Customer_Churn_Analysis_Feature_Engineering.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Required Library and Functions\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from catboost import CatBoostClassifier\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score,roc_auc_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import GridSearchCV, cross_validate\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import warnings\n",
    "warnings.simplefilter(action=\"ignore\")\n",
    "\n",
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.width', 170)\n",
    "pd.set_option('display.max_rows', None)\n",
    "pd.set_option('display.float_format', lambda x: '%.3f' % x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>customerID</th>\n",
       "      <th>gender</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>tenure</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>InternetService</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>OnlineBackup</th>\n",
       "      <th>DeviceProtection</th>\n",
       "      <th>TechSupport</th>\n",
       "      <th>StreamingTV</th>\n",
       "      <th>StreamingMovies</th>\n",
       "      <th>Contract</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>PaymentMethod</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7590-VHVEG</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>29.850</td>\n",
       "      <td>29.85</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5575-GNVDE</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>34</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>56.950</td>\n",
       "      <td>1889.5</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3668-QPYBK</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>53.850</td>\n",
       "      <td>108.15</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7795-CFOCW</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>45</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>42.300</td>\n",
       "      <td>1840.75</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9237-HQITU</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>70.700</td>\n",
       "      <td>151.65</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   customerID  gender  SeniorCitizen Partner Dependents  tenure PhoneService     MultipleLines InternetService OnlineSecurity OnlineBackup DeviceProtection TechSupport  \\\n",
       "0  7590-VHVEG  Female              0     Yes         No       1           No  No phone service             DSL             No          Yes               No          No   \n",
       "1  5575-GNVDE    Male              0      No         No      34          Yes                No             DSL            Yes           No              Yes          No   \n",
       "2  3668-QPYBK    Male              0      No         No       2          Yes                No             DSL            Yes          Yes               No          No   \n",
       "3  7795-CFOCW    Male              0      No         No      45           No  No phone service             DSL            Yes           No              Yes         Yes   \n",
       "4  9237-HQITU  Female              0      No         No       2          Yes                No     Fiber optic             No           No               No          No   \n",
       "\n",
       "  StreamingTV StreamingMovies        Contract PaperlessBilling              PaymentMethod  MonthlyCharges TotalCharges Churn  \n",
       "0          No              No  Month-to-month              Yes           Electronic check          29.850        29.85    No  \n",
       "1          No              No        One year               No               Mailed check          56.950       1889.5    No  \n",
       "2          No              No  Month-to-month              Yes               Mailed check          53.850       108.15   Yes  \n",
       "3          No              No        One year               No  Bank transfer (automatic)          42.300      1840.75    No  \n",
       "4          No              No  Month-to-month              Yes           Electronic check          70.700       151.65   Yes  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"../datasets/Telco-Customer-Churn.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7043, 21)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7043 entries, 0 to 7042\n",
      "Data columns (total 21 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   customerID        7043 non-null   object \n",
      " 1   gender            7043 non-null   object \n",
      " 2   SeniorCitizen     7043 non-null   int64  \n",
      " 3   Partner           7043 non-null   object \n",
      " 4   Dependents        7043 non-null   object \n",
      " 5   tenure            7043 non-null   int64  \n",
      " 6   PhoneService      7043 non-null   object \n",
      " 7   MultipleLines     7043 non-null   object \n",
      " 8   InternetService   7043 non-null   object \n",
      " 9   OnlineSecurity    7043 non-null   object \n",
      " 10  OnlineBackup      7043 non-null   object \n",
      " 11  DeviceProtection  7043 non-null   object \n",
      " 12  TechSupport       7043 non-null   object \n",
      " 13  StreamingTV       7043 non-null   object \n",
      " 14  StreamingMovies   7043 non-null   object \n",
      " 15  Contract          7043 non-null   object \n",
      " 16  PaperlessBilling  7043 non-null   object \n",
      " 17  PaymentMethod     7043 non-null   object \n",
      " 18  MonthlyCharges    7043 non-null   float64\n",
      " 19  TotalCharges      7043 non-null   object \n",
      " 20  Churn             7043 non-null   object \n",
      "dtypes: float64(1), int64(2), object(18)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TotalCharges must be a numeric variable\n",
    "df[\"TotalCharges\"] = pd.to_numeric(df[\"TotalCharges\"], errors='coerce')\n",
    "\n",
    "df[\"Churn\"] = df[\"Churn\"].apply(lambda x : 1 if x == \"Yes\" else 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##################################\n",
    "### TASK 1: EXPLORATORY DATA ANALYSIS\n",
    "##################################"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "################################## GENERAL PICTURE ##################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "##################### Shape #####################\n",
      "(7043, 21)\n",
      "##################### Types #####################\n",
      "customerID           object\n",
      "gender               object\n",
      "SeniorCitizen         int64\n",
      "Partner              object\n",
      "Dependents           object\n",
      "tenure                int64\n",
      "PhoneService         object\n",
      "MultipleLines        object\n",
      "InternetService      object\n",
      "OnlineSecurity       object\n",
      "OnlineBackup         object\n",
      "DeviceProtection     object\n",
      "TechSupport          object\n",
      "StreamingTV          object\n",
      "StreamingMovies      object\n",
      "Contract             object\n",
      "PaperlessBilling     object\n",
      "PaymentMethod        object\n",
      "MonthlyCharges      float64\n",
      "TotalCharges        float64\n",
      "Churn                 int64\n",
      "dtype: object\n",
      "##################### Head #####################\n",
      "   customerID  gender  SeniorCitizen Partner Dependents  tenure PhoneService     MultipleLines InternetService OnlineSecurity OnlineBackup DeviceProtection TechSupport  \\\n",
      "0  7590-VHVEG  Female              0     Yes         No       1           No  No phone service             DSL             No          Yes               No          No   \n",
      "1  5575-GNVDE    Male              0      No         No      34          Yes                No             DSL            Yes           No              Yes          No   \n",
      "2  3668-QPYBK    Male              0      No         No       2          Yes                No             DSL            Yes          Yes               No          No   \n",
      "3  7795-CFOCW    Male              0      No         No      45           No  No phone service             DSL            Yes           No              Yes         Yes   \n",
      "4  9237-HQITU  Female              0      No         No       2          Yes                No     Fiber optic             No           No               No          No   \n",
      "\n",
      "  StreamingTV StreamingMovies        Contract PaperlessBilling              PaymentMethod  MonthlyCharges  TotalCharges  Churn  \n",
      "0          No              No  Month-to-month              Yes           Electronic check          29.850        29.850      0  \n",
      "1          No              No        One year               No               Mailed check          56.950      1889.500      0  \n",
      "2          No              No  Month-to-month              Yes               Mailed check          53.850       108.150      1  \n",
      "3          No              No        One year               No  Bank transfer (automatic)          42.300      1840.750      0  \n",
      "4          No              No  Month-to-month              Yes           Electronic check          70.700       151.650      1  \n",
      "##################### Tail #####################\n",
      "      customerID  gender  SeniorCitizen Partner Dependents  tenure PhoneService     MultipleLines InternetService OnlineSecurity OnlineBackup DeviceProtection  \\\n",
      "7038  6840-RESVB    Male              0     Yes        Yes      24          Yes               Yes             DSL            Yes           No              Yes   \n",
      "7039  2234-XADUH  Female              0     Yes        Yes      72          Yes               Yes     Fiber optic             No          Yes              Yes   \n",
