week-8/8-logistic-regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import pylab as pl\n",
    "import numpy as np\n",
    "%matplotlib inline \n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2024-01-13 10:04:57--  https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%203/data/ChurnData.csv\n",
      "Resolving cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud (cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud)... 169.45.118.108\n",
      "Connecting to cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud (cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud)|169.45.118.108|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 35943 (35K) [text/csv]\n",
      "Saving to: ‘ChurnData.csv’\n",
      "\n",
      "ChurnData.csv       100%[===================>]  35,10K  --.-KB/s    in 0,001s  \n",
      "\n",
      "2024-01-13 10:05:00 (36,3 MB/s) - ‘ChurnData.csv’ saved [35943/35943]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Download data\n",
    "# !wget -O ChurnData.csv https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%203/data/ChurnData.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- LogisticRegression\n",
    "- confusion_matrix\n",
    "- https://en.wikipedia.org/wiki/Logistic_function\n",
    "- https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "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>tenure</th>\n",
       "      <th>age</th>\n",
       "      <th>address</th>\n",
       "      <th>income</th>\n",
       "      <th>ed</th>\n",
       "      <th>employ</th>\n",
       "      <th>equip</th>\n",
       "      <th>callcard</th>\n",
       "      <th>wireless</th>\n",
       "      <th>longmon</th>\n",
       "      <th>...</th>\n",
       "      <th>pager</th>\n",
       "      <th>internet</th>\n",
       "      <th>callwait</th>\n",
       "      <th>confer</th>\n",
       "      <th>ebill</th>\n",
       "      <th>loglong</th>\n",
       "      <th>logtoll</th>\n",
       "      <th>lninc</th>\n",
       "      <th>custcat</th>\n",
       "      <th>churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.40</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.482</td>\n",
       "      <td>3.033</td>\n",
       "      <td>4.913</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>33.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.45</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.246</td>\n",
       "      <td>3.240</td>\n",
       "      <td>3.497</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>23.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.30</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.841</td>\n",
       "      <td>3.240</td>\n",
       "      <td>3.401</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>38.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.05</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.800</td>\n",
       "      <td>3.807</td>\n",
       "      <td>4.331</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.10</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.960</td>\n",
       "      <td>3.091</td>\n",
       "      <td>4.382</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   tenure   age  address  income   ed  employ  equip  callcard  wireless  \\\n",
       "0    11.0  33.0      7.0   136.0  5.0     5.0    0.0       1.0       1.0   \n",
       "1    33.0  33.0     12.0    33.0  2.0     0.0    0.0       0.0       0.0   \n",
       "2    23.0  30.0      9.0    30.0  1.0     2.0    0.0       0.0       0.0   \n",
       "3    38.0  35.0      5.0    76.0  2.0    10.0    1.0       1.0       1.0   \n",
       "4     7.0  35.0     14.0    80.0  2.0    15.0    0.0       1.0       0.0   \n",
       "\n",
       "   longmon  ...  pager  internet  callwait  confer  ebill  loglong  logtoll  \\\n",
       "0     4.40  ...    1.0       0.0       1.0     1.0    0.0    1.482    3.033   \n",
       "1     9.45  ...    0.0       0.0       0.0     0.0    0.0    2.246    3.240   \n",
       "2     6.30  ...    0.0       0.0       0.0     1.0    0.0    1.841    3.240   \n",
       "3     6.05  ...    1.0       1.0       1.0     1.0    1.0    1.800    3.807   \n",
       "4     7.10  ...    0.0       0.0       1.0     1.0    0.0    1.960    3.091   \n",
       "\n",
       "   lninc  custcat  churn  \n",
       "0  4.913      4.0    1.0  \n",
       "1  3.497      1.0    1.0  \n",
       "2  3.401      3.0    0.0  \n",
       "3  4.331      4.0    0.0  \n",
       "4  4.382      3.0    0.0  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "churn_df = pd.read_csv(\"ChurnData.csv\")\n",
    "churn_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200, 28)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "churn_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['tenure', 'age', 'address', 'income', 'ed', 'employ', 'equip',\n",
