Machine Learning Project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "eba8327a",
   "metadata": {},
   "source": [
    "# Machine Learning Final Project  <img style=\"display: inline; align-right: 300px; position: absolute; right: 0px;\" src=\"files/Logo-AIT-Red600x-8.webp\" width=\"130\"/>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a41b73b9",
   "metadata": {},
   "source": [
    "<font color=green>SQUAD ForeCasting</font>\n",
    "\n",
    "👉 Sergio Salvador</br>\n",
    "👉 Berta Pfaff</br>\n",
    "👉 Jesús Moncho</br>\n",
    "👉 Francesc Vilaró</br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "179042b1",
   "metadata": {},
   "source": [
    "On this project, Adult dataset will be used. This dataset comes from the following University of California Irvine path (Url: https://archive.ics.uci.edu/ml/datasets/Census+Income)\n",
    "\n",
    "The objective of the problem is to predict if a person has a salary of more than 50 thousand dollars per year or not, based on their characteristics.\n",
    "\n",
    "The Census Income dataset has <strong>32,561 entries</strong>. Each entry contains the following information about an individual:\n",
    "\n",
    "<font style = \"margin-left: 30px;\">✅ <strong>age:</strong> The age of an individual</font><br>\n",
    "<font style = \"margin-left: 30px;\">✅ <strong>workclass:</strong> A general term to represent the employment status of an individual</font><br>\n",
    "<font style = \"margin-left: 30px;\">✅ <strong>fnlwgt:</strong> Final weight. In other words, this is the number of people the census believes the entry represents..</font><br>\n",
    "<font style = \"margin-left: 30px;\">✅ <strong>education:</strong> The highest level of education achieved by an individual.</font><br>\n",
    "<font style = \"margin-left: 30px;\">✅ <strong>education-num:</strong> The highest level of education achieved in numerical form.</font><br>\n",
    "<font style = \"margin-left: 30px;\">✅ <strong>marital-status:</strong> Marital status of an individual.</font><br>\n",
    "<font style = \"margin-left: 30px;\">✅ <strong>occupation:</strong> The general type of occupation of an individual</font><br>\n",
    "<font style = \"margin-left: 30px;\">✅ <strong>relationship:</strong> Represents what this individual is relative to others</font><br>\n",
    "<font style = \"margin-left: 30px;\">✅ <strong>race:</strong> Descriptions of an individual’s race</font><br>\n",
    "<font style = \"margin-left: 30px;\">✅ <strong>sex:</strong> The biological sex of the individual</font><br>\n",
    "<font style = \"margin-left: 30px;\">✅ <strong>capital-gain:</strong> Capital gains for an individual</font><br>\n",
    "<font style = \"margin-left: 30px;\">✅ <strong>capital-loss:</strong> Capital loss for an individual</font><br>\n",
    "<font style = \"margin-left: 30px;\">✅ <strong>hours-per-week:</strong> The hours an individual has reported to work per week</font><br>\n",
    "<font style = \"margin-left: 30px;\">✅ <strong>native-country:</strong> Country of origin for an individual</font><br>\n",
    "<font style = \"margin-left: 30px;\">✅ <strong>TARGET:</strong> Whether or not an individual makes more than 50k annually.</font><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0832f78",
   "metadata": {},
   "source": [
    "In this project the following libraries have been used"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bf7ed555",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from matplotlib.ticker import FuncFormatter\n",
    "import plotly.express as px\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import StandardScaler, OneHotEncoder, LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import confusion_matrix, plot_confusion_matrix, accuracy_score, precision_score, recall_score\n",
    "from sklearn.metrics import f1_score, roc_auc_score, roc_curve, classification_report\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from mglearn.plots import plot_2d_separator\n",
    "from sklearn.tree import DecisionTreeClassifier, plot_tree"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62023d09",
   "metadata": {},
   "source": [
    "## Importing CSV file and initial transformations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93063601",
   "metadata": {},
   "source": [
    "CSV is imported using Pandas and headers are defined, since the original file doesn't have them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f021e65b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"files/adult.csv\", index_col=False, names = [\"age\", \"Work class\", \"fnlwgt\", \"Education\", \"Education-num\", \"Marital-status\", \"Occupation\", \"Relationship\", \"Race\", \"Sex\", \"Capital-gain\", \"Capital-loss\", \"Hours-per-week\", \"Native-country\", \"TARGET\" ])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4c7f80e",
   "metadata": {},
   "source": [
    "Verification that the file has been imported correctly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b6677918",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>age</th>\n",
       "      <th>Work class</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>Education</th>\n",
       "      <th>Education-num</th>\n",
       "      <th>Marital-status</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Relationship</th>\n",
       "      <th>Race</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Capital-gain</th>\n",
       "      <th>Capital-loss</th>\n",
       "      <th>Hours-per-week</th>\n",
       "      <th>Native-country</th>\n",
       "      <th>TARGET</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>77516</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>83311</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age         Work class  fnlwgt   Education  Education-num  \\\n",
       "0   39          State-gov   77516   Bachelors             13   \n",
       "1   50   Self-emp-not-inc   83311   Bachelors             13   \n",
       "2   38            Private  215646     HS-grad              9   \n",
       "3   53            Private  234721        11th              7   \n",
       "4   28            Private  338409   Bachelors             13   \n",
       "\n",
       "        Marital-status          Occupation    Relationship    Race      Sex  \\\n",
       "0        Never-married        Adm-clerical   Not-in-family   White     Male   \n",
       "1   Married-civ-spouse     Exec-managerial         Husband   White     Male   \n",
       "2             Divorced   Handlers-cleaners   Not-in-family   White     Male   \n",
       "3   Married-civ-spouse   Handlers-cleaners         Husband   Black     Male   \n",
       "4   Married-civ-spouse      Prof-specialty            Wife   Black   Female   \n",
       "\n",
       "   Capital-gain  Capital-loss  Hours-per-week  Native-country  TARGET  \n",
       "0          2174             0              40   United-States   <=50K  \n",
       "1             0             0              13   United-States   <=50K  \n",
       "2             0             0              40   United-States   <=50K  \n",
       "3             0             0              40   United-States   <=50K  \n",
       "4             0             0              40            Cuba   <=50K  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16af72b3",
   "metadata": {},
   "source": [
    "Column type is checked to determine if any column needs a transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cd11f935",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 32561 entries, 0 to 32560\n",
      "Data columns (total 15 columns):\n",
      " #   Column          Non-Null Count  Dtype \n",
      "---  ------          --------------  ----- \n",
      " 0   age             32561 non-null  int64 \n",
      " 1   Work class      32561 non-null  object\n",
      " 2   fnlwgt          32561 non-null  int64 \n",
      " 3   Education       32561 non-null  object\n",
      " 4   Education-num   32561 non-null  int64 \n",
      " 5   Marital-status  32561 non-null  object\n",
      " 6   Occupation      32561 non-null  object\n",
      " 7   Relationship    32561 non-null  object\n",
      " 8   Race            32561 non-null  object\n",
      " 9   Sex             32561 non-null  object\n",
      " 10  Capital-gain    32561 non-null  int64 \n",
      " 11  Capital-loss    32561 non-null  int64 \n",
      " 12  Hours-per-week  32561 non-null  int64 \n",
      " 13  Native-country  32561 non-null  object\n",
      " 14  TARGET          32561 non-null  object\n",
      "dtypes: int64(6), object(9)\n",
      "memory usage: 3.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bd470e2",
   "metadata": {},
   "source": [
    "Object type columns are transformed to categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b8613a98",
   "metadata": {},
   "outputs": [],
   "source": [
    "only_obj = df.select_dtypes(include=[\"object\"])\n",
    "only_numeric = df.select_dtypes(include=[\"int64\", \"float64\"])\n",
    "\n",
    "for column_ in only_obj.columns:\n",
    "    df[column_] = pd.Categorical(df[column_])\n",
    "    \n",
    "only_cat = df.select_dtypes(include=[\"category\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "724271c9",
   "metadata": {},
   "source": [
    "We can now see that object type columns have been transformed to category correctly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9c9a7777",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 32561 entries, 0 to 32560\n",
      "Data columns (total 15 columns):\n",
      " #   Column          Non-Null Count  Dtype   \n",
      "---  ------          --------------  -----   \n",
      " 0   age             32561 non-null  int64   \n",
      " 1   Work class      32561 non-null  category\n",
      " 2   fnlwgt          32561 non-null  int64   \n",
      " 3   Education       32561 non-null  category\n",
      " 4   Education-num   32561 non-null  int64   \n",
      " 5   Marital-status  32561 non-null  category\n",
      " 6   Occupation      32561 non-null  category\n",
      " 7   Relationship    32561 non-null  category\n",
      " 8   Race            32561 non-null  category\n",
      " 9   Sex             32561 non-null  category\n",
      " 10  Capital-gain    32561 non-null  int64   \n",
      " 11  Capital-loss    32561 non-null  int64   \n",
      " 12  Hours-per-week  32561 non-null  int64   \n",
      " 13  Native-country  32561 non-null  category\n",
      " 14  TARGET          32561 non-null  category\n",
      "dtypes: category(9), int64(6)\n",
      "memory usage: 1.8 MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38450dc4",
   "metadata": {},
   "source": [
    "## EDA"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19e5c6cc",
   "metadata": {},
   "source": [
    "### Target variable"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e8921a9",
   "metadata": {},
   "source": [
    "The original dataset contains a distribution of 23.93% entries labeled with >50k and 76.07% entries labeled with <=50k. For a better prediction, training and test sets will have to maintain the above distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "86e596f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Count of values <=50K: 24720, 75.92%\n",
      "Count of values  >50K:  7841, 24.08%\n",
      "\n"
     ]
    }
   ],
   "source": [
    "n_bigger_50k = df[\"TARGET\"].value_counts()[1]\n",
    "n_lower_50k = df[\"TARGET\"].value_counts()[0]\n",
    "total = n_bigger_50k + n_lower_50k\n",
    "\n",
    "print(f\"\"\"\n",
    "Count of values <=50K: {n_lower_50k}, {round((n_lower_50k/total)*100, 2)}%\n",
    "Count of values  >50K:  {n_bigger_50k}, {round((n_bigger_50k/total)*100, 2)}%\n",
    "\"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82a57c1f",
   "metadata": {},
   "source": [
    "### Independent variables"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "773ad3be",
   "metadata": {},
   "source": [
    "#### Categorical features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c006d798",
   "metadata": {},
   "source": [
    "Unique values are evaluated for categorical columns to check if there are missing or mispelled values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ccb6f2e2",
   "metadata": {},
   "source": [
    "##### Unique values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edab87f4",
   "metadata": {},
   "source": [
    "It is observed that some categorical columns present missing values as '?'. These will have to be set to NA and then dropped from the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ae337e8f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[' State-gov',\n",
       " ' Self-emp-not-inc',\n",
       " ' Private',\n",
       " ' Federal-gov',\n",
       " ' Local-gov',\n",
       " ' ?',\n",
       " ' Self-emp-inc',\n",
       " ' Without-pay',\n",
       " ' Never-worked']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Work class\"].unique().to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d201d8e7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[' Bachelors',\n",
       " ' HS-grad',\n",
       " ' 11th',\n",
       " ' Masters',\n",
       " ' 9th',\n",
       " ' Some-college',\n",
       " ' Assoc-acdm',\n",
       " ' Assoc-voc',\n",
       " ' 7th-8th',\n",
       " ' Doctorate',\n",
       " ' Prof-school',\n",
       " ' 5th-6th',\n",
       " ' 10th',\n",
       " ' 1st-4th',\n",
       " ' Preschool',\n",
       " ' 12th']"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Education\"].unique().to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "cab4472d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[' Never-married',\n",
       " ' Married-civ-spouse',\n",
       " ' Divorced',\n",
       " ' Married-spouse-absent',\n",
       " ' Separated',\n",
       " ' Married-AF-spouse',\n",
       " ' Widowed']"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Marital-status\"].unique().to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9bb6a04f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[' Adm-clerical',\n",
       " ' Exec-managerial',\n",
       " ' Handlers-cleaners',\n",
       " ' Prof-specialty',\n",
       " ' Other-service',\n",
       " ' Sales',\n",
       " ' Craft-repair',\n",
       " ' Transport-moving',\n",
       " ' Farming-fishing',\n",
       " ' Machine-op-inspct',\n",
       " ' Tech-support',\n",
       " ' ?',\n",
       " ' Protective-serv',\n",
       " ' Armed-Forces',\n",
       " ' Priv-house-serv']"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Occupation\"].unique().to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c46b62da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[' Not-in-family',\n",
       " ' Husband',\n",
       " ' Wife',\n",
       " ' Own-child',\n",
       " ' Unmarried',\n",
       " ' Other-relative']"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Relationship\"].unique().to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "426db56f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[' White', ' Black', ' Asian-Pac-Islander', ' Amer-Indian-Eskimo', ' Other']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Race\"].unique().to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d00968d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[' Male', ' Female']"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Sex\"].unique().to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a9de7526",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[' United-States',\n",
       " ' Cuba',\n",
       " ' Jamaica',\n",
       " ' India',\n",
       " ' ?',\n",
       " ' Mexico',\n",
       " ' South',\n",
       " ' Puerto-Rico',\n",
       " ' Honduras',\n",
       " ' England',\n",
       " ' Canada',\n",
       " ' Germany',\n",
       " ' Iran',\n",
       " ' Philippines',\n",
       " ' Italy',\n",
       " ' Poland',\n",
       " ' Columbia',\n",
       " ' Cambodia',\n",
       " ' Thailand',\n",
       " ' Ecuador',\n",
       " ' Laos',\n",
       " ' Taiwan',\n",
       " ' Haiti',\n",
       " ' Portugal',\n",
       " ' Dominican-Republic',\n",
       " ' El-Salvador',\n",
       " ' France',\n",
       " ' Guatemala',\n",
       " ' China',\n",
       " ' Japan',\n",
       " ' Yugoslavia',\n",
       " ' Peru',\n",
       " ' Outlying-US(Guam-USVI-etc)',\n",
       " ' Scotland',\n",
       " ' Trinadad&Tobago',\n",
       " ' Greece',\n",
       " ' Nicaragua',\n",
       " ' Vietnam',\n",
       " ' Hong',\n",
       " ' Ireland',\n",
       " ' Hungary',\n",
       " ' Holand-Netherlands']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Native-country\"].unique().to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "86dac7ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[' <=50K', ' >50K']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"TARGET\"].unique().to_list()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1cfc183",
   "metadata": {},
   "source": [
    "##### Bar plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "3d09a4ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x17000 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tempList1 = only_cat.columns\n",
    "\n",
    "subplot = 1\n",
    "plt.figure(figsize = (10,170))\n",
    "for i in tempList1:\n",
    "    plt.subplot(9,1,subplot)\n",
    "    sns.countplot(data = df, x = i, hue=\"TARGET\")\n",
    "    plt.xticks(rotation=90)\n",
    "    subplot = subplot + 1\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1f42132",
   "metadata": {},
   "source": [
    "#### Numerical features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6960de06",
   "metadata": {},
   "source": [
    "##### Heatmap"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32c92f3f",
   "metadata": {},
   "source": [
    "A heatmap is generated to see if there is any correlation between independent variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9617c2e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "\n",
    "sns.heatmap(df.corr(),\n",
    "            cmap='BrBG',\n",
    "            fmt='.2f',\n",
    "            linewidths=2,\n",