      "7040  4801-JZAZL  Female              0     Yes        Yes      11           No  No phone service             DSL            Yes           No               No   \n",
      "7041  8361-LTMKD    Male              1     Yes         No       4          Yes               Yes     Fiber optic             No           No               No   \n",
      "7042  3186-AJIEK    Male              0      No         No      66          Yes                No     Fiber optic            Yes           No              Yes   \n",
      "\n",
      "     TechSupport StreamingTV StreamingMovies        Contract PaperlessBilling              PaymentMethod  MonthlyCharges  TotalCharges  Churn  \n",
      "7038         Yes         Yes             Yes        One year              Yes               Mailed check          84.800      1990.500      0  \n",
      "7039          No         Yes             Yes        One year              Yes    Credit card (automatic)         103.200      7362.900      0  \n",
      "7040          No          No              No  Month-to-month              Yes           Electronic check          29.600       346.450      0  \n",
      "7041          No          No              No  Month-to-month              Yes               Mailed check          74.400       306.600      1  \n",
      "7042         Yes         Yes             Yes        Two year              Yes  Bank transfer (automatic)         105.650      6844.500      0  \n",
      "##################### NA #####################\n",
      "customerID           0\n",
      "gender               0\n",
      "SeniorCitizen        0\n",
      "Partner              0\n",
      "Dependents           0\n",
      "tenure               0\n",
      "PhoneService         0\n",
      "MultipleLines        0\n",
      "InternetService      0\n",
      "OnlineSecurity       0\n",
      "OnlineBackup         0\n",
      "DeviceProtection     0\n",
      "TechSupport          0\n",
      "StreamingTV          0\n",
      "StreamingMovies      0\n",
      "Contract             0\n",
      "PaperlessBilling     0\n",
      "PaymentMethod        0\n",
      "MonthlyCharges       0\n",
      "TotalCharges        11\n",
      "Churn                0\n",
      "dtype: int64\n",
      "##################### Quantiles #####################\n",
      "                0.000  0.050    0.500    0.950    0.990    1.000\n",
      "SeniorCitizen   0.000  0.000    0.000    1.000    1.000    1.000\n",
      "tenure          0.000  1.000   29.000   72.000   72.000   72.000\n",
      "MonthlyCharges 18.250 19.650   70.350  107.400  114.729  118.750\n",
      "TotalCharges   18.800 49.605 1397.475 6923.590 8039.883 8684.800\n",
      "Churn           0.000  0.000    0.000    1.000    1.000    1.000\n"
     ]
    }
   ],
   "source": [
    "def check_df(dataframe, head=5):\n",
    "    print(\"##################### Shape #####################\")\n",
    "    print(dataframe.shape)\n",
    "    print(\"##################### Types #####################\")\n",
    "    print(dataframe.dtypes)\n",
    "    print(\"##################### Head #####################\")\n",
    "    print(dataframe.head(head))\n",
    "    print(\"##################### Tail #####################\")\n",
    "    print(dataframe.tail(head))\n",
    "    print(\"##################### NA #####################\")\n",
    "    print(dataframe.isnull().sum())\n",
    "    print(\"##################### Quantiles #####################\")\n",
    "    # Filter to numeric columns before computing quantiles\n",
    "    numeric_cols = dataframe.select_dtypes(include=[np.number])\n",
    "    if not numeric_cols.empty:\n",
    "        print(numeric_cols.quantile([0, 0.05, 0.50, 0.95, 0.99, 1]).T)\n",
    "    else:\n",
    "        print(\"No numeric columns to display quantiles.\")\n",
    "\n",
    "# Use the function to check the DataFrame\n",
    "check_df(df)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "######################## CAPTURING NUMERIC AND CATEGORICAL VARIABLES ########################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Observations: 7043\n",
      "Variables: 21\n",
      "cat_cols: 17\n",
      "num_cols: 3\n",
      "cat_but_car: 1\n",
      "num_but_cat: 2\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['customerID']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def grab_col_names(dataframe, cat_th=10, car_th=20):\n",
    "    \"\"\"\n",
    "\n",
    "    Returns the names of categorical, numeric and categorical but cardinal variables in the data set.\n",
    "    Note Categorical variables include categorical variables with numeric appearance.\n",
    "\n",
    "    Parameters\n",
    "    ------\n",
    "        dataframe: dataframe\n",
    "                Dataframe to get variable names\n",
    "        cat_th: int, optional\n",
    "                class threshold for numeric but categorical variables\n",
    "        car_th: int, optional\n",
    "                class threshold for categorical but cardinal variables\n",