       "       'callcard', 'wireless', 'longmon', 'tollmon', 'equipmon', 'cardmon',\n",
       "       'wiremon', 'longten', 'tollten', 'cardten', 'voice', 'pager',\n",
       "       'internet', 'callwait', 'confer', 'ebill', 'loglong', 'logtoll',\n",
       "       'lninc', 'custcat', 'churn'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "churn_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 200 entries, 0 to 199\n",
      "Data columns (total 28 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   tenure    200 non-null    float64\n",
      " 1   age       200 non-null    float64\n",
      " 2   address   200 non-null    float64\n",
      " 3   income    200 non-null    float64\n",
      " 4   ed        200 non-null    float64\n",
      " 5   employ    200 non-null    float64\n",
      " 6   equip     200 non-null    float64\n",
      " 7   callcard  200 non-null    float64\n",
      " 8   wireless  200 non-null    float64\n",
      " 9   longmon   200 non-null    float64\n",
      " 10  tollmon   200 non-null    float64\n",
      " 11  equipmon  200 non-null    float64\n",
      " 12  cardmon   200 non-null    float64\n",
      " 13  wiremon   200 non-null    float64\n",
      " 14  longten   200 non-null    float64\n",
      " 15  tollten   200 non-null    float64\n",
      " 16  cardten   200 non-null    float64\n",
      " 17  voice     200 non-null    float64\n",
      " 18  pager     200 non-null    float64\n",
      " 19  internet  200 non-null    float64\n",
      " 20  callwait  200 non-null    float64\n",
      " 21  confer    200 non-null    float64\n",
      " 22  ebill     200 non-null    float64\n",
      " 23  loglong   200 non-null    float64\n",
      " 24  logtoll   200 non-null    float64\n",
      " 25  lninc     200 non-null    float64\n",
      " 26  custcat   200 non-null    float64\n",
      " 27  churn     200 non-null    float64\n",
      "dtypes: float64(28)\n",
      "memory usage: 43.9 KB\n"
     ]
    }
   ],
   "source": [
    "churn_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "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>tenure</th>\n",
       "      <th>age</th>\n",
       "      <th>address</th>\n",
       "      <th>income</th>\n",
       "      <th>ed</th>\n",
       "      <th>employ</th>\n",
       "      <th>equip</th>\n",
       "      <th>callcard</th>\n",
       "      <th>wireless</th>\n",
       "      <th>churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>33.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>23.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>38.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   tenure   age  address  income   ed  employ  equip  callcard  wireless  \\\n",
       "0    11.0  33.0      7.0   136.0  5.0     5.0    0.0       1.0       1.0   \n",
       "1    33.0  33.0     12.0    33.0  2.0     0.0    0.0       0.0       0.0   \n",
       "2    23.0  30.0      9.0    30.0  1.0     2.0    0.0       0.0       0.0   \n",
       "3    38.0  35.0      5.0    76.0  2.0    10.0    1.0       1.0       1.0   \n",
       "4     7.0  35.0     14.0    80.0  2.0    15.0    0.0       1.0       0.0   \n",
       "\n",
       "   churn  \n",
       "0      1  \n",
       "1      1  \n",
       "2      0  \n",
       "3      0  \n",
       "4      0  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "churn_df = churn_df[['tenure', 'age', 'address', 'income', 'ed', 'employ', 'equip',   'callcard', 'wireless','churn']]\n",
    "churn_df['churn'] = churn_df['churn'].astype('int')\n",
    "churn_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200, 10)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "churn_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 11.,  33.,   7., 136.,   5.,   5.,   0.],\n",
       "       [ 33.,  33.,  12.,  33.,   2.,   0.,   0.],\n",
       "       [ 23.,  30.,   9.,  30.,   1.,   2.,   0.],\n",
       "       [ 38.,  35.,   5.,  76.,   2.,  10.,   1.],\n",
       "       [  7.,  35.,  14.,  80.,   2.,  15.,   0.]])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = np.asarray(churn_df[['tenure', 'age', 'address', 'income', 'ed', 'employ', 'equip']])\n",
    "X[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 0, 0, 0])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = np.asarray(churn_df['churn'])\n",
    "y[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.14, -0.63, -0.46,  0.48,  1.7 , -0.58, -0.86],\n",
       "       [-0.12, -0.63,  0.03, -0.33, -0.64, -1.14, -0.86],\n",
       "       [-0.58, -0.86, -0.26, -0.35, -1.42, -0.92, -0.86],\n",
       "       [ 0.12, -0.47, -0.66,  0.01, -0.64, -0.03,  1.16],\n",
       "       [-1.32, -0.47,  0.23,  0.04, -0.64,  0.53, -0.86]])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Normalize the data\n",
    "from sklearn import preprocessing\n",
    "X = preprocessing.StandardScaler().fit(X).transform(X)\n",