    "            annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b421330",
   "metadata": {},
   "source": [
    "##### Histograms"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b81d6805",
   "metadata": {},
   "source": [
    "Histograms of the numerical variables are generated to observe the data distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e4c3859f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+fZSfn2/32Nja7HbuY3Mebl9l3DPmPNy+yrhHy3moXNWtz7dt26bu3bvL29vbLua7777T8ePHb/i4PG6iD1DDGYah8ePH64EHHlBUVJQkKSsrS5IUGhpqFxsaGqoTJ044PEdH2Ldvn6Kjo3Xp0iXVqVNH69atU+vWrc0/gK7SFwkJCUpNTdXu3btLtbna56Jz58765z//qRYtWujUqVN65ZVX1LVrVx04cMDl+uKbb77RokWLNH78eL3wwgvauXOnxo4dK29vb/361792uf4o8e677+rMmTMaNmyYJNf7MzJ58mTZbDbdfffdcnd3V1FRkV599VU99thjklyrPwICAhQdHa0///nPatWqlUJDQ/Wvf/1LO3bsUPPmzV2qL1C5cnJyVFRUVOZnp+Rzda2srKwy4y9fvqycnByFh4dXWb5V5Vb64VqzZs3ShQsXNGTIkKpI0WFupS+OHDmi559/Xlu2bJGHR+35p96t9MU333yjrVu3ysfHR+vWrVNOTo5GjhypH374Qf/4xz8ckXalu5V+6Nq1q1atWqVHH31Uly5d0uXLlzVo0CDNnz/fESlXG7Xx+7Ky1JbvzFtRW78zAWcq69qjq6joOpuruN41JVdwvesnrojrJKVdunRJzz//vIYOHarAwEBnp+NQM2bMkIeHh8aOHevsVJwiOztb58+f12uvvaZXXnlFM2bMUGJioh555BFt2rRJ3bt3d3aKqOEq47oafnSr99WzsrLk5eWlevXqlYopeX9WVpZCQkJK7TMkJMQu5tr91KtXT15eXi5xPm/3Pjbn4fZVxj1jzsPtq4x7tJyHylXd+jwrK0tNmjQptZ+StqZNm97QcXF11IWMHj1ae/fu1datW0u1WSwWu9eGYZRaV1u0bNlSaWlpOnPmjNasWaMnn3xSycnJZrsr9MXJkyc1btw4bdiwodTsWVdzhb6QpH79+pn/36ZNG0VHR+vOO+/U8uXL1aVLF0mu0xfFxcXq2LGjpk2bJklq3769Dhw4oEWLFtld6HWV/iixZMkS9evXz66qX3Kdfnj77be1cuVKrV69Wvfcc4/S0tIUFxeniIgIPfnkk2acq/THihUr9NRTT+mOO+6Qu7u77rvvPg0dOlR79uwxY1ylL1D5bvazU1Z8Wetrmlv9M/Svf/1L8fHxeu+998r8R1dNdKN9UVRUpKFDh+qll15SixYtHJWeQ93M56K4uFgWi0WrVq2S1WqVJM2ePVu/+MUv9Ne//lW+vr5Vnm9VuZl+OHjwoMaOHas//elP6tOnjzIzM/Xcc8/p2Wef1ZIlSxyRbrVRW78vb0dt/M68Ua7wnQk4Q0XXHmuzG73OVtvd6DWl2uxGr5+4Iq6TXFFYWKhf/epXKi4u1sKFC52djkOlpqbqjTfe0J49e1zy3EtXvicl6eGHH9bvf/97SdK9996rlJQUvfnmmxQPotLwnVs5Kvu++rUxZcXfSkxtVJX3sTkPN64q7xlzHm5cVd6j5TzcnurU55Vx7ZvHFruIMWPG6P3339emTZvUsGFDc31YWJgklaoEzs7OLlXBWlt4eXnprrvuUseOHTV9+nS1a9dOb7zxhkv1RWpqqrKzs9WhQwd5eHjIw8NDycnJmjdvnjw8POwqka9WG/uiLP7+/mrTpo2OHDniUp8LSQoPD1fr1q3t1rVq1Urp6emSXPM748SJE9q4caOefvppc52r9cNzzz2n559/Xr/61a/Upk0bxcbG6ve//72mT58uyfX6484771RycrLOnz+vkydPaufOnSosLFTTpk1dri9QeYKDg+Xu7n5Tn52wsLAy4z08PFS/fv0qy7Uq3Uo/lHj77bc1fPhw/b//9/9qxaPibrYvzp07p927d2v06NHm+O7ll1/Wf//7X3l4eOjTTz91VOqV7lY+F+Hh4brjjjvMwkHpypjGMAxlZGRUab5V5Vb6Yfr06erWrZuee+45tW3bVn369NHChQv1j3/8Q5mZmY5Iu1qojd+Xt6u2fWferNr8nQk4S3nXHl3B9a6zFRUVOTtFh7jeNSVXcL3rJ66I6yQ/Kiws1JAhQ3Ts2DElJSW53KyDW7ZsUXZ2tiIjI83vyhMnTmjChAmlZkqprYKDg+Xh4eHy35WoOrdzXQ32bue+elhYmAoKCpSbm1thzKlTp0rt9/vvv7eLuXY/ubm5KiwsrPXnszLuY3Mebl9l3DPmPNy+yrhHy3moXNWtz8u79i2Vnh2xIhQP1nKGYWj06NFau3atPv3001JTUpYUOiQlJZnrCgoKlJycrK5duzo6XacwDEP5+fku1Rc9e/bUvn37lJaWZi4dO3bU448/rrS0NDVr1sxl+qIs+fn5OnTokMLDw13qcyFJ3bp10+HDh+3WffXVV2rcuLEk1/zOWLp0qUJCQtS/f39znav1w8WLF+XmZj9kcHd3N38t62r9UcLf31/h4eHKzc3V+vXr9fDDD7tsX+D2eXl5qUOHDnafHUlKSkoq97MTHR1dKn7Dhg3q2LGjPD09qyzXqnQr/SBdmT1r2LBhWr16td33dU12s30RGBhYanz37LPPmrNud+7c2VGpV7pb+Vx069ZN3333nc6fP2+u++qrr+Tm5lZjCxpupR/K+ztc+vHXh66gNn5f3o7a+J15s2rzdybgaNe79ugKrnedreTv3trueteUXMH1rp+4Iq6TXFFSOHjkyBFt3LjRJX/AEhsbq71799p9V0ZEROi5557T+vXrnZ2eQ3h5een+++93+e9KVJ1bva6GH1XGffUOHTrI09PTLiYzM1P79+83Y6Kjo2Wz2bRz504zZseOHbLZbHYx+/fvt/vx54YNG+Tt7a0OHTpU/sFXI5VxH5vzcPsq454x5+H2VcY9Ws5D5apufR4dHa3PPvtMBQUFdjERERE39yMdA7Xa7373O8NqtRqbN282MjMzzeXixYtmzGuvvWZYrVZj7dq1xr59+4zHHnvMCA8PN86ePevEzKvGlClTjM8++8w4duyYsXfvXuOFF14w3NzcjA0bNhiG4Vp9ca3u3bsb48aNM1+7Ul9MmDDB2Lx5s/HNN98Y27dvNwYMGGAEBAQYx48fNwzDtfpi586dhoeHh/Hqq68aR44cMVatWmX4+fkZK1euNGNcqT+KioqMyMhIY/LkyaXaXKkfnnzySeOOO+4wPvzwQ+PYsWPG2rVrjeDgYGPSpElmjCv1R2JiovGf//zH+Oabb4wNGzYY7dq1Mzp16mQUFBQYhuFafYHKlZCQYHh6ehpLliwxDh48aMTFxRn+/v7m30fPP/+8ERsba8Z/8803hp+fn/H73//eOHjwoLFkyRLD09PT+Pe//+2sQ6gUN9sPq1evNjw8PIy//vWvduPdM2fOOOsQKs3N9sW1XnzxRaNdu3YOyrZq3WxfnDt3zmjYsKHxi1/8wjhw4ICRnJxsNG/e3Hj66aeddQiV4mb7YenSpYaHh4excOFC4+jRo8bWrVuNjh07Gp06dXLWIVSKc+fOGV988YXxxRdfGJKM2bNnG1988YVx4sQJwzBc5/vSMG6+L2rzd+bN9sW1atN3JuBIN3Lt0RVde53NFdzINaXa7kaun9RG1/s72BWuk1TUB4WFhcagQYOMhg0bGmlpaXbflfn5+c5OvVJd77NwrcaNGxtz5sxxbJJV7Hp9sHbtWsPT09P429/+Zhw5csSYP3++4e7ubmzZssXJmaO2uN51A1Sssu6rP/vss0bDhg2NjRs3Gnv27DF++tOfGu3atTMuX75sxvTt29do27atsW3bNmPbtm1GmzZtjAEDBpjtly9fNqKiooyePXsae/bsMTZu3Gg0bNjQGD16tGM6o5q5lfvYnIfbU1n3jDkPt6ey7tFyHm5OZfwbx1F9fubMGSM0NNR47LHHjH379hlr1641AgMDjZkzZ97UMVM8WMtJKnNZunSpGVNcXGy8+OKLRlhYmOHt7W089NBDxr59+5yXdBV66qmnjMaNGxteXl5GgwYNjJ49e5qFg4bhWn1xrWsHXa7UF48++qgRHh5ueHp6GhEREcYjjzxiHDhwwGx3pb4wDMP44IMPjKioKMPb29u4++67jb/97W927a7UH+vXrzckGYcPHy7V5kr9cPbsWWPcuHFGZGSk4ePjYzRr1syYOnWq3QVOV+qPt99+22jWrJnh5eVlhIWFGaNGjbK74e5KfYHK99e//tUcq9x3331GcnKy2fbkk08a3bt3t4vfvHmz0b59e8PLy8to0qSJsWjRIgdnXDVuph+6d+9e5nj3ySefdHziVeBmPxNXq22FMDfbF4cOHTJ69epl+Pr6Gg0bNjTGjx9fKwoZbrYf5s2bZ7Ru3drw9fU1wsPDjccff9zIyMhwcNaVa9OmTRX+uXel78ub7Yva/J15K5+Lq9W270zAUW7k2qMrcsXiQcO4/jWl2u5Grp/URtf7O9gVrpNU1AfHjh0r97ty06ZNzk69Ul3vs3Ct2lg8eCN9sGTJEuOuu+4yfHx8jHbt2hnvvvuu8xJGrVTRdQNUrLLuq+fl5RmjR482goKCDF9fX2PAgAFGenq6Xczp06eNxx9/3AgICDACAgKMxx9/3MjNzbWLOXHihNG/f3/D19fXCAoKMkaPHm1cunSpqg6/WruV+9ich9tXGfeMOQ+3p7Lu0XIebk5l/BvHkX2+d+9e48EHHzS8vb2NsLAwIz4+3iguLr6pY7YYhgs9qwgAAAAAAAAAAAAAAAAAAMjt+iEAAAAAAAAAAAAAAAAAAKA2oXgQAAAAAAAAAAAAAAAAAAAXQ/EgAAAAAAAAAAAAAAAAAAAuhuJBAAAAAAAAAAAAAAAAAABcDMWDAAAAAAAAAAAAAAAAAAC4GIoHAQAAAAAAAAAAAAAAAABwMRQPAgAAAAAAAAAAAAAAAADgYigeBAAAAAAAAAAAAAAAAADAxVA8CAAAAADALTAMQ7/97W8VFBQki8WitLS0677HYrHo3XffrfLcADjfZ599poEDByoiIuKW/+wbhqGZM2eqRYsW8vb2VqN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      "text/plain": [
       "<Figure size 3200x1800 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tempList1 = only_numeric.columns\n",
    "\n",
    "subplot = 1\n",
    "plt.figure(figsize = (32,18))\n",
    "for i in tempList1:\n",
    "    plt.subplot(3,4,subplot)\n",
    "    sns.histplot(data = df, x = i)\n",
    "    subplot = subplot + 1\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a93d110",
   "metadata": {},
   "source": [
    "##### Boxplots"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "195ae81b",
   "metadata": {},
   "source": [
    "To detect the presence of outliers, boxplots are generated. \n",
    "\n",
    "It is observed that various variables present a substancial amount of outliers. This will be adressed further on and will require a different approach for every variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3837b71b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AAFIRugAAAKQidAEAAEhF6AIAAJCK0AUAACAVoQsAAEAqQhcAAIBUhC4AAACpCF0AAABSEboAAACkInQBAABIRegCAACQitAFAAAgFaELAABAKkIXAACAVIQuAAAAqQhdAAAAUhG6AAAApCJ0AQAASEXoAgAAkIrQBQAAIBWhCwAAQCpCFwAAgFSELgAAAKkIXQAAAFIRugAAAKQidAEAAEhF6AIAAJCK0AUAACAVoQsAAEAqQhcAAIBUhC4AAACpCF0AAABSEboAAACkInQBAABIRegCAACQitAFAAAgFaELAABAKkIXAACAVIQuAAAAqQhdAAAAUhG6AAAApCJ0AQAASEXoAgAAkIrQBQAAIBWhCwAAQCo1lR4A1oRS0+IoKj0EzS35sOXPSa/UtLjSIwAAyQld1grrzPxJpUegDQ1P3VbpEQAASMRLlwEAAEjFEV3Sqquri8mTJ1d6DFpRFEUsWrQoIiJqa2ujVCpVeCIqoa6urtIjAAAJCV3SKpVKUV9fX+kxaEOfPn0qPQIAAAl56TIAAACpCF0AAABSEboAAACkInQBAABIRegCAACQitAFAAAgFaELAABAKkIXAACAVIQuAAAAqQhdAAAAUhG6AAAApCJ0AQAASEXoAgAAkIrQBQAAIBWhCwAAQCpCFwAAgFSELgAAAKkIXQAAAFIRugAAAKQidAEAAEhF6AIAAJCK0AUAACAVoQsAAEAqQhcAAIBUhC4AAACpCF0AAABSEboAAACkInQBAABIRegCAACQSk1nr1gURUREzJs3b7UNAwAAAK1Z2p9Le7Q1nQ7d+fPnR0TEyJEjO7sLAAAA6LD58+dH//79W728VKwshVvR1NQUc+bMiYaGhiiVSp0ecG0yb968GDlyZLzyyivRr1+/So/DKrKeuVjPPKxlLtYzF+uZi/XMpaesZ1EUMX/+/Bg+fHhUVbX+m7idPqJbVVUVI0aM6OzV12r9+vXr1l88dIz1zMV65mEtc7GeuVjPXKxnLj1hPds6kruUN6MCAAAgFaELAABAKkJ3DaqtrY3zzz8/amtrKz0Kq4H1zMV65mEtc7GeuVjPXKxnLtnWs9NvRgUAAADdkSO6AAAApCJ0AQAASEXoAgAAkIrQBQAAIBWhu5pdfPHF8dGPfjQaGhpi8ODB8alPfSqee+65ZtsURREXXHBBDB8+POrr66OxsTGeeeaZCk1MW6655prYbrvtyn84e7fddovJkyeXL7eWPdfFF18cpVIpJkyYUD7PevYsF1xwQZRKpWYfQ4cOLV9uPXuWP/7xj3H00UfHwIEDo0+fPrHDDjvEjBkzypdbz55jww03XOF7s1QqxcknnxwR1rKnWbx4cZx77rmx0UYbRX19fWy88cZx4YUXRlNTU3kba9qzzJ8/PyZMmBAbbLBB1NfXx+677x7Tp08vX55lPYXuajZt2rQ4+eST49e//nU88MADsXjx4jjggAPi/fffL29z2WWXxZVXXhlXX311TJ8+PYYOHRr7779/zJ8/v4KT05IRI0bEJZdcEo8//ng8/vjj8Td/8zfxyU9+svzNbi17punTp8d1110X2223XbPzrWfPs80228Rrr71W/nj66afLl1nPnuPtt9+OPfbYI3r16hWTJ0+OZ599Nq644ooYMGBAeRvr2XNMnz692fflAw88EBERn//85yPCWvY0l156aVx77bVx9dVXx6xZs+Kyyy6Lyy+/PL73ve+Vt7GmPcsJJ5wQDzzwQPzrv/5rPP3003HAAQfEfvvtF3/84x8jItF6FnSpuXPnFhFRTJs2rSiKomhqaiqGDh1aXHLJJeVtFi5cWPTv37+49tprKzUmHbDuuusWP/rRj6xlDzV//vxis802Kx544IFin332KcaPH18Uhe/Nnuj8888vtt9++xYvs549y6RJk4o999yz1cutZ882fvz4YpNNNimampqsZQ90yCGHFMcff3yz8z7zmc8URx99dFEUvj97mgULFhTV1dXFvffe2+z87bffvjjnnHNSracjul3s3XffjYiI9dZbLyIiXn755Xj99dfjgAMOKG9TW1sb++yzTzz66KMVmZH2WbJkSdx2223x/vvvx2677WYte6iTTz45DjnkkNhvv/2anW89e6YXXnghhg8fHhtttFEcccQR8dJLL0WE9exp7r777hgzZkx8/vOfj8GDB8eOO+4YP/zhD8uXW8+e64MPPoibb745jj/++CiVStayB9pzzz3jwQcfjOeffz4iIp566ql45JFH4uCDD44I3589zeLFi2PJkiVRV1fX7Pz6+vp45JFHUq2n0O1CRVHExIkTY88994zRo0dHRMTrr78eERFDhgxptu2QIUPKl9G9PP3007HOOutEbW1tnHTSSXHnnXfG1ltvbS17oNtuuy1mzJgRF1988QqXWc+eZ9ddd42bbrop7r///vjhD38Yr7/+euy+++7x5ptvWs8e5qWXXoprrrkmNttss7j//vvjpJNOilNPPTVuuummiPD92ZPddddd8c4778QXv/jFiLCWPdGkSZPiyCOPjC233DJ69eoVO+64Y0yYMCGOPPLIiLCmPU1DQ0Pstttu8Y1vfCPmzJkTS5YsiZtvvjkee+yxeO2111KtZ02lB8jsK1/5Svz2t7+NRx55ZIXLSqVSs9NFUaxwHt3DFltsETNnzox33nknfvazn8Vxxx0X06ZNK19uLXuGV155JcaPHx9TpkxZ4X8xl2U9e46xY8eWP992221jt912i0022SR+/OMfx8c+9rGIsJ49RVNTU4wZMyYuuuiiiIjYcccd45lnnolrrrkmjj322PJ21rPnuf7662Ps2LExfPjwZudby57j9ttvj5tvvjluvfXW2GabbWLmzJkxYcKEGD58eBx33HHl7axpz/Gv//qvcfzxx8f6668f1dXVsdNOO8W4cePiiSeeKG+TYT0d0e0ip5xyStx9993x0EMPxYgRI8rnL31H0OX/R2Tu3Lkr/M8J3UPv3r1j0003jTFjxsTFF18c22+/fVx11VXWsoeZMWNGzJ07N3beeeeoqamJmpqamDZtWnz3u9+Nmpqa8ppZz56rb9++se2228YLL7zg+7OHGTZsWGy99dbNzttqq61i9uzZEeFnZ0/1hz/8IX7xi1/ECSecUD7PWvY8Z555Zpx99tlxxBFHxLbbbhvHHHNMnHbaaeVXR1nTnmeTTTaJadOmxXvvvRevvPJK/OY3v4kPP/wwNtpoo1TrKXRXs6Io4itf+Urccccd8V//9V+x0UYbNbt86RfQ0ncgjPjr769MmzYtdt999zU9Lp1QFEUsWrTIWvYw++67bzz99NMxc+bM8seYMWPiqKOOipkzZ8bGG29sPXu4RYsWxaxZs2LYsGG+P3uYPfbYY4U/xff888/HBhtsEBF+dvZUN9xwQwwePDgOOeSQ8nnWsudZsGBBVFU1T4bq6urynxeypj1X3759Y9iwYfH222/H/fffH5/85CdzrWeF3gQrrb//+78v+vfvX0ydOrV47bXXyh8LFiwob3PJJZcU/fv3L+64447i6aefLo488shi2LBhxbx58yo4OS356le/Wvzyl78sXn755eK3v/1t8Y//+I9FVVVVMWXKlKIorGVPt+y7LheF9expTj/99GLq1KnFSy+9VPz6178uPvGJTxQNDQ3F73//+6IorGdP8pvf/KaoqakpvvWtbxUvvPBCccsttxR9+vQpbr755vI21rNnWbJkSTFq1Khi0qRJK1xmLXuW4447rlh//fWLe++9t3j55ZeLO+64oxg0aFBx1llnlbexpj3LfffdV0yePLl46aWXiilTphTbb799scsuuxQffPBBURR51lPormYR0eLHDTfcUN6mqampOP/884uhQ4cWtbW1xd577108/fTTlRuaVh1//PHFBhtsUPTu3bv4yEc+Uuy7777lyC0Ka9nTLR+61rNn+cIXvlAMGzas6NWrVzF8+PDiM5/5TPHMM8+UL7eePcs999xTjB49uqitrS223HLL4rrrrmt2ufXsWe6///4iIornnntuhcusZc8yb968Yvz48cWoUaOKurq6YuONNy7OOeecYtGiReVtrGnPcvvttxcbb7xx0bt372Lo0KHFySefXLzzzjvly7OsZ6koiqKCB5QBAABgtfI7ugAAAKQidAEAAEhF6AIAAJCK0AUAACAVoQsAAEAqQhcAAIBUhC4AAACpCF0AAABSEboAAACkInQBAABIRegCwGpy3333xZ577hkDBgyIgQMHxic+8Yl48cUXy5c/+uijscMOO0RdXV2MGTMm7rrrriiVSjFz5szyNs8++2wcfPDBsc4668SQIUPimGOOiTfeeKMC9wYAei6hCwCryfvvvx8TJ06M6dOnx4MPPhhVVVXx6U9/OpqammL+/Plx6KGHxrbbbhtPPPFEfOMb34hJkyY1u/5rr70W++yzT+ywww7x+OOPx3333Rd/+tOf4vDDD6/QPQKAnqlUFEVR6SEAIKM///nPMXjw4Hj66afjkUceiXPPPTdeffXVqKuri4iIH/3oR/G3f/u38eSTT8YOO+wQ5513Xjz22GNx//33l/fx6quvxsiRI+O5556LzTffvFJ3BQB6FEd0AWA1efHFF2PcuHGx8cYbR79+/WKjjTaKiIjZs2fHc889F9ttt105ciMidtlll2bXnzFjRjz00EOxzjrrlD+23HLL8r4BgPapqfQAAJDFoYceGiNHjowf/vCHMXz48GhqaorRo0fHBx98EEVRRKlUarb98i+qampqikMPPTQuvfTSFfY9bNiwLp0dADIRugCwGrz55psxa9as+Od//ufYa6+9IiLikUceKV++5ZZbxi233BKLFi2K2traiIh4/PHHm+1jp512ip/97Gex4YYbRk2NH9EA0FleugwAq8G6664bAwcOjOuuuy7+93//N/7rv/4rJk6cWL583Lhx0dTUFH/3d38Xs2bNivvvvz++/e1vR0SUj/SefPLJ8dZbb8WRRx4Zv/nNb+Kll16KKVOmxPHHHx9LliypyP0CgJ5I6ALAalBVVRW33XZbzJgxI0aPHh2nnXZaXH755eXL+/XrF/fcc0/MnDkzdthhhzjnnHPivPPOi4go/97u8OHD41e/+lUsWbIkDjzwwBg9enSMHz8++vfvH1VVfmQDQHt512UAqJBbbrklvvSlL8W7774b9fX1lR4HANLwC0AAsIbcdNNNsfHGG8f6668fTz31VEyaNCkOP/xwkQsAq5nQBYA15PXXX4/zzjsvXn/99Rg2bFh8/vOfj29961uVHgsA0vHSZQAAAFLxzhYAAACkInQBAABIRegCAACQitAFAAAgFaELAABAKkIXAACAVIQuAAAAqQhdAAAAUhG6AAAApCJ0AQAASEXoAgAAkIrQBQAAIBWhCwAAQCpCFwAAgFSELgAAAKkIXQAAAFIRugAAAKQidAEAAEhF6AIAAJCK0AUAACAVoQsAAEAqNZ29YlNTU8yZMycaGhqiVCqtzpkAAABgBUVRxPz582P48OFRVdX6cdtOh+6cOXNi5MiRnb06AAAAdMorr7wSI0aMaPXyToduQ0ND+Qb69evX2d0AAABAu8ybNy9GjhxZ7tHWdDp0l75cuV+/fkIXAACANWZlvz7rzagAAABIRegCAACQitAFAAAgFaELAABAKkIXAACAVIQuAAAAqQhdAAAAUhG6AAAApCJ0AQAASEXoAgAAkIrQBQAAIBWhCwAAQCpCFwAAgFSELgAAAKkIXQAAAFIRugAAAKQidAEAAEhF6AIAAJCK0AUAACAVoQsAAEAqQhcAAIBUhC4AAACpCF0AAABSEboAAACkInQBAABIRegCAACQitAFAAAgFaELAABAKjWVHoDVqyiKWLhwYaXHiKIoYtGiRRERUVtbG6VSqcITRdTV1XWLOQAAgK4ldJNZuHBhjB07ttJjdEuTJ0+O+vr6So8BAAB0MS9dBgAAIBVHdBN7b4cjo6iq0BIv+TAanrotIiLmb39ERHWvioxRaloc68z8SUVuGwAAqAyhm1hRVVOxwGymulfF5igqcqsAAEAleekyAAAAqQhdAAAAUhG6AAAApCJ0AQAASEXoAgAAkIrQBQAAIBWhCwAAQCpCFwAAgFSELgAAAKkIXQAAAFIRugAAAKQidAEAAEhF6AIAAJCK0AUAACAVoQsAAEAqQhcAAIBUhC4AAACpCF0AAABSEboAAACkInQBAABIRegCAACQitAFAAAgFaELAABAKkIXAACAVIQuAAAAqQhdAAAAUhG6AAAApCJ0AQAASEXoAgAAkIrQBQAAIBWhCwAAQCpCFwAAgFSELgAAAKkIXQAAAFIRugAAAKQidAEAAEhF6AIAAJCK0AUAACAVoQsAAEAqQhcAAIBUhC4AAACpCF0AAABSEboAAACkInQBAABIRegCAACQitAFAAAgFaELAABAKkIXAACAVIQuAAAAqQhdAAAAUhG6AAAApCJ0AQAASEXoAgAAkIrQBQAAIBWhCwAAQCpCFwAAgFSELgAAAKkIXQAAAFIRugAAAKQidAEAAEhF6AIAAJBKTaUH6EpFUcTChQsjIqKuri5KpVKFJwKW8v0JAEBXSX1Ed+HChTF27NgYO3Zs+Qk10D34/gQAoKukDl0AAADWPkIXAACAVIQuAAAAqQhdAAAAUhG6AAAApCJ0AQAASEXoAgAAkIrQBQAAIBWhCwAAQCpCFwAAgFSELgAAAKkIXQAAAFIRugAAAKQidAEAAEhF6AIAAJCK0AUAACAVoQsAAEAqQhcAAIBUhC4AAACpCF0AAABSEboAAACkInQBAABIRegCAACQitAFAAAgFaELAABAKkIXAACAVIQuAAAAqQhdAAAAUhG6AAAApCJ0AQAASEXoAgAAkIrQBQAAIBWhCwAAQCpCFwAAgFSELgAAAKkIXQAAAFIRugAAAKQidAEAAEhF6AIAAJCK0AUAACAVoQsAAEAqQhcAAIBUhC4AAACpCF0AAABSEboAAACkInQBAABIRegCAACQitAFAAAgFaELAABAKkIXAACAVIQuAAAAqQhdAAAAUhG6AAAApCJ0AQAASEXoAgAAkIrQBQAAIBWhCwAAQCpCFwAAgFSELgAAAKkIXQAAAFIRugAAAKRSU+kBAMaOHVvpEbqNQYMGxRtvvNFl+29oaIj58+dHTU1N1NbWxl/+8pdoamoqX15dXR1LlixZ4XqjRo2KOXPmxOLFi5udX1VVFXvvvXdMmzYtiqKIiIjGxsa44IILyts8+uijcdVVV0VtbW3Mnj272fUbGxtj5syZERFx1llnxaxZs+KWW26JvffeO6ZPnx4LFiyI6urqWLx4cVRVVUVTU1NUVVVFdXV1fPjhhzFq1Kh49dVXY+utt47f/e53ERHRt2/fOOecc2LWrFlx8803R+/evaO+vj4OPfTQmDJlSmy11VYxbdq0qKqqiiVLlpT3cdRRR8WTTz4Zv/vd76K6ujrq6uoiIqJXr15x1llnxZQpU2Lq1KnR2NgYI0eOjFtuuSWOOuqo+PKXv9zsvl522WXx4Ycfls9bev3dd989Lrjggpg6dWr5sSuKIo4++ujYaqut4qqrrooDDjgg7rnnnvLjERFx2WWXlU/vvvvu7VrnpY/5+PHjIyLK+54yZUqMHz8+dt9997j++uvLj/WsWbPK5y973fbeXnvmaOs2V8e+27v90sdh+cdjVfffkeu0tt3qfOzba+maHHXUUeWvwzVx+23d11V9HLricezKr4c1OVN32n+lZbt/q+P+ZHtMSsXSZyYdNG/evOjfv3+8++670a9fv9U912rxl7/8pfwEevLkyVFfX1/hibresvd5/k7HRFT3qswgSz6Mhif+tVvNsbZ8DfQU//Iv/xI33XRTpcegi9x+++0xZMiQWLhwYRx99NHtivd111033nnnnejkj6VmBgwYEO+8806z80qlUqf3ve6668bbb7+9wvlVVVVxxx13xIABA2LhwoVx1FFHxZtvvrnCdgMHDozvfOc7ceyxx65wWalUinXXXTfeeuutZjOut956ERHx1ltvlfdxyy23lAO8Ncs+5gMHDoyIiDfffLP8HwWDBg2K73//+3HkkUc2+0+OQYMGxY9+9KM44YQT4o033ohBgwbFzTffvNLba88cbd1mZ25j+X2vbB/Lbr/sf5gsfTyWv35H99+R67S2XWduc1W988478ZnPfCaampqafR129e23dV9X9XHoisexK78e1uRM3Wn/lZbt/q2O+9OTHpP2dqiXLgMVIXJzO+WUUyIi4pZbbmkx/Fry9ttvr5bIjYgVIjciVmnfLUVuRERTU1Ocd955EdH2fX3zzTfjpJNOavGyoijKMbvsjG+99Vb5/KX7uPXWW1c667JzvPnmm+XPlwbmm2++Gaecckqz4Fx6/te+9rVm123P7bV3jq985Sst3mZnbmP5fa9sH8tuv3SGZR+P5a/f0f135DqtbdeZ21xVX/va18qPw7Jfh119+23d11V9HLricezKr4c1OVN32n+lZbt/q+P+ZHtMIpIf0V2wYEEcfPDBERFx5513dtv/lVidFi5cGJ/+9KcjImL+jkdH1PSuzCDd5Yju4g+i4cmbI2Lt+RroCY444oh49913Kz0GXezEE0+MH/3oRy2+FDqbs88+Oy6//PIuv6/V1dXx4x//OEaMGNHi5a+++mocd9xxq22OmpqauPHGG1u9vdZ0ZI6O3kZL+25rH+2ZZdnrd3T/HZmpte0uueSSmDRpUoduc1U9/vjjccYZZ7R6eVfdfluPVUR0+LFv7747ez+68uuhs3r6/ist2/1bHfenpz0m7e3QdofuokWLYtGiRc1uYOTIkd06dN9+++1y9K2N5m9/RETvPpW58e4Suh8siIanbqvMbQNrjdZ+t7krjBkzJi6//PIolUrNzi+KIs4666x44oknVtss1dXVsdNOO8Vll122wu21pqNzlEqlGDNmTLtuo7V9tzbn0u1nzJixwtHkZS29/qWXXhqTJk1q9/47MlNr21VVVcU666wT77//frtvc1U1NTXFpz71qZg3b16r23TF7a/ssSqKIp588slOPQ4d/dpYHfOuytdDZ/X0/Vdatvu3Ou5PT3xMVvtLly+++OLo379/+WPkyJGrZVAA6OnW5FHrxx9/fIU39YqImD17dkyfPn21zrJkyZKYPn16i7fXmo7OURRFu2+jtX23NufS7duK3GWv/9hjj3Vo/x2ZqbXtmpqaYt68eR26zVX12GOPtRm5XXX7K3usHn/88U4/Dh392lgd867K10Nn9fT9V1q2+7c67k+2x2RZ7X7X5a9+9asxceLE8umlR3S7s9ra2vLna8vLVpd96XJUeVPtZR+DteVroLt7991344gjjqj0GLBarckjuh/96Edj1KhRK5w/atSo+OhHP7raj+juvPPOLd5eazo6R6lUavU+tXffrc25dPv2HNHdeeedY9ddd+3Q/jsyU2vbtXVEt6OPfXvtuuuu0a9fv5Ue0V3dt7+yx6qpqanFI7rtmaOjXxurY95V+XrorJ6+/0rLdv9Wx/3J9pgsq91HdGtra6Nfv37NPrq7ZQ+z19XVRX19ffqPZiHXzV5mUBFr4ddAd/8YOnRorLvuuhX8omBNOemkk6K6urrSY6wRZ5111hq5r9XV1TFhwoQWX0ZWKpXKf05odVm6z468bK2jc1RXV7f7Nlrbd2tztnf+pdtVVVV1aP8dmam17aqqquL888/v0G2uqqqqqvKbqLWmK25/ZY/VhAkTOj1HR782Vse8q/L10Fk9ff+Vlu3+rY77k+0xWZZ3XQbWuDvvvLPSI9DFBg8eHEcccUSMGzeuR/+QbI/tttsuDjzwwBg3blyb2/Xps+rvmXDUUUfF+uuv3+rlI0aMWOljXiqVYvDgwS2ev+222zYLs3HjxrV5e+2do1QqxUc+8pEWb7Ojt9HSvtvax8oek+Wv39H9d+Q6rW238847d/g2V9WYMWNi2223bfGyrrz9th6rzjz27d13V8y7JufItP9Ky3b/Vsf9yfaYLCV0gYpo6e+Jksf3vve9iPhrmC39W64rs9566622KG7pVQOrsu/WXoVQVVUVF154YUS0fV8HDRoU1157bav7WPo3c5edceDAgeXzl+5jZTG9/ByDBg0qf15VVVU+73vf+1759LL7/8Y3vtHsuu25vfbOcfXVV7d4m525jeX3vbJ9LLv90hmWfTyWv35H99+R67S2XWduc1V94xvfaPZ4LP166+rbb+u+rurj0BWPY1d+PazJmbrT/ist2/1bHfcn22MSIXSBCjnyyCMrPUK3NGjQoC7df0NDQ0T89c8G9O3bd4XwaO3lt6NGjYqamhV/77+qqioaGxubBVpjY2MMGTIkIv76KwMTJ06MIUOGtPh7Po2NjTFgwIAYMGBAnHHGGXH00UeX99m3b98olUrl2132CXmvXr3Kc1VVVcXo0aPL++zbt2+ceeaZccwxx0SpVIra2toYMGBAHH300TFkyJDyvEvv69J9HHPMMeX9VFdXR9++faNv374xYMCAOPPMM6OxsbE88zHHHBNVVVVx1FFHxYABA8r39fTTT48BAwaUr7v0+hMnToxRo0aV97H0fpRKpTjqqKPijDPOiCFDhsTRRx9dfjxOP/30OOOMM8qnJ06c2K73GVj2MZ84cWKcfvrpMWTIkDjqqKNiyJAhcdppp5VPL32sl56/9HaWnl6V9zVYdo62brMzt7H8vle2j2W3X/o4LPt4LH/9ju6/I9dpbbvO3OaqGjBgQHlNlv067Orbb+u+rurj0BWPY1d+PazJmbrT/ist2/1bHfcn22MSkfzv6P7lL3+JsWPHRkTE5MmTo76+vsITdb1l73NF/6xPd/nzQsvMsbZ8DfQUa+P3JwAAq2a1/3khAAAA6AmELgAAAKkIXQAAAFIRugAAAKQidAEAAEhF6AIAAJCK0AUAACAVoQsAAEAqQhcAAIBUhC4AAACpCF0AAABSEboAAACkInQBAABIRegCAACQitAFAAAgFaELAABAKkIXAACAVIQuAAAAqQhdAAAAUhG6AAAApCJ0AQAASEXoAgAAkIrQBQAAIBWhCwAAQCpCFwAAgFSELgAAAKkIXQAAAFIRugAAAKQidAEAAEhF6AIAAJCK0AUAACAVoQsAAEAqQhcAAIBUhC4AAACpCF0AAABSEboAAACkInQBAABIRegCAACQitAFAAAgFaELAABAKkIXAACAVIQuAAAAqQhdAAAAUhG6AAAApCJ0AQAASEXoAgAAkIrQBQAAIBWhCwAAQCpCFwAAgFSELgAAAKkIXQAAAFIRugAAAKQidAEAAEhF6AIAAJCK0AUAACAVoQsAAEAqQhcAAIBUhC4AAACpCF0AAABSEboAAACkUlPpAbpSXV1dTJ48ufw50H34/gQAoKukDt1SqRT19fWVHgNoge9PAAC6ipcuAwAAkIrQBQAAIBWhCwAAQCpCFwAAgFSELgAAAKkIXQAAAFIRugAAAKQidAEAAEhF6AIAAJCK0AUAACAVoQsAAEAqQhcAAIBUhC4AAACpCF0AAABSEboAAACkInQBAABIRegCAACQitAFAAAgFaELAABAKkIXAACAVIQuAAAAqQhdAAAAUhG6AAAApCJ0AQAASEXoAgAAkIrQBQAAIBWhCwAAQCpCFwAAgFSELgAAAKkIXQAAAFIRugAAAKQidAEAAEhF6AIAAJCK0AUAACAVoQsAAEAqQhcAAIBUhC4AAACpCF0AAABSEboAAACkInQBAABIRegCAACQitAFAAAgFaELAABAKkIXAACAVIQuAAAAqQhdAAAAUhG6AAAApCJ0AQAASEXoAgAAkIrQBQAAIBWhCwAAQCpCFwAAgFSELgAAAKkIXQAAAFIRugAAAKQidAEAAEhF6AIAAJCK0AUAACAVoQsAAEAqQhcAAIBUhC4AAACp1FR6ALpOqWlxFJW68SUftvz5GlZqWlyx2wYAACpD6Ca2zsyfVHqEiIhoeOq2So8AAACsRbx0GQAAgFQc0U2mrq4uJk+eXOkxoiiKWLRoUURE1NbWRqlUqvBEf31sAACA/IRuMqVSKerr6ys9RkRE9OnTp9IjAAAAayEvXQYAACAVoQsAAEAqQhcAAIBUhC4AAACpCF0AAABSEboAAACkInQBAABIRegCAACQitAFAAAgFaELAABAKkIXAACAVIQuAAAAqQhdAAAAUhG6AAAApCJ0AQAASEXoAgAAkIrQBQAAIBWhCwAAQCpCFwAAgFSELgAAAKkIXQAAAFIRugAAAKQidAEAAEhF6AIAAJCK0AUAACAVoQsAAEAqQhcAAIBUhC4AAACpCF0AAABSqensFYuiiIiIefPmrbZhAAAAoDVL+3Npj7am06E7f/78iIgYOXJkZ3cBAAAAHTZ//vzo379/q5eXipWlcCuamppizpw50dDQEKVSqdMDrg7z5s2LkSNHxiuvvBL9+vWr6CysGmuZh7XMw1rmYB3zsJZ5WMs8rOWaUxRFzJ8/P4YPHx5VVa3/Jm6nj+hWVVXFiBEjOnv1LtGvXz9fWElYyzysZR7WMgfrmIe1zMNa5mEt14y2juQu5c2oAAAASEXoAgAAkEqK0K2trY3zzz8/amtrKz0Kq8ha5mEt87CWOVjHPKxlHtYyD2vZ/XT6zagAAACgO0pxRBcAAACWEroAAACkInQBAABIRegCAACQSo8J3R/84Aex0UYbRV1dXey8887x8MMPt7n9tGnTYuedd466urrYeOON49prr11Dk7IyHVnLO+64I/bff//4yEc+Ev369Yvddtst7r///jU4LW3p6PflUr/61a+ipqYmdthhh64dkHbp6DouWrQozjnnnNhggw2itrY2Ntlkk/iXf/mXNTQtbenoWt5yyy2x/fbbR58+fWLYsGHxpS99Kd588801NC2t+eUvfxmHHnpoDB8+PEqlUtx1110rvY7nPd1TR9fS857uqTPfk0t5zlM5PSJ0b7/99pgwYUKcc8458eSTT8Zee+0VY8eOjdmzZ7e4/csvvxwHH3xw7LXXXvHkk0/GP/7jP8app54aP/vZz9bw5Cyvo2v5y1/+Mvbff//4+c9/HjNmzIiPf/zjceihh8aTTz65hidneR1dy6XefffdOPbYY2PfffddQ5PSls6s4+GHHx4PPvhgXH/99fHcc8/FT37yk9hyyy3X4NS0pKNr+cgjj8Sxxx4bX/7yl+OZZ56Jn/70pzF9+vQ44YQT1vDkLO/999+P7bffPq6++up2be95T/fV0bX0vKd76ug6LuU5T4UVPcAuu+xSnHTSSc3O23LLLYuzzz67xe3POuusYsstt2x23oknnlh87GMf67IZaZ+OrmVLtt566+LrX//66h6NDursWn7hC18ozj333OL8888vtt9++y6ckPbo6DpOnjy56N+/f/Hmm2+uifHogI6u5eWXX15svPHGzc777ne/W4wYMaLLZqTjIqK4884729zG856eoT1r2RLPe7qXjqyj5zyV1e2P6H7wwQcxY8aMOOCAA5qdf8ABB8Sjjz7a4nX++7//e4XtDzzwwHj88cfjww8/7LJZaVtn1nJ5TU1NMX/+/FhvvfW6YkTaqbNrecMNN8SLL74Y559/flePSDt0Zh3vvvvuGDNmTFx22WWx/vrrx+abbx5nnHFG/OUvf1kTI9OKzqzl7rvvHq+++mr8/Oc/j6Io4k9/+lP8+7//exxyyCFrYmRWI8978vK8p+fynKfyaio9wMq88cYbsWTJkhgyZEiz84cMGRKvv/56i9d5/fXXW9x+8eLF8cYbb8SwYcO6bF5a15m1XN4VV1wR77//fhx++OFdMSLt1Jm1fOGFF+Lss8+Ohx9+OGpquv0/PWuFzqzjSy+9FI888kjU1dXFnXfeGW+88Ub8wz/8Q7z11lt+T7eCOrOWu+++e9xyyy3xhS98IRYuXBiLFy+Oww47LL73ve+tiZFZjTzvycvznp7Jc57uodsf0V2qVCo1O10UxQrnrWz7ls5nzevoWi71k5/8JC644IK4/fbbY/DgwV01Hh3Q3rVcsmRJjBs3Lr7+9a/H5ptvvqbGo5068j3Z1NQUpVIpbrnllthll13i4IMPjiuvvDJuvPFGR3W7gY6s5bPPPhunnnpqnHfeeTFjxoy477774uWXX46TTjppTYzKauZ5Tz6e9/RMnvN0H93+vxgGDRoU1dXVK/yP9Ny5c1f438ulhg4d2uL2NTU1MXDgwC6blbZ1Zi2Xuv322+PLX/5y/PSnP4399tuvK8ekHTq6lvPnz4/HH388nnzyyfjKV74SEX8NpqIooqamJqZMmRJ/8zd/s0Zm5//rzPfksGHDYv3114/+/fuXz9tqq62iKIp49dVXY7PNNuvSmWlZZ9by4osvjj322CPOPPPMiIjYbrvtom/fvrHXXnvFN7/5TUcBexDPe/LxvKfn8pyn++j2R3R79+4dO++8czzwwAPNzn/ggQdi9913b/E6u+222wrbT5kyJcaMGRO9evXqsllpW2fWMuKv/6P5xS9+MW699Va/O9ZNdHQt+/XrF08//XTMnDmz/HHSSSfFFltsETNnzoxdd911TY3OMjrzPbnHHnvEnDlz4r333iuf9/zzz0dVVVWMGDGiS+eldZ1ZywULFkRVVfOnAdXV1RHx/48G0jN43pOL5z09m+c83Uhl3gOrY2677baiV69exfXXX188++yzxYQJE4q+ffsWv//974uiKIqzzz67OOaYY8rbv/TSS0WfPn2K0047rXj22WeL66+/vujVq1fx7//+75W6C/yfjq7lrbfeWtTU1BTf//73i9dee6388c4771TqLvB/OrqWy/MOhN1DR9dx/vz5xYgRI4rPfe5zxTPPPFNMmzat2GyzzYoTTjihUneB/9PRtbzhhhuKmpqa4gc/+EHx4osvFo888kgxZsyYYpdddqnUXeD/zJ8/v3jyySeLJ598soiI4sorryyefPLJ4g9/+ENRFJ739CQdXUvPe7qnjq7j8jznqYweEbpFURTf//73iw022KDo3bt3sdNOOxXTpk0rX3bccccV++yzT7Ptp06dWuy4445F7969iw033LC45ppr1vDEtKYja7nPPvsUEbHCx3HHHbfmB2cFHf2+XJZ/9LuPjq7jrFmziv3226+or68vRowYUUycOLFYsGDBGp6alnR0Lb/73e8WW2+9dVFfX18MGzasOOqoo4pXX311DU/N8h566KE2f/Z53tNzdHQtPe/pnjrzPbksz3kqo1QUXp8EAABAHt3+d3QBAACgI4QuAAAAqQhdAAAAUhG6AAAApCJ0AQAASEXoAgAAkIrQBQAAIBWhCwDtUBRF/N3f/V2st956USqVYubMmSu9TqlUirvuuqvLZwOA7uKXv/xlHHrooTF8+PBO/xwsiiK+/e1vx+abbx61tbUxcuTIuOiiizq0D6ELAO1w3333xY033hj33ntvvPbaazF69OhKjxQbbrhh/NM//VOlxwCAsvfffz+23377uPrqqzu9j/Hjx8ePfvSj+Pa3vx3/8z//E/fcc0/ssssuHdpHTadvHQDWIi+++GIMGzYsdt9990qPAgDd1tixY2Ps2LGtXv7BBx/EueeeG7fccku88847MXr06Lj00kujsbExIiJmzZoV11xzTfzud7+LLbbYotNzOKILACvxxS9+MU455ZSYPXt2lEql2HDDDaOxsTFOPfXUOOuss2K99daLoUOHxgUXXNDqPj772c/GKaecUj49YcKEKJVK8cwzz0RExOLFi6OhoSHuv//+iIiYP39+HHXUUdG3b98YNmxYfOc734nGxsaYMGFCREQ0NjbGH/7whzjttNOiVCpFqVTqsvsPAKvLl770pfjVr34Vt912W/z2t7+Nz3/+83HQQQfFCy+8EBER99xzT2y88cZx7733xkYbbRQbbrhhnHDCCfHWW2916HaELgCsxFVXXRUXXnhhjBgxIl577bWYPn16RET8+Mc/jr59+8Zjjz0Wl112WVx44YXxwAMPtLiPxsbGmDp1avn0tGnTYtCgQTFt2rSIiJg+fXosXLgw9thjj4iImDhxYvzqV7+Ku+++Ox544IF4+OGH44knnihf/4477ogRI0bEhRdeGK+99lq89tprXXTvAWD1ePHFF+MnP/lJ/PSnP4299torNtlkkzjjjDNizz33jBtuuCEiIl566aX4wx/+ED/96U/jpptuihtvvDFmzJgRn/vc5zp0W166DAAr0b9//2hoaIjq6uoYOnRo+fztttsuzj///IiI2GyzzeLqq6+OBx98MPbff/8V9tHY2Bjjx4+PN954I6qrq+OZZ56J888/P6ZOnRr/8A//EFOnTo2dd9451llnnZg/f378+Mc/jltvvTX23XffiIi44YYbYvjw4eX9rbfeelFdXR0NDQ3NZgKA7uqJJ56Ioihi8803b3b+okWLYuDAgRER0dTUFIsWLYqbbrqpvN31118fO++8czz33HPtfjmz0AWATtpuu+2anR42bFjMnTu3xW1Hjx4dAwcOjGnTpkWvXr1i++23j8MOOyy++93vRkTE1KlTY5999omIv/5v9ocfftjsjTf69++/Sr+rBACV1tTUFNXV1TFjxoyorq5udtk666wTEX/9WVpTU9MshrfaaquIiJg9e7bQBYCu1qtXr2anS6VSNDU1tbhtqVSKvffeO6ZOnRq9e/eOxsbGGD16dCxZsiSefvrpePTRR8u/f1sURfk6y1p6PgD0RDvuuGMsWbIk5s6dG3vttVeL2+yxxx6xePHiePHFF2OTTTaJiIjnn38+IiI22GCDdt+W39EFgDVk6e/pTp06NRobG6NUKsVee+0V3/72t+Mvf/lL+fdzN9lkk+jVq1f85je/KV933rx55TfqWKp3796xZMmSNXofAKAt7733XsycObP89+ZffvnlmDlzZsyePTs233zzOOqoo+LYY4+NO+64I15++eWYPn16XHrppfHzn/88IiL222+/2GmnneL444+PJ598MmbMmBEnnnhi7L///iu85LktQhcA1pDGxsZ45pln4umnny7/T3ZjY2PccsstsdNOO0W/fv0iIqKhoSGOO+64OPPMM+Ohhx6KZ555Jo4//vioqqpqdpR3ww03jF/+8pfxxz/+Md54442K3CcAWNbjjz8eO+64Y+y4444R8dc3V9xxxx3jvPPOi4i/vufEscceG6effnpsscUWcdhhh8Vjjz0WI0eOjIiIqqqquOeee2LQoEGx9957xyGHHBJbbbVV3HbbbR2aw0uXAWANGT16dAwaNCg22GCDctTus88+sWTJkvLv5y515ZVXxkknnRSf+MQnol+/fnHWWWfFK6+8EnV1deVtLrzwwjjxxBNjk002iUWLFnlpMwAV19jY2ObPo169esXXv/71+PrXv97qNsOHD4+f/exnqzRHqfBTEQC6vffffz/WX3/9uOKKK+LLX/5ypccBgG7NEV0A6IaefPLJ+J//+Z/YZZdd4t13340LL7wwIiI++clPVngyAOj+hC4AdFPf/va347nnnovevXvHzjvvHA8//HAMGjSo0mMBQLfnpcsAAACk4l2XAQAASEXoAgAAkIrQBQAAIBWhCwAAQCpCFwAAgFSELgAAAKkIXQAAAFIRugAAAKQidAEAAEhF6AIAAJCK0AUAACAVoQsAAEAqQhcAAIBUhC4AAACpCF0AAABSEboAAACkInQBAABIRegCAACQitAFAAAgFaELAABAKkIXAACAVGo6e8WmpqaYM2dONDQ0RKlUWp0zAQAAwAqKooj58+fH8OHDo6qq9eO2nQ7dOXPmxMiRIzt7dQAAAOiUV155JUaMGNHq5Z0O3YaGhvIN9OvXr7O7AQAAgHaZN29ejBw5styjrel06C59uXK/fv2ELgAAAGvMyn591ptRAQAAkIrQBQAAIBWhCwAAQCpCFwAAgFSELgAAAKkIXQAAAFIRugAAAKQidAEAAEhF6AIAAJCK0AUAACAVoQsAAEAqQhcAAIBUhC4AAACpCF0AAABSEboAAACkInQBAABIRegCAACQitAFAAAgFaELAABAKkIXAACAVIQuAAAAqQhdAAAAUhG6AAAApCJ0AQAASEXoAgAAkIrQBQAAIBWhCwAAQCpCFwAAgFRqKj0AANB1iqKIhQsXVnqMLlMURSxatCgiImpra6NUKlV4IlZFXV2dNQRWC6ELAIktXLgwxo4dW+kxoF0mT54c9fX1lR4DSMBLlwEAAEjFEV0AWEu8t8ORUVQl+9G/5MNoeOq2iIiYv/0REdW9KjwQHVVqWhzrzPxJpccAkkn20w4AaE1RVZM7BKt75b5/SRWVHgBIyUuXAQAASEXoAgAAkIrQBQAAIBWhCwAAQCpCFwAAgFSELgAAAKkIXQAAAFIRugAAAKQidAEAAEhF6AIAAJCK0AUAACAVoQsAAEAqQhcAAIBUhC4AAACpCF0AAABSEboAAACkInQBAABIRegCAACQitAFAAAgFaELAABAKkIXAACAVIQuAAAAqQhdAAAAUhG6AAAApCJ0AQAASEXoAgAAkIrQBQAAIBWhCwAAQCpCFwAAgFSELgAAAKkIXQAAAFIRugAAAKQidAEAAEhF6AIAAJCK0AUAACAVoQsAAEAqQhcAAIBUhC4AAACpCF0AAABSEboAAACkInQBAABIRegCAACQitAFAAAgFaELAABAKkIXAACAVIQuAAAAqQhdAAAAUhG6AAAApCJ0AQAASEXoAgAAkIrQBQAAIBWhCwAAQCpCFwAAgFSELgAAAKkIXQAAAFIRugAAAKQidAEAAEhF6AIAAJCK0AUAACAVoQsAAEAqQhcAAIBUaio9AJBbURSxcOHCiIioq6uLUqlU4YkAAFhWxudrjugCXWrhwoUxduzYGDt2bPkfUAAAuo+Mz9eELgAAAKkIXQAAAFIRugAAAKQidAEAAEhF6AIAAJCK0AUAACAVoQsAAEAqQhcAAIBUhC4AAACpCF0AAABSEboAAACkInQBAABIRegCAACQitAFAAAgFaELAABAKkIXAACAVIQuAAAAqQhdAAAAUhG6AAAApCJ0AQAASEXoAgAAkIrQBQAAIBWhCwAAQCpCFwAAgFSELgAAAKkIXQAAAFIRugAAAKQidAEAAEhF6AIAAJCK0AUAACAVoQsAAEAqQhcAAIBUhC4AAACpCF0AAABSEboAAACkInQBAABIRegCAACQitAFAAAgFaELAABAKkIXAACAVIQuAAAAqQhdAAAAUhG6AAAApCJ0AQAASEXoAgAAkIrQBQAAIBWhCwAAQCpCFwAAgFSELgAAAKkIXQAAAFIRugAAAKQidAEAAEhF6AIAAJCK0AUAACAVoQsAAEAqQhcAAIBUhC4AAACpCF0AAABSEboAAACkInQBAABIRegCAACQSk2lB+hqjY2N5c+nTp1asTm6Svb7F5H/Pma/fwAAsKalPqL7qU99qs3TPd2ygdTS6Qyy38fs9w8AACohdei+8847bZ4GAAAgn7QvXW7tyFhjY2OKl4dmv38R+e9j9vu3VFEU5c8XLlxYwUlg7dTs+26Z70foNvycgIpb9nuvSPKzot2hu2jRoli0aFH59Lx587pkoNVhxowZK7185513XkPTrH5HHHHESi+/7bbb1tA0XWNlL+Ht6TGY/f4ta9l/Nz796U9XcBIgmhZHRO9KTwHNNS0uf+rnBFTeokWLok+fPpUeY5W1+6XLF198cfTv37/8MXLkyK6ca5Wcfvrpq3R5d/f666+v0uUAAACZtfuI7le/+tWYOHFi+fS8efO6bexeccUVbcbsFVdcsQanWf2GDh3aZswOHTp0DU4DbautrS1/fuedd0ZdXV0Fp4G1z8KFC///UbKqtL+xRE+2zNelnxNQGcv+rFj2uVtP1u6feLW1tT3mTq/sZck9+WXLERG33XZbmy997ekvW47465/Zaes+9vSX9Wa/f8sqlUrlz+vq6qK+vr6C08BabpnvR+g2/JyAbqWU5GdF2nddbi0UsgRE9vsXkf8+Zr9/AABQKWlDNyJiwIABbZ4GAAAgn9She9ddd7V5uqdb/shfxiOB2e9j9vsHAACVkP5dKbKHQ/b7F5H/Pma/fwAAsKalPqILAADA2kfoAgAAkIrQBQAAIBWhCwAAQCpCFwAAgFSELgAAAKkIXQAAAFIRugAAAKQidAEAAEhF6AIAAJCK0AUAACAVoQsAAEAqQhcAAIBUhC4AAACpCF0AAABSEboAAACkInQBAABIRegCAACQitAFAAAgFaELAABAKkIXAACAVIQuAAAAqQhdAAAAUhG6AAAApCJ0AQAASEXoAgAAkIrQBQAAIBWhCwAAQCpCFwAAgFSELgAAAKkIXQAAAFIRugAAAKQidAEAAEhF6AIAAJCK0AUAACAVoQsAAEAqQhcAAIBUhC4AAACpCF0AAABSEboAAACkInQBAABIRegCAACQitAFAAAgFaELAABAKkIXAACAVIQuAAAAqQhdAAAAUhG6AAAApCJ0AQAASEXoAgAAkIrQBQAAIBWhCwAAQCpCFwAAgFSELgAAAKkIXQAAAFIRugAAAKQidAEAAEhF6AIAAJCK0AUAACAVoQsAAEAqNZUeAMitrq4uJk+eXP4cAIDuJePzNaELdKlSqRT19fWVHgMAgFZkfL7mpcsAAACkInQBAABIRegCAACQitAFAAAgFaELAABAKkIXAACAVIQuAAAAqQhdAAAAUhG6AAAApCJ0AQAASEXoAgAAkIrQBQAAIBWhCwAAQCpCFwAAgFSELgAAAKkIXQAAAFIRugAAAKQidAEAAEhF6AIAAJCK0AUAACAVoQsAAEAqQhcAAIBUhC4AAACpCF0AAABSEboAAACkInQBAABIRegCAACQitAFAAAgFaELAABAKkIXAACAVIQuAAAAqQhdAAAAUhG6AAAApCJ0AQAASEXoAgAAkIrQBQAAIBWhCwAAQCpCFwAAgFSELgAAAKkIXQAAAFIRugAAAKQidAEAAEhF6AIAAJCK0AUAACAVoQsAAEAqQhcAAIBUhC4AAACpCF0AAABSEboAAACkInQBAABIRegCAACQitAFAAAgFaELAABAKkIXAACAVIQuAAAAqQhdAAAAUhG6AAAApCJ0AQAASEXoAgAAkIrQBQAAIBWhCwAAQCo1lR4AAFgzSk2Lo6j0EKvbkg9b/pweo9S0uNIjAAkJXQBYS6wz8yeVHqFLNTx1W6VHAKCb8NJlAAAAUnFEFwASq6uri8mTJ1d6jC5TFEUsWrQoIiJqa2ujVCpVeCJWRV1dXaVHAJIQugCQWKlUivr6+kqP0aX69OlT6REA6Ga8dBkAAIBUhC4AAACpCF0AAABSEboAAACkInQBAABIRegCAACQitAFAAAgFaELAABAKkIXAACAVIQuAAAAqQhdAAAAUhG6AAAApCJ0AQAASEXoAgAAkIrQBQAAIBWhCwAAQCpCFwAAgFSELgAAAKkIXQAAAFIRugAAAKQidAEAAEhF6AIAAJCK0AUAACAVoQsAAEAqQhcAAIBUhC4AAACpCF0AAABSEboAAACkInQBAABIpaazVyyKIiIi5s2bt9qGAQAAgNYs7c+lPdqaTofu/PnzIyJi5MiRnd0FAAAAdNj8+fOjf//+rV5eKlaWwq1oamqKOXPmRENDQ5RKpU4PyKqZN29ejBw5Ml555ZXo169fpcehE6xhz2cNez5r2LNZv57PGvZ81rDn6ylrWBRFzJ8/P4YPHx5VVa3/Jm6nj+hWVVXFiBEjOnt1VrN+/fp16y9IVs4a9nzWsOezhj2b9ev5rGHPZw17vp6whm0dyV3Km1EBAACQitAFAAAgFaHbw9XW1sb5558ftbW1lR6FTrKGPZ817PmsYc9m/Xo+a9jzWcOeL9sadvrNqAAAAKA7ckQXAACAVIQuAAAAqQhdAAAAUhG6AAAApCJ0e6iLL744PvrRj0ZDQ0MMHjw4PvWpT8Vzzz1X6bHopIsvvjhKpVJMmDCh0qPQQX/84x/j6KOPjoEDB0afPn1ihx12iBkzZlR6LNph8eLFce6558ZGG20U9fX1sfHGG8eFF14YTU1NlR6NVvzyl7+MQw89NIYPHx6lUinuuuuuZpcXRREXXHBBDB8+POrr66OxsTGeeeaZygxLi9paww8//DAmTZoU2267bfTt2zeGDx8exx57bMyZM6dyA7OClX0fLuvEE0+MUqkU//RP/7TG5qNt7Vm/WbNmxWGHHRb9+/ePhoaG+NjHPhazZ89e88OuIqHbQ02bNi1OPvnk+PWvfx0PPPBALF68OA444IB4//33Kz0aHTR9+vS47rrrYrvttqv0KHTQ22+/HXvssUf06tUrJk+eHM8++2xcccUVMWDAgEqPRjtceumlce2118bVV18ds2bNissuuywuv/zy+N73vlfp0WjF+++/H9tvv31cffXVLV5+2WWXxZVXXhlXX311TJ8+PYYOHRr7779/zJ8/fw1PSmvaWsMFCxbEE088EV/72tfiiSeeiDvuuCOef/75OOywwyowKa1Z2ffhUnfddVc89thjMXz48DU0Ge2xsvV78cUXY88994wtt9wypk6dGk899VR87Wtfi7q6ujU86arz54WS+POf/xyDBw+OadOmxd57713pcWin9957L3baaaf4wQ9+EN/85jdjhx128L+ePcjZZ58dv/rVr+Lhhx+u9Ch0wic+8YkYMmRIXH/99eXzPvvZz0afPn3iX//1Xys4Ge1RKpXizjvvjE996lMR8dejucOHD48JEybEpEmTIiJi0aJFMWTIkLj00kvjxBNPrOC0tGT5NWzJ9OnTY5dddok//OEPMWrUqDU3HO3S2hr+8Y9/jF133TXuv//+OOSQQ2LChAletdYNtbR+RxxxRPTq1SvFz0FHdJN49913IyJivfXWq/AkdMTJJ58chxxySOy3336VHoVOuPvuu2PMmDHx+c9/PgYPHhw77rhj/PCHP6z0WLTTnnvuGQ8++GA8//zzERHx1FNPxSOPPBIHH3xwhSejM15++eV4/fXX44ADDiifV1tbG/vss088+uijFZyMVfHuu+9GqVTySpkepKmpKY455pg488wzY5tttqn0OHRAU1NT/Od//mdsvvnmceCBB8bgwYNj1113bfPl6d2Z0E2gKIqYOHFi7LnnnjF69OhKj0M73XbbbTFjxoy4+OKLKz0KnfTSSy/FNddcE5tttlncf//9cdJJJ8Wpp54aN910U6VHox0mTZoURx55ZGy55ZbRq1ev2HHHHWPChAlx5JFHVno0OuH111+PiIghQ4Y0O3/IkCHly+hZFi5cGGeffXaMGzcu+vXrV+lxaKdLL700ampq4tRTT630KHTQ3Llz47333otLLrkkDjrooJgyZUp8+tOfjs985jMxbdq0So/XYTWVHoBV95WvfCV++9vfxiOPPFLpUWinV155JcaPHx9Tpkzpkb/zwF81NTXFmDFj4qKLLoqIiB133DGeeeaZuOaaa+LYY4+t8HSszO233x4333xz3HrrrbHNNtvEzJkzY8KECTF8+PA47rjjKj0enVQqlZqdLopihfPo/j788MM44ogjoqmpKX7wgx9UehzaacaMGXHVVVfFE0884fuuB1r6Zoyf/OQn47TTTouIiB122CEeffTRuPbaa2Offfap5Hgd5ohuD3fKKafE3XffHQ899FCMGDGi0uPQTjNmzIi5c+fGzjvvHDU1NVFTUxPTpk2L7373u1FTUxNLliyp9Ii0w7Bhw2Lrrbdudt5WW23VI9+ZcG105plnxtlnnx1HHHFEbLvttnHMMcfEaaed5lUWPdTQoUMjIlY4ejt37twVjvLSvX344Ydx+OGHx8svvxwPPPCAo7k9yMMPPxxz586NUaNGlZ/f/OEPf4jTTz89Ntxww0qPx0oMGjQoampq0jy3cUS3hyqKIk455ZS48847Y+rUqbHRRhtVeiQ6YN99942nn3662Xlf+tKXYsstt4xJkyZFdXV1hSajI/bYY48V/qzX888/HxtssEGFJqIjFixYEFVVzf+/t7q62p8X6qE22mijGDp0aDzwwAOx4447RkTEBx98ENOmTYtLL720wtPRXksj94UXXoiHHnooBg4cWOmR6IBjjjlmhfcdOfDAA+OYY46JL33pSxWaivbq3bt3fPSjH03z3Ebo9lAnn3xy3HrrrfEf//Ef0dDQUP4f7P79+0d9fX2Fp2NlGhoaVvh96r59+8bAgQP9nnUPctppp8Xuu+8eF110URx++OHxm9/8Jq677rq47rrrKj0a7XDooYfGt771rRg1alRss8028eSTT8aVV14Zxx9/fKVHoxXvvfde/O///m/59MsvvxwzZ86M9dZbL0aNGhUTJkyIiy66KDbbbLPYbLPN4qKLLoo+ffrEuHHjKjg1y2prDYcPHx6f+9zn4oknnoh77703lixZUn5+s95660Xv3r0rNTbLWNn34fL/OdGrV68YOnRobLHFFmt6VFqwsvU788wz4wtf+ELsvffe8fGPfzzuu+++uOeee2Lq1KmVG7qzCnqkiGjx44Ybbqj0aHTSPvvsU4wfP77SY9BB99xzTzF69Oiitra22HLLLYvrrruu0iPRTvPmzSvGjx9fjBo1qqirqys23njj4pxzzikWLVpU6dFoxUMPPdTiz77jjjuuKIqiaGpqKs4///xi6NChRW1tbbH33nsXTz/9dGWHppm21vDll19u9fnNQw89VOnR+T8r+z5c3gYbbFB85zvfWaMz0rr2rN/1119fbLrppkVdXV2x/fbbF3fddVflBl4F/o4uAAAAqXgzKgAAAFIRugAAAKQidAEAAEhF6AIAAJCK0AUAACAVoQsAAEAqQhcAAIBUhC4AqZVKpbjrrrsqPUZ88YtfjE996lOVHgMA1gpCF4Ae44tf/GKUSqUVPg466KBKj1b2+9//PkqlUsycObPZ+VdddVXceOONFZkJANY2NZUeAAA64qCDDoobbrih2Xm1tbUVmqb9+vfvX+kRAGCt4YguAD1KbW1tDB06tNnHuuuuGxERL7zwQuy9995RV1cXW2+9dTzwwAPNrjt16tQolUrxzjvvlM+bOXNmlEql+P3vf18+71e/+lXss88+0adPn1h33XXjwAMPjLfffjsiIu67777Yc889Y8CAATFw4MD4xCc+ES+++GL5uhtttFFEROy4445RKpWisbExIlZ86fKiRYvi1FNPjcGDB0ddXV3sueeeMX369BVmffDBB2PMmDHRp0+f2H333eO5555r8/FZejvf/va3Y9iwYTFw4MA4+eST48MPPyxv09LLuQcMGFA+4rz0qPS//du/xV577RX19fXx0Y9+NJ5//vmYPn16jBkzJtZZZ5046KCD4s9//nOb8wBAJQhdAFJoamqKz3zmM1FdXR2//vWv49prr41JkyZ1eD8zZ86MfffdN7bZZpv47//+73jkkUfi0EMPjSVLlkRExPvvvx8TJ06M6dOnx4MPPhhVVVXx6U9/OpqamiIi4je/+U1ERPziF7+I1157Le64444Wb+ess86Kn/3sZ/HjH/84nnjiidh0003jwAMPjLfeeqvZduecc05cccUV8fjjj0dNTU0cf/zxK70PDz30ULz44ovx0EMPxY9//OO48cYbO/Wy6fPPPz/OPffceOKJJ6KmpiaOPPLIOOuss+Kqq66Khx9+OF588cU477zzOrxfAOhqXroMQI9y7733xjrrrNPsvEmTJsWuu+4as2bNit///vcxYsSIiIi46KKLYuzYsR3a/2WXXRZjxoyJH/zgB+Xzttlmm/Lnn/3sZ5ttf/3118fgwYPj2WefjdGjR8dHPvKRiIgYOHBgDB06tMXbeP/99+Oaa66JG2+8sTzfD3/4w3jggQfi+uuvjzPPPLO87be+9a3YZ599IiLi7LPPjkMOOSQWLlwYdXV1rd6HddddN66++uqorq6OLbfcMg455JB48MEH42//9m879FicccYZceCBB0ZExPjx4+PII4+MBx98MPbYY4+IiPjyl7/s944B6JYc0QWgR/n4xz8eM2fObPZx8sknx6xZs2LUqFHlyI2I2G233Tq8/6VHdFvz4osvxrhx42LjjTeOfv36lV+qPHv27HbfxosvvhgffvhhORgjInr16hW77LJLzJo1q9m22223XfnzYcOGRUTE3LlzY/bs2bHOOuuUPy666KLydttss01UV1c3u97cuXPbPV9Ltz1kyJCIiNh2222bndeZ/QJAV3NEF4AepW/fvrHpppuucH5RFCucVyqVmp2uqqpaYdtlf3c1IqK+vr7N2z/00ENj5MiR8cMf/jCGDx8eTU1NMXr06Pjggw/afR+W3v7y8xVFscJ5vXr1Kn++9LKmpqYYMWJEs3d2Xm+99Vq8ztLrLX1p9dLTyz9eyz8Ord328uctu18A6C4c0QUgha233jpmz54dc+bMKZ/33//93822Wfqy4tdee6183vJ/Bmi77baLBx98sMXbePPNN2PWrFlx7rnnxr777htbbbVV+U2qlurdu3dERPl3eluy6aabRu/eveORRx4pn/fhhx/G448/HltttVUb9/L/q6mpiU033bT8sWzorsxHPvKRZo/BCy+8EAsWLGj39QGgu3NEF4AeZdGiRfH66683O6+mpib222+/2GKLLeLYY4+NK664IubNmxfnnHNOs+023XTTGDlyZFxwwQXxzW9+M1544YW44oormm3z1a9+Nbbddtv4h3/4hzjppJOid+/e8dBDD8XnP//5WG+99WLgwIFx3XXXxbBhw2L27Nlx9tlnN7v+4MGDo76+Pu67774YMWJE1NXVrfCnhfr27Rt///d/H2eeeWast956MWrUqLjssstiwYIF8eUvf3k1Plot+5u/+Zu4+uqr42Mf+1g0NTXFpEmTVjgKDAA9mSO6APQo9913XwwbNqzZx5577hlVVVVx5513xqJFi2KXXXaJE044Ib71rW81u26vXr3iJz/5SfzP//xPbL/99nHppZfGN7/5zWbbbL755jFlypR46qmnYpdddonddtst/uM//iNqamqiqqoqbrvttpgxY0aMHj06TjvttLj88subXb+mpia++93vxj//8z/H8OHD45Of/GSL9+OSSy6Jz372s3HMMcfETjvtFP/7v/8b999/f/lPJXWlK664IkaOHBl77713jBs3Ls4444zo06dPl98uAKwppaKlX2oCAACAHsoRXQAAAFIRugAAAKQidAEAAEhF6AIAAJCK0AUAACAVoQsAAEAqQhcAAIBUhC4AAACpCF0AAABSEboAAACkInQBAABIRegCAACQitAFAAAgFaELAABAKkIXAACAVIQuAAAAqQhdAAAAUhG6AAAApCJ0AQAASEXoAgAAkIrQBQAAIBWhCwAAQCpCFwAAgFSELgAAAKnUdPaKTU1NMWfOnGhoaIhSqbQ6ZwIAAIAVFEUR8+fPj+HDh0dVVevHbTsdunPmzImRI0d29uoAAADQKa+88kqMGDGi1cs7HboNDQ3lG+jXr19ndwMAAADtMm/evBg5cmS5R1vT6dBd+nLlfv36CV0AAADWmJX9+qw3owIAACAVoQsAAEAqQhcAAIBUhC4AAACpCF0AAABSEboAAACkInQBAABIRegCAACQitAFAAAgFaELAABAKkIXAACAVIQuAAAAqQhdAAAAUhG6AAAApCJ0AQAASEXoAgAAkIrQBQAAIBWhCwAAQCpCFwAAgFSELgAAAKkIXQAAAFIRugAAAKQidAEAAEhF6AIAAJCK0AUAACAVoQsAAEAqQhcAAIBUhC4AAACp1FR6gK5UFEUsXLgwIiLq6uqiVCpVeCIAAAC6WuojugsXLoyxY8fG2LFjy8ELAABAbulDt6XPAQAAyCt16AIAALD2EboAAACkInQBAABIRegCAACQitAFAAAgFaELAABAKkIXAACAVIQuAAAAqQhdAAAAUhG6AAAApCJ0AQAASEXoAgAAkIrQBQAAIBWhCwAAQCpCFwAAgFSELgAAAKkIXQAAAFIRugAAAKQidAEAAEhF6AIAAJCK0AUAACAVoQsAAEAqQhcAAIBUhC4AAACpCF0AAABSEboAAACkInQBAABIRegCAACQitAFAAAgFaELAABAKkIXAACAVIQuAAAAqQhdAAAAUhG6AAAApCJ0AQAASEXoAgAAkIrQBQAAIBWhCwAAQCpCFwAAgFSELgAAAKkIXQAAAFIRugAAAKQidAEAAEhF6AIAAJCK0AUAACAVoQsAAEAqQhcAAIBUhC4AAACpCF0AAABSEboAAACkInQBAABIRegCAACQitAFAAAgFaELAABAKkIXAACAVIQuAAAAqQhdAAAAUhG6AAAApCJ0AQAASEXoAgAAkIrQBQAAIBWhCwAAQCpCFwAAgFRSh25TU1OLnwMAAJBX6tCdN29ei58DAACQV+rQBQAAYO0jdAEAAEhF6AIAAJCK0AUAACAVoQsAAEAqQhcAAIBUhC4AAACpCF0AAABSEboAAACkInQBAABIRegCAACQitAFAAAgFaELAABAKkIXAACAVIQuAAAAqQhdAAAAUhG6AAAApCJ0AQAASEXoAgAAkIrQBQAAIBWhCwAAQCpCFwAAgFSELgAAAKkIXQAAAFIRugAAAKQidAEAAEhF6AIAAJCK0AUAACAVoQsAAEAqQhcAAIBUhC4AAACpCF0AAABSEboAAACkInQBAABIRegCAACQitAFAAAgFaELAADA/2vvzuOrqO/98b8TAglLCCAKIqDQ4hqtCu4b7uLe3i4KgkuvvVpBcddrrV6txWrl1qVq67VqFYttXa61asUq7itIAbWWXq24WyybImvm94e/nC8hJyeHkJDw8fl8PPJ4ZGY+5zOfmfOZOfM6s5ykCLoAAAAkRdAFAAAgKYIuAAAASRF0AQAASIqgCwAAQFIEXQAAAJIi6AIAAJAUQRcAAICkCLoAAAAkRdAFAAAgKYIuAAAASRF0AQAASIqgCwAAQFIEXQAAAJIi6AIAAJAUQRcAAICkCLoAAAAkRdAFAAAgKYIuAAAASRF0AQAASIqgCwAAQFIEXQAAAJIi6AIAAJAUQRcAAICklLV2A9aW448/Pnr27BnDhg2L22+/PTe+Y8eO8fnnn7diyxpX28ahQ4fGAQccEFdffXV89NFHDZafPHlyDB06tMHp5eXlcdFFF8Wuu+4aEVGn7OTJk3P/r1pHdXV1XHfddfXqW7lcSUlJPP7443nne/PNN8eECRNixIgR8d3vfrfB9jVk1XbmW8aG2r/y+IiIb37zmzFnzpzo2bNn/P73v1/ttjSkqctYqK3FePbZZ+Pqq6+O0047Lfe+tpQ1bevarrc1jRo1KmbPnh39+/ePX//6163dnLViTbdzAKB1pHYs9qU6oztnzpw6ITci2nzIjfh/bZw8eXJceeWVBUNuRP2AuqolS5bET3/601i8eHEceuihdabVDl9++eX1Xjdz5syYPXt2nXHf+MY36gxnWRb3339/vdfOmzcvJkyYEDU1NTFhwoSYN29ewTau6vzzz68z3NAy/uAHP8g7feXhmTNnxpw5cyLiiz4xc+bM1WpLQ5q6jKuGgdUNB4sXL47x48fHRx99FOPHj4/Fixev1utXx6rbz6rDTXXyyScXHF4XzZo1K7e9zJ49O2bNmtXKLWp5a7qdAwCto9Cx87rqSxV0UzB37txmqedf//pX3HnnnfHpp5/WGV87/PDDD+d93UknnVSvnlWNHz++3rgLL7wwampqIiKipqYmfvjDH65We5977rmiyj399NONlhkzZkzB4aZq6jL+3//9X8HhxkyYMCE++eSTiIj45JNP4s4771yt16+Om2++ueBwU73++usFh9dFKYb3xqzpdg4A0FySDrq33HJLazehTWvoUspC3+AsWrQofvOb3zRa7phjjsn9//LLL8eMGTPqTJ8+fXq8/PLLRbVzdb9Raqj80KFD48orr4wsy+qMz7IsrrzyytWax6qauoyF2lqMd999N+68887cMmVZFnfeeWe8++67Rb1+dXz9619frfHFWtN10BbdeOONsXz58jrjli9fHjfeeGMrtajlrel2DgC0