+uqrWbJkSfs1XXWAyZMnM378eEaOHMmoUaO4/vrraWxsZMKECbk2Le+YNGkSc+bMYd68eZSWlrb7EZWXl1NUVJRj6/KP0tLSbv5DxcXFVFZWql9REv74xz+y4447cvnll3PEEUewYMECZsyYMaBXkVXIKEoann/+eb766iu++uqrbkJPtHg8Rx55JEuWLGHKlCksXLiQrbfemmeeeaabA7ACt956KwC77757l/P33HNP2m1ORcmE7bffnkcffZTzzz+fSy65hHXWWYfrr7+eY489Ntem9RtG9F9iRVEURVEKFN3oVxRFURSlYFEhoyiKoihKwaJCRlEURVGUgkWFjKIoiqIoBYsKGUVRFEVRChYVMoqiKIqiFCwqZBRFURRFKVhUyCiKoiiKUrCokFEUpVccf/zxCSuHf/XVV30y/r333sugQYP6ZKye8uqrr3LAAQcwfPhwjDE89thjObVHUZQOVMgoitJr9tlnH3755Zcur3wsiBiJRHrUr7Gxka222oqbb765jy1SFKW3qJBRFKXXBAIBVltttS6vtsrE8+bNY9tttyUYDLLuuuty8cUXE41G2/tee+21bLHFFhQXFzNixAhOO+00GhoaAHj55ZeZMGECtbW17Ss9F110EUDClZFBgwZx7733AvDdd99hjGHu3LnstttuBINBZs+eDcCdd97JJptsQjAYZOONN+aWW25J+f723XdfLr30Ug4++OA+eFqKovQlWjRSUZR+41//+hfjxo3jxhtvZJddduHrr7/mpJNOAmDq1KkAOI7DjTfeyDrrrMM333zDaaedxp///GduueUWdtxxR66//nqmTJnC559/DnRUa8+U8847j2uuuYZtttmmXcxMmTKFm266iW222YYPPviAiRMnUlxczPjx4/v2ASiK0v+IoihKLxg/frx4PB4pLi5ufx122GEiIrLnnnvK5Zdf3qX9zJkzZdiwYUnHe/DBB6WysrL9+J577pHy8vJu7QB59NFHu5wrLy+Xe+65R0REvv32WwHk+uuv79JmvfXWkzlz5nQ599e//lV22GGHdG816X0VRckduiKjKEqv2WOPPbj11lvbj4uLiwH497//zeuvv85ll13Wfi0Wi9HS0kJTUxOhUIgXXniBadOm8dlnn1FXV0c0Gu1yvbeMHDmy/ffGxka+/vprTjjhBCZOnNh+PhqNUl5e3ut7KYqy4lEhoyhKrykuLmb99dfvdr6hoYGLL76YQw45pNu1YDDId999x/7778+pp57KZZddRkVFBa+99honnHAC4XA4pZAxxiAiXc4lcuZtE1Vt9gDccccdjB49uku7Np8eRVEKCxUyiqL0G9tuuy2ff/55QpED8N577+G6Ltdccw2OY2MPHnjggS5t/H4/sVisW99VVlmFX375pf34yy+/pKmpKaU9q666KsOHD+ebb77h2GOPzfbtKIqSh6iQURSl35gyZQr7778/a665JocddhiO4/Dvf/+bjz/+mEsvvZT111+fSCTC9OnTOeCAA3j99de57bbbuoyx9tpr09DQwPz589lqq60IhUKEQiF+/etfc9NNN7HDDjsQi8U499xz8fl8aW26+OKLOfPMMykvL2efffahtbWVd999l+rqaiZPnpywT0NDQ5e8ON9++y0ffvghFRUVrLnmmr17SIqi9I5cO+koilLYjB8/Xg466KCk15955hnZcccdpaioSMrKymTUqFEyY8aM9uvXXnutDBs2TIqKimTMmDHy97//XQCprq5ub3PKKadIZWWlADJ16lQREfnpp59k7733luLiYtlggw3kqaeeSujs+8EHH3Szafbs2bL11luL3++XwYMHy6677iqPPPJI0vfw0ksvCdDtNX78+CyelKIo/YERWW6TWVEURVEUpUDQhHiKoiiKohQsKmQURVEURSlYVMgoiqIoilKwqJBRFEVRFKVgUSGjKIqiKErBokJGURRFUZSCRYWMoiiKoigFiwoZRVEURVEKFhUyiqIoiqIULCpkFEVRFEUpWFTIKIqiKIpSsKiQURRFURSlYPl/Mxlu4eNp2HYAAAAASUVORK5CYII=",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "plt.scatter(X[:, 0], X[:, 1], c=y)\n",
+        "\n",
+        "x1_min, x1_max = X[:, 0].min(), X[:, 0].max()\n",
+        "x2_min, x2_max = X[:, 1].min(), X[:, 1].max()\n",
+        "\n",
+        "xx1, xx2 = np.meshgrid(np.linspace(x1_min, x1_max), np.linspace(x2_min, x2_max))\n",
+        "grid = np.c_[xx1.ravel(), xx2.ravel()]\n",
+        "probs = (np.dot(grid, w)-b).reshape(xx1.shape)\n",
+        "plt.contour(xx1, xx2, probs, levels=[0.5], colors='blue')\n",
+        "plt.xlabel('Feature 1')\n",
+        "plt.ylabel('Feature 2')\n",
+        "plt.title('Decision Boundary')"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/Week 1/SVM-Example/LinearSVM.ipynb b/Week 1/SVM-Example/LinearSVM.ipynb
index 6bbb228..00ff596 100644
--- a/Week 1/SVM-Example/LinearSVM.ipynb	
+++ b/Week 1/SVM-Example/LinearSVM.ipynb	
@@ -1,272 +1,282 @@
 {
-  "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": 6,
-      "metadata": {
-        "id": "yJ2qCHbR6afK"
-      },
-      "outputs": [],
-      "source": [
-        "import numpy as np\n",
-        "from sklearn.datasets import make_blobs\n",
-        "from sklearn.model_selection import train_test_split\n",
-        "from sklearn.metrics import accuracy_score"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 38,
-      "metadata": {
-        "id": "fRpU6rQE6evG"