    "X[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set: (160, 7) (160,)\n",
      "Test set: (40, 7) (40,)\n"
     ]
    }
   ],
   "source": [
    "# Train/test split\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, random_state=10)\n",
    "print ('Train set:', X_train.shape,  y_train.shape)\n",
    "print ('Test set:', X_test.shape,  y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(C=0.01, solver=&#x27;liblinear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(C=0.01, solver=&#x27;liblinear&#x27;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(C=0.01, solver='liblinear')"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "LR = LogisticRegression(C=0.01, solver=\"liblinear\").fit(X_train, y_train)\n",
    "LR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yhat = LR.predict(X_test)\n",
    "yhat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.42, 0.58],\n",
       "       [0.72, 0.28],\n",
       "       [0.65, 0.35],\n",
       "       [0.51, 0.49],\n",
       "       [0.5 , 0.5 ]])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# probabilities of predictions\n",
    "yhat_prob = LR.predict_proba(X_test)\n",
    "yhat_prob[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7058823529411765"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# jaccard index\n",
    "from sklearn.metrics import jaccard_score\n",
    "jaccard_score(y_test, yhat, pos_label=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.75      0.92      0.83        26\n",
      "           1       0.75      0.43      0.55        14\n",
      "\n",
      "    accuracy                           0.75        40\n",
      "   macro avg       0.75      0.68      0.69        40\n",
      "weighted avg       0.75      0.75      0.73        40\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# F1-score\n",
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test, yhat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5817876037226268"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Log loss\n",
    "from sklearn.metrics import log_loss\n",
    "log_loss(y_test, yhat_prob)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "# confusion matrix\n",
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
    "cnf_matrix = confusion_matrix(y_test, yhat)\n",
    "# cnf_matrix = confusion_matrix(y_test, yhat, labels=[0,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[24,  2],\n",
       "       [ 8,  6]])"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cnf_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f98029e2230>"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "disp = ConfusionMatrixDisplay(confusion_matrix=cnf_matrix, display_labels=[1,0])\n",
    "# disp = ConfusionMatrixDisplay(confusion_matrix=cnf_matrix, display_labels=LR.classes_)\n",
    "disp.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model with different solver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "LR2 = LogisticRegression(C=0.01, solver=\"sag\").fit(X_train, y_train)\n",
    "yhat2 = LR2.predict(X_test)\n",
    "yhat_prob2 = LR2.predict_proba(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.65      1.00      0.79        26\n",
      "           1       0.00      0.00      0.00        14\n",
      "\n",
      "    accuracy                           0.65        40\n",
      "   macro avg       0.33      0.50      0.39        40\n",
      "weighted avg       0.42      0.65      0.51        40\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/varfo/Projects/data-science/.conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/home/varfo/Projects/data-science/.conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/home/varfo/Projects/data-science/.conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "# f1 score for new model\n",
    "print(classification_report(y_test, yhat2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5744491618468478"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# log loss for new model\n",
    "log_loss(y_test, yhat_prob2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.65"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# jaccard index for new model\n",
    "jaccard_score(y_test, yhat2, pos_label=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NOTES\n",
    "- How to use GridSearchCV for best parameters in LogisticRegression()\n",
    "- Robust Scaler for preprocessing "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise\n",
    "Write custom script for plotting confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[24  2]\n",
      " [ 8  6]]\n",
      "Normalized confusion matrix\n",
      "[[0.92 0.08]\n",
      " [0.57 0.43]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Aslıhan's example\n",
    "\n",
    "titles_options = [\n",
    "    (\"Confusion matrix, without normalization\", None),\n",
    "    (\"Normalized confusion matrix\", \"true\"),\n",
    "]\n",