jhSPxSJWI+guWbIkFixYUOevLVu6dGk8+eSTrd2MJP3iF7+Il156qWCZd999Nz799NOoqamJSy65JG+ZSy65JHf2pyHvvfdek9uZzx//+McGxy9durRJdTZ1GRu7lLWx6VmWxdVXX93g+FUD/ZqYP39+g1cTzJ07N+bPn9+ket988801mt4WLVu2LCZOnJh32sSJE2PZsmVruUUtb023cwCgdRx//PFrNL0tKzrojhs3LqqqqnJ//fr1a8l2rbFx48a1dhOSdvbZZzda5vjjj48XXnihwS9FFixYEC+88ELBOkaMGNGk9jVFU/tMU5fxxBNPLFhvY9Nnz54dL730UqxYsaLO+BUrVsRLL71U737qNTF8+PA1mt6QE044YY2mt0XXXnvtGk1fF63pdg4AtI633nprjaa3ZUUH3fPPPz/mz5+f+3vnnXdasl1rbNWHF9G8irnU95Zbbomddtopunbtmnd6VVVV7LTTTgXrmDBhQpPa1xRN7TNNXcabbrqpYL2NTe/fv3/ssMMO0a5duzrj27VrFzvuuGP079+/4OtXR2P3/Tb1vuBf/epXazS9LWrsnu/muie8LVnT7RwAaB0DBgxYo+ltWdFBt7y8PLp27Vrnry3r0KFD7Lnnnq3djCSdfPLJscMOOxQs079//+jSpUuUlpY2+ECaiy66KEpLC3fBjTbaqMntzOeQQw7JO/6www6LDh06NKnOpi7joEGDCtbb2PSSkpI47bTTGhxfUlJS8PWro6qqKrp37553Wo8ePaKqqqpJ9Q4cOHCNprdF7du3j6OOOirvtOHDh0f79u3Xcota3ppu5wBA62jsmUbr8jOPkj76WJevKV8bRo0alXd8od/N6tSpU3znO99ptNzKD7oaMmRIbL311nWmb7PNNrH99tsX1c7V/R2vhspPnjw5zj777HoBsKSkJM4888zVmseqmrqMhdpajL59+8bw4cNzy1RSUhLDhw9v9i8IIiLuvffevOPvueeeNap3TddBW3TSSSdFWVndnykvKyuL733ve63Uopa3pts5ANA6UjwWi0g86KaoobNqq6tHjx4xfPjw6NKlS53xtcMHHXRQ3tet+tTYHj161Ctzxhln1Bt36aWX5s7qlJaWNvjgmobssssuRZXbfffdGy2z6j2SzXXPZFOX8Stf+UrB4caMGDEi1ltvvYiI6NmzZ5Pvly3Gmv7mb0O22GKLgsProhtuuKHgcIrWdDsHAGguX6qg27Nnzxg5cmSdcR07dmyl1hSvto1Dhw6Ns88+O3r16lWwfGPfvpSXl8dZZ50VFRUV8cADD9SZVjt83nnn1XtddXV1vfs+Vz2bV1JSEocffni913br1i1GjBgRpaWlMWLEiOjWrVvBNq5q1QdFNbSMP/rRj/JOX3m4uro6evbsGRFf9Inq6urVaktDmrqMa/rbtBUVFXHGGWdEr1694vTTT4+KiorVev3qWHX7WXW4qVIMhYMGDcptL/3792/0UvQUrOl2DgC0jkLHzuuqkqyJv0GyYMGCqKqqivnz57fZ+3Xfeuut3OXLt9xyyzp9MzUAAMCXXbE59Et1RhcAAID0CboAAAAkRdAFAAAgKYIuAAAASRF0AQAASIqgCwAAQFIEXQAAAJIi6AIAAJAUQRcAAICkCLoAAAAkRdAFAAAgKYIuAAAASRF0AQAASIqgCwAAQFIEXQAAAJIi6AIAAJAUQRcAAICkCLoAAAAkRdAFAAAgKYIuAAAASRF0AQAASIqgCwAAQFIEXQAAAJIi6AIAAJAUQRcAAICkCLoAAAAkRdAFAAAgKYIuAAAASRF0AQAASIqgCwAAQFIEXQAAAJIi6AIAAJAUQRcAAICkCLoAAAAkRdAFAAAgKYIuAAAASRF0AQAASIqgCwAAQFIEXQAAAJIi6AIAAJAUQRcAAICkCLoAAAAkRdAFAAAgKYIuAAAASRF0AQAASIqgCwAAQFIEXQAAAJIi6AIAAJAUQRcAAICkCLoAAAAkRdAFAAAgKYIuAAAASRF0AQAASIqgCwAAQFIEXQAAAJIi6AIAAJAUQRcAAICkCLoAAAAkRdAFAAAgKYIuAAAASUk66Hbt2jXv/wAAAKQr6aBbWlqa938AAADSJf0BAACQFEEXAACApAi6AAAAJEXQBQAAICmCLgAAAEkRdAEAAEiKoAsAAEBSBF0AAACSIugCAACQFEEXAACApAi6AAAAJEXQBQAAICmCLgAAAEkRdAEAAEiKoAsAAEBSBF0AAACSIugCAACQFEEXAACApAi6AAAAJEXQBQAAICmCLgAAAEkRdAEAAEiKoAsAAEBSBF0AAACSIugCAACQFEEXAACApAi6AAAAJEXQBQAAICmCLgAAAEkRdAEAAEiKoAsAAEBSBF0AAACSIugCAACQFEEXAACApAi6AAAAJEXQBQAAICmCLgAAAEkRdAEAAEiKoAsAAEBSBF0AAACSIugCAACQFEEXAACApAi6AAAAJEXQBQAAICmCLgAAAEkRdAEAAEiKoAsAAEBSBF0AAACSIugCAACQFEEXAACApAi6AAAAJEXQBQAAICmCLgAAAEkRdAEAAEiKoAsAAEBSBF0AAACSIugCAACQFEEXAACApAi6AAAAJEXQBQAAICmCLgAAAEkRdAEAAEiKoAsAAEBSkg66FRUVef8HAAAgXWWt3YCWVFFREQ899FDufwAAANKXdNAtKSmJjh07tnYzAAAAWIuSvnQZAACALx9BFwAAgKQIugAAACRF0AUAACApgi4AAABJEXQBAABIiqALAABAUgRdAAAAkiLoAgAAkBRBFwAAgKQIugAAACRF0AUAACApgi4AAABJEXQBAABIiqALAABAUgRdAAAAkiLoAgAAkBRBFwAAgKQIugAAACRF0AUAACApgi4AAABJEXQBAABIiqALAABAUgRdAAAAkiLoAgAAkBRBFwAAgKQIugAAACRF0AUAACApgi4AAABJKWvqC7Msi4iIBQsWNFtjAAAAoCG1+bM2jzakyUF34cKFERHRr1+/plYBAAAAq23hwoVRVVXV4PSSrLEo3ICampp4//33o7KyMkpKSprcwJa2YMGC6NevX7zzzjvRtWvX1m4O1KOP0tbpo7R1+ijrAv2Utm5d6aNZlsXChQujT58+UVra8J24TT6jW1paGn379m3qy9e6rl27tuk3DPRR2jp9lLZOH2VdoJ/S1q0LfbTQmdxaHkYFAABAUgRdAAAAkpJ80C0vL4+LLrooysvLW7spkJc+Slunj9LW6aOsC/RT2rrU+miTH0YFAAAAbVHyZ3QBAAD4chF0AQAASIqgCwAAQFIEXQAAAJKSfNC9/vrrY8CAAVFRURGDBw+Op556qrWbxDpu3LhxscMOO0RlZWVssMEGceSRR8Ybb7xRp0yWZXHxxRdHnz59omPHjjF06NB49dVX65RZsmRJjBkzJnr27BmdO3eOww8/PN599906ZebOnRsjR46MqqqqqKqqipEjR8a8efPqlJk9e3Ycdthh0blz5+jZs2eceuqpsXTp0hZZdtZN48aNi5KSkhg7dmxunD5Ka3vvvffimGOOifXWWy86deoU2267bUyZMiU3XR+lNS1fvjx+8IMfxIABA6Jjx44xcODAuOSSS6KmpiZXRh9lbXvyySfjsMMOiz59+kRJSUncd999daa3tT45Y8aM2GuvvaJjx46x0UYbxSWXXBJr9TnIWcImTpyYtW/fPrvpppuy1157LTvttNOyzp07Z2+//XZrN4112IEHHpjdcsst2cyZM7Np06ZlhxxySNa/f//s008/zZW5/PLLs8rKyuzuu+/OZsyYkX3nO9/JNtxww2zBggW5MieddFK20UYbZZMmTcqmTp2a7b333tnXvva1bPny5bkyBx10UFZdXZ09++yz2bPPPptVV1dnhx56aG768uXLs+rq6mzvvffOpk6dmk2aNCnr06dPNnr06LWzMmjzXnzxxWyTTTbJttlmm+y0007LjddHaU3/+te/so033jg77rjjshdeeCF76623skcffTT7+9//niujj9KafvSjH2Xrrbde9sADD2RvvfVW9rvf/S7r0qVL9rOf/SxXRh9lbXvwwQezCy64ILv77ruziMjuvffeOtPbUp+cP39+1qtXr+yoo47KZsyYkd19991ZZWVl9tOf/rTlVtAqkg66O+64Y3bSSSfVGbf55ptn5513Xiu1iBR9/PHHWURkTzzxRJZlWVZTU5P17t07u/zyy3NlFi9enFVVVWU33nhjlmVZNm/evKx9+/bZxIkTc2Xee++9rLS0NHv44YezLMuy1157LYuI7Pnnn8+Vee6557KIyP76179mWfbFDq+0tDR77733cmV+85vfZOXl5dn8+fNbbqFZJyxcuDAbNGhQNmnSpGyvvfbKBV19lNZ27rnnZrvvvnuD0/VRWtshhxySnXDCCXXGfeMb38iOOeaYLMv0UVrfqkG3rfXJ66+/PquqqsoWL16cKzNu3LisT58+WU1NTTOuiYYle+ny0qVLY8qUKXHAAQfUGX/AAQfEs88+20qtIkXz58+PiIgePXpERMRbb70VH374YZ2+V15eHnvttVeu702ZMiWWLVtWp0yfPn2iuro6V+a5556Lqqqq2GmnnXJldt5556iqqqpTprq6Ovr06ZMrc+CBB8aSJUvqXALIl9Mpp5wShxxySOy33351xuujtLb7778/hgwZEt/61rdigw02iO222y5uuumm3HR9lNa2++67x5///Of429/+FhERf/nLX+Lpp5+Ogw8+OCL0UdqettYnn3vuudhrr72ivLy8Tpn3338//vGPfzT/CsijbK3MpRXMmTMnVqxYEb169aozvlevXvHhhx+2UqtITZZlccYZZ8Tuu+8e1dXVERG5/pWv77399tu5Mh06dIju3bvXK1P7+g8//DA22GCDevPcYIMN6pRZdT7du3ePDh066OdfchMnTowpU6bEyy+/XG+aPkpre/PNN+OGG26IM844I/7zP/8zXnzxxTj11FOjvLw8Ro0apY/S6s4999yYP39+bL755tGuXbtYsWJFXHbZZXH00UdHhP0obU9b65MffvhhbLLJJvXmUzttwIABTVnM1ZJs0K1VUlJSZzjLsnrjoKlGjx4d06dPj6effrretKb0vVXL5CvflDJ8ubzzzjtx2mmnxSOPPBIVFRUNltNHaS01NTUxZMiQ+PGPfxwREdttt128+uqrccMNN8SoUaNy5fRRWstdd90Vd9xxR9x5552x1VZbxbRp02Ls2LHRp0+fOPbYY3Pl9FHamrbUJ/O1paHXtoRkL13u2bNntGvXrt43XR9//HG9byCgKcaMGRP3339/PP7449G3b9/c+N69e0dEFOx7vXv3jqVLl8bcuXMLlvnoo4/qzfef//xnnTKrzmfu3LmxbNky/fxLbMqUKfHxxx/H4MGDo6ysLMrKyuKJJ56Ia665JsrKyup8o7oyfZS1ZcMNN4wtt9yyzrgtttgiZs+eHRH2o7S+s88+O84777w46qijYuutt46RI0fG6aefHuPGjYsIfZS2p631yXxlPv7444iof9a5pSQbdDt06BCDBw+OSZMm1Rk/adKk2HXXXVupVaQgy7IYPXp03HPPPfHYY4/Vu/RiwIAB0bt37zp9b+nSpfHEE0/k+t7gwYOjffv2dcp88MEHMXPmzFyZXXbZJebPnx8vvvhirswLL7wQ8+fPr1Nm5syZ8cEHH+TKPPLII1FeXh6DBw9u/oVnnbDvvvvGjBkzYtq0abm/IUOGxIgRI2LatGkxcOBAfZRWtdtuu9X7Wba//e1vsfHGG0eE/Sitb9GiRVFaWvcwuV27drmfF9JHaWvaWp/cZZdd4sknn6zzk0OPPPJI9OnTp94lzS1mrTzyqpXU/rzQzTffnL322mvZ2LFjs86dO2f/+Mc/WrtprMNOPvnkrKqqKps8eXL2wQcf5P4WLVqUK3P55ZdnVVVV2T333JPNmDEjO/roo/M+3r1v377Zo48+mk2dOjXbZ5998j7efZtttsmee+657Lnnnsu23nrrvI9333fffbOpU6dmjz76aNa3b18/OUA9Kz91Ocv0UVrXiy++mJWVlWWXXXZZNmvWrGzChAlZp06dsjvuuCNXRh+lNR177LHZRhttlPt5oXvuuSfr2bNnds455+TK6KOsbQsXLsxeeeWV7JVXXskiIhs/fnz2yiuv5H46tS31yXnz5mW9evXKjj766GzGjBnZPffck3Xt2tXPCzWnn//859nGG2+cdejQIdt+++1zPwEDTRURef9uueWWXJmamprsoosuynr37p2Vl5dne+65ZzZjxow69Xz++efZ6NGjsx49emQdO3bMDj300Gz27Nl1ynzyySfZiBEjssrKyqyysjIbMWJENnfu3Dpl3n777eyQQw7JOnbsmPXo0SMbPXp0nUe5Q5bVD7r6KK3tD3/4Q1ZdXZ2Vl5dnm2++efbLX/6yznR9lNa0YMGC7LTTTsv69++fVVRUZAMHDswuuOCCbMmSJbky+ihr2+OPP573GPTYY4/Nsqzt9cnp06dne+yxR1ZeXp717t07u/jii9faTwtlWZaVZNn/f1cwAAAAJCDZe3QBAAD4chJ0AQAASIqgCwAAQFIEXQAAAJIi6AIAAJAUQRcAAICkCLoAAAAkRdAFgDVw8cUXx7bbbttm6mnM5MmTo6SkJObNm9fi8wKA1iLoApCsDz/8MMaMGRMDBw6M8vLy6NevXxx22GHx5z//udnmcdZZZ9Wp77jjjosjjzyy2epvbrvuumt88MEHUVVV1dpNAYAWU9baDQCAlvCPf/wjdtttt+jWrVtcccUVsc0228SyZcviT3/6U5xyyinx17/+tVnm06VLl+jSpUuz1LU2dOjQIXr37t3azQCAFuWMLgBJ+v73vx8lJSXx4osvxje/+c3YdNNNY6uttoozzjgjnn/++YiIGD9+fGy99dbRuXPn6NevX3z/+9+PTz/9NFfHrbfeGt26dYv77rsvNt1006ioqIj9998/3nnnnVyZlS85vvjii+O2226L//3f/42SkpIoKSmJyZMnR0TEueeeG5tuuml06tQpBg4cGBdeeGEsW7ZstZZp4cKFMWLEiOjcuXNsuOGG8d///d8xdOjQGDt2bK7MHXfcEUOGDInKysro3bt3DB8+PD7++OPc9FUvXa5dxj/96U+xxRZbRJcuXeKggw6KDz74YLXaBgBtiaALQHL+9a9/xcMPPxynnHJKdO7cud70bt26RUREaWlpXHPNNTFz5sy47bbb4rHHHotzzjmnTtlFixbFZZddFrfddls888wzsWDBgjjqqKPyzvess86Kb3/727mg+MEHH8Suu+4aERGVlZVx6623xmuvvRZXX3113HTTTfHf//3fq7VcZ5xxRjzzzDNx//33x6RJk+Kpp56KqVOn1imzdOnSuPTSS+Mvf/lL3HffffHWW2/FcccdV7DeRYsWxU9/+tO4/fbb48knn4zZs2fHWWedtVptA4C2xKXLACTn73//e2RZFptvvnnBciufCR0wYEBceumlcfLJJ8f111+fG79s2bK47rrrYqeddoqIiNtuuy222GKLePHFF2PHHXesU1+XLl2iY8eOsWTJknqXB//gBz/I/b/JJpvEmWeeGXfddVe9YN2QhQsXxm233RZ33nln7LvvvhERccstt0SfPn3qlDvhhBNy/w8cODCuueaa2HHHHePTTz9t8BLrZcuWxY033hhf+cpXIiJi9OjRcckllxTVLgBoi5zRBSA5WZZFRERJSUnBco8//njsv//+sdFGG0VlZWWMGjUqPvnkk/jss89yZcrKymLIkCG54c033zy6desWr7/++mq16fe//33svvvu0bt37+jSpUtceOGFMXv27Lxln3rqqdy9v126dIkJEybEm2++GcuWLasTrquqqmKzzTar89pXXnkljjjiiNh4442jsrIyhg4dGhHR4LwiIjp16pQLuRERG264YZ3LnQFgXSPoApCcQYMGRUlJScEw+vbbb8fBBx8c1dXVcffdd8eUKVPi5z//eUREvXtn8wXmxkL0yp5//vk46qijYtiwYfHAAw/EK6+8EhdccEEsXbo0b/khQ4bEtGnTcn+HH354g+G9dnxExGeffRYHHHBAdOnSJe6444546aWX4t57742IaHBeERHt27evt2wr1wsA6xpBF4Dk9OjRIw488MD4+c9/XufsbK158+bFyy+/HMuXL4+rrroqdt5559h0003j/fffr1d2+fLl8fLLL+eG33jjjZg3b16Dl0V36NAhVqxYUWfcM888ExtvvHFccMEFMWTIkBg0aFC8/fbbDba/Y8eO8dWvfjX3V1lZGV/5yleiffv28eKLL+bKLViwIGbNmpUb/utf/xpz5syJyy+/PPbYY4/YfPPNnZkF4EtJ0AUgSddff32sWLEidtxxx7j77rtj1qxZ8frrr8c111wTu+yyS3zlK1+J5cuXx7XXXhtvvvlm3H777XHjjTfWq6d9+/YxZsyYeOGFF2Lq1Klx/PHHx84771zv/txam2yySUyfPj3eeOONmDNnTixbtiy++tWvxuzZs2PixInxf//3f3HNNdfkzrQWq7KyMo499tg4++yz4/HHH49XX301TjjhhCgtLc2d5e3fv3906NAht0z3339/XHrppau/8gBgHSfoApCkAQMGxNSpU2PvvfeOM888M6qrq2P//fePP//5z3HDDTfEtttuG+PHj4+f/OQnUV1dHRMmTIhx48bVq6dTp05x7rnnxvDhw2OXXXaJjh07xsSJExuc74knnhibbbZZDBkyJNZff/145pln4ogjjojTTz89Ro8eHdtuu208++yzceGFF672Mo0fPz522WWXOPTQQ2O//faL3XbbLbbYYouoqKiIiIj1118/br311vjd734XW265ZVx++eXx05/+dLXnAwDrupLMTTgAkNett94aY8eOzf3mbFvz2WefxUYbbRRXXXVVfPe7323t5gBAm+HnhQBgHfHKK6/EX//619hxxx1j/vz5uZ8AOuKII1q5ZQDQtgi6ALAO+elPfxpvvPFGdOjQIQYPHhxPPfVU9OzZs7WbBQBtikuXAQAASIqHUQEAAJAUQRcAAICkCLoAAAAkRdAFAAAgKYIuAAAASRF0AQAASIqgCwAAQFIEXQAAAJIi6AIAAJAUQRcAAICkCLoAAAAkRdAFAAAgKYIuAAAASRF0AQAASIqgCwAAQFIEXQAAAJIi6AIAAJAUQRcAAICkCLoAAAAkRdAFAAAgKYIuAAAASSlr6gtramri/fffj8rKyigpKWnONgEAAEA9WZbFwoULo0+fPlFa2vB52yYH3ffffz/69evX1JcDAABAk7zzzjvRt2/fBqc3OehWVlbmZtC1a9emVgMAAABFWbBgQfTr1y+XRxvS5KBbe7ly165dBV0AAADWmsZun/UwKgAAAJIi6AIAAJAUQRcAAICkCLoAAAAkRdAFAAAgKYIuAAAASRF0AQAASIqgCwAAQFIEXQAAAJIi6AIAAJAUQRcAAICkCLoAAAAkRdAFAAAgKYIuAAAASRF0AQAASIqgCwAAQFIEXQAAAJIi6AIAAJAUQRcAAICkCLoAAAAkRdAFAAAgKYIuAAAASRF0AQAASIqgCwAAQFIEXQAAAJIi6AIAAJAUQRcAAICkCLoAAAAkpay1G9CSsiyLxYsXR0RERUVFlJSUtHKLAAAAaGlJn9FdvHhxDBs2LIYNG5YLvAAAAKQt+aCb738AAADSlXTQBQAA4MtH0AUAACApgi4AAABJEXQBAABIiqALAABAUgRdAAAAkiLoAgAAkBRBFwAAgKQIugAAACRF0AUAACApgi4AAABJEXQBAABIiqALAABAUgRdAAAAkiLoAgAAkBRBFwAAgKQIugAAACRF0AUAACApgi4AAABJEXQBAABIiqALAABAUgRdAAAAkiLoAgAAkBRBFwAAgKQIugAAACRF0AUAACApgi4AAABJEXQBAABIiqALAABAUgRdAAAAkiLoAgAAkBRBFwAAgKQIugAAACRF0AUAACApgi4AAABJEXQBAABIiqALAABAUgRdAAAAkiLoAgAAkBRBFwAAgKQIugAAACRF0AUAACApgi4AAABJEXQBAABIiqALAABAUgRdAAAAkiLoAgAAkBRBFwAAgKQIugAAACRF0AUAACApgi4AAABJEXQBAABIiqALAABAUgRdAAAAkiLoAgAAkBRBFwAAgKQIugAAACRF0AUAACApgi4AAABJEXQBAABIiqALAABAUgRdAAAAkiLoAgAAkJSkg25NTU3e/wEAAEhX0kF3wYIFef8HAAAgXUkHXQAAAL58BF0AAACSIugCAACQFEEXAACApAi6AAAAJEXQBQAAICmCLgAAAEkRdAEAAEiKoAsAAEBSBF0AAACSIugCAACQFEEXAACApAi6AAAAJEXQBQAAICmCLgAAAEkRdAEAAEiKoAsAAEBSBF0AAACSIugCAACQFEEXAACApAi6AAAAJEXQBQAAICmCLgAAAEkRdAEAAEiKoAsAAEBSBF0AAACSIugCAACQFEEXAACApAi6AAAAJEXQBQAAICmCLgAAAEkRdAEAAEiKoAsAAEBSBF0AAACSIugCAACQFEEXAACApAi6AAAAJEXQBQAAICmCLgAAAEkRdAEAAEiKoAsAAEBSBF0AAACSIugCAACQFEEXAACApAi6AAAAJEXQBQAAICmCLgAAAEkRdAEAAEiKoAsAAEBSBF0AAACSIugCAACQFEEXAACApAi6AAAAJEXQBQAAICmCLgAAAEkRdAEAAEiKoAsAAEBSBF0AAACSIugCAACQFEEXAACApAi6AAAAJKWstRuQqmeffTauvvrqOO2002LXXXctWHb06NExc+bMqK6ujuuuu67Z2nDxxRfH5MmTY+jQoXHxxRe3ej0tZe+9944sy6KkpCQef/zx3PjWavfNN98cEyZMiBEjRsR3v/vdol/3zW9+M+bMmRM9e/aM3//+92t9/sU47LDDYuHChVFZWRl/+MMf1vr813Rbaaz9+aYPHTo0N/3HP/5xo9vzylZ+bW1fXHl4Zfn2GSuXL6RDhw6xdOnSOuMmT54cN998c9x+++0FyzTW7p49e8acOXNywyNHjszVWaiOWiu3ofZ9e/bZZ+M///M/69XR2PLmm1dD+9pV68o3j1XrO+CAA+qto5W3x2L266vud1beHrbYYou4+uqrY8GCBfH555/XaUdD66S2vv79+8fs2bPzznPo0KFxwAEHFP2Zs/I6GDlyZJ3tdeVptX2mQ4cO8cgjj9R7bW0bV94H77XXXjF58uSorq6Od999NyIizjnnnIiIJn0mvvrqq3n377VGjRoVs2fPjv79+8evf/3rgvU2p6bs5/bbb79Yvnx5lJWVxaOPPtrCLSysJffTrHuaoz+sjT6l37a8tn7Mv7qc0W0BixcvjvHjx8dHH30U48ePj8WLFzdYdvbs2TFz5syIiJg5c2aDBzKr66OPPsodhEyePDk++uijVq2npdx///2RZVlERGRZFvfff39EtF67582bFxMmTIiampqYMGFCzJs3r6jXzZw5Mxcm5syZk+sTa2v+xZg6dWosXLgwIiIWLlwYU6dOXavzX9NtpbH255t+5ZVX1ilzySWXFNyeV3bkkUfWGV41eN1www25//PtMy666KKi5hMR9cJZRMRNN91UJ5DmK3PyySfXGzdy5Mg6wyuH3IioU2dExMSJExts17x58+qUnzlzZsyaNatOoIv4Yl0de+yxDdZTa9V12NC+duV1u/Jrv/3tb9cZt/Lwk08+mXcd1W6PxezXV93vzJo1K7c93HHHHfHTn/40PvroozohNyLilltuiUsvvbTOuNtvv71OfYX6++TJk+PKK68s6jPnpptuqjef2u318ssvrzOtdn0sXbo0nnzyyfjGN75RZ/o3vvGNevvg2vbOnDkz5s2bF/PmzYurrroqrrrqqiZ9Jubbv9eaNWtWbr3Mnj07Zs2a1WC9zakp+7lHH300li9fHhERy5cvb9Wg25L7adY9zdEf1kaf0m9bXls/5m+Kkqz2U2Q1LViwIKqqqmL+/PnRtWvX5m5Xs3jrrbfi+OOPj4gvDiQGDBiwVuZ78803xx133JH7FnrkyJFxwgkn5C178MEHx6JFi3LDnTp1igcffHCN2/Ctb30r/vnPf+aGN9hgg/jtb3/bavW0lHxngCZPntxq7R4zZkzMmDEjN7zNNtvENddc0+jras+I1Gro7EVLzb8YDa3rtTX/Nd1WGmt/sWdPR40a1eD23Nj8Gpp/vn3G2jo7tep7WOx6KFRHrVX7Q0REWVlZ7oC/KVaeV0P72tVZhmLOJpeUlMQxxxzT6H591f3Omi7r+uuvX6e+YjT2mdOU97c5re5n4qpWfv9rz5DWWltnSpuynytm/7m2tOR+mnVPc/SHtdGn9NuW19aP+VdWbA51RreZvfvuu3HnnXfW+Rb6zjvvzF3CtbLf/OY39T7QFy1aFL/5zW/WqA0PP/xwvYOjjz/+OB5++OFWqaelHHPMMXnHf/3rX2+Vdr/88sv1DuqnT58eL7/8csHXXXnllbHq901ZltU7m9hS8y9GQ5evrDy+Jee/pttKY+1fnctzfv3rX+fdnldWbJg4+OCD8+4z1uYlmCu3takh6Jvf/Ga9cfn6Q0SsUfCL+H9tbGhfe9BBB612faeeemrBMlmWxe23315wv55vf7mmy7q6IbehttU69NBD16g9zWF1PxNXVbvfv/HGG+ut3+XLl8eNN97YfI3Noyn7uf/4j/9YrfEtqSX306x7mqM/rI0+pd+2vLZ+zN9URZ/RXbJkSSxZsiQ3vGDBgujXr58zuivJsizOOeecmDp1aqxYsSI3vl27drH99tvHFVdcESUlJRHxxQfyfvvt12Bdjz76aJSVrf4t1CtWrIgDDjigzvxXbscjjzwS7dq1W2v1tJRPP/10tQ/aWrLdNTU1ceSRR8aCBQvqTevatWvcd999UVpa/3ulpUuXxgEHHNBgvY888kh06NChxeZfjCVLlsSBBx7Y4PQ//elP0b59+xab/5puK421//7774/DDz98tdr0ta99LX72s5/ltueVTZkyJc4888yi6xo0aFC8+eabebe1teVXv/pVLF26NE466aQm13H//ffnPgtqamri0EMPbTS0rIkddtih3r52bVp5v15TU9Pg/rI15PvMmTt3bnz9619v5ZZ9oSmfiSu79957Cy7LpEmTon379s3S1pU1ZT+7aNGiOPjggxus88EHH4xOnTo1e1vzacnPCdY9zdEf1kaf0m9bXls/5s+n2c/ojhs3LqqqqnJ//fr1a5aGpmT27Nnx0ksv1esoK1asiJdeeqnOPVaNnbFp6hmdBx54oMGDrRUrVsQDDzywVutpKbVfYKyOlmz3Cy+8kHcnHPHFxvjCCy/knTZu3LiC9TY2fU3nX4wLL7yw0ektOf813VYaa39DVwYU8pe//KXBeyZXJ+RGfHGfYWsHpBNOOGGNQm5E3fX4wgsvtGjIjYi8+9q1aeX9eqH9ZWvI95lz1FFHtWKL6mrKZ+LKGluWa6+9tsltK6Qp+7l898GvzvTm1JL7adY9zdEf1kaf0m9bXls/5l8TRQfd888/P+bPn5/7e+edd1qyXeuk/v37xw477FDvW4927drFjjvuGP3798+NGzVqVMG6GpvekEMPPbTBb13KysqKPgvaXPW0lFtuuWW1X9OS7d5pp50a/Eapqqoqdtppp7zTzj///IL1NjZ9TedfjFUfkpNvekvOf023lcbaf8cdd6x2m7bddts62/PKrrrqqtWqa9CgQa3+TemvfvWrNb7kc+X1uNNOO7X4Wap8+9q1aeX9eqH9ZWvI95lT6KFha1tTPhNX1tiyjBkzpsltK6Qp+7l8D0ZbnenNqSX306x7mqM/rI0+pd+2vLZ+zL8mig665eXl0bVr1zp/1FVSUhKnnXZag+NXvsyxrKyswftzTj755CZdthzxxQHE2WefnXfaOeecU/TBWHPV01K6dOkSffv2zTutR48eece3ZLtLS0vjhz/8Yd5pF110UYOX1XTo0CEOOeSQvNMOO+ywoi5bXpP5F6O8vLzBezf32WefKC8vb9H5r+m20lj7u3btutr3pp599tl5L1uOiBg8eHDR9XTq1Gm1nq7cUgYOHBibb755k1+//vrr1/lMKC0tjUsuuaQ5mpbX5MmT8+5ry8rKoqKiYrXr22abbVb7NSvv1wvtL1tDvs+c7t27R5cuXVqxVf/P6n4mrqx///7RvXv3Bs/qDh8+vEUuW45o2n62U6dOsdlmm+V9zRZbbLHWLluOaNnPCdY9zdEf1kaf0m9bXls/5l8Tekcz69u3bwwfPjz3AV5SUhLDhw+PjTbaqF7Zo48+ut6HXKdOneI73/nOGrXhoIMOivXXX7/OuA022KDgvaAtWU9LaehM3D333NMq7R4yZEhsvfXWdcZts802sf322xd8Xb7QVFJSstqXwDZ1/sVo6GFNK3/4tOT813Rbaaz9q/MwqlGjRuXdnldW7NNUH3zwwbz7jKZe0dEUK7e1qU+B/d3vfldvXL7+EBFN/hKvVm0bG9rXru6DMyZPntzokztrnxRcaL+eb3+5psu6an3FKPSZ0xYuP1vdz8RV1V7ifNJJJ9Vbv2VlZfG9732v+RqbR1P2c7/4xS/yjl+bZ3NrteR+mnVPc/SHtdGn9NuW19aP+ZtK0G0BI0aMiPXWWy8iInr27BnDhw9vsOyqlws21xMjr7vuujrDTb1nqbnqaSlnnHFG3uHWavell16a+3Zxdc5qrdq+pra3qfMvxvjx4wsOt/T813Rbaaz9+YZXPdteUVFRcHteWbdu3QpOXzmk59tn7LXXXkXNpyEjRoxotMwWW2xRb9zqPn+h0L29+S4bz3dw361bt9h4441Xa74RDe9rG/oCZIMNNmhwuFBfvfbaa4var6+637nhhhty20NJSUmDV5sce+yx0bFjxzrjvvvd79arr5Du3bsXbFuthvpFaWlpwSdWX3LJJfXa36NHj3r74HzWW2+9Jn8mrmzVea3al9ZWcGzKfu4HP/hBweG1qSX306x7mqM/rI0+pd+2vLZ+zN8Ugm4LqKioiDPOOCN69eoVp59+esFL6fr37x/V1dUREVFdXd3gfX+rq1evXrnLMYcOHRq9evVq1XpayuGHH17nLEvt03Nbq93dunWLESNGRGlpaYwYMaLRsFOruro6evbsGRFfHAjW9om1Nf9ibL/99lFZWRkREZWVlXm/SW3J+a/pttJY+/NNX/VSnh/+8IdFXxp733331Rle9Wzpyg+hybfP+K//+q+i5hMReS9xP/HEE2PkyJEFy+QLBrfffnud4dp+WWvlOiMKPxioW7dudcpXV1fHoEGD4sc//nGdcvfdd1/cdtttDdZTa9V12NC+Nt8DfiZPnlzv9wBXHt5zzz3zrqPa7bGY/fqq+51BgwbltodjjjkmzjrrrOjVq1e9UHv88cfXe2jayJEj69RXqL8PHTo0zj777KI+c0488cR686ndXs8777w602rXR4cOHWLPPfeMe+65p870e+65p94+uLa91dXV0a1bt+jWrVuceeaZceaZZzbpMzHf/r3WoEGDcuulf//+MWjQoAbrbU5N2c/tt99+uTPQZWVlRT9huiW05H6adU9z9Ie10af025bX1o/5m6LonxdaVbGPdW5Na/vnhQAAAGg5zf7zQgAAALAuEHQBAABIiqALAABAUgRdAAAAkiLoAgAAkBRBFwAAgKQIugAAACRF0AUAACApgi4AAABJEXQBAABIiqALAABAUgRdAAAAkiLoAgAAkBRBFwAAgKQIugAAACRF0AUAACApgi4AAABJEXQBAABIiqALAABAUgRdAAAAkiLoAgAAkBRBFwAAgKQIugAAACRF0AUAACApgi4AAABJEXQBAABIiqALAABAUgRdAAAAkiLoAgAAkBRBFwAAgKQIugAAACRF0AUAACApgi4AAABJEXQBAABIiqALAABAUgRdAAAAkiLoAgAAkBRBFwAAgKQIugAAACRF0AUAACApgi4AAABJEXQBAABIiqALAABAUgRdAAAAkiLoAgAAkBRBFwAAgKQIugAAACRF0AUAACApgi4AAABJEXQBAABIiqALAABAUgRdAAAAkiLoAgAAkBRBFwAAgKQIugAAACRF0AUAACApgi4AAABJEXQBAABIiqALAABAUgRdAAAAkpJ00O3atWve/wEAAEhX0kG3tLQ07/8AAACkS/oDAAAgKYIuAAAASRF0AQAASIqgCwAAQFIEXQAAAJIi6AIAAJAUQRcAAICkCLoAAAAkRdAFAAAgKYIuAAAASRF0AQAASIqgCwAAQFIEXQAAAJIi6AIAAJAUQRcAAICkCLoAAAAkRdAFAAAgKYIuAAAASRF0AQAASIqgCwAAQFIEXQAAAJIi6AIAAJAUQRcAAICkCLoAAAAkRdAFAAAgKYIuAAAASRF0AQAASIqgCwAAQFIEXQAAAJIi6AIAAJAUQRcAAICkCLoAAAAkRdAFAAAgKYIuAAAASRF0AQAASIqgCwAAQFIEXQAAAJIi6AIAAJAUQRcAAICkCLoAAAAkRdAFAAAgKYIuAAAASRF0AQAASIqgCwAAQFIEXQAAAJIi6AIAAJAUQRcAAICkCLoAAAAkRdAFAAAgKYIuAAAASRF0AQAASIqgCwAAQFIEXQAAAJIi6AIAAJAUQRcAAICkCLoAAAAkRdAFAAAgKYIuAAAASRF0AQAASIqgCwAAQFIEXQAAAJIi6AIAAJAUQRcAAICkJB10Kyoq8v4PAABAuspauwEtqaKiIh566KHc/wAAAKQv6aBbUlISHTt2bO1mAAAAsBYlfekyAAAAXz6CLgAAAEkRdAEAAEiKoAsAAEBSBF0AAACSIugCAACQFEEXAACApAi6AAAAJEXQBQAAICmCLgAAAEkRdAEAAEiKoAsAAEBSBF0AAACSIugCAACQFEEXAACApAi6AAAAJEXQBQAAICmCLgAAAEkRdAEAAEiKoAsAAEBSBF0AAACSIugCAACQFEEXAACApAi6AAAAJEXQBQAAICmCLgAAAEkRdAEAAEiKoAsAAEBSBF0AAACSUtbUF2ZZFhERCxYsaLbGAAAAQENq82dtHm1Ik4PuwoULIyKiX79+Ta0CAAAAVtvChQujqqqqweklWWNRuAE1NTXx/vvvR2VlZZSUlDS5gS1twYIF0a9fv3jnnXeia9eurd0caBW2A7AdQITtACJsB+u6LMti4cKF0adPnygtbfhO3Caf0S0tLY2+ffs29eVrXdeuXXVkvvRsB2A7gAjbAUTYDtZlhc7k1vIwKgAAAJIi6AIAAJCU5INueXl5XHTRRVFeXt7aTYFWYzsA2wFE2A4gwnbwZdHkh1EBAABAW5T8GV0AAAC+XARdAAAAkiLoAgAAkBRBFwAAgKQkH3Svv/76GDBgQFRUVMTgwYPjqaeeau0mQZM8+eSTcdhhh0WfPn2ipKQk7rvvvjrTsyyLiy++OPr06RMdO3aMoUOHxquvvlqnzJIlS2LMmDHRs2fP6Ny5cxx++OHx7rvv1ikzd+7cGDlyZFRVVUVVVVWMHDky5s2b18JLB8UZN25c7LDDDlFZWRkbbLBBHHnkkfHGG2/UKWNbIHU33HBDbLPNNtG1a9fo2rVr7LLLLvHQQw/lptsG+LIZN25clJSUxNixY3PjbAckHXTvuuuuGDt2bFxwwQXxyiuvxB577BHDhg2L2bNnt3bTYLV99tln8bWvfS2uu+66vNOvuOKKGD9+fFx33XXx0ksvRe/evWP//fePhQsX5sqMHTs27r333pg4cWI8/fTT8emnn8ahhx4aK1asyJUZPnx4TJs2LR5++OF4+OGHY9q0aTFy5MgWXz4oxhNPPBGnnHJKPP/88zFp0qRYvnx5HHDAAfHZZ5/lytgWSF3fvn3j8ssvj5dffjlefvnl2GeffeKII47IHcTbBvgyeemll+KXv/xlbLPNNnXG2w6ILGE77rhjdtJJJ9UZt/nmm2fnnXdeK7UImkdEZPfee29uuKamJuvdu3d2+eWX58YtXrw4q6qqym688cYsy7Js3rx5Wfv27bOJEyfmyrz33ntZaWlp9vDDD2dZlmWvvfZaFhHZ888/nyvz3HPPZRGR/fWvf23hpYLV9/HHH2cRkT3xxBNZltkW+PLq3r179j//8z+2Ab5UFi5cmA0aNCibNGlSttdee2WnnXZalmU+C/hCsmd0ly5dGlOmTIkDDjigzvgDDjggnn322VZqFbSMt956Kz788MM6/b28vDz22muvXH+fMmVKLFu2rE6ZPn36RHV1da7Mc889F1VVVbHTTjvlyuy8885RVVVlu6FNmj9/fkRE9OjRIyJsC3z5rFixIiZOnBifffZZ7LLLLrYBvlROOeWUOOSQQ2K//farM952QEREWWs3oKXMmTMnVqxYEb169aozvlevXvHhhx+2UqugZdT26Xz9/e23386V6dChQ3Tv3r1emdrXf/jhh7HBBhvUq3+DDTaw3dDmZFkWZ5xxRuy+++5RXV0dEbYFvjxmzJgRu+yySyxevDi6dOkS9957b2y55Za5g2/bAKmbOHFiTJkyJV5++eV603wWEJFw0K1VUlJSZzjLsnrjIBVN6e+rlslX3nZDWzR69OiYPn16PP300/Wm2RZI3WabbRbTpk2LefPmxd133x3HHntsPPHEE7nptgFS9s4778Rpp50WjzzySFRUVDRYznbw5Zbspcs9e/aMdu3a1fu25eOPP6737Q6s63r37h0RUbC/9+7dO5YuXRpz584tWOajjz6qV/8///lP2w1typgxY+L++++Pxx9/PPr27Zsbb1vgy6JDhw7x1a9+NYYMGRLjxo2Lr33ta3H11VfbBvhSmDJlSnz88ccxePDgKCsri7KysnjiiSfimmuuibKyslwftR18uSUbdDt06BCDBw+OSZMm1Rk/adKk2HXXXVupVdAyBgwYEL17967T35cuXRpPPPFErr8PHjw42rdvX6fMBx98EDNnzsyV2WWXXWL+/Pnx4osv5sq88MILMX/+fNsNbUKWZTF69Oi455574rHHHosBAwbUmW5b4Msqy7JYsmSJbYAvhX333TdmzJgR06ZNy/0NGTIkRowYEdOmTYuBAwfaDkj7qcsTJ07M2rdvn918883Za6+9lo0dOzbr3Llz9o9//KO1mwarbeHChdkrr7ySvfLKK1lEZOPHj89eeeWV7O23386yLMsuv/zyrKqqKrvnnnuyGTNmZEcffXS24YYbZgsWLMjVcdJJJ2V9+/bNHn300Wzq1KnZPvvsk33ta1/Lli9fnitz0EEHZdtss0323HPPZc8991y29dZbZ4ceeuhaX17I5+STT86qqqqyyZMnZx988EHub9GiRbkytgVSd/7552dPPvlk9tZbb2XTp0/P/vM//zMrLS3NHnnkkSzLbAN8Oa381OUssx2QZUkH3SzLsp///OfZxhtvnHXo0CHbfvvtcz9BAeuaxx9/PIuIen/HHntslmVfPEr/oosuynr37p2Vl5dne+65ZzZjxow6dXz++efZ6NGjsx49emQdO3bMDj300Gz27Nl1ynzyySfZiBEjssrKyqyysjIbMWJENnfu3LW0lFBYvm0gIrJbbrklV8a2QOpOOOGE3LHN+uuvn+277765kJtltgG+nFYNurYDSrIsy1rnXDIAAAA0v2Tv0QUAAODLSdAFAAAgKYIuAAAASRF0AQAASIqgCwAAQFIEXQAAAJIi6AIAAJAUQRcA1tDFF18c22677VqpZ+jQoTF27Ng1nhcApEzQBSBpH374YYwZMyYGDhwY5eXl0a9fvzjssMPiz3/+c7PN46yzzqpT33HHHRdHHnlks9UPAKyestZuAAC0lH/84x+x2267Rbdu3eKKK66IbbbZJpYtWxZ/+tOf4pRTTom//vWvzTKfLl26RJcuXZqlLgBgzTmjC0Cyvv/970dJSUm8+OKL8c1vfjM23XTT2GqrreKMM86I559/PiIixo8fH1tvvXV07tw5+vXrF9///vfj008/zdVx6623Rrdu3eK+++6LTTfdNCoqKmL//fePd955J1dm5UuOL7744rjtttvif//3f6OkpCRKSkpi8uTJERFx7rnnxqabbhqdOnWKgQMHxoUXXhjLli1bo2WcO3dujBo1Krp37x6dOnWKYcOGxaxZs3LT33777TjssMOie/fu0blz59hqq63iwQcfzL12xIgRsf7660fHjh1j0KBBccstt6xRewCgLXBGF4Ak/etf/4qHH344LrvssujcuXO96d26dYuIiNLS0rjmmmtik002ibfeeiu+//3vxznnnBPXX399ruyiRYvisssui9tuuy06dOgQ3//+9+Ooo46KZ555pl69Z511Vrz++uuxYMGCXGjs0aNHRERUVlbGrbfeGn369IkZM2bEiSeeGJWVlXHOOec0eTmPO+64mDVrVtx///3RtWvXOPfcc+Pggw+O1157Ldq3bx+nnHJKLF26NJ588sno3LlzvPbaa7mzzxdeeGG89tpr8dBDD0XPnj3j73//e3z++edNbgsAtBWCLgBJ+vvf/x5ZlsXmm29esNzKD3YaMGBAXHrppXHyySfXCbrLli2L6667LnbaaaeIiLjttttiiy22iBdffDF23HHHOvV16dIlOnbsGEuWLInevXvXmfaDH/wg9/8mm2wSZ555Ztx1111NDrq1AfeZZ56JXXfdNSIiJkyYEP369Yv77rsvvvWtb8Xs2bPj3/7t32LrrbeOiIiBAwfmXj979uzYbrvtYsiQIbk2AUAKXLoMQJKyLIuIiJKSkoLlHn/88dh///1jo402isrKyhg1alR88skn8dlnn+XKlJWV5cJgRMTmm28e3bp1i9dff3212vT73/8+dt999+jdu3d06dIlLrzwwpg9e3besk899VTu3t8uXbrEhAkT6pV5/fXXo6ysLBfAIyLWW2+92GyzzXJtO/XUU+NHP/pR7LbbbnHRRRfF9OnTc2VPPvnkmDhxYmy77bZxzjnnxLPPPrtaywMAbZWgC0CSBg0aFCUlJQXD6Ntvvx0HH3xwVFdXx9133x1TpkyJn//85xER9e6dzReYGwvRK3v++efjqKOOimHDhsUDDzwQr7zySlxwwQWxdOnSvOWHDBkS06ZNy/0dfvjh9crUhvl842vb9u///u/x5ptvxsiRI2PGjBkxZMiQuPbaayMiYtiwYfH222/H2LFj4/3334999903zjrrrKKXCQDaKkEXgCT16NEjDjzwwPj5z39e5+xsrXnz5sXLL78cy5cvj6uuuip23nnn2HTTTeP999+vV3b58uXx8ssv54bfeOONmDdvXoOXRXfo0CFWrFhRZ9wzzzwTG2+8cVxwwQUxZMiQGDRoULz99tsNtr9jx47x1a9+NfdXWVlZr8yWW24Zy5cvjxdeeCE37pNPPom//e1vscUWW+TG9evXL0466aS455574swzz4ybbropN2399deP4447Lu6444742c9+Fr/85S8bbBMArCsEXQCSdf3118eKFStixx13jLvvvjtmzZoVr7/+elxzzTWxyy67xFe+8pVYvnx5XHvttfHmm2/G7bffHjfeeGO9etq3bx9jxoyJF154IaZOnRrHH3987LzzzvXuz621ySabxPTp0+ONN96IOXPmxLJly+KrX/1qzJ49OyZOnBj/93//F9dcc03ce++9a7R8gwYNiiOOOCJOPPHEePrpp+Mvf/lLHHPMMbHRRhvFEUccERFf3IP8pz/9Kd56662YOnVqPPbYY7kQ/MMf/jD+93//N/7+97/Hq6++Gg888ECdgAwA6ypBF4BkDRgwIKZOnRp77713nHnmmVFdXR37779//PnPf44bbrghtt122xg/fnz85Cc/ierq6pgwYUKMGzeuXj2dOnWKc889N4YPHx677LJLdOzYMSZOnNjgfE888cTYbLPNYsiQIbH++uvHM888E0cccUScfvrpMXr06Nh2223j2WefjQsvvHCNl/GWW26JwYMHx6GHHhq77LJLZFkWDz74YLRv3z4iIlasWBGnnHJKbLHFFnHQQQfFZpttlnvQVocOHeL888+PbbbZJvbcc89o165dweUCgHVFSdbQDT4AQNx6660xduzYmDdvXms3BQAokjO6AAAAJEXQBQAAICkuXQYAACApzugCAACQFEEXAACApAi6AAAAJEXQBQAAICmCLgAAAEkRdAEAAEiKoAsAAEBSBF0AAACSIugCAACQFEEXAACApAi6AAAAJEXQBQAAICmCLgAAAEkRdAEAAEiKoAsAAEBSBF0AAACSIugCAACQFEEXAACApAi6AAAAJEXQBQAAICmCLgAAAEkpa+oLa2pq4v3334/KysooKSlpzjYBAABAPVmWxcKFC6NPnz5RWtrwedsmB933338/+vXr19SXAwAAQJO888470bdv3wanNznoVlZW5mbQtWvXplYDAAAARVmwYEH069cvl0cb0uSgW3u5cteuXQVdAAAA1prGbp/1MCoAAACSIugCAACQFEEXAACApAi6AAAAJEXQBQAAICmCLgAAAEkRdAEAAEiKoAsAAEBSBF0AAACSIugCAACQFEEXAACApAi6AAAAJEXQBQAAICmCLgAAAEkRdAEAAEiKoAsAAEBSBF0AAACSIugCAACQFEEXAACApAi6AAAAJEXQBQAAICmCLgAAAEkRdAEAAEiKoAsAAEBSBF0AAACSIugCAACQFEEXAACApAi6AAAAJKWstRsAQNtUU1MT8+fPj4iI8vLyKCkpaeUWFaeiomKdaSsA0DIEXQDymj9/fnz9619v7Wastoceeig6duzY2s0AAFqRS5cBAABIijO6ADTq062/FVn7itZuRoNKapZHl2m/ae1mAABthKALQKOy0nYR7dq3djMalLV2AwCANsWlywAAACRF0AUAACApgi4AAABJEXQBAABIiqALAABAUgRdAAAAkiLoAgAAkBRBFwAAgKQIugAAACRF0AUAACApgi4AAABJEXQBAABIiqALAABAUgRdAAAAkiLoAgAAkBRBFwAAgKQIugAAACRF0AUAACApgi4AAABJEXQBAABIiqALAABAUgRdAAAAkiLoAgAAkBRBFwAAgKQIugAAACRF0AUAACApgi4AAABJEXQBAABIiqALAABAUgRdAAAAkiLoAgAAkBRBFwAAgKQIugAAACRF0AUAACApgi4AAABJEXQBAABIiqALAABAUgRdAAAAkiLoAgAAkBRBFwAAgKQIugAAACRF0AUAACApgi4AAABJEXQBAABIiqALAABAUgRdAAAAkiLoAgAAkBRBFwAAgKQIugAAACRF0AUAACApgi4AAABJEXQBAABIiqALAABAUgRdAAAAkiLoAgAAkBRBFwAAgKQIugAAACRF0AUAACApgi4AAABJEXQBAABIiqALAABAUspauwEA64Isy2Lx4sUREVFRURElJSWt3CK+zPRHACjMGV2AIixevDiGDRsWw4YNywUMaC36IwAUJugCAACQFEEXAACApAi6AAAAJEXQBQAAICmCLgAAAEkRdAEAAEiKoAsAAEBSBF0AAACSIugCAACQFEEXAACApAi6AAAAJEXQBQAAICmCLgAAAEkRdAEAAEiKoAsAAEBSBF0AAACSIugCAACQFEEXAACApAi6AAAAJEXQBQAAICmCLgAAAEkRdAEAAEiKoAsAAEBSBF0AAACSIugCAACQFEEXAACApAi6AAAAJEXQBQAAICmCLgAAAEkRdAEAAEiKoAsAAEBSBF0AAACSIugCAACQFEEXAACApAi6AAAAJEXQBQAAICmCLgAAAEkRdAEAAEiKoAsAAEBSBF0AAACSIugCAACQFEEXAACApAi6AAAAJEXQBQAAICmCLgAAAEkRdAEAAEiKoAsAAEBSBF0AAACSIugCAACQFEEXAACApAi6AAAAJEXQBQAAICmCLgAAAEkRdAEAAEiKoAsAAEBSBF0AAACSIugCAACQFEEXAACApAi6AAAAJEXQBQAAICmCLgAAAEkpa+0GtLShQ4fm/p88eXKTyjRHHanNZ5999omampooLS2Nxx57rE23dW3NZ11q69qaT3O19bDDDouFCxdGZWVl/OEPf2hSHaNGjYrZs2dH//7949e//nWT2wLrkpT2JxGNb8fF1HHzzTfHhAkTYsSIEfHd7363SXXst99+sXz58igrK4tHH300b5m1tU5Gjx4dM2fOjOrq6rjuuuvqTd97770jy7IoKSmJxx9/PG8djWmufWNj9TT23kREPPvss3H11VfHaaedFrvuumuT5nPxxRfH5MmTY+jQoXHxxRfnreOb3/xmzJkzJ3r27Bm///3v600fNmxYfP7559GxY8d46KGH8tZRjOZoazHrZE3bUUyZ5nr/GqunmPkUszyNrdvmWK/NsbwRje/7iqmjuY7d1yVJn9H97//+74LDERFjxowpOHzssccWHI744iC80HBExIEHHlhweN999y04HBHxgx/8oOBwRN0Omm/4hhtuKDhcTB0PPfRQ1NTURERETU1N3h18Y3VERFx22WUFh08++eSCw8XOp7EyZ555ZsHhYuqYOHFiweFi23rJJZcUHP7Od75TcDgi4sorryw4XExbimnr6aefvkbDxc5n6tSpsXDhwoiIWLhwYUydOnW165g1a1bMnj07IiJmz54ds2bNalJbYF1y0UUXFRweMWJEweGI5tnH3nTTTQWHIyLOOeecgsMRjW/HxbR13rx5MWHChKipqYkJEybEvHnz6kwv5jPy0UcfjeXLl0dExPLly/MG3cY+E4ppazHrbfbs2TFz5syIiJg5c2Zu/dS6//77I8uyiIjIsizuv//+enU0prn2jY3V09h7ExGxePHiGD9+fHz00Ucxfvz4WLx4cb0ye++9d8Hhjz76KHcAP3ny5Pjoo4/q1TFz5syYM2dORETMmTMnt45rPf/88/H5559HRMTnn38ezz//fL06itHYOimmrcWsk8Z8+9vfLjgcEXH33XcXHG6u96+xeoqZTzHHdY2t2+ZYr82xvBGN7/uKqaO5jt3XNUkH3f/93/8tOBwRMWPGjILDb7/9dsHhiMgdhDc0HBGxZMmSgsMrVqwoOBwR8fTTTxccLsZdd91VcLgYP/nJTwoOF2vSpEkFh19//fWCw81lypQpBYeLceONNxYcLtaq37CtOrzqjjjfh94f//jHgsPN5ZVXXlmj4WKdccYZBYeLUcyXJpCaJ554ouDwe++9V3C4uUyYMKHgcETEiy++WHA4onm24wsvvLDOwd4Pf/jDOtOL+Yz80Y9+VHA4onk+E4pZbyeddFLB4fHjxxccbksae28ivlgHn3zySUREfPLJJ3HnnXfWK1Mb7BsaHj16dJ3hVU9w5Bu36vB5551XcLi5FNPWYtZJYz7++OOCwxER1157bcHh5nr/GqunmPkUc1zX2LptjvXaHMsb0fi+r5g6muvYfV2TbNA94IADGh3f0DcVteMbm95cZdbWfA4++OC801ce31gdxx9/fN7pK48vpq35zlivPH5dWvff/OY3805feXwx89lnn33ylqkdX0wd+++/f94yK49fG+ukud6/hi7Tqh1fTB033nhj7uxLreXLl9c58CymnpUPlhYvXhyff/558n91vn2ue6zY9nzJ3p+V35tVD+Qj6p/FWnX82trHHnrooXmnrzy+ObbjYup4+eWX632ZPX369Hj55ZcjorjPyP/4j//IW2bl8Y19JhTT1mLW229+85tYtGhRnemLFi2K3/zmNxERccwxx+Sto6HxjbWpmPFNraex9yYi4t13340777yzzhnqO++8M959992i5/Pwww/HP//5zzrTPv7443j44Ydzw1deeWXesFx7dVS+qw0KjW9Ic7S1mHWypu2IiDjqqKPylqkd31zvX2P1FDOfYpansXXbHOu1OZY3ovF9XzF1NNex+7qo6Ht0lyxZUucs5IIFC1qkQc1hzpw5sXTp0rzTli5dGnPmzIlPP/20YB1/+tOfCk5/4403Yv78+QXLvPjiiw22o1a+b4JXNnny5Nh0000Llnn//fcb/WZm5MiR9T4Qay1atCj+9a9/xTe+8Y2CdRTq7G+99VZ89tlnMWrUqIJ1/Nu//Vtcf/31ec9YR3xxJvu5554rWMebb74ZJ5xwQpPbWmyZd955J6ZPn16wzG9+85vc5U2rmjNnTixYsCAOP/zwRtsxceLE3Ldxq6qpqWnwHrBaM2fOjF69esWyZcvyTl+2bFn885//jG9961uNtqWx6Q3d31qrsSsNZs+e3ehlXr/97W/jiCOOaPD+kNp7agoZOnRoTJo0Ke9l5BFfXEr43e9+t8EvB1auZ/LkyXX2f1//+tcLviZJNcsbL9OaVmrfl+39WbJkSXTq1Ck3/MEHH+QNvxFfHGw1dG9Wrddff73Rs6VDhw7NeyvPysaPH9/gZ+2nn34ac+fOjc8++6xgHe+++2706tWr4Hbc0LSV2/rYY4/VuxWk1iWXXBK/+tWvGv2MrKioiDfeeCNvmTfeeCMWLVoUy5cvL/iZUMx+69577210vVVWVsYvfvGLvGV+8YtfxLBhwxo8KH/33Xfj008/jS5dujTalsamF3MPX2PHUw899FDeS8Qjvnhv7rvvvigpKYmrr7663vQsy+Lqq6+OK664otH7jydNmpT3Vp6IL8Lt/vvvHytWrGjwKqg//vGP8R//8R95rzaI+OKYb/HixVFRUVGwHRERF1xwQcHp5513Xrz00ksF21paWtroOikpKSk4n1UDUr7pm2yySXz44Yd5p3/44Ycxf/78gttWse9flmUF67nnnnsanU9jV6W888470adPn4L9YL/99lvj9VpbtlAdjS3vfffdFytWrCi47zv++OMbrePzzz+Pt956K2+Z2mP3Bx98sODy/O53v2v0+LGtKska+jRcxcUXXxz/9V//VW/8/Pnzo2vXrs3esDWx7777NhikIiLatWtXcPqXUfv27RsMScXq06dPvP/++83UojR07dp1rX0pVFpa2mBYLmZ6W7Pjjjs2eEBRrMMPP7zgPWmNTa81efLkmDt37pcuQK1sYfU3Izq2rX19HUsXReVfCgeeVN17773RvXv33PC68g18eXl5vdt48il2Oy1k3Lhxcf755zc4vbHjgvbt20efPn3y3r5Ua+ONN465c+eu8T6/sfVSXl4e3/nOdwp+6dixY8fcPaT5rL/++vG73/2uYDuK6UfFBN017Y/jxo2LPn36FPxi5bbbbmv0i5fGnH766TFt2rSCgbmxz/Stt9663iW9+azpOjn99NNj2223bXSdbLzxxmvcju7du8fcuXMbnN65c+eCX1gV+/69//77BbfRUaNGFezzjW3jtU4//fS8z+wpdj7FrNe33357jZd33Lhx8dxzzxXc9zV2nDRu3Li49tprCx6bF3vs3tYeTLVgwYKoqqpqNIcWfUb3/PPPr3Nv3IIFC6Jfv35r1soWctdddzV46VDt9E8//TSOO+64Bsucf/75MW7cuAan/+IXv4j58+cXvFTliiuuiKVLl+Z9aFSt/fbbr+DZuosvvjg23XTTGD58eINl7rzzzvjJT34Sf/nLXxos069fv3jnnXcanH7XXXc1eka3MTfddFOMGjUqdz9CPuutt15cf/31eR+iVKuxndWvfvWrRs/oNofbb789pk+f3uA3fxFfXK7W0LfqERF33HFHo2d0I774Zq6hS4MivnjwWKGz/9ddd1306tWr4Ddud911V7N8I/frX/+64Jn7H/3oRwX7/K9//et4/vnn4/rrr2+wzPe///044ogj6j20bXWNGTOm4IdEY9NXVl5envv/3nvvLepb+3XdvHnz4uijj/5ioLSNP6R/pfZ9Gd6fxYsX5754WblvRnxxpUnufcvjhz/8YYNnASK+eABTMfe/HnvssXHbbbc1OL2xgDpx4sT47LPPCl5Ke8cdd0SvXr3WOOjutNNODYaUqqqquPnmmxs9bqioqGjw8uaIL9bb8uXLi9rnFzJx4sSCX6pNnDgxKisrCx6M/+Y3v4kjjzyywem33HLLmjRxtTR2PHXuuefGDTfc0OB7s9NOO0VJSUnssMMOMXXq1DpfSLRr1y4GDx4c/fv3b7RfX3DBBXH55Zfn/UKjrKwsDj300Bg2bFjBoNvYZ3qh44WV7bbbbvHMM880OH3nnXeOl156qWBbS0tLG10njbn22mvz3ve78vRNNtkk70NWa915550xcuTINX7/+vXrV3AbHTVqVNx3330F53P77bfHyJEjG2zr7bffHn369IlrrrmmwXU7atSoeP3119dovfbv33+Nl3ennXaKIUOGFNz3XXrppfGtb32rYB3bbLNNHHLIIQ3WcdNNN8WDDz4YP//5zxssc8oppzQ4ra0r+h7d8vLy6Nq1a52/tqpnz57RoUOHvNM6dOgQPXv2jE022aRgHY0dYG+22Wax4447Fiyz4447xu67716wTKFAEPHFt219+vQpWKZPnz55L5FY2e23317n0raVderUKXr06NHotzWTJ0+OAQMG5J32la98JTp37lzvKXyruvvuu6NXr17Rrl27vNPbtWsXu+yyS8E6Bg4cWFRbiylTSL9+/QruHCIijj766OjZs2feaeuvv3507dq1qHb07t07Skvzb46lpaWx3377Fayjuro61l9//Wjfvn3e6e3bt4/1119/jdfJ5MmTG93JN9bn+/fvn/eJjiv79re/HeXl5Q1+27zPPvsU1db27ds3+AXC8OHDo3379kWvk5UvVaqoqIiOHTsm/1cnLBa+Uqv1fcnen5Xfm1Uvo9twww0bvLSupKSkwecB1Npiiy2K2i4auu+r1hlnnNHg5bFdunSJ7t27R9++fQvW0bdv30a342LaWlpamvcBLRFfPI26Z8+ejX5GdurUKTbbbLO8ZbbYYovo1KlTdO3ateBnQjFt7d69e6PrraysrMH7hU8++eTo1q1bg+u2f//+jV62XNuWNZleq7HjqWHDhhV8b0pLS6OkpCROO+20etNrxxfTr/fff/84++yz804755xzol27dtGhQ4cGP/cPO+yw6Nq1a4PHfjvvvHPRX7Ct+isTq7r88ssbbWsx66QxW2+9daPTKysro3fv3nmnb7jhhlFVVdUs719j22hZWVmj82nsBFy/fv2iXbt2BddtWVnZGq/X5lje0tLSRvd95eXljdbRuXPnRo/dGzsJsq5ethyR8MOoHnnkkUbHF7r/r5jpzVVmbc2noWvwVx7fWB0NfQt88803r1Zb//znP+ctUzt+XVr3+X5fLyLqXBZWzHwaum+udnwxdaz65Op849fGOmmu96+hh1HV7tiLqeOkk06KsrK6ZyPLysrie9/73mq1BdYlDZ2Vqh2/tvaxDzzwQN7pK49vju24mDqGDBlS78B+m222ie233z4iivuMbOgKnpXvMW3sM6GYthaz3o4++uh64bxTp065K6buuOOOvHU09pyFhtpUzPim1tPYexPxxZcew4cPzwWNkpKSGD58eGy00UZFz+eggw6K9ddfv860DTbYoM6DSs8+++x6YaakpCT3EzVXXHFF3nlcfvnlecc3pDnaWsw6WdN2ROT/ycSIyD34rLnev8bqKWY+xSxPY+u2OdZrcyxvROP7vmLqaK5j93VRskE3IuKII44oOBxR/9usVYdXvQ4/33X5lZWVBYcj6l9aturwqmc4853xXPVMWWNnzvIp5ndYG3PuuecWHC7Wqg8BWnV4iy22KDjcXAYPHlxwuBiN/cRDsVb9RnrV4V69ehUcjoh630Y3dla6qbbbbrs1Gi5Wc/w8RjG/jQmp2WuvvQoOr3rQtjoHcaujmN/rXfUsWb6zZs2xHV966aW5q2dKS0ub9FvlxfymfXN8JhSz3hr7GaPm+Hm2taWx9ybii3Ww3nrrRcQXV+/lu60rX0hd2XXXXVdnON89tY39lM6qoXZ1Q26ximlrMeukMRtssEHB4YjGf3Kpud6/xuopZj7FHNc1tm6bY702x/JGNL7vK6aO5jp2X9ckHXRPP/30gsMRje/MVr3/KN/9SH/4wx8KDkfUf+rgqsOrnuHMd8azmN/uW/Wbl1WHi/kdwsbqGDZsWJ0NatiwYatdR0T9pw6uOlzMQU0x82mszFVXXVVwuJg6Vr20JN+lJsW0ddVLUJryG4+rXpKT7xKdxtpSTFtXfZjD6g4XO5/tt98+9+VRZWVlnW8pi61j0KBBuUuu+/fvH4MGDWpSW2BdsuoDJFcdLuZ3WptjH3viiScWHI6of5Ys31mzxrbjYtrarVu3GDFiRJSWlsaIESOiW7dudaYX8xm533775c6wlJWV5b21pLHPhGLaWsx669+/f1RXV0fEF7ewrHpryeGHH17njFJT7h9urn1jY/U09t5EfHFbwhlnnBG9evWK008/Pe+lwqtezbDqcK9evXK3xQwdOjTvF8bV1dW5S9B79uyZW8e1dt555+jYsWNEfPHgr5133rleHcVobJ0U09Zi1kljfvvb3xYcjvjiVzMKDTfX+9dYPcXMp5jjusbWbXOs1+ZY3ojG933F1NFcx+7rmqKfuryqYp92BZCCzz//PPfB8NBDD+UOclK28pOmF37tqIgO+e9hbBNWLIvKqbdHxJfj/fky9kcAiCg+hyZ9RhcAAIAvH0EXAACApAi6AAAAJEXQBQAAICmCLgAAAEkRdAEAAEiKoAsAAEBSBF0AAACSIugCAACQFEEXAACApAi6AAAAJEXQBQAAICmCLgAAAEkRdAEAAEiKoAsAAEBSBF0AAACSIugCAACQFEEXAACApAi6AAAAJEXQBQAAICmCLgAAAEkRdAEAAEiKoAsAAEBSBF0AAACSIugCAACQFEEXAACApAi6AAAAJEXQBQAAICmCLgAAAEkRdAEAAEiKoAsAAEBSBF0AAACSIugCAACQFEEXAACApAi6AAAAJEXQBQAAICmCLgAAAEkRdAEAAEiKoAsAAEBSBF0AAACSIugCAACQFEEXAACApAi6AAAAJEXQBQAAICmCLgAAAEkRdAEAAEiKoAsAAEBSBF0AAACSIugCAACQFEEXAACApAi6AAAAJEXQBQAAICmCLgAAAEkRdAEAAEiKoAsAAEBSBF0AAACSIugCAACQFEEXAACApAi6AAAAJEXQBQAAICllrd0AgHVBRUVFPPTQQ7n/oTXpjwBQmKALUISSkpLo2LFjazcDIkJ/BIDGuHQZAACApAi6AAAAJEXQBQAAICmCLgAAAEkRdAEAAEiKoAsAAEBSBF0AAACSIugCAACQFEEXAACApAi6AAAAJEXQBQAAICmCLgAAAEkRdAEAAEiKoAsAAEBSBF0AAACSIugCAACQFEEXAACApAi6AAAAJEXQBQAAICmCLgAAAEkRdAEAAEiKoAsAAEBSBF0AAACSIugCAACQFEEXAACApAi6AAAAJEXQBQAAICmCLgAAAEkRdAEAAEiKoAsAAEBSBF0AAACSIugCAACQFEEXAACApAi6AAAAJEXQBQAAICmCLgAAAEkRdAEAAEiKoAsAAEBSBF0AAACSIugCAACQFEEXAACApAi6AAAAJEXQBQAAICmCLgAAAEkRdAEAAEiKoAsAAEBSBF0AAACSIugCAACQFEEXAACApAi6AAAAJEXQBQAAICmCLgAAAEkRdAEAAEiKoAsAAEBSBF0AAACSIugCAACQFEEXAACApAi6AAAAJEXQBQAAICmCLgAAAEkRdAEAAEhKWWs3AIC2r6RmRWQrlrV2MxpUUrO8tZsAALQhgi4Ajeoy43et3QQAgKK5dBkAAICkOKMLQF5VVVVx7733RkREeXl5lJSUtHKLilNRUdHaTQAAWpmgC0BepaWl0b1799ZuBgDAanPpMgAAAEkRdAEAAEiKoAsAAEBSBF0AAACSIugCAACQFEEXAACApAi6AAAAJEXQBQAAICmCLgAAAEkRdAEAAEiKoAsAAEBSBF0AAACSIugCAACQFEEXAACApAi6AAAAJEXQBQAAICmCLgAAAEkRdAEAAEiKoAsAAEBSBF0AAACSIugCAACQFEEXAACApAi6AAAAJEXQBQAAICmCLgAAAEkRdAEAAEiKoAsAAEBSBF0AAACSIugCAACQlLKmvjDLsoiIWLBgQbM1BgAAABpSmz9r82hDmhx0Fy5cGBER/fr1a2oVAAAAsNoWLlwYVVVVDU4vyRqLwg2oqamJ999/PyorK6OkpKTJDVxTCxYsiH79+sU777wTXbt2bbV2QDH0V9Y1+izrEv2VdY0+y7qkrfTXLMti4cKF0adPnygtbfhO3Caf0S0tLY2+ffs29eXNrmvXrnYQrDP0V9Y1+izrEv2VdY0+y7qkLfTXQmdya3kYFQAAAEkRdAEAAEjKOh90y8vL46KLLory8vLWbgo0Sn9lXaPPsi7RX1nX6LOsS9a1/trkh1EBAABAW7TOn9EFAACAlQm6AAAAJEXQBQAAICmCLgAAAElZp4Pu9ddfHwMGDIiKiooYPHhwPPXUU63dJIhx48bFDjvsEJWVlbHBBhvEkUceGW+88UadMlmWxcUXXxx9+vSJjh07xtChQ+PVV19tpRbD/zNu3LgoKSmJsWPH5sbpr7Q17733XhxzzDGx3nrrRadOnWLbbbeNKVOm5Kbrs7Qly5cvjx/84AcxYMCA6NixYwwcODAuueSSqKmpyZXRZ2ktTz75ZBx22GHRp0+fKCkpifvuu6/O9GL65pIlS2LMmDHRs2fP6Ny5cxx++OHx7rvvrsWlyG+dDbp33XVXjB07Ni644IJ45ZVXYo899ohhw4bF7NmzW7tpfMk98cQTccopp8Tzzz8fkyZNiuXLl8cBBxwQn332Wa7MFVdcEePHj4/rrrsuXnrppejdu3fsv//+sXDhwlZsOV92L730Uvzyl7+MbbbZps54/ZW2ZO7cubHbbrtF+/bt46GHHorXXnstrrrqqujWrVuujD5LW/KTn/wkbrzxxrjuuuvi9ddfjyuuuCKuvPLKuPbaa3Nl9Flay2effRZf+9rX4rrrrss7vZi+OXbs2Lj33ntj4sSJ8fTTT8enn34ahx56aKxYsWJtLUZ+2Tpqxx13zE466aQ64zbffPPsvPPOa6UWQX4ff/xxFhHZE088kWVZltXU1GS9e/fOLr/88lyZxYsXZ1VVVdmNN97YWs3kS27hwoXZoEGDskmTJmV77bVXdtppp2VZpr/S9px77rnZ7rvv3uB0fZa25pBDDslOOOGEOuO+8Y1vZMccc0yWZfosbUdEZPfee29uuJi+OW/evKx9+/bZxIkTc2Xee++9rLS0NHv44YfXWtvzWSfP6C5dujSmTJkSBxxwQJ3xBxxwQDz77LOt1CrIb/78+RER0aNHj4iIeOutt+LDDz+s03/Ly8tjr7320n9pNaecckoccsghsd9++9UZr7/S1tx///0xZMiQ+Na3vhUbbLBBbLfddnHTTTflpuuztDW77757/PnPf46//e1vERHxl7/8JZ5++uk4+OCDI0Kfpe0qpm9OmTIlli1bVqdMnz59orq6utX7b1mrzr2J5syZEytWrIhevXrVGd+rV6/48MMPW6lVUF+WZXHGGWfE7rvvHtXV1RERuT6ar/++/fbba72NMHHixJgyZUq8/PLL9abpr7Q1b775Ztxwww1xxhlnxH/+53/Giy++GKeeemqUl5fHqFGj9FnanHPPPTfmz58fm2++ebRr1y5WrFgRl112WRx99NERYT9L21VM3/zwww+jQ4cO0b1793plWjuXrZNBt1ZJSUmd4SzL6o2D1jR69OiYPn16PP300/Wm6b+0Be+8806cdtpp8cgjj0RFRUWD5fRX2oqampoYMmRI/PjHP46IiO222y5effXVuOGGG2LUqFG5cvosbcVdd90Vd9xxR9x5552x1VZbxbRp02Ls2LHRp0+fOPbYY3Pl9Fnaqqb0zbbQf9fJS5d79uwZ7dq1q/ctwccff1zvGwdoLWPGjIn7778/Hn/88ejbt29ufO/evSMi9F/ahClTpsTHH38cgwcPjrKysigrK4snnngirrnmmigrK8v1Sf2VtmLDDTeMLbfcss64LbbYIvcwSvtY2pqzzz47zjvvvDjqqKNi6623jpEjR8bpp58e48aNiwh9lrarmL7Zu3fvWLp0acydO7fBMq1lnQy6HTp0iMGDB8ekSZPqjJ80aVLsuuuurdQq+EKWZTF69Oi455574rHHHosBAwbUmT5gwIDo3bt3nf67dOnSeOKJJ/Rf1rp99903ZsyYEdOmTcv9DRkyJEaMGBHTpk2LgQMH6q+0Kbvttlu9n2z729/+FhtvvHFE2MfS9ixatChKS+secrdr1y7380L6LG1VMX1z8ODB0b59+zplPvjgg5g5c2br999WewzWGpo4cWLWvn377Oabb85ee+21bOzYsVnnzp2zf/zjH63dNL7kTj755KyqqiqbPHly9sEHH+T+Fi1alCtz+eWXZ1VVVdk999yTzZgxIzv66KOzDTfcMFuwYEErthy+sPJTl7NMf6VtefHFF7OysrLssssuy2bNmpVNmDAh69SpU3bHHXfkyuiztCXHHntsttFGG2UPPPBA9tZbb2X33HNP1rNnz+ycc87JldFnaS0LFy7MXnnlleyVV17JIiIbP3589sorr2Rvv/12lmXF9c2TTjop69u3b/boo49mU6dOzfbZZ5/sa1/7WrZ8+fLWWqwsy7JsnQ26WZZlP//5z7ONN94469ChQ7b99tvnfr4FWlNE5P275ZZbcmVqamqyiy66KOvdu3dWXl6e7bnnntmMGTNar9GwklWDrv5KW/OHP/whq66uzsrLy7PNN988++Uvf1lnuj5LW7JgwYLstNNOy/r3759VVFRkAwcOzC644IJsyZIluTL6LK3l8ccfz3vceuyxx2ZZVlzf/Pzzz7PRo0dnPXr0yDp27Jgdeuih2ezZs1thaeoqybIsa51zyQAAAND81sl7dAEAAKAhgi4AAABJEXQBAABIiqALAABAUgRdAAAAkiLoAgAAkBRBFwAAgKQIugDAatlkk03iZz/7WWs3AwAaJOgC0OYdd9xxceSRR9YbP3ny5CgpKYl58+at9TYBAG2XoAsABSxbtqy1m5DTltoCAG2ZoAtAMu6+++7Yaqutory8PDbZZJO46qqr6kwvKSmJ++67r864bt26xa233hoREf/4xz+ipKQkfvvb38bQoUOjoqIi7rjjjnj77bfjsMMOi+7du0fnzp1jq622igcffLDBdtx6663RrVu3uO+++2LTTTeNioqK2H///eOdd96pU+4Pf/hDDB48OCoqKmLgwIHxX//1X7F8+fI67b3xxhvjiCOOiM6dO8ePfvSjevMaPHhwneU88sgjo6ysLBYsWBARER9++GGUlJTEG2+8ERERS5cujXPOOSc22mij6Ny5c+y0004xefLkOnU+++yzseeee0bHjh2jX79+ceqpp8Znn33W4PLecsstUVVVFZMmTWqwDACsTYIuAEmYMmVKfPvb346jjjoqZsyYERdffHFceOGFuRC7Os4999w49dRT4/XXX48DDzwwTjnllFiyZEk8+eSTMWPGjPjJT34SXbp0KVjHokWL4rLLLovbbrstnnnmmViwYEEcddRRuel/+tOf4phjjolTTz01XnvttfjFL34Rt956a1x22WV16rnoooviiCOOiBkzZsQJJ5xQbz5Dhw7NBdUsy+Kpp56K7t27x9NPPx0REY8//nj07t07Nttss4iIOP744+OZZ56JiRMnxvTp0+Nb3/pWHHTQQTFr1qyIiJgxY0YceOCB8Y1vfCOmT58ed911Vzz99NMxevTovMv505/+NM4666z405/+FPvvv39xKxgAWloGAG3csccem7Vr1y7r3Llznb+KioosIrK5c+dmw4cPz/bff/86rzv77LOzLbfcMjccEdm9995bp0xVVVV2yy23ZFmWZW+99VYWEdnPfvazOmW23nrr7OKLLy66vbfccksWEdnzzz+fG/f6669nEZG98MILWZZl2R577JH9+Mc/rvO622+/Pdtwww3rtHfs2LEF53X//fdnVVVV2YoVK7Jp06Zl66+/fnb66adnZ599dpZlWfa9730v+853vpNlWZb9/e9/z0pKSrL33nuvTh377rtvdv7552dZlmUjR47Mvve979WZ/tRTT2WlpaXZ559/nmVZlm288cbZf//3f2fnnXdetuGGG2bTp08vet0AwNrgjC4A64S99947pk2bVufvf/7nf3LTX3/99dhtt93qvGa33XaLWbNmxYoVK1ZrXkOGDKkzfOqpp8aPfvSj2G233eKiiy6K6dOn56ZttdVW0aVLl+jSpUsMGzYsN76srKxOPZtvvnl069YtXn/99Yj44gz0JZdcknttly5d4sQTT4wPPvggFi1alLctw4YNy5XdaqutIiJizz33jIULF8Yrr7wSTzzxROy1116x9957xxNPPBERXzywa6+99oqIiKlTp0aWZbHpppvWme8TTzwR//d//5dr16233lpn+oEHHhg1NTXx1ltv5dpy1VVXxS9+8Yt4+umnY+utt16t9QsALa2stRsAAMXo3LlzfPWrX60z7t133839n2VZlJSU1JmeZVmd4ZKSknrj8j3gqXPnznWG//3f/z0OPPDA+OMf/xiPPPJIjBs3Lq666qoYM2ZMPPjgg7k6OnbsWG9+q6odV1NTE//1X/8V3/jGN+qVqaioyNuW//mf/4nPP/88IiLat28fERFVVVWx7bbbxuTJk+PZZ5+NffbZJ/bYY4+YNm1azJo1K/72t7/F0KFDc/Ns165dTJkyJdq1a1dnnrWXYtfU1MR//Md/xKmnnlqvXf3798/9v8cee8Qf//jH+O1vfxvnnXdevbIA0JoEXQCSsOWWW+buS6317LPPxqabbpoLdeuvv3588MEHuemzZs2qc/a0kH79+sVJJ50UJ510Upx//vlx0003xZgxY2LjjTfOW3758uXx8ssvx4477hgREW+88UbMmzcvNt9884iI2H777eONN96oF94L2WijjfKOHzp0aDz++OPxwgsvxCWXXBLdunWLLbfcMn70ox/FBhtsEFtssUVERGy33XaxYsWK+Pjjj2OPPfbIW9f2228fr776aqPt2nHHHWPMmDFx4IEHRrt27eLss88uejkAoKUJugAk4cwzz4wddtghLr300vjOd74Tzz33XFx33XVx/fXX58rss88+cd1118XOO+8cNTU1ce655+bOjBYyduzYGDZsWGy66aYxd+7ceOyxx3LhsSHt27ePMWPGxDXXXBPt27eP0aNHx84775wLvj/84Q/j0EMPjX79+sW3vvWtKC0tjenTp8eMGTPyPl25kKFDh8bVV18dPXr0iC233DI37tprr61zxnjTTTeNESNGxKhRo+Kqq66K7bbbLubMmROPPfZYbL311nHwwQfHueeeGzvvvHOccsopceKJJ0bnzp3j9ddfj0mTJsW1115bZ7677LJLPPTQQ3HQQQdFWVlZnH766avVbgBoKe7RBSAJ22+/ffz2t7+NiRMnRnV1dfzwhz+MSy65JI477rhcmauuuir69esXe+65ZwwfPjzOOuus6NSpU6N1r1ixIk455ZTYYost4qCDDorNNtusToDOp1OnTnHuuefG8OHDY5dddomOHTvGxIkTc9MPPPDAeOCBB2LSpEmxww47xM477xzjx49v8AxxIXvuuWdEROy11165S6P32muvWLFiRe7+3Fq33HJLjBo1Ks4888zYbLPN4vDDD48XXngh+vXrFxER22yzTTzxxBMxa9as2GOPPWK77baLCy+8MDbccMO8895tt93ij3/8Y1x44YVxzTXXrHbbAaAllGSr3qwEAKyRW2+9NcaOHRvz5s1r7aYAwJeSM7oAAAAkRdAFAAAgKS5dBgAAICnO6AIAAJAUQRcAAICkCLoAAAAkRdAFAAAgKYIuAAAASRF0AQAASIqgCwAAQFIEXQAAAJIi6AIAAJCU/w83HJVIEI7hIwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1200x3000 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tempList1 = only_numeric.columns.to_list()\n",