    "\n",
    "    Returns\n",
    "    ------\n",
    "        cat_cols: list\n",
    "                Categorical variable list\n",
    "        num_cols: list\n",
    "                Numeric variable list\n",
    "        cat_but_car: list\n",
    "                List of cardinal variables with categorical view\n",
    "\n",
    "    Examples\n",
    "    ------\n",
    "        import seaborn as sns\n",
    "        df = sns.load_dataset(\"iris\")\n",
    "        print(grab_col_names(df))\n",
    "\n",
    "\n",
    "    Notes\n",
    "    ------\n",
    "        cat_cols + num_cols + cat_but_car = total number of variables\n",
    "        num_but_cat is inside cat_cols.\n",
    "\n",
    "    \"\"\"\n",
    "    # cat_cols, cat_but_car\n",
    "    cat_cols = [col for col in dataframe.columns if dataframe[col].dtypes == \"O\"]\n",
    "    num_but_cat = [col for col in dataframe.columns if dataframe[col].nunique() < cat_th and dataframe[col].dtypes != \"O\"]\n",
    "    cat_but_car = [col for col in dataframe.columns if dataframe[col].nunique() > car_th and dataframe[col].dtypes == \"O\"]\n",
    "    cat_cols = cat_cols + num_but_cat\n",
    "    cat_cols = [col for col in cat_cols if col not in cat_but_car]\n",
    "\n",
    "    # num_cols\n",
    "    num_cols = [col for col in dataframe.columns if dataframe[col].dtypes != \"O\"]\n",
    "    num_cols = [col for col in num_cols if col not in num_but_cat]\n",
    "\n",
    "    print(f\"Observations: {dataframe.shape[0]}\")\n",
    "    print(f\"Variables: {dataframe.shape[1]}\")\n",
    "    print(f'cat_cols: {len(cat_cols)}')\n",
    "    print(f'num_cols: {len(num_cols)}')\n",
    "    print(f'cat_but_car: {len(cat_but_car)}')\n",
    "    print(f'num_but_cat: {len(num_but_cat)}')\n",
    "\n",
    "    return cat_cols, num_cols, cat_but_car\n",
    "\n",
    "\n",
    "cat_cols, num_cols, cat_but_car = grab_col_names(df)\n",
    "\n",
    "cat_cols\n",
    "num_cols\n",
    "cat_but_car\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "################################## ANALYSIS OF CATEGORICAL VARIABLES #############################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        gender  Ratio\n",
      "gender               \n",
      "Male      3555 50.476\n",
      "Female    3488 49.524\n",
      "##########################################\n",
      "         Partner  Ratio\n",
      "Partner                \n",
      "No          3641 51.697\n",
      "Yes         3402 48.303\n",
      "##########################################\n",
      "            Dependents  Ratio\n",
      "Dependents                   \n",
      "No                4933 70.041\n",
      "Yes               2110 29.959\n",
      "##########################################\n",
      "              PhoneService  Ratio\n",
      "PhoneService                     \n",
      "Yes                   6361 90.317\n",
      "No                     682  9.683\n",
      "##########################################\n",
      "                  MultipleLines  Ratio\n",
      "MultipleLines                         \n",
      "No                         3390 48.133\n",
      "Yes                        2971 42.184\n",
      "No phone service            682  9.683\n",
      "##########################################\n",
      "                 InternetService  Ratio\n",
      "InternetService                        \n",
      "Fiber optic                 3096 43.959\n",
      "DSL                         2421 34.375\n",
      "No                          1526 21.667\n",
      "##########################################\n",
      "                     OnlineSecurity  Ratio\n",
      "OnlineSecurity                            \n",
      "No                             3498 49.666\n",
      "Yes                            2019 28.667\n",
      "No internet service            1526 21.667\n",
      "##########################################\n",
      "                     OnlineBackup  Ratio\n",
      "OnlineBackup                            \n",
      "No                           3088 43.845\n",
      "Yes                          2429 34.488\n",
      "No internet service          1526 21.667\n",
      "##########################################\n",
      "                     DeviceProtection  Ratio\n",
      "DeviceProtection                            \n",
      "No                               3095 43.944\n",
      "Yes                              2422 34.389\n",
      "No internet service              1526 21.667\n",
      "##########################################\n",
      "                     TechSupport  Ratio\n",
      "TechSupport                            \n",
      "No                          3473 49.311\n",
      "Yes                         2044 29.022\n",
      "No internet service         1526 21.667\n",
      "##########################################\n",
      "                     StreamingTV