    "for title, normalize in titles_options:\n",
    "    disp = ConfusionMatrixDisplay.from_estimator(\n",
    "        LR,\n",
    "        X_test,\n",
    "        y_test,\n",
    "        display_labels=[0,1],\n",
    "        cmap=plt.cm.Blues,\n",
    "        normalize=normalize,\n",
    "    )\n",
    "    disp.ax_.set_title(title)\n",
    "\n",
    "    print(title)\n",
    "    print(disp.confusion_matrix)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAo0AAAIjCAYAAABmuyHTAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8g+/7EAAAACXBIWXMAAA9hAAAPYQGoP6dpAABFM0lEQVR4nO3de1xU9b7/8feAOqAiSioXU7yQt7xmRnj3SKKVealMsy2Y2q7EVLzlbnuvZm+1NNO0i4pWVnZRU8vykpqp5bW0jCOImil4SxFMNFi/P/o5pwlwMco4I/N6nsd6PPZ8Z63vfGbOrvM57+93LSyGYRgCAAAArsLH3QUAAADA89E0AgAAwBRNIwAAAEzRNAIAAMAUTSMAAABM0TQCAADAFE0jAAAATNE0AgAAwBRNIwAAAEzRNAK4qgMHDqhjx44KDAyUxWLRsmXLinT+Q4cOyWKxKDExsUjnvZm1a9dO7dq1c3cZAOCAphG4CaSkpOif//ynatasKT8/P5UrV04tW7bUK6+8ot9//92lnx0bG6u9e/fqhRde0Ntvv60777zTpZ93I8XFxclisahcuXL5/o4HDhyQxWKRxWLRtGnTnJ7/2LFjmjBhgvbs2VME1QKAe5VwdwEArm7VqlV6+OGHZbVa1bdvXzVo0ECXLl3S5s2bNXLkSP3444964403XPLZv//+u7Zu3arnnntO8fHxLvmM8PBw/f777ypZsqRL5jdTokQJXbhwQStWrFDPnj0d3nv33Xfl5+enixcvXtPcx44d08SJE1W9enU1adKk0Nd9+eWX1/R5AOBKNI2AB0tNTVWvXr0UHh6u9evXKzQ01P7eoEGDlJycrFWrVrns80+ePClJKl++vMs+w2KxyM/Pz2Xzm7FarWrZsqXee++9PE3j4sWLdd999+njjz++IbVcuHBBpUuXVqlSpW7I5wGAM1ieBjzYlClTlJmZqXnz5jk0jFdERERoyJAh9td//PGHJk+erFq1aslqtap69er617/+pezsbIfrqlevrvvvv1+bN2/WXXfdJT8/P9WsWVOLFi2ynzNhwgSFh4dLkkaOHCmLxaLq1atL+nNZ98p//qsJEybIYrE4jK1Zs0atWrVS+fLlVbZsWdWpU0f/+te/7O8XtKdx/fr1at26tcqUKaPy5cura9eu2r9/f76fl5ycrLi4OJUvX16BgYHq16+fLly4UPAP+zePPvqoPv/8c509e9Y+tn37dh04cECPPvponvPPnDmjESNGqGHDhipbtqzKlSunzp076/vvv7efs2HDBjVv3lyS1K9fP/sy95Xv2a5dOzVo0EA7d+5UmzZtVLp0afvv8vc9jbGxsfLz88vz/WNiYlShQgUdO3as0N8VAK4VTSPgwVasWKGaNWuqRYsWhTp/wIABGjdunO644w5Nnz5dbdu2lc1mU69evfKcm5ycrIceekj33HOPXnrpJVWoUEFxcXH68ccfJUk9evTQ9OnTJUm9e/fW22+/rRkzZjhV/48//qj7779f2dnZmjRpkl566SU98MAD+uabb6563dq1axUTE6MTJ05owoQJSkhI0JYtW9SyZUsdOnQoz/k9e/bU+fPnZbPZ1LNnTyUmJmrixImFrrNHjx6yWCz65JNP7GOLFy9W3bp1dccdd+Q5/+DBg1q2bJnuv/9+vfzyyxo5cqT27t2rtm3b2hu4evXqadKkSZKkJ554Qm+//bbefvtttWnTxj7P6dOn1blzZzVp0kQzZsxQ+/bt863vlVdeUaVKlRQbG6ucnBxJ0uuvv64vv/xSr776qsLCwgr9XQHgmhkAPNK5c+cMSUbXrl0Ldf6ePXsMScaAAQMcxkeMGGFIMtavX28fCw8PNyQZmzZtso+dOHHCsFqtxvDhw+1jqamphiRj6tSpDnPGxsYa4eHheWoYP3688dd/rUyfPt2QZJw8ebLAuq98xoIFC+xjTZo0MSpXrmycPn3aPvb9998bPj4+Rt++ffN83uOPP+4wZ/fu3Y1bbrmlwM/86/coU6aMYRiG8dBDDxkdOnQwDMMwcnJyjJCQEGPixIn5/gYXL140cnJy8nwPq9VqTJo0yT62ffv2PN/tirZt2xqSjLlz5+b7Xtu2bR3GvvjiC0OS8fzzzxsHDx40ypYta3Tr1s30OwJAUSFpBDxURkaGJCkgIKBQ53/22WeSpISEBIfx4cOHS1KevY/169dX69at7a8rVaqkOnXq6ODBg9dc899d2Qu5fPly5ebmFuqa48ePa8+ePYqLi1NQUJB9vFGjRrrnnnvs3/OvnnzySYfXrVu31unTp+2/YWE8+uij2rBhg9LS0rR+/XqlpaXluzQt/bkP0sfnz3995uTk6PTp0/al9127dhX6M61Wq/r161eoczt27Kh//vOfmjRpknr06CE/Pz+9/vrrhf4sALheNI2AhypXrpwk6fz584U6//Dhw/Lx8VFERITDeEhIiMqXL6/Dhw87jFerVi3PHBUqVNBvv/12jRXn9cgjj6hly5YaMGCAgoOD1atXLy1ZsuSqDeSVOuvUqZPnvXr16unUqVPKyspyGP/7d6lQoYIkOfVd7r33XgUEBOiDDz7Qu+++q+bNm+f5La/Izc3V9OnTddttt8lqtapixYqqVKmSfvjhB507d67Qn1mlShWnbnqZNm2agoKCtGfPHs2cOVOVK1cu9LUAcL1oGgEPVa5cOYWFhWnfvn1OXff3G1EK4uvrm++4YRjX/BlX9ttd4e/vr02bNmnt2rX6xz/+oR9++EGPPPKI7rnnnjznXo/r+S5XWK1W9ejRQwsXLtTSpUsLTBkl6cUXX1RCQoLatGmjd955R1988YXWrFmj22+/vdCJqvTn7+OM3bt368SJE5KkvXv3OnUtAFwvmkbAg91///1KSUnR1q1bTc8NDw9Xbm6uDhw44DCenp6us2fP2u+ELgoVKlRwuNP4ir+nmZLk4+OjDh066OWXX9ZPP/2kF154QevXr9dXX32V79xX6kxKSsrz3s8//6yKFSuqTJky1/cFCvDoo49q9+7dOn/+fL43D13x0UcfqX379po3b5569eqljh07Kjo6Os9vUtgGvjCysrLUr18/1a9fX0888YSmTJmi7du3F9n8AGCGphHwYKNGjVKZMmU0YMAApaen53k/JSVFr7zyiqQ/l1cl5bnD+eWXX5Yk3XfffUVWV61atXTu3Dn98MMP9rHjx49r6dKlDuedOXMmz7VXHnL998cAXREaGqomTZpo4cKFDk3Yvn379OWXX9q/pyu0b99ekydP1qxZsxQSElLgeb6+vnlSzA8//FC//vqrw9iV5ja/BttZo0eP1pEjR7Rw4UK9/PLLql69umJjYwv8HQGgqPFwb8CD1apVS4sXL9YjjzyievXqOfxFmC1btujDDz9UXFycJKlx48aKjY3VG2+8obNnz6pt27b67rvvtHDhQnXr1q3Ax7lci169emn06NHq3r27nnnmGV24cEFz5sxR7dq1HW4EmTRpkjZt2qT77rtP4eHhOnHihF577TXdeuutatWqVYHzT506VZ07d1ZUVJT69++v33//Xa+++qoCAwM1YcKEIvsef+fj46N///vfpufdf//9mjRpkvr166cWLVpo7969evfdd1WzZk2H82rVqqXy5ctr7ty5CggIUJkyZRQZGakaNWo4Vdf69ev12muvafz48fZHAC1YsEDt2rXT2LFjNWXKFKfmA4BrQdIIeLgHHnhAP/zwgx566CEtX75cgwYN0rPPPqtDhw7ppZde0syZM+3nvvXWW5o4caK2b9+uoUOHav369RozZozef//9Iq3plltu0dKlS1W6dGmNGjVKCxculM1mU5cuXfLUXq1aNc2fP1+DBg3S7Nmz1aZNG61fv16BgYEFzh8dHa3Vq1frlltu0bhx4zRt2jTdfffd+uabb5xuuFzhX//6l4YPH64vvvhCQ4YM0a5du7Rq1SpVrVrV4bySJUtq4cKF8vX11ZNPPqnevXtr48aNTn3W+fPn9fjjj6tp06Z67rnn7OOtW7fWkCFD9NJLL2nbtm1F8r0A4GoshjM7xQEAAOCVSBoBAABgiqYRAAAApmgaAQAAYIqmEQAAAKZoGgEAAGCKphEAAACmaBoBAABgqlj+RRj/pvHuLgGAi/y2fZa7SwDgIn5u7Epc2Tv8vrt4/HuLpBEAAACmimXSCAAA4BQLOZoZmkYAAACLxd0VeDzaagAAAJgiaQQAAGB52hS/EAAAAEyRNAIAALCn0RRJIwAAAEyRNAIAALCn0RS/EAAAAEyRNAIAALCn0RRNIwAAAMvTpviFAAAAYIqkEQAAgOVpUySNAAAAMEXSCAAAwJ5GU/xCAAAAMEXSCAAAwJ5GUySNAAAAMEXSCAAAwJ5GUzSNAAAALE+boq0GAACAKZJGAAAAlqdN8QsBAADAFEkjAAAASaMpfiEAAACYomkEAADwsbjucILNZlPz5s0VEBCgypUrq1u3bkpKSrK/f+bMGQ0ePFh16tSRv7+/