    "subplot = 1\n",
    "plt.figure(figsize = (12,30))\n",
    "for i in tempList1:\n",
    "    plt.subplot(6,1,subplot)\n",
    "    sns.boxplot(data = df, x = i, whis=1.5)\n",
    "    subplot = subplot + 1\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58574d01",
   "metadata": {},
   "source": [
    "## Preprocessing and data transformation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fd4955e",
   "metadata": {},
   "source": [
    "### Missing values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b94952c3",
   "metadata": {},
   "source": [
    "As seen in the preliminary analysis, NA values are represented with the string '?' in this database. They are replaced with None values and later deleted from the data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1bbe1e41",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.replace(\" ?\", None, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3285d97",
   "metadata": {},
   "source": [
    "After missing values are replaced with None values, it is observed that columns \"Work class\", \"Occupation\" and \"Native-country\" present null values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "23113fae",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 32561 entries, 0 to 32560\n",
      "Data columns (total 15 columns):\n",
      " #   Column          Non-Null Count  Dtype   \n",
      "---  ------          --------------  -----   \n",
      " 0   age             32561 non-null  int64   \n",
      " 1   Work class      30725 non-null  category\n",
      " 2   fnlwgt          32561 non-null  int64   \n",
      " 3   Education       32561 non-null  category\n",
      " 4   Education-num   32561 non-null  int64   \n",
      " 5   Marital-status  32561 non-null  category\n",
      " 6   Occupation      30718 non-null  category\n",
      " 7   Relationship    32561 non-null  category\n",
      " 8   Race            32561 non-null  category\n",
      " 9   Sex             32561 non-null  category\n",
      " 10  Capital-gain    32561 non-null  int64   \n",
      " 11  Capital-loss    32561 non-null  int64   \n",
      " 12  Hours-per-week  32561 non-null  int64   \n",
      " 13  Native-country  31978 non-null  category\n",
      " 14  TARGET          32561 non-null  category\n",
      "dtypes: category(9), int64(6)\n",
      "memory usage: 1.8 MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1913fc77",
   "metadata": {},
   "source": [
    "As decided in the rules of this project, missing values are deleted from database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "cfb53e7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f844bb2",
   "metadata": {},
   "source": [
    "All rows now present the same amount of non-null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "663902e1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 30162 entries, 0 to 32560\n",
      "Data columns (total 15 columns):\n",
      " #   Column          Non-Null Count  Dtype   \n",
      "---  ------          --------------  -----   \n",
      " 0   age             30162 non-null  int64   \n",
      " 1   Work class      30162 non-null  category\n",
      " 2   fnlwgt          30162 non-null  int64   \n",
      " 3   Education       30162 non-null  category\n",
      " 4   Education-num   30162 non-null  int64   \n",
      " 5   Marital-status  30162 non-null  category\n",
      " 6   Occupation      30162 non-null  category\n",
      " 7   Relationship    30162 non-null  category\n",
      " 8   Race            30162 non-null  category\n",
      " 9   Sex             30162 non-null  category\n",
      " 10  Capital-gain    30162 non-null  int64   \n",
      " 11  Capital-loss    30162 non-null  int64   \n",
      " 12  Hours-per-week  30162 non-null  int64   \n",
      " 13  Native-country  30162 non-null  category\n",
      " 14  TARGET          30162 non-null  category\n",
      "dtypes: category(9), int64(6)\n",
      "memory usage: 1.9 MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f040e87b",
   "metadata": {},
   "source": [
    "### Feature removal"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce2c5ad6",
   "metadata": {},
   "source": [
    "Some features need to be removed from database since they are either were not useful for our analysis or have too much bad data (zero values)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a50a93e2",
   "metadata": {},
   "source": [
    "#### \"fnlwgt\" feature removal"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b9bd6a6",
   "metadata": {},
   "source": [
    "This feature is a weight assigned by the Census Bureau to each sample. Two samples with similar final weight are more likely to share the same values for every feature. \n",
    "\n",
    "This feature is not ultimately relevant to our model so it's decided to delete it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "15192bc3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(\"fnlwgt\", axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afc14e98",
   "metadata": {},
   "source": [
    "#### \"Capital-gain\" and \"Capital-loss\" removal"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12c7f2c6",
   "metadata": {},
   "source": [
    "Both features present an enourmous quantity of zero values, so they will not be relevant to the model. It is decided to delete them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "aa256372",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Capital-gain\n",
       "0               27624\n",
       "15024             337\n",
       "7688              270\n",
       "7298              240\n",
       "99999             148\n",
       "                ...  \n",
       "1639                1\n",
       "5060                1\n",
       "2387                1\n",
       "6097                1\n",
       "7978                1\n",
       "Length: 118, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[\"Capital-gain\"]].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b58cc2fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(\"Capital-gain\", axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "653c70c9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Capital-loss\n",
       "0               28735\n",
       "1902              194\n",
       "1977              162\n",
       "1887              155\n",
       "1848               50\n",
       "                ...  \n",
       "419                 1\n",
       "1944                1\n",
       "2080                1\n",
       "2282                1\n",
       "4356                1\n",
       "Length: 90, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[\"Capital-loss\"]].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c46f11b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(\"Capital-loss\", axis=1) #La dropeamos porque la mayoria de sus valores son cero."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f239e82",
   "metadata": {},
   "source": [
    "#### \"Education-num\" removal"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7253ba0",
   "metadata": {},
   "source": [
    "This feature is basically the same as the \"Education\" feature but label encoded. It is decided to delete this feature and label encode \"Education\" feature later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b5809caa",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(\"Education-num\", axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "82ca38c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 30162 entries, 0 to 32560\n",
      "Data columns (total 11 columns):\n",
      " #   Column          Non-Null Count  Dtype   \n",
      "---  ------          --------------  -----   \n",
      " 0   age             30162 non-null  int64   \n",
      " 1   Work class      30162 non-null  category\n",
      " 2   Education       30162 non-null  category\n",
      " 3   Marital-status  30162 non-null  category\n",
      " 4   Occupation      30162 non-null  category\n",
      " 5   Relationship    30162 non-null  category\n",
      " 6   Race            30162 non-null  category\n",
      " 7   Sex             30162 non-null  category\n",
      " 8   Hours-per-week  30162 non-null  int64   \n",
      " 9   Native-country  30162 non-null  category\n",
      " 10  TARGET          30162 non-null  category\n",
      "dtypes: category(9), int64(2)\n",
      "memory usage: 976.1 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9c14566",
   "metadata": {},
   "source": [
    "### Outlier treatment"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca60c6ad",
   "metadata": {},
   "source": [
    "#### Function definition"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a5de898",
   "metadata": {},
   "source": [
    "A function is defined to detect the number of outliers of a particular feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f0f0707c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def has_outliers(df, column, whis = 1.5):\n",
    "    \"\"\"\n",
    "    Inputs:\n",
    "    - df: Pandas DataFrame\n",
    "    - column: String. DataFrame column name.\n",
    "    - whis: Float. Interquartilic Range (IQR) multiplier\n",
    "    \n",
    "    Output:\n",
    "    - df_no_outliers: Original DataFrame with no outliers\n",
    "    \n",
    "    Also, prints the number of outliers of a column from a provided DataFrame\n",
    "    \n",
    "    Every data point outside the range:\n",
    "        Q1 - whis * IQR < x < Q3 + whis * IQR\n",
    "    is considered an outlier\n",
    "    \"\"\"\n",
    "    \n",
    "    # First and third quartiles are obtained for the provided feature\n",
    "    q1 = df[column].quantile(0.25)\n",
    "    q3 = df[column].quantile(0.75)\n",
    "    \n",
    "    # IQR calculation\n",
    "    IQR = q3-q1\n",
    "    \n",
    "    # Every data point that is not inside the valid range is considered as an outlier.\n",
    "    df_outliers = df[~((df[column] >= q1 - whis * IQR) & (df[column] <= q3 + whis * IQR))]\n",
    "    \n",
    "    # DataFrame with no outliers on the provided feature is obtained\n",
    "    df_no_outliers = df[((df[column] >= q1 - whis * IQR) & (df[column] <= q3 + whis * IQR))]\n",
    "    \n",
    "    print(f\"Provided column has {len(df_outliers)} outliers\\n{round(len(df_outliers)*100/len(df[column]),2)}% of data\")\n",
    "    \n",
    "    return df_no_outliers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e882da6",
   "metadata": {},
   "source": [
    "Also, a function is defined to eliminate the outliers from a particular feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a8309086",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def delete_outliers(df, column, whis=1.5):\n",
    "    \"\"\"\n",
    "    Inputs:\n",
    "    - df: Pandas DataFrame\n",
    "    - column: String. DataFrame column name.\n",
    "    - whis: Float. Interquartilic Range (IQR) multiplier\n",
    "    \n",
    "    Output:\n",
    "    - No output is generated in this function\n",
    "    \n",
    "    Deletes outliers from a provided feature of a Data Frame\n",
    "    \n",
    "    Every data point outside the range:\n",
    "        Q1 - whis * IQR < x < Q3 + whis * IQR\n",
    "    is considered an outlier\n",
    "    \"\"\"\n",
    "    \n",
    "    # First and third quartiles are obtained for the provided feature\n",
    "    q1 = df[column].quantile(0.25)\n",
    "    q3 = df[column].quantile(0.75)\n",
    "    \n",
    "    # IQR calculation\n",
    "    IQR = q3 - q1\n",
    "    \n",
    "    # A list with the indexes of the upper and lower outliers is generated\n",
    "    outlier_index_upper = df.index[df[\"age\"] >= (q3+whis*IQR)].to_list()\n",
    "    outlier_index_lower = df.index[df[\"age\"] <= (q1-whis*IQR)].to_list()\n",
    "\n",
    "    # Outliers are dropped from Data Frame\n",
    "    df.drop(outlier_index_upper, inplace = True)\n",
    "    df.drop(outlier_index_lower, inplace = True)\n",
    "    \n",
    "    print(f\"{len(outlier_index_lower)+len(outlier_index_upper)} outliers have been deleted from variable {column}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16ad0c35",
   "metadata": {},
   "source": [
    "#### Outlier treatment"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62239734",
   "metadata": {},
   "source": [
    "Some features present outliers. Through the has_outliers function, these outliers are detected. \n",
    "\n",
    "In some features direct outlier elimination was decided due to the low number of outliers compared to the total of data. This elimination was done through the delete_outlier function.\n",
    "\n",
    "In some other features a more complex approach was made, since direct outlier elimination was not suitable."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d10d4193",
   "metadata": {},
   "source": [
    "##### \"age\" feature outlier treatment"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4da22be",
   "metadata": {},
   "source": [
    "This feature presents a small quantity of outliers (<strong>0,56% of total</strong>), so it is decided to eliminate them directly from the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "1b133085",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Provided column has 169 outliers\n",
      "0.56% of data\n"
     ]
    }
   ],
   "source": [
    "age_outliers = has_outliers(df, \"age\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "10d6038f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "166 outliers have been deleted from variable age\n"
     ]
    }
   ],
   "source": [
    "delete_outliers(df, \"age\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40c2e292",
   "metadata": {},
   "source": [
    "##### \"Hours-per-week\" feature outlier treatment"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0483336",
   "metadata": {},
   "source": [
    "This feature presents a substantial quantity of outliers when compared to other features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "b262881c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Provided column has 5902 outliers\n",
      "19.68% of data\n"
     ]
    }
   ],
   "source": [
    "df_hourspw_no_outliers = has_outliers(df, \"Hours-per-week\", whis= 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b63c0a54",
   "metadata": {},
   "source": [
    "A custom function is created to divide values in this feature into 3 intervals:\n",
    "\n",
    "- Less than 40 hours\n",
    "- Exactly 40 hours\n",
    "- More than 40 hours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d84504be",
   "metadata": {},
   "outputs": [],
   "source": [
    "def replace_hours(x):\n",
    "    if x < 40:\n",
    "        return \"<40\"\n",
    "    elif x > 40:\n",
    "        return \">40\"\n",
    "    else:\n",
    "        return \"40\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6463294b",
   "metadata": {},
   "source": [
    "The previous function is applied to all the feature simultaneosly, as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "093ef763",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df['Hours-per-week'] = df['Hours-per-week'].apply(replace_hours)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b07ab1c5",
   "metadata": {},
   "source": [
    "DataFrame is printed to ensure that the changes have been made correctly. As seen below, \"Hours-per-week\" feature now only has 3 unique values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "85320324",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>age</th>\n",
       "      <th>Work class</th>\n",
       "      <th>Education</th>\n",
       "      <th>Marital-status</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Relationship</th>\n",
       "      <th>Race</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Hours-per-week</th>\n",
       "      <th>Native-country</th>\n",
       "      <th>TARGET</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>&lt;40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>11th</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age         Work class   Education       Marital-status  \\\n",
       "0   39          State-gov   Bachelors        Never-married   \n",
       "1   50   Self-emp-not-inc   Bachelors   Married-civ-spouse   \n",
       "2   38            Private     HS-grad             Divorced   \n",
       "3   53            Private        11th   Married-civ-spouse   \n",