-      },
-      "outputs": [],
-      "source": [
-        "class LinearSVM:\n",
-        "    def __init__(self, learning_rate=0.01, lambda_param=0.01, num_iterations=1000):\n",
-        "        self.learning_rate = learning_rate\n",
-        "        self.lambda_param = lambda_param\n",
-        "        self.num_iterations = num_iterations\n",
-        "\n",
-        "    def fit(self, X, y):\n",
-        "        y = np.where(y <= 0, -1, 1)\n",
-        "        num_samples, num_features = X.shape\n",
-        "        self.w = np.zeros(num_features)\n",
-        "        self.b = 0\n",
-        "\n",
-        "        for _ in range(self.num_iterations):\n",
-        "            for idx, x_i in enumerate(X):\n",
-        "                condition = y[idx] * (np.dot(x_i, self.w) - self.b) >= 1\n",
-        "                if condition:\n",
-        "                    self.w -= self.learning_rate * (2 * self.lambda_param * self.w)\n",
-        "                else:\n",
-        "                    self.w -= self.learning_rate * (2 * self.lambda_param * self.w - np.dot(x_i, y[idx]))\n",
-        "                    self.b -= self.learning_rate * y[idx]\n",
-        "         \n",
-        "      #Vanilla gd\n",
-        "        for _ in range(self.num_iterations):\n",
-        "            scores = np.dot(X, self.w) - self.b\n",
-        "            margins = 1 - scores * y\n",
-        "\n",
-        "            if np.any(margins > 0):\n",
-        "                gradients = -np.dot(X.T, y * (margins > 0)) / num_samples\n",
-        "                self.w -= self.learning_rate * (gradients + 2 * self.lambda_param * self.w)\n",
-        "                self.b -= self.learning_rate * np.mean(-y * (margins > 0))           \n",
-        "        return (self.w,self.b)\n",
-        "        \n",
-        "    def predict(self, X):\n",
-        "        prediction = np.dot(X, self.w) - self.b\n",
-        "        return np.where(prediction<=0,0,1)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "class NonlinearSVM:\n",
-        "    def __init__(self, learning_rate=0.01, lambda_param=0.01, num_iterations=1000, degree=3):\n",
-        "        self.learning_rate = learning_rate\n",
-        "        self.lambda_param = lambda_param\n",
-        "        self.num_iterations = num_iterations\n",
-        "        self.degree = degree\n",
-        "        self.w = None\n",
-        "        self.b = None\n",
-        "\n",
-        "    def fit(self, X, y):\n",
-        "        y = np.where(y <= 0, -1, 1)\n",
-        "        num_samples, num_features = X.shape\n",
-        "\n",
-        "        # Initialize weights and bias\n",
-        "        self.w = np.zeros(num_samples)\n",
-        "        self.b = 0\n",
-        "\n",
-        "        # Gradient Descent\n",
-        "        for _ in range(self.num_iterations):\n",
-        "            scores = np.dot(self.w, self.kernel(X)) - self.b\n",
-        "            margins = 1 - scores * y\n",
-        "\n",
-        "            # Compute gradients\n",
-        "            dw = -np.dot(y * (margins > 0), self.kernel(X)) / num_samples + 2 * self.lambda_param * self.w\n",
-        "            db = -np.mean(y * (margins > 0))\n",
-        "\n",
-        "            # Update weights and bias\n",
-        "            self.w -= self.learning_rate * dw\n",
-        "            self.b -= self.learning_rate * db\n",