qlWrpmeeeUbnzp276rxxcXGyWCwOR6dOnZz7iZw6GwAAAC6zceNGDRo0SNu2bdOaNWt0+fJldezYUVlZWZKkY8eO6dixY5o2bZr27dunxMRErV69Wv379zedu1OnTjp+/Lj9eO+995yqjT2NAAAAHrKncfXq1Q6vExMTVblyZe3cuVNt2rRRgwYN9PHHH9vfr1Wrll544QU99thj+uOPP1SiRMGtndVqVUhIyDXX5hm/EAAAgDtZLC47srOzlZGR4XBkZ2cXqqwry85BQUFXPadcuXJXbRglacOGDapcubLq1Kmjp556SqdPny787yOaRgAAAJey2WwKDAx0OGw2m+l1ubm5Gjp0qFq2bKkGDRrke86pU6c0efJkPfHEE1edq1OnTlq0aJHWrVun//73v9q4caM6d+6snJycQn8Pi2EYRqHPvkn4N413dwkAXOS37bPcXQIAF/Fz46Y5/+j/uGzus6uG5UkWrVarrFbrVa976qmn9Pnnn2vz5s269dZb87yfkZGhe+65R0FBQfr0009VsmTJQtd08OBB1apVS2vXrlWHDh0KdQ1JIwAAgAtZrVaVK1fO4TBrGOPj47Vy5Up99dVX+TaM58+fV6dOnRQQEKClS5c61TBKUs2aNVWxYkUlJycX+hpuhAEAALA492gcVzEMQ4MHD9bSpUu1YcMG1ahRI885GRkZiomJkdVq1aeffio/Pz+nP+fo0aM6ffq0QkNDC30NSSMAAICHGDRokN555x0tXrxYAQEBSktLU1pamn7//XdJfzaMVx7BM2/ePGVkZNjP+ev+xLp162rp0qWSpMzMTI0cOVLbtm3ToUOHtG7dOnXt2lURERGKiYkpdG0kjQAAAB7yyJ05c+ZIktq1a+cwvmDBAsXFxWnXrl369ttvJUkREREO56Smpqp69eqSpKSkJPud176+vvrhhx+0cOFCnT17VmFhYerYsaMmT55sukz+VzSNAAAAHsLs/uR27dqZnvP3efz9/fXFF19cd200jQAAAB6yp9GT0TQCAAB4yPK0J+MXAgAAgCmSRgAAAJanTZE0AgAAwBRJIwAAAHsaTfELAQAAwBRJIwAAAHsaTZE0AgAAwBRJIwAAAHsaTdE0AgAA0DSa4hcCAACAKZJGAAAAboQxRdIIAAAAUySNAAAA7Gk0xS8EAAAAUySNAAAA7Gk0RdIIAAAAUySNAAAA7Gk0RdMIAADA8rQp2moAAACYImkEAABez0LSaIqkEQAAAKZIGgEAgNcjaTRH0ggAAABTJI0AAAAEjaZIGgEAAGCKpBEAAHg99jSao2kEAABej6bRHMvTAAAAMEXSCAAAvB5JozmSRgAAAJgiaQQAAF6PpNEcSSMAAABMkTQCAAAQNJoiaQQAAIApkkYAAOD12NNojqQRAAAApkgaAQCA1yNpNEfTCAAAvB5NozmWpwEAAGCKpBEAAHg9kkZzJI0AAAAewmazqXnz5goICFDlypXVrVs3JSUlOZxz8eJFDRo0SLfccovKli2rBx98UOnp6Ved1zAMjRs3TqGhofL391d0dLQOHDjgVG00jQAAABYXHk7YuHGjBg0apG3btmnNmjW6fPmyOnbsqKysLPs5w4YN04oVK/Thhx9q48aNOnbsmHr06HHVeadMmaKZM2dq7ty5+vbbb1WmTBnFxMTo4sWLha7NYhiG4dzX8Xz+TePdXQIAF/lt+yx3lwDARfzcuGnultj3XDb36YW9r/nakydPqnLlytq4caPatGmjc+fOqVKlSlq8eLEeeughSdLPP/+sevXqaevWrbr77rvzzGEYhsLCwjR8+HCNGDFCknTu3DkFBwcrMTFRvXr1KlQtJI0AAMDrWSwWlx3Z2dnKyMhwOLKzswtV17lz5yRJQUFBkqSdO3fq8uXLio6Otp9Tt25dVatWTVu3bs13jtTUVKWlpTlcExgYqMjIyAKvyQ9NIwAAgAvZbDYFBgY6HDabzfS63NxcDR06VC1btlSDBg0kSWlpaSpVqpTKly/vcG5wcLDS0tLynefKeHBwcKGvyQ93TwMAAK/nyrunx4wZo4SEBIcxq9Vqet2gQYO0b98+bd682VWlOYWmEQAAeD1XNo1Wq7VQTeJfxcfHa+XKldq0aZNuvfVW+3hISIguXbqks2fPOqSN6enpCgkJyXeuK+Pp6ekKDQ11uKZJkyaFronlaQAAAA9hGIbi4+O1dOlSrV+/XjVq1HB4v1mzZipZsqTWrVtnH0tKStKRI0cUFRWV75w1atRQSEiIwzUZGRn69ttvC7wmPySNAAAAHvJs70GDBmnx4sVavny5AgIC7HsOAwMD5e/vr8DAQPXv318JCQkKCgpSuXLlNHjwYEVFRTncOV23bl3ZbDZ1795dFotFQ4cO1fPPP6/bbrtNNWrU0NixYxUWFqZu3boVujaaRgAAAA8xZ84cSVK7du0cxhcsWKC4uDhJ0vTp0+Xj46MHH3xQ2dnZiomJ0WuvveZwflJSkv3Oa0kaNWqUsrKy9MQTT+js2bNq1aqVVq9eLT8/v0LXxnMaAdxUeE4jUHy58zmNwQM+dNnc6W897LK5byT2NAIAAMAUy9MAAMDrufLu6eKCpBEAAACmSBoBAIDXI2k0R9MIAAC8Hk2jOZanAQAAYIqkEQAAgKDRFEkjAAAATJE0AgAAr8eeRnMkjQAAADBF0ggAALweSaM5kkYAAACYImkEAABej6TRHE0jAAAAPaMplqcBAABgiqQRAAB4PZanzZE0AgAAwBRJIwAA8HokjeZIGgEAAGCKpBE3hRGPd1S3/2ms2tWD9Xv2ZX37/UE998pyHTh8It/zl816SjEtb1fPYW9oxYYfbnC1AK7HvDdf17o1Xyo19aCsfn5q0qSphiaMUPUaNd1dGooxkkZzJI24KbS+I0JzP9iktn2n6f6nZqlECV+tnBOv0n6l8pw7uE97GYYbigRQJHZs/06P9O6jt99botffXKA//vhDTw7srwsXLri7NMCrkTTiptA1/jWH10+Mf0e/rP+Pmtavqm92pdjHG9WuoiH/+B+17DNFh9babnSZAIrAnDfmObye9MJ/1L51lPb/9KOa3dncTVWhuCNpNOfWpvHUqVOaP3++tm7dqrS0NElSSEiIWrRoobi4OFWqVMmd5cGDlSvrJ0n67dz/JQ/+fiWVaIvT0P8sUfrp8+4qDUARyzz/5z/P5QID3VwJijV6RlNuW57evn27ateurZkzZyowMFBt2rRRmzZtFBgYqJkzZ6pu3brasWOH6TzZ2dnKyMhwOIzcnBvwDeAuFotFU0c8pC27U/RTynH7+JThD2rb96lauWGvG6sDUJRyc3M15b8vqknTO3TbbbXdXQ7g1dyWNA4ePFgPP/yw5s6dmycSNgxDTz75pAYPHqytW7dedR6bzaaJEyc6jPkGN1fJ0LuKvGZ4hhljeur2iFB16DfdPnZf24Zqd1dt3d3rP26sDEBRe/H5iUo5cECJby92dyko5lieNmcxDPfcMuDv76/du3erbt26+b7/888/q2nTpvr999+vOk92drays7Mdxiq3Hi2Lj2+R1QrPMX30w7q/XSNF95+hw8dO28enjnhQT/duq9zc//uvc4kSvsrJydU3u1MUM/AVd5QLF/ht+yx3l4Ab5MXnJ2nDV+s0f+E7uvXWqu4uBzeAnxs3zdVM+Mxlcx98+V6XzX0jue1/PSEhIfruu+8KbBq/++47BQcHm85jtVpltVodxmgYi6fpox/WA//TWB0HvuLQMErStAVfasHSLQ5jOz96TqNe+lirNu67kWUCuE6GYcj2wmStX7dG8xLfpmHEDUHSaM5tTeOIESP0xBNPaOfOnerQoYO9QUxPT9e6dev05ptvatq0ae4qDx5mxpieeqTznXp42BvKzLqo4FsCJEnnMi/qYvZlpZ8+n+/NL78c/y1PgwnAs704eaI+/2ylZrz6msqULqNTJ09KksoGBMjPz8/N1QHey21N46BBg1SxYkVNnz5dr732mnJy/rx5xdfXV82aNVNiYqJ69uzprvLgYf7Zs40kac1bQx3GB457W++s+NYNFQFwlSUfvCdJ6h/3D4fxSc/b1LV7D3eUBC9A0GjObXsa/+ry5cs6deqUJKlixYoqWbLkdc3n3zS+KMoC4IHY0wgUX+7c0xgx4nOXzZ08rbPL5r6RPOLh3iVLllRoaKi7ywAAAF6KPY3mPKJpBAAAcCd6RnP87WkAAACYImkEAABej+VpcySNAAAAMEXSCAAAvB5BozmSRgAAAJgiaQQAAF7Px4eo0QxJIwAAAEyRNAIAAK/HnkZzNI0AAMDr8cgdcyxPAwAAeJBNmzapS5cuCgsLk8Vi0bJlyxzet1gs+R5Tp04tcM4JEybkOb9u3bpO1UXSCAAAvJ4nBY1ZWVlq3LixHn/8cfXo0SPP+8ePH3d4/fnnn6t///568MEHrzrv7bffrrVr19pflyjhXBtI0wgAAOBBOnfurM6dOxf4fkhIiMPr5cuXq3379qpZs+ZV5y1RokSea51B0wgAALyeK/c0ZmdnKzs722HMarXKarVe99zp6elatWqVFi5caHrugQMHFBYWJj8/P0VFRclms6latWqF/iz2NAIAALiQzWZTYGCgw2Gz2Ypk7oULFyogICDfZey/ioyMVGJiolavXq05c+YoNTVVrVu31vnz5wv9WSSNAADA67kyaRwzZowSEhIcxooiZZSk+fPnq0+fPvLz87vqeX9d7m7UqJEiIyMVHh6uJUuWqH///oX6LJpGAAAAFyqqpei/+/rrr5WUlKQPPvjA6WvLly+v2rVrKzk5udDXsDwNAAC8nsXiusNV5s2bp2bNmqlx48ZOX5uZmamUlBSFhoYW+hqaRgAA4PUKevZhURzOyszM1J49e7Rnzx5JUmpqqvbs2aMjR47Yz8nIyNCHH36oAQMG5DtHhw4dNGvWLPvrESNGaOPGjTp06JC2bNmi7t27y9fXV7179y50XSxPAwAAeJAdO3aoffv29tdX9kPGxsYqMTFRkvT+++/LMIwCm76UlBSdOnXK/vro0aPq3bu3Tp8+rUqVKqlVq1batm2bKlWqVOi6LIZhGNfwfTyaf9N4d5cAwEV+2z7L/CQANyU/N0ZZd0xa77K5d437H5fNfSOxPA0AAABTLE8DAACv58pH7hQXJI0AAAAwRdIIAAC8HkGjOZJGAAAAmCJpBAAAXo89jeZIGgEAAGCKpBEAAHg9gkZzNI0AAMDrsTxtjuVpAAAAmCJpBAAAXo+g0RxJIwAAAEyRNAIAAK/HnkZzJI0AAAAwRdIIAAC8HkGjOZJGAAAAmCJpBAAAXo89jeZoGgEAgNejZzTH8jQAAABMkTQCAACvx/K0OZJGAAAAmCJpBAAAXo+k0RxJIwAAAEyRNAIAAK9H0GiOpBEAAACmSBoBAIDXY0+jOZpGAADg9egZzbE8DQAAAFMkjQAAwOuxPG2OpBEAAACmSBoBAIDXI2g0R9IIAAAAUySNAADA6/kQNZoiaQQAAIApkkYAAOD1CBrN0TQCAACvxyN3zLE8DQAAAFMkjQAAwOv5EDSaImkEAACAKZJGAADg9djTaI6kEQAAAKZIGgEAgNcjaDRH0ggAAOBBNm3apC5duigsLEwWi0XLli1zeD8uLk4Wi8Xh6NSpk+m8s2fPVvXq1eXn56fIyEh99913TtVF0wgAALyexYX/46ysrCw1btxYs2fPLvCcTp066fjx4/bjvffeu+qcH3zwgRISEjR+/Hjt2rVLjRs3VkxMjE6cOFHoulieBgAAXs+THrnTuXNnde7c+arnWK1WhYSEFHrOl19+WQMHDlS/fv0kSXPnztWqVas0f/58Pfvss4Wag6QRAADAhbKzs5WRkeFwZGdnX9ecGzZsUOXKlVWnTh099dRTOn36dIHnXrp0STt37lR0dLR9zMfHR9HR0dq6dWuhP5OmEQAAeL2/7xEsysNmsykwMNDhsNls11xrp06dtGjRIq1bt07//e9/tXHjRnXu3Fk5OTn5nn/q1Cnl5OQoODjYYTw4OFhpaWmF/lyWpwEAAFxozJgxSkhIcBizWq3XPF+vXr3s/7lhw4Zq1KiRatWqpQ0bNqhDhw7XPK8ZmkYAAOD1XPnIHavVel1NopmaNWuqYsWKSk5OzrdprFixonx9fZWenu4wnp6e7tS+SJanAQAAbmJHjx7V6dOnFRoamu/7pUqVUrNmzbRu3Tr7WG5urtatW6eoqKhCfw5JIwAA8Ho+HvR078zMTCUnJ9tfp6amas+ePQoKClJQUJAmTpyoBx98UCEhIUpJSdGoUaMUERGhmJgY+zUdOnRQ9+7dFR8fL0lKSEhQbGys7rzzTt11112aMWOGsrKy7HdTFwZNIwAAgAfZsWOH2rdvb399ZT9kbGys5syZox9++EELFy7U2bNnFRYWpo4dO2ry5MkOS+ApKSk6deqU/fUjjzyikydPaty4cUpLS1OTJk20evXqPDfHXI3FMAyjCL6fR/FvGu/uEgC4yG/bZ7m7BAAu4ufGKOvB+TtdNvfHjzdz2dw3EkkjAADwehYPWp72VNwIAwAAAFMkjQAAwOsRNJorVNP46aefFnrCBx544JqLAQAAgGcqVNPYrVu3Qk1msVgK/BM2AAAAnsqTHrnjqQrVNObm5rq6DgAAAHiw67oR5uLFi0VVBwAAgNtYXHgUF043jTk5OZo8ebKqVKmismXL6uDBg5KksWPHat68eUVeIAAAANzP6abxhRdeUGJioqZMmaJSpUrZxxs0aKC33nqrSIsDAAC4ESwWi8uO4sLppnHRokV644031KdPH/n6+trHGzdurJ9//rlIiwMAALgRfCyuO4oLp5vGX3/9VREREXnGc3Nzdfny5SIpCgAAAJ7F6aaxfv36+vrrr/OMf/TRR2ratGmRFAUAAHAjsTxtzum/CDNu3DjFxsbq119/VW5urj755BMlJSVp0aJFWrlypStqBAAAgJs5nTR27dpVK1as0Nq1a1WmTBmNGzdO+/fv14oVK3TPPfe4okYAAACXslhcdxQX1/S3p1u3bq01a9YUdS0AAADwUNfUNErSjh07tH//fkl/7nNs1qxZkRUFAABwIxWnvYeu4nTTePToUfXu3VvffPONypcvL0k6e/asWrRooffff1+33nprUdcIAAAAN3N6T+OAAQN0+fJl7d+/X2fOnNGZM2e0f/9+5ebmasCAAa6oEQAAwKV4TqM5p5PGjRs3asuWLapTp459rE6dOnr11VfVunXrIi0OAADgRmB52pzTSWPVqlXzfYh3Tk6OwsLCiqQoAAAAeBanm8apU6dq8ODB2rFjh31sx44dGjJkiKZNm1akxQEAANwIFhcexUWhlqcrVKjgENtmZWUpMjJSJUr8efkff/yhEiVK6PHHH1e3bt1cUigAAADcp1BN44wZM1xcBgAAgPv4sKfRVKGaxtjYWFfXAQAAAA92zQ/3lqSLFy/q0qVLDmPlypW7roIAAABuNIJGc07fCJOVlaX4+HhVrlxZZcqUUYUKFRwOAAAAFD9ON42jRo3S+vXrNWfOHFmtVr311luaOHGiwsLCtGjRIlfUCAAA4FIWi8VlR3Hh9PL0ihUrtGjRIrVr1079+vVT69atFRERofDwcL377rvq06ePK+oEAACAGzmdNJ45c0Y1a9aU9Of+xTNnzkiSWrVqpU2bNhVtdQAAADeAxeK6o7hwummsWbOmUlNTJUl169bVkiVLJP2ZQJYvX75IiwMAALgRfCwWlx3FhdNNY79+/fT9999Lkp599lnNnj1bfn5+GjZsmEaOHFnkBQIAAMD9nN7TOGzYMPt/jo6O1s8//6ydO3cqIiJCjRo1KtLiAAAAboRiFAi6zHU9p1GSwsPDFR4eXhS1AAAAwEMVqmmcOXNmoSd85plnrrkYAAAAdyhOj8ZxlUI1jdOnTy/UZBaLhaYRAACgGCpU03jlbumbxefvT3J3CQBcJDk9090lAHCRBlXKuu2znb4z2AvxGwEAAMDUdd8IAwAAcLNjT6M5mkYAAOD1fOgZTbE8DQAAAFMkjQAAwOuRNJq7pqTx66+/1mOPPaaoqCj9+uuvkqS3335bmzdvLtLiAAAAvM2mTZvUpUsXhYWFyWKxaNmyZfb3Ll++rNGjR6thw4YqU6aMwsLC1LdvXx07duyqc06YMEEWi8XhqFu3rlN1Od00fvzxx4qJiZG/v792796t7OxsSdK5c+f04osvOjsdAACA2/29oSrKw1lZWVlq3LixZs+enee9CxcuaNeuXRo7dqx27dqlTz75RElJSXrggQdM57399tt1/Phx++Fs2Of08vTzzz+vuXPnqm/fvnr//fft4y1bttTzzz/v7HQAAAD4i86dO6tz5875vhcYGKg1a9Y4jM2aNUt33XWXjhw5omrVqhU4b4kSJRQSEnLNdTndNCYlJalNmzZ5xgMDA3X27NlrLgQAAMBdXLmnMTs7274ye4XVapXVai2S+c+dOyeLxaLy5ctf9bwDBw4oLCxMfn5+ioqKks1mu2qT+XdOL0+HhIQoOTk5z/jmzZtVs2ZNZ6cDAAAo1mw2mwIDAx0Om81WJHNfvHhRo0ePVu/evVWuXLkCz4uMjFRiYqJWr16tOXPmKDU1Va1bt9b58+cL/VlOJ40DBw7UkCFDNH/+fFksFh07dkxbt27ViBEjNHbsWGenAwAAcDtXPtt7zJgxSkhIcBgripTx8uXL6tmzpwzD0Jw5c6567l+Xuxs1aqTIyEiFh4dryZIl6t+/f6E+z+mm8dlnn1Vubq46dOigCxcuqE2bNrJarRoxYoQGDx7s7HQAAABu5+PCrrEol6KvuNIwHj58WOvXr79qypif8uXLq3bt2vmuHhfE6eVpi8Wi5557TmfOnNG+ffu0bds2nTx5UpMnT3Z2KgAAADjpSsN44MABrV27VrfccovTc2RmZiolJUWhoaGFvuaaH+5dqlQp1a9f/1ovBwAA8Bie9CfyMjMzHRLA1NRU7dmzR0FBQQoNDdVDDz2kXbt2aeXKlcrJyVFaWpokKSgoSKVKlZIkdejQQd27d1d8fLwkacSIEerSpYvCw8N17NgxjR8/Xr6+vurdu3eh63K6aWzfvv1Vnzm0fv16Z6cEAADA/7djxw61b9/e/vrKfsjY2FhNmDBBn376qSSpSZMmDtd99dVXateunSQpJSVFp06dsr939OhR9e7dW6dPn1alSpXUqlUrbdu2TZUqVSp0XU43jX8v8PLly9qzZ4/27dun2NhYZ6cDAABwO1feCOOsdu3ayTCMAt+/2ntXHDp0yOH1X5+tfa2cbhqnT5+e7/iECROUmZl53QUBAADA8xTZEv5jjz2m+fPnF9V0AAAAN4yPxeKyo7gosqZx69at8vPzK6rpAAAA4EGcXp7u0aOHw2vDMHT8+HHt2LGDh3sDAICbUjEKBF3G6aYxMDDQ4bWPj4/q1KmjSZMmqWPHjkVWGAAAwI3iyr89XVw41TTm5OSoX79+atiwoSpUqOCqmgAAAOBhnNrT6Ovrq44dO+rs2bMuKgcAAODG40YYc07fCNOgQQMdPHjQFbUAAADAQzndND7//PMaMWKEVq5cqePHjysjI8PhAAAAuNlYLK47iotC72mcNGmShg8frnvvvVeS9MADDzj8OUHDMGSxWJSTk1P0VQIAAMCtCt00Tpw4UU8++aS++uorV9YDAABww3H3tLlCN41X/s5h27ZtXVYMAAAAPJNTj9yxFKeFeQAAgP/PInocM041jbVr1zZtHM+cOXNdBQEAANxoLE+bc6ppnDhxYp6/CAMAAIDiz6mmsVevXqpcubKragEAAHALkkZzhX5OI/sZAQAAvJfTd08DAAAUN4Rj5grdNObm5rqyDgAAAHgwp/Y0AgAAFEfsaTTn9N+eBgAAgPchaQQAAF6PLY3maBoBAIDX86FrNMXyNAAAAEyRNAIAAK/HjTDmSBoBAABgiqQRAAB4PbY0miNpBAAAgCmSRgAA4PV8RNRohqQRAAAApkgaAQCA12NPozmaRgAA4PV45I45lqcBAABgiqQRAAB4Pf6MoDmSRgAAAJgiaQQAAF6PoNEcSSMAAABMkTQCAACvx55GcySNAAAAMEXSCAAAvB5BozmaRgAA4PVYejXHbwQAAABTNI0AAMDrWSwWlx3O2rRpk7p06aKwsDBZLBYtW7bM4X3DMDRu3DiFhobK399f0dHROnDggOm8s2fPVvXq1eXn56fIyEh99913TtVF0wgAAOBBsrKy1LhxY82ePTvf96dMmaKZM2dq7ty5+vbbb1WmTBnFxMTo4sWLBc75wQcfKCEhQePHj9euXbvUuHFjxcTE6MSJE4Wuy2IYhuH0t/FwG5LOuLsEAC5SsWwpd5cAwEUaVCnrts9etOMXl83d986q13ytxWLR0qVL1a1bN0l/poxhYWEaPny4RowYIUk6d+6cgoODlZiYqF69euU7T2RkpJo3b65Zs2ZJknJzc1W1alUNHjxYzz77bKFqIWkEAABwoezsbGVkZDgc2dnZ1zRXamqq0tLSFB0dbR8LDAxUZGSktm7dmu81ly5d0s6dOx2u8fHxUXR0dIHX5IemEQAAeD0fi8Vlh81mU2BgoMNhs9muqc60tDRJUnBwsMN4cHCw/b2/O3XqlHJycpy6Jj88cgcAAMCFxowZo4SEBIcxq9XqpmquHU0jAADweq58trfVai2yJjEkJESSlJ6ertDQUPt4enq6mjRpku81FStWlK+vr9LT0x3G09PT7fMVBsvTAADA61ksrjuKUo0aNRQSEqJ169bZxzIyMvTtt98qKioq32tKlSqlZs2aOVyTm5urdevWFXhNfkgaAQAAPEhmZqaSk5Ptr1NTU7Vnzx4FBQWpWrVqGjp0qJ5//nnddtttqlGjhsaOHauwsDD7HdaS1KFDB3Xv3l3x8fGSpISEBMXGxurOO+/UXXfdpRkzZigrK0v9+vUrdF00jQAAwOtdy0O4XWXHjh1q3769/fWV/ZCxsbFKTEzUqFGjlJWVpSeeeEJnz55Vq1attHr1avn5+dmvSUlJ0alTp+yvH3nkEZ08eVLjxo1TWlqamjRpotWrV+e5OeZqeE4jgJsKz2kEii93Pqfxvd2/umzu3k2ruGzuG4mkEQAAeD1u8jDHbwQAAABTJI0AAMDredKeRk9F0ggAAABTJI0AAMDrkTOaI2kEAACAKZJGAADg9djTaI6mEQAAeD2WXs3xGwEAAMAUSSMAAPB6LE+bI2kEAACAKZJGAADg9cgZzZE0AgAAwBRJIwAA8HpsaTRH0ggAAABTJI0AAMDr+bCr0RRNIwAA8HosT5tjeRoAAACmSBoBAIDXs7A8bYqkEQAAAKZIGgEAgNdjT6M5kkYAAACYImkEAABej0fumCNpBAAAgCmSRgAA4PXY02iOphEAAHg9mkZzLE8DAADAFEkjAADwejzc2xxJIwAAAEyRNAIAAK/nQ9BoiqQRAAAApkgaAQCA12NPozmSRgAAAJgiaQQAAF6P5zSao2kEAABej+VpcyxPAwAAwBRJIwAA8Ho8csccSSMAAABMkTQCAACvx55GcySNAAAAMEXSiJtSbk6OVrz3lr7d8IUyzp5WYFAltfife3XvI/1k4bkJwE3v9MkTeufNmdr13RZdunhRIVVu1aBRExRRp767S0Mxxf/pMEfSiJvS6o/f1sbPl6r3P4drwuz31SP2aX2x9F19tfJDd5cG4Dplns/Qc888Ll/fEvq3baZmLPhQsU8OU9myAe4uDXC56tWry2Kx5DkGDRqU7/mJiYl5zvXz83NJbSSNuCkd/HmvmkS2VsPmLSVJFYNDtX3TGqX+709urgzA9Vr6XqIqVg5W/OgJ9rHg0CruKwhewVOCxu3btysnJ8f+et++fbrnnnv08MMPF3hNuXLllJSUZH/tqhU3mkbclGrWbajNXy5X+q9HFFylmn5JPaDkn77Xw/2HuLs0ANdpx9ZNanJnlKZNGKUff9ilWypWVswDD+me+3u4uzQUYz4esj5dqVIlh9f/+c9/VKtWLbVt27bAaywWi0JCQlxdmmc3jb/88ovGjx+v+fPnF3hOdna2srOzHcYuXcpWqVJWV5cHN+r0UF9d/P2Cxj/dSxYfHxm5uer62D8V2S7G3aUBuE7px37VF59+pC4P91GPPo8rOeknzZ81TSVKllT7mC7uLg9wWn69itVqldV69V7l0qVLeuedd5SQkHDV9DAzM1Ph4eHKzc3VHXfcoRdffFG33357kdT+Vx69p/HMmTNauHDhVc+x2WwKDAx0OBa/PuPGFAi32bl5nb7b+IX6D5+of09PVNzQsVqzbLG2rlvl7tIAXCfDyFXN2+qqz4B41bytrjre30PR93XTlys+dndpKMYsLjzy61VsNptpTcuWLdPZs2cVFxdX4Dl16tTR/PnztXz5cr3zzjvKzc1VixYtdPTo0Wv5Ga7KrUnjp59+etX3Dx48aDrHmDFjlJCQ4DC27XDWddUFz/dx4izFPPgPNW9zjySpSvUInT6Rps8/WqSoDve5uToA16N8UEXdWr2Gw1iVajW0bdN6N1UEXJ/8ehWzlFGS5s2bp86dOyssLKzAc6KiohQVFWV/3aJFC9WrV0+vv/66Jk+efO1F58OtTWO3bt1ksVhkGEaB55ht5swv3i1V6o8iqQ+e61L2RflYHINyHx+fq/53CcDNoW6Dxjr2y2GHseNHj6hScKibKoJXcOGWxsIsRf/d4cOHtXbtWn3yySdOXVeyZEk1bdpUycnJTl1XGG5dng4NDdUnn3yi3NzcfI9du3a5szx4sEbNW+mzDxO1d/s3OpV+XLu3btDa5e+ryd0FbxQGcHPo8lAf/e9Pe/Xxu/N1/Ndf9PW6z7Vm1Sfq1K3gu0eB4mbBggWqXLmy7rvPudWznJwc7d27V6GhRf//ZLk1aWzWrJl27typrl275vu+WQoJ79XriQQtf/cNLZ47TefPnVFgUCW17tRN9z/yuLtLA3CdIurerlGTpundt2bpw0VvqnJomPo9PVxtou91d2koxjzpzwjm5uZqwYIFio2NVYkSjq1a3759VaVKFfueyEmTJunuu+9WRESEzp49q6lTp+rw4cMaMGBAkdfl1qZx5MiRysoqeP9hRESEvvrqqxtYEW4WfqXL6JGBw/TIwGHuLgWAC9wZ1UZ3RrVxdxmAW6xdu1ZHjhzR44/nDUKOHDkiH5//Wyj+7bffNHDgQKWlpalChQpq1qyZtmzZovr1i/6vJ1mMYhjlbUg64+4SALhIxbKl3F0CABdpUKWs2z77u4PnXDb3XTUDXTb3jeTRz2kEAAC4ETxncdpzefRzGgEAAOAZSBoBAACIGk2RNAIAAMAUSSMAAPB6nvTIHU9F0ggAAABTJI0AAMDrmfzVYoikEQAAAIVA0ggAALweQaM5mkYAAAC6RlMsTwMAAMAUSSMAAPB6PHLHHEkjAAAATJE0AgAAr8cjd8yRNAIAAMAUSSMAAPB6BI3mSBoBAABgiqQRAACAqNEUTSMAAPB6PHLHHMvTAAAAMEXSCAAAvB6P3DFH0ggAAABTJI0AAMDrETSaI2kEAACAKZJGAAAAokZTJI0AAAAwRdIIAAC8Hs9pNEfSCAAAAFMkjQAAwOvxnEZzNI0AAMDr0TOaY3kaAAAApkgaAQAAiBpNkTQCAADAFEkjAADwejxyxxxJIwAAAEyRNAIAAK/HI3fMkTQCAADAFEkjAADwegSN5mgaAQAA6BpNsTwNAAAAUySNAADA6/HIHXMkjQAAADBF0wgAALyexeK6wxkTJkyQxWJxOOrWrXvVaz788EPVrVtXfn5+atiwoT777LPr+CUKRtMIAADgQW6//XYdP37cfmzevLnAc7ds2aLevXurf//+2r17t7p166Zu3bpp3759RV4XTSMAAPB6FhcezipRooRCQkLsR8WKFQs895VXXlGnTp00cuRI1atXT5MnT9Ydd9yhWbNmXcMnXx1NIwAAgAtlZ2crIyPD4cjOzi7w/AMHDigsLEw1a9ZUnz59dOTIkQLP3bp1q6Kjox3GYmJitHXr1iKr/wqaRgAAABdGjTabTYGBgQ6HzWbLt4zIyEglJiZq9erVmjNnjlJTU9W6dWudP38+3/PT0tIUHBzsMBYcHKy0tLTr+DHyxyN3AACA13PlI3fGjBmjhIQEhzGr1ZrvuZ07d7b/50aNGikyMlLh4eFasmSJ+vfv77IaC4OmEQAAwIWsVmuBTaKZ8uXLq3bt2kpOTs73/ZCQEKWnpzuMpaenKyQk5Jo+72pYngYAAF7PUx6583eZmZlKSUlRaGhovu9HRUVp3bp1DmNr1qxRVFTU9X1wPmgaAQAAPMSIESO0ceNGHTp0SFu2bFH37t3l6+ur3r17S5L69u2rMWPG2M8fMmSIVq9erZdeekk///yzJkyYoB07dig+Pr7Ia2N5GgAAeD1P+SOCR48eVe/evXX69GlVqlRJrVq10rZt21SpUiVJ0pEjR+Tj83+ZX4sWLbR48WL9+9//1r/+9S/ddtttWrZsmRo0aFDktVkMwzCKfFY325B0xt0lAHCRimVLubsEAC7SoEpZt332oVMXXTZ39Yp+Lpv7RiJpBAAA8JSo0YOxpxEAAACmSBoBAIDXc+VzGosLmkYAAOD1rvfRON6A5WkAAACYImkEAABej6DRHEkjAAAATJE0AgAAr8eeRnMkjQAAADBF0ggAAMCuRlMkjQAAADBF0ggAALweexrN0TQCAACvR89ojuVpAAAAmCJpBAAAXo