       "4   28            Private   Bachelors   Married-civ-spouse   \n",
       "\n",
       "           Occupation    Relationship    Race      Sex Hours-per-week  \\\n",
       "0        Adm-clerical   Not-in-family   White     Male             40   \n",
       "1     Exec-managerial         Husband   White     Male            <40   \n",
       "2   Handlers-cleaners   Not-in-family   White     Male             40   \n",
       "3   Handlers-cleaners         Husband   Black     Male             40   \n",
       "4      Prof-specialty            Wife   Black   Female             40   \n",
       "\n",
       "   Native-country  TARGET  \n",
       "0   United-States   <=50K  \n",
       "1   United-States   <=50K  \n",
       "2   United-States   <=50K  \n",
       "3   United-States   <=50K  \n",
       "4            Cuba   <=50K  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f758bfa",
   "metadata": {},
   "source": [
    "This feature now presents only 3 unique values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "eec4e4b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['40', '<40', '>40'], dtype=object)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Hours-per-week\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "6393ca20",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "40     14211\n",
       ">40     9175\n",
       "<40     6607\n",
       "Name: Hours-per-week, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Hours-per-week\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80f4c027",
   "metadata": {},
   "source": [
    "The feature type is changed to categorical, since it no longer represents number of hours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0d6dfa11",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Hours-per-week\"] = pd.Categorical(df[\"Hours-per-week\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3705ed21",
   "metadata": {},
   "source": [
    "A bar plot is generated to observe the distribution between classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "1035d729",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "orden_categorias = [\"<40\", \"40\" , \">40\"] # Crea una lista con el orden deseado de las categorías\n",
    "\n",
    "sns.countplot(data=df, x=\"Hours-per-week\", hue=\"TARGET\", order=orden_categorias)\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27519da4",
   "metadata": {},
   "source": [
    "##### \"Native-country\" feature outlier treatment"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00083b30",
   "metadata": {},
   "source": [
    "The vast majority of entries belong to the United States, so it is decided to transform every other country to class \"Other\". To make this change, a custom function is built."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3e9e0f22",
   "metadata": {},
   "outputs": [],
   "source": [
    "def replace_countries(x):\n",
    "    if x == \" United-States\":\n",
    "        return \" United-States\"\n",
    "    else:\n",
    "        return \"Other\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edb01c62",
   "metadata": {},
   "source": [
    "Function is applied to the feature as follows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "54b94b61",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Native-country'] = df['Native-country'].apply(replace_countries)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5392de12",
   "metadata": {},
   "source": [
    "Now, feature only has two unique elements:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "39a483f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([' United-States', 'Other'], dtype=object)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Native-country'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6631ed26",
   "metadata": {},
   "source": [
    "Feature is transformed again into categorical:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "8d467f82",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Native-country\"] = pd.Categorical(df[\"Native-country\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6c1ec12",
   "metadata": {},
   "source": [
    "Data types are printed to ensure that the changes have been made succesfully. As observed below, \"Native-country\" feature is categorical again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "de349a91",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 29993 entries, 0 to 32560\n",
      "Data columns (total 11 columns):\n",
      " #   Column          Non-Null Count  Dtype   \n",
      "---  ------          --------------  -----   \n",
      " 0   age             29993 non-null  int64   \n",
      " 1   Work class      29993 non-null  category\n",
      " 2   Education       29993 non-null  category\n",
      " 3   Marital-status  29993 non-null  category\n",
      " 4   Occupation      29993 non-null  category\n",
      " 5   Relationship    29993 non-null  category\n",
      " 6   Race            29993 non-null  category\n",
      " 7   Sex             29993 non-null  category\n",
      " 8   Hours-per-week  29993 non-null  category\n",
      " 9   Native-country  29993 non-null  category\n",
      " 10  TARGET          29993 non-null  category\n",
      "dtypes: category(10), int64(1)\n",
      "memory usage: 764.5 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "629c2d31",
   "metadata": {},
   "source": [
    "A bar plot is printed to observe the class distribution on this feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "9e98c8d7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=df, x=\"Native-country\", hue=\"TARGET\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed5ce558",
   "metadata": {},
   "source": [
    "##### \"races\" feature outlier treatment"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7aa9181a",
   "metadata": {},
   "source": [
    "In the same manner as in the previous feature, the vast majority of entries of this particular feature belong to the same class (\"White\"). A custom function is built to clasify all entries that have a different race from \"White\" into \"Other\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "fcf72af2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def replace_races(x):\n",
    "    if x == \" White\":\n",
    "        return \" White\"\n",
    "    else:\n",
    "        return \"Other\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f90a6abf",
   "metadata": {},
   "source": [
    "Function is then applied to all the feature simultaneosly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b1d2ebb9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Race'] = df['Race'].apply(replace_races)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb5f5f6c",
   "metadata": {},
   "source": [
    "After the changes have been made, feature only presents two unique values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "8a035c66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([' White', 'Other'], dtype=object)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Race'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32b5fc23",
   "metadata": {},
   "source": [
    "Feature is transformed again into categorical:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ebf9506b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Race\"] = pd.Categorical(df[\"Race\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a436d86b",
   "metadata": {},
   "source": [
    "Data types are printed to ensure that the changes have been made succesfully. As observed below, \"Native-country\" feature is categorical again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "7882ca6e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 29993 entries, 0 to 32560\n",
      "Data columns (total 11 columns):\n",
      " #   Column          Non-Null Count  Dtype   \n",
      "---  ------          --------------  -----   \n",
      " 0   age             29993 non-null  int64   \n",
      " 1   Work class      29993 non-null  category\n",
      " 2   Education       29993 non-null  category\n",
      " 3   Marital-status  29993 non-null  category\n",
      " 4   Occupation      29993 non-null  category\n",
      " 5   Relationship    29993 non-null  category\n",
      " 6   Race            29993 non-null  category\n",
      " 7   Sex             29993 non-null  category\n",
      " 8   Hours-per-week  29993 non-null  category\n",
      " 9   Native-country  29993 non-null  category\n",
      " 10  TARGET          29993 non-null  category\n",
      "dtypes: category(10), int64(1)\n",
      "memory usage: 764.4 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f2f04bb",
   "metadata": {},
   "source": [
    "A bar plot is printed to observe the new class distribution on this feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "c438ec97",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=df, x=\"Race\", hue=\"TARGET\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0fbfbb5b",
   "metadata": {},
   "source": [
    "### Data Modelling"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1feb3a22",
   "metadata": {},
   "source": [
    "#### Train and test sets generation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c955ff77",
   "metadata": {},
   "source": [
    "Main dataset is divided into a subset containing the dependent variables and another one containing the target variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d06b4b61",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop([\"TARGET\"],axis =  1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "23d9f300",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df[\"TARGET\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3255397b",
   "metadata": {},
   "source": [
    "Test size is kept at 20%, since it's a standard value. This can be later changed if improvement in model accuracy is needed.\n",
    "\n",
    "Stratify parameter is set to 'y' to maintain original distribution of labels with >50k (23.93%) and labels with <=50k (76.07%)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "4c820493",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20, stratify= y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56d62ec7",
   "metadata": {},
   "source": [
    "Sets dimensions are printed to ensure that no mistakes where made"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "093d6cdc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((25916, 10), (6479, 10), (25916,), (6479,))"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(X_train.shape, X_test.shape, y_train.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "111fdb54",
   "metadata": {},
   "source": [
    "#### Data normalization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c9b34e9",
   "metadata": {},
   "source": [
    "Numerical features are standardized using a standard scaler. This improves algorithm accuracy for Machile Learning models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "0a4702d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "sc = StandardScaler()\n",
    "X_train_num = X_train.select_dtypes(include=[\"int64\", \"float64\"])\n",
    "X_test_num = X_test.select_dtypes(include=[\"int64\", \"float64\"])\n",
    "\n",
    "X_train_num = sc.fit_transform(X_train_num)\n",
    "X_test_num = sc.fit_transform(X_test_num)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d5b5e30",
   "metadata": {},
   "source": [
    "#### Data encoding"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad2b03e3",
   "metadata": {},
   "source": [
    "Categorical features and target variable are enconded as a one-hot numeric array. \n",
    "\n",
    "Distinction is made between binary categorical features (only two options) and non binary categorical features (more than two options) to enable label encoding to the later if needed in future variations of this project."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0f77490",
   "metadata": {},
   "source": [
    "##### Independent variables"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "789e2b75",
   "metadata": {},
   "source": [
    "Binary categorical features are selected and then encoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "46098776",
   "metadata": {},
   "outputs": [],
   "source": [
    "cats_binary_train = X_train[[\"Race\", \"Native-country\", \"Sex\", \"Relationship\"]]\n",
    "cats_binary_test = X_test[[\"Race\", \"Native-country\", \"Sex\", \"Relationship\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "9a1934e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "encoder = OneHotEncoder(drop=\"first\")\n",
    "\n",
    "cats_binary_train = encoder.fit_transform(cats_binary_train).toarray()\n",
    "cats_binary_test = encoder.fit_transform(cats_binary_test).toarray()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4cffdbfa",
   "metadata": {},
   "source": [
    "Non binary categorical features are selected and then encoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "1036e76c",
   "metadata": {},
   "outputs": [],
   "source": [
    "cats_non_binary_train = X_train[[\"Work class\",\"Hours-per-week\", \"Marital-status\", \"Occupation\", \"Education\"]]\n",
    "cats_non_binary_test = X_test[[\"Work class\",\"Hours-per-week\", \"Marital-status\", \"Occupation\", \"Education\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "c7559f8f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['x0_ Local-gov' 'x0_ Never-worked' 'x0_ Private' 'x0_ Self-emp-inc'\n",
      " 'x0_ Self-emp-not-inc' 'x0_ State-gov' 'x0_ Without-pay' 'x0_nan'\n",
      " 'x1_<40' 'x1_>40' 'x2_ Married-AF-spouse' 'x2_ Married-civ-spouse'\n",
      " 'x2_ Married-spouse-absent' 'x2_ Never-married' 'x2_ Separated'\n",
      " 'x2_ Widowed' 'x3_ Armed-Forces' 'x3_ Craft-repair' 'x3_ Exec-managerial'\n",
      " 'x3_ Farming-fishing' 'x3_ Handlers-cleaners' 'x3_ Machine-op-inspct'\n",
      " 'x3_ Other-service' 'x3_ Priv-house-serv' 'x3_ Prof-specialty'\n",
      " 'x3_ Protective-serv' 'x3_ Sales' 'x3_ Tech-support'\n",
      " 'x3_ Transport-moving' 'x3_nan' 'x4_ 11th' 'x4_ 12th' 'x4_ 1st-4th'\n",
      " 'x4_ 5th-6th' 'x4_ 7th-8th' 'x4_ 9th' 'x4_ Assoc-acdm' 'x4_ Assoc-voc'\n",
      " 'x4_ Bachelors' 'x4_ Doctorate' 'x4_ HS-grad' 'x4_ Masters'\n",
      " 'x4_ Preschool' 'x4_ Prof-school' 'x4_ Some-college']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\franc\\anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "encoder = OneHotEncoder(drop=\"first\")\n",
    "\n",
    "cats_non_binary_train = encoder.fit_transform(cats_non_binary_train)\n",
    "cats_non_binary_test = encoder.transform(cats_non_binary_test).toarray()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78550837",
   "metadata": {},
   "source": [
    "All features are finally concatenated to obtain the set that will feed the Machine Learning algorithms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "75f08cae",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = np.concatenate((X_train_num, cats_binary_train,cats_non_binary_train.toarray()), axis=1)\n",
    "X_test = np.concatenate((X_test_num, cats_binary_test,cats_non_binary_test), axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbde589e",
   "metadata": {},
   "source": [
    "##### Target variable"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fd4051f",
   "metadata": {},
   "source": [
    "Target variable is also one hot encoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "9179726d",
   "metadata": {},
   "outputs": [],
   "source": [
    "encoder = OneHotEncoder(drop=\"first\")\n",
    "\n",
    "y_train = pd.DataFrame(y_train)\n",
    "y_train = encoder.fit_transform(y_train).toarray()\n",
    "\n",
    "y_test = pd.DataFrame(y_test)\n",
    "y_test = encoder.fit_transform(y_test).toarray()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3bbe2bcf",
   "metadata": {},
   "source": [
    "Encoded target variable is printed to ensure that changes have been made correctly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "bfd1e565",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.0000e+00 1.9667e+04]\n",
      " [1.0000e+00 6.2490e+03]]\n"
     ]
    }
   ],
   "source": [
    "unique, counts = np.unique(y_train, return_counts=True)\n",
    "\n",
    "print(np.asarray((unique, counts)).T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "d7afe960",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.000e+00 4.917e+03]\n",
      " [1.000e+00 1.562e+03]]\n"
     ]
    }
   ],
   "source": [
    "unique, counts = np.unique(y_test, return_counts=True)\n",
    "\n",
    "print(np.asarray((unique, counts)).T)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a73a4a69",
   "metadata": {},
   "source": [
    "## Application of algorithms"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d36949b",
   "metadata": {},
   "source": [
    "### Logistic Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5259b571",
   "metadata": {},
   "source": [
    "#### Model instantiation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d96f3d46",
   "metadata": {},
   "source": [
    "Model is first instantiated:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "726b3a06",
   "metadata": {},
   "outputs": [],
   "source": [
    "logisticRegr = LogisticRegression(max_iter=10000)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6717584",
   "metadata": {},
   "source": [
    "#### Model fitting"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e57c0b9",
   "metadata": {},
   "source": [
    "Model is then fitted according to the given training data (X and y training sets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "3fb47b20",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\franc\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(max_iter=10000)"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logisticRegr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b8b785b",
   "metadata": {},
   "source": [
    "#### Label prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd5c0658",
   "metadata": {},
   "source": [