-        "\n",
-        "        return self.w, self.b\n",
-        "\n",
-        "    def predict(self, X):\n",
-        "        prediction = np.dot(self.w, self.kernel(X)) - self.b\n",
-        "        return np.where(prediction <= 0, 0, 1)\n",
-        "\n",
-        "    def kernel(self, X):\n",
-        "        return np.power(np.dot(X, X.T) + 1, self.degree)"
-      ]
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "id": "yJ2qCHbR6afK"
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from sklearn.datasets import make_blobs\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import accuracy_score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "id": "fRpU6rQE6evG"
+   },
+   "outputs": [],
+   "source": [
+    "class LinearSVM:\n",
+    "    def __init__(self, learning_rate=0.01, lambda_param=0.01, num_iterations=1000):\n",
+    "        self.learning_rate = learning_rate\n",
+    "        self.lambda_param = lambda_param\n",
+    "        self.num_iterations = num_iterations\n",
+    "\n",
+    "    def fit(self, X, y):\n",
+    "        y = np.where(y <= 0, -1, 1)\n",
+    "        num_samples, num_features = X.shape\n",
+    "        self.w = np.zeros(num_features)\n",
+    "        self.b = 0\n",
+    "\n",
+    "        for _ in range(self.num_iterations):\n",
+    "            for idx, x_i in enumerate(X):\n",
+    "                condition = y[idx] * (np.dot(x_i, self.w) - self.b) >= 1\n",
+    "                if condition:\n",
+    "                    self.w -= self.learning_rate * (2 * self.lambda_param * self.w)\n",
+    "                else:\n",
+    "                    self.w -= self.learning_rate * (2 * self.lambda_param * self.w - np.dot(x_i, y[idx]))\n",
+    "                    self.b -= self.learning_rate * y[idx]\n",
+    "         \n",
+    "      #Vanilla gd\n",
+    "        for _ in range(self.num_iterations):\n",
+    "            scores = np.dot(X, self.w) - self.b\n",
+    "            margins = 1 - scores * y\n",
+    "\n",
+    "            if np.any(margins > 0):\n",
+    "                gradients = -np.dot(X.T, y * (margins > 0)) / num_samples\n",
+    "                self.w -= self.learning_rate * (gradients + 2 * self.lambda_param * self.w)\n",
+    "                self.b -= self.learning_rate * np.mean(-y * (margins > 0))           \n",
+    "        return (self.w,self.b)\n",
+    "        \n",
+    "    def predict(self, X):\n",
+    "        prediction = np.dot(X, self.w) - self.b\n",
+    "        return np.where(prediction<=0,0,1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class NonlinearSVM:\n",
+    "    def __init__(self, learning_rate=0.01, lambda_param=0.01, num_iterations=1000, degree=3):\n",
+    "        self.learning_rate = learning_rate\n",
+    "        self.lambda_param = lambda_param\n",
+    "        self.num_iterations = num_iterations\n",
+    "        self.degree = degree\n",
+    "        self.w = None\n",
+    "        self.b = None\n",
+    "\n",
+    "    def fit(self, X, y):\n",
+    "        y = np.where(y <= 0, -1, 1)\n",
+    "        num_samples, num_features = X.shape\n",
+    "\n",
+    "        # Initialize weights and bias\n",
+    "        self.w = np.zeros(num_samples)\n",
+    "        self.b = 0\n",
+    "\n",
+    "        # Gradient Descent\n",
+    "        for _ in range(self.num_iterations):\n",
+    "            scores = np.dot(self.w, self.kernel(X)) - self.b\n",