/laXMkjQAAADBF0ggAALyehV2NpkgaAQAAYIqkEQAAgKDRFEkjAAAATJE0AgAAr0fQaI6mEQAAeD0euWOO5WkAAACYImkEAABej0fumCNpBAAAgCmSRgAAAIJGUySNAAAAMEXSCAAAvB5BozmSRgAAAJgiaQQAAF6P5zSao2kEAABej0fumGN5GgAAAKZIGgEAgNdjedocSSMAAABM0TQCAADAFE0jAAAATLGnEQAAeD32NJojaQQAAIApkkYAAOD1eE6jOZpGAADg9VieNsfyNAAAAEzRNAIAAK9nceHhDJvNpubNmysgIECVK1dWt27dlJSUdNVrEhMTZbFYHA4/Pz8nP9kcTSMAAICH2LhxowYNGqRt27ZpzZo1unz5sjp27KisrKyrXleuXDkdP37cfhw+fLjIa2NPIwAAgIfsaVy9erXD68TERFWuXFk7d+5UmzZtCrzOYrEoJCTEpbWRNAIAALhQdna2MjIyHI7s7OxCXXvu3DlJUlBQ0FXPy8zMVHh4uKpWraquXbvqxx9/vO66/46mEQAAeD2LC//HZrMpMDDQ4bDZbKY15ebmaujQoWrZsqUaNGhQ4Hl16tTR/PnztXz5cr3zzjvKzc1VixYtdPTo0aL8iWQxDMMo0hk9wIakM+4uAYCLVCxbyt0lAHCRBlXKuu2zM7Nd1w6V1KU8yaLVapXVar3qdU899ZQ+//xzbd68WbfeemuhP+/y5cuqV6+eevfurcmTJ19TzflhTyMAAPB6rnxOo7WUeYP4d/Hx8Vq5cqU2bdrkVMMoSSVLllTTpk2VnJzs1HVmWJ4GAADwEIZhKD4+XkuXLtX69etVo0YNp+fIycnR3r17FRoaWqS1kTQCAACv5yE3T2vQoEFavHixli9froCAAKWlpUmSAgMD5e/vL0nq27evqlSpYt8XOWnSJN19992KiIjQ2bNnNXXqVB0+fFgDBgwo0tpoGgEAADyka5wzZ44kqV27dg7jCxYsUFxcnCTpyJEj8vH5v8Xi3377TQMHDlRaWpoqVKigZs2aacuWLapfv36R1saNMABuKtwIAxRf7rwR5sJl17VDpUt6SEd6nUgaAQCA17N4StTowbgRBgAAAKZIGgEAgNdz5SN3iguSRgAAAJgqljfCwHtkZ2fLZrNpzJgxTj84FYBn459vwLPQNOKmlpGRocDAQJ07d07lypVzdzkAihD/fAOeheVpAAAAmKJpBAAAgCmaRgAAAJiiacRNzWq1avz48WySB4oh/vkGPAs3wgAAAMAUSSMAAABM0TQCAADAFE0jAAAATNE0AgAAwBRNI25qs2fPVvXq1eXn56fIyEh999137i4JwHXatGmTunTporCwMFksFi1btszdJQEQTSNuYh988IESEhI0fvx47dq1S40bN1ZMTIxOnDjh7tIAXIesrCw1btxYs2fPdncpAP6CR+7gphUZGanmzZtr1qxZkqTc3FxVrVpVgwcP1rPPPuvm6gAUBYvFoqVLl6pbt27uLgXweiSNuCldunRJO3fuVHR0tH3Mx8dH0dHR2rp1qxsrAwCgeKJpxE3p1KlTysnJUXBwsMN4cHCw0tLS3FQVAADFF00jAAAATNE04qZUsWJF+fr6Kj093WE8PT1dISEhbqoKAIDii6YRN6VSpUqpWbNmWrdunX0sNzdX69atU1RUlBsrAwCgeCrh7gKAa5WQkKDY2FjdeeeduuuuuzRjxgxlZWWpX79+7i4NwHXIzMxUcnKy/XVqaqr27NmjoKAgVatWzY2VAd6NR+7gpjZr1ixNnTpVaWlpatKkiWbOnKnIyEh3lwXgOmzYsEHt27fPMx4bG6vExMQbXxAASTSNAAAAKAT2NAIAAMAUTSMAAABM0TQCAADAFE0jAAAATNE0AgAAwBRNIwAAAEzRNAIAAMAUTSMAAABM0TQCKHJxcXHq1q2b/XW7du00dOjQG17Hhg0bZLFYdPbs2QLPsVgsWrZsWaHnnDBhgpo0aXJddR06dEgWi0V79uy5rnkA4EaiaQS8RFxcnCwWiywWi0qVKqWIiAhNmjRJf/zxh8s/+5NPPtHkyZMLdW5hGj0AwI1Xwt0FALhxOnXqpAULFig7O1ufffaZBg0apJIlS2rMmDF5zr106ZJKlSpVJJ8bFBRUJPMAANyHpBHwIlarVSEhIQoPD9dTTz2l6Ohoffrpp5L+b0n5hRdeUFhYmOrUqSNJ+uWXX9SzZ0+VL19eQUFB6tq1qw4dOmSfMycnRwkJCSpfvrxuueUWjRo1Sn//k/Z/X57Ozs7W6NGjVbVqVVmtVkVERGjevHk6dOiQ2rdvL0mqUKGCLBaL4uLiJEm5ubmy2WyqUaOG/P391bhxY3300UcOn/PZZ5+pdu3a8vf3V/v27R3qLKzRo0erdu3aKl26tGrWrKmxY8fq8uXLec57/fXXVbVqVZUuXVo9e/bUuXPnHN5/6623VK9ePfn5+alu3bp67bXXnK4FADwJTSPgxfz9/XXp0iX763Xr1ikpKUlr1qzRypUrdfnyZcXExCggIEBff/21vvnmG5UtW1adOnWyX/fSSy8pMTFR8+fP1+bNm3XmzBktXbr0qp/bt29fvffee5o5c6b279+v119/XWXLllXVqlX18ccfS5KSkpJ0/PhxvfLKK5Ikm82mRYsWae7cufrxxx81bNgwPfbYY9q4caOkP5vbHj16qEuXLtqzZ48GDBigZ5991unfJCAgQImJifrpp5/0yiuv6M0339T06dMdzklOTtaSJUu0YsUKrV69Wrt379bTTz9tf//dd9/VuHHj9MILL2j//v168cUXNXbsWC1cuNDpegDAYxgAvEJsbKzRtWtXwzAMIzc311izZo1htVqNESNG2N8PDg42srOz7de8/fbbRp06dYzc3Fz7WHZ2tuHv72988cUXhmEYRmhoqDFlyhT7+5cvXzZuvfVW+2cZhmG0bdvWGDJkiGEYhpGUlGRIMtasWZNvnV999ZUhyfjtt9/sYxcvXjRKly5tbNmyxeHc/v37G7179zYMwzDGjBlj1K9f3+H90aNH55nr7yQZS5cuLfD9qVOnGs2aNbO/Hj9+vOHr62scPXrUPvb5558bPj4+xvHjxw3DMIxatWoZixcvdphn8uTJRlRUlGEYhpGammpIMnbv3l3g5wKAp2FPI+BFVq5cqbJly+ry5cvKzc3Vo48+qgkTJtjfb9iwocM+xu+//17JyckKCAhwmOfixYtKSUnRuXPndPz4cUVGRtrfK1GihO688848S9RX7NmzR76+vmrbtm2h605OTtaFCxd0zz33OIxfunRJTZs2lSTt37/foQ5JioqKKvRnXPHBBx9o5syZSklJUWZmpv744w+VK1fO4Zxq1aqpSpUqDp+Tm5urpKQkBQQEKCUlRf3799fAgQPt5/zxxx8KDAx0uh4A8BQ0jYAXad++vebMmaNSpUopLCxMJUo4/iugTJkyDq8zMzPVrFkzvfvuu3nmqlSp0jXV4O/v7/Q1mZmZkqRVq1Y5NGvSn/s0i8rWrVvVp08fTZw4UTExMQoMDNT777+vl156yela33zzzTxNrK+vb5HVCgA3Gk0j4EXKlCmjiIiIQp9/xx136IMPPlDlypXzpG1XhIaG6ttvv1WbNm0k/Zmo7dy5U3fccUe+5zds2FC5ubnauHGjoqOj87x/JenMycmxj9WvX19Wq1VHjhwpMKGsV6+e/aaeK7Zt22b+Jf9iy5YtCg8P13PPPWcfO3z4cJ7zjhw5omPHjiksLMz+OT4+PqpTp46Cg4MVFhamgwcPqk+fPk59PgB4Mm6EAVCgPn36qGLFiuratau+/vprpaamasOGDXrmmWd09OhRSdKQIUP0n//8R8uWLdPPP/+sp59++qrPWKxevbpiY2P1+OOPa9myZfY5lyxZIkkKDw+XxWLRypUrdfLkSWVmZiogIEAjRozQsGHDtHDhQqWkpGjXrl169dVX7TeXPPnkkzpw4IBGjhyppKQkLV68WImJiU5939tuu01HjhzR+++/r5SUFM2cOTPfm3r8/PwUGxur77//Xl9//bWeeeYZ9ezZUyEhIZKkiRMnymazaebMmfrf//1f7d27VwsWLNDLL7/sVD0A4EloGgEUqHTp0tq0aZOqVaumHj16qF69eurfv78uXrxoTx6HDx+uf/zjH4qNjVVUVJQCAgLUvXv3q847Z84cPfTQQ3r66adVt25dDRw4UFlZWZKkKlWqaOLEiXr22WcVHBys+Ph4SdLkyZM1duxY2Ww21atXT506ddKqVatUo0YNSX/uM/z444+1bNkyNW7cWHPnztWLL77o1Pd94IEHNGzYMMXHx6tJkybasmWLxo4dm+e8iIgI9ejRQ/fee686duyoRo0aOTxSZ8CAAXrrrbe0YMECNWzYUG3btlViYqK9VgC4GVmMgnarAwAAAP8fSSMAAABM0TQCAADAFE0jAAAATNE0AgAAwBRNIwAAAEzRNAIAAMAUTSMAAABM0TQCAADAFE0jAAAATNE0AgAAwBRNIwAAAEz9P8dIJYUhPgvFAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Yasemin's example\n",
    "import seaborn as sns\n",
    "\n",
    "plt.figure(figsize=(8,6))\n",
    "sns.heatmap(cnf_matrix, annot=True, fmt='d', cmap='Blues')\n",
    "plt.xlabel('Predicted label')\n",
    "plt.ylabel('True label')\n",
    "plt.title('Confusion Matrix')\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}