    "Class labels are then predicted for samples in X test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "d0861b7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = logisticRegr.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eddc387e",
   "metadata": {},
   "source": [
    "#### Evaluation metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "112c612f",
   "metadata": {},
   "source": [
    "##### Accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69d9ee80",
   "metadata": {},
   "source": [
    "Mean accuracy is obtained for the prediction made on X test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "7b34fded",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.83\n"
     ]
    }
   ],
   "source": [
    "print('Accuracy: {:.2f}'.format(logisticRegr.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca5ec503",
   "metadata": {},
   "source": [
    "##### Confusion Matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99965a0f",
   "metadata": {},
   "source": [
    "Confusion matrix is computed and then ploted to evaluate the accuracy of the clasification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "9d746a45",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[4130  373]\n",
      " [ 623  873]]\n"
     ]
    }
   ],
   "source": [
    "confusion_matrix = confusion_matrix(y_test, y_pred)\n",
    "print(confusion_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "7d9371d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\franc\\anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function plot_confusion_matrix is deprecated; Function `plot_confusion_matrix` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[4130  373]\n",
      " [ 623  873]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "class_names = ['<=50k', '>50k']\n",
    "\n",
    "disp = plot_confusion_matrix(logisticRegr, X_test, y_test,\n",
    "                              display_labels=class_names,\n",
    "                              cmap=plt.cm.Blues)\n",
    "\n",
    "print(disp.confusion_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a286c589",
   "metadata": {},
   "source": [
    "##### Other evaluation metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5414de60",
   "metadata": {},
   "source": [
    "The following metrics are also calculated:\n",
    "- Accuracy\n",
    "- Precision\n",
    "- Recall\n",
    "- F1 score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "d39ffce8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.833972\n",
      "Precision: 0.700642\n",
      "Recall: 0.583556\n",
      "F1 score: 0.636761\n"
     ]
    }
   ],
   "source": [
    "# accuracy: (tp + tn) / (p + n)\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print('Accuracy: %f' % accuracy)\n",
    "\n",
    "# precision tp / (tp + fp)\n",
    "precision = precision_score(y_test, y_pred)\n",
    "print('Precision: %f' % precision)\n",
    "\n",
    "# recall: tp / (tp + fn)\n",
    "recall = recall_score(y_test, y_pred)\n",
    "print('Recall: %f' % recall)\n",
    "\n",
    "# f1: 2 tp / (2 tp + fp + fn)\n",
    "f1 = f1_score(y_test, y_pred)\n",
    "print('F1 score: %f' % f1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a628122",
   "metadata": {},
   "source": [
    "##### ROC Curve"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbb34c9c",
   "metadata": {},
   "source": [
    "Compute Receiver operating characteristic (ROC) is also computed and then plotted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "cd8319a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "# encode the target variable\n",
    "le = LabelEncoder()\n",
    "y_test = le.fit_transform(y_test)\n",
    "\n",
    "\n",
    "logit_roc_auc = roc_auc_score(y_test, logisticRegr.predict(X_test))\n",
    "fpr, tpr, thresholds = roc_curve(y_test, logisticRegr.predict_proba(X_test)[:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "cf9f3f33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(fpr, tpr, label='Logistic Regression (area = %0.2f)' % logit_roc_auc)\n",
    "plt.plot([0, 1], [0, 1],'r--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.savefig('Log_ROC')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4544f5f4",
   "metadata": {},
   "source": [
    "### K Nearest Neighbors"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50851db6",
   "metadata": {},
   "source": [
    "#### Model instantiation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbf84b43",
   "metadata": {},
   "source": [
    "Model is first instantiated:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "403b736a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\franc\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=50)"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = KNeighborsClassifier(n_neighbors=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c01bc7f",
   "metadata": {},
   "source": [
    "#### Model fitting"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc2963f1",
   "metadata": {},
   "source": [
    "Model is then fitted according to the given training data (X and y training sets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "97f41c65",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c3a4862",
   "metadata": {},
   "source": [
    "#### Label prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7cf8f35b",
   "metadata": {},
   "source": [
    "Class labels are then predicted for samples in X test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "edb3d27e",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test_pred = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0f29dec",
   "metadata": {},
   "source": [
    "#### Evaluation metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30fa8735",
   "metadata": {},
   "source": [
    "##### Accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25da92ab",
   "metadata": {},
   "source": [
    "Mean accuracy is obtained for train and test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "ce35021b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\franc\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Accuracy in train set: 0.8373343335833958\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\franc\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Accuracy in test  set: 0.8366394399066511\n"
     ]
    }
   ],
   "source": [
    "print('- Accuracy in train set: {}'.format(clf.score(X_train, y_train)))\n",
    "print('- Accuracy in test  set: {}'.format(clf.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "385bffc7",
   "metadata": {},
   "source": [
    "##### Other evaluation metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c68a148",
   "metadata": {},
   "source": [
    "The following metrics are also calculated:\n",
    "- Accuracy\n",
    "- Precision\n",
    "- Recall\n",
    "- F1 score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "c24fcef5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Accuracy:  0.84\n",
      "- Precision: 0.7\n",
      "- Recall: 0.6109625668449198\n",
      "- Fscore: 0.65\n"
     ]
    }
   ],
   "source": [
    "print(\"- Accuracy: \", round(accuracy_score(y_test, y_test_pred), 2))\n",
    "print(\"- Precision:\", round(precision_score(y_test, y_test_pred,pos_label=1),2))\n",
    "print(\"- Recall:\", recall_score(y_test, y_test_pred,pos_label=1))\n",
    "print(\"- Fscore:\", round(f1_score(y_test, y_test_pred, pos_label=1##### Other evaluation metrics\n",
    "\n",
    "The following metrics are also calculated:\n",
    "- Accuracy\n",
    "- Precision\n",
    "- Recall\n",
    "- F1 score),2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "779e36b0",
   "metadata": {},
   "source": [
    "##### Confusion Matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3865845d",
   "metadata": {},
   "source": [
    "Confusion matrix is computed to evaluate the accuracy of the clasification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "b56ffa1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4105,  398],\n",
       "       [ 582,  914]], dtype=int64)"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test, y_test_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "34115a87",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.91      0.89      4503\n",
      "           1       0.70      0.61      0.65      1496\n",
      "\n",
      "    accuracy                           0.84      5999\n",
      "   macro avg       0.79      0.76      0.77      5999\n",
      "weighted avg       0.83      0.84      0.83      5999\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_true=y_test,y_pred = y_test_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "b3e04a96",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\franc\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\franc\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\franc\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\franc\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\franc\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\franc\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\franc\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\franc\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\franc\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\franc\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\franc\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\franc\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\franc\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\franc\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\franc\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\franc\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\franc\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\franc\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\franc\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\franc\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\franc\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\franc\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\franc\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\franc\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\franc\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\franc\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\franc\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\franc\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\franc\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\franc\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\franc\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\franc\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\franc\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\franc\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\franc\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\franc\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\franc\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\franc\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\franc\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\franc\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\franc\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\franc\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\franc\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\franc\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\franc\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\franc\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\franc\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\franc\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\franc\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "C:\\Users\\franc\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\franc\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\franc\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n"
     ]
    }
   ],
   "source": [
    "valores_k = list(range(1,50,4))\n",
    "resultados_train_u = []\n",
    "resultados_test_u  = []\n",
    "resultados_train_w = []\n",
    "resultados_test_w  = []\n",
    "\n",
    "for k in valores_k:\n",
    "    # instanciamos el modelo uniforme\n",
    "    clf_u = KNeighborsClassifier(n_neighbors=k, weights='uniform')\n",
    "    clf_u.fit(X_train, y_train)\n",
    "    y_train_pred = clf_u.predict(X_train)\n",
    "    y_pred = clf_u.predict(X_test)\n",
    "    resultados_train_u.append(accuracy_score(y_train, y_train_pred))\n",
    "    resultados_test_u.append(accuracy_score(y_test, y_pred))\n",
    "\n",
    "    clf_w = KNeighborsClassifier(n_neighbors=k, weights='distance')\n",
    "    clf_w.fit(X_train, y_train)\n",
    "    y_train_pred = clf_w.predict(X_train)\n",
    "    y_pred = clf_w.predict(X_test)\n",
    "    resultados_train_w.append(accuracy_score(y_train, y_train_pred))\n",
    "    resultados_test_w.append(accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "90b1db7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# veamos que paso en cada caso\n",
    "f, ax = plt.subplots(1,2,figsize=(14,5),sharey=True)\n",
    "ax[0].plot(valores_k, resultados_train_u, valores_k, resultados_test_u);\n",
    "ax[0].legend(['pesos uniformes - train', 'pesos uniformes - test']);\n",
    "ax[0].set(xlabel='k',ylabel='accuracy');\n",
    "\n",
    "ax[1].plot(valores_k, resultados_train_w, valores_k, resultados_test_w);\n",
    "ax[1].legend(['pesos distancia - train', 'pesos distancia - test']);\n",
    "ax[1].set(xlabel='k');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38218b96",
   "metadata": {},
   "source": [
    "### Decision Tree"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6c3f8f5",
   "metadata": {},
   "source": [
    "#### Model instantiation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdf176b9",
   "metadata": {},
   "source": [
    "Model is first instantiated:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "6cf49dff",
   "metadata": {},
   "outputs": [],
   "source": [
    "arbol = DecisionTreeClassifier(criterion='gini', splitter=\"best\", max_depth=8, min_samples_leaf=1, min_samples_split=2, ccp_alpha=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a65e028e",
   "metadata": {},
   "source": [
    "#### Model fitting"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4a19ec8",
   "metadata": {},
   "source": [
    "Model is then fitted according to the given training data (X and y training sets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "cfa06b49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(ccp_alpha=0, max_depth=8)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arbol.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "1d274e01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8367417811390647"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_train, arbol.predict(X_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5187e9b",
   "metadata": {},
   "source": [
    "#### Label prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22be896a",
   "metadata": {},
   "source": [
    "Class labels are then predicted for samples in X test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "d0afb43e",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=arbol.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8779335c",
   "metadata": {},
   "source": [
    "#### Evaluation metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68806f42",
   "metadata": {},
   "source": [
    "##### General metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fda60f9c",
   "metadata": {},
   "source": [
    "The following metrics are calculated in a classification report:\n",
    "- Accuracy\n",
    "- Precision\n",
    "- Recall\n",
    "- F1 score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "561eda6b",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.87      0.91      0.89      4503\n",
      "         1.0       0.68      0.58      0.63      1496\n",
      "\n",
      "    accuracy                           0.83      5999\n",
      "   macro avg       0.77      0.75      0.76      5999\n",
      "weighted avg       0.82      0.83      0.82      5999\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_true=y_test,y_pred=arbol.predict(X_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a41ab261",
   "metadata": {},
   "source": [
    "##### Confusion matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ec99484",
   "metadata": {},
   "source": [
    "Confusion matrix is computed and then ploted to evaluate the accuracy of the clasification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "f8be7f3f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cf_matrix = confusion_matrix(y_test, arbol.predict(X_test))\n",
    "sns.heatmap(cf_matrix, annot=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b91c375",
   "metadata": {},
   "source": [
    "##### Accuracy vs. depth"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "300f28ae",
   "metadata": {},
   "source": [
    "To ensure that the chosen depth for the model is optimal, accuracy is ploted against the depth for the training and test sets. As seen in the plot below, optimal value for depth happens around 8."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "61e684b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "profundidad = list(range(1,20))\n",
    "resultados_train = []\n",
    "resultados_test  = []\n",
    "\n",
    "for depth in profundidad:\n",
    "    # instanciamos el modelo uniforme\n",
    "    arbol = DecisionTreeClassifier(criterion='gini', max_depth=depth, min_samples_leaf=1, min_samples_split=2, ccp_alpha=0, class_weight=\"balanced\")\n",
    "    arbol.fit(X_train, y_train)\n",
    "    y_train_pred = arbol.predict(X_train)\n",
    "    y_pred = arbol.predict(X_test)\n",
    "    resultados_train.append(accuracy_score(y_train, y_train_pred))\n",
    "    resultados_test.append(accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "621a121d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(1,1,figsize=(14,5),sharey=True)\n",
    "ax.plot(profundidad, resultados_train, profundidad, resultados_test);\n",
    "ax.legend(['accuracy train', 'accuracy test']);\n",
    "ax.set(xlabel='profundidad',ylabel='accuracy');"
   ]
  }
 ],
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