+    "            margins = 1 - scores * y\n",
+    "\n",
+    "            # Compute gradients\n",
+    "            dw = -np.dot(y * (margins > 0), self.kernel(X)) / num_samples + 2 * self.lambda_param * self.w\n",
+    "            db = -np.mean(y * (margins > 0))\n",
+    "\n",
+    "            # Update weights and bias\n",
+    "            self.w -= self.learning_rate * dw\n",
+    "            self.b -= self.learning_rate * db\n",
+    "\n",
+    "        return self.w, self.b\n",
+    "\n",
+    "    def predict(self, X):\n",
+    "        prediction = np.dot(self.w, self.kernel(X)) - self.b\n",
+    "        return np.where(prediction <= 0, 0, 1)\n",
+    "\n",
+    "    def kernel(self, X):\n",
+    "        return np.power(np.dot(X, X.T) + 1, self.degree)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "id": "-0OXjMNm6sR5"
+   },
+   "outputs": [],
+   "source": [
+    "X, y = make_blobs(n_samples=1000, n_features=2, centers=2, random_state=42)\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 472
     },
+    "id": "8nQW96-yJRUI",
+    "outputId": "bcc5cc1b-b4bf-4590-8e02-b89355ecdabf"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 39,
-      "metadata": {
-        "id": "-0OXjMNm6sR5"
-      },
-      "outputs": [],
-      "source": [
-        "X, y = make_blobs(n_samples=1000, n_features=2, centers=2, random_state=42)\n",
-        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "plt.scatter(X[:, 0], X[:, 1], c=y)\n",
+    "plt.xlabel('Feature 1')\n",
+    "plt.ylabel('Feature 2')\n",
+    "plt.title('Generated Data')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
     },
+    "id": "DBPM-4ChBAy7",
+    "outputId": "075d9c0e-7be1-4b84-e66b-a93b8b78ec5c"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 40,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 472
-        },
-        "id": "8nQW96-yJRUI",
-        "outputId": "bcc5cc1b-b4bf-4590-8e02-b89355ecdabf"
-      },
-      "outputs": [
-        {
-          "data": {
-            "image/png": 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",
-            "text/plain": [
-              "<Figure size 640x480 with 1 Axes>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "import matplotlib.pyplot as plt\n",
-        "plt.scatter(X[:, 0], X[:, 1], c=y)\n",
-        "plt.xlabel('Feature 1')\n",
-        "plt.ylabel('Feature 2')\n",
-        "plt.title('Generated Data')\n",
-        "plt.show()"
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 1.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "svm = LinearSVM()\n",
+    "w,b = svm.fit(X_train, y_train)\n",
+    "\n",
+    "# Make predictions on the test set\n",
+    "y_pred = svm.predict(X_test)\n",
+    "\n",
+    "# Calculate the accuracy\n",
+    "accuracy = accuracy_score(y_test, y_pred)\n",
+    "print(\"Accuracy:\", accuracy)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
     },
+    "id": "9xRJtVo8SoiC",
+    "outputId": "a7507743-e293-453c-bca3-2c6035b8f3b6"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 41,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "DBPM-4ChBAy7",
-        "outputId": "075d9c0e-7be1-4b84-e66b-a93b8b78ec5c"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Accuracy: 1.0\n"
-          ]
-        }
-      ],
-      "source": [
-        "svm = LinearSVM()\n",
-        "w,b = svm.fit(X_train, y_train)\n",
-        "\n",
-        "# Make predictions on the test set\n",
-        "y_pred = svm.predict(X_test)\n",
-        "\n",
-        "# Calculate the accuracy\n",
-        "accuracy = accuracy_score(y_test, y_pred)\n",
-        "print(\"Accuracy:\", accuracy)"
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 0.35026469]\n",
+      " [-0.29642149]] [[-1.225325]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "w=w.reshape(-1,1)\n",
+    "b=b.reshape(-1,1)\n",
+    "print(w,b)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 490
     },
+    "id": "Jqf3nUwwKb87",
+    "outputId": "efce2b66-5381-439c-f17a-fa45433f084a"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 42,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "9xRJtVo8SoiC",
-        "outputId": "a7507743-e293-453c-bca3-2c6035b8f3b6"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "[[ 0.35026469]\n",
-            " [-0.29642149]] [[-1.225325]]\n"
-          ]
-        }
-      ],
-      "source": [
-        "w=w.reshape(-1,1)\n",
-        "b=b.reshape(-1,1)\n",
-        "print(w,b)"
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Decision Boundary')"
       ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
     },
     {
-      "cell_type": "code",
-      "execution_count": 44,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 490
-        },
-        "id": "Jqf3nUwwKb87",
-        "outputId": "efce2b66-5381-439c-f17a-fa45433f084a"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "Text(0.5, 1.0, 'Decision Boundary')"
-            ]
-          },
-          "execution_count": 44,
-          "metadata": {},
-          "output_type": "execute_result"
-        },
-        {
-          "data": {
-            "image/png": 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",
-            "text/plain": [
-              "<Figure size 640x480 with 1 Axes>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "plt.scatter(X[:, 0], X[:, 1], c=y)\n",
-        "\n",
-        "x1_min, x1_max = X[:, 0].min(), X[:, 0].max()\n",
-        "x2_min, x2_max = X[:, 1].min(), X[:, 1].max()\n",
-        "\n",
-        "xx1, xx2 = np.meshgrid(np.linspace(x1_min, x1_max), np.linspace(x2_min, x2_max))\n",
-        "grid = np.c_[xx1.ravel(), xx2.ravel()]\n",
-        "probs = (np.dot(grid, w)-b).reshape(xx1.shape)\n",
-        "plt.contour(xx1, xx2, probs, levels=[0.5], colors='blue')\n",
-        "plt.xlabel('Feature 1')\n",
-        "plt.ylabel('Feature 2')\n",
-        "plt.title('Decision Boundary')"
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
-  ],
-  "metadata": {
-    "colab": {
-      "provenance": []
-    },
-    "kernelspec": {
-      "display_name": "Python 3",
-      "name": "python3"
-    },
-    "language_info": {
-      "name": "python"
-    }
+   ],
+   "source": [
+    "plt.scatter(X[:, 0], X[:, 1], c=y)\n",
+    "\n",
+    "x1_min, x1_max = X[:, 0].min(), X[:, 0].max()\n",
+    "x2_min, x2_max = X[:, 1].min(), X[:, 1].max()\n",
+    "\n",
+    "xx1, xx2 = np.meshgrid(np.linspace(x1_min, x1_max), np.linspace(x2_min, x2_max))\n",
+    "grid = np.c_[xx1.ravel(), xx2.ravel()]\n",
+    "probs = (np.dot(grid, w)-b).reshape(xx1.shape)\n",
+    "plt.contour(xx1, xx2, probs, levels=[0.5], colors='blue')\n",
+    "plt.xlabel('Feature 1')\n",
+    "plt.ylabel('Feature 2')\n",
+    "plt.title('Decision Boundary')"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
   },
-  "nbformat": 4,
-  "nbformat_minor": 0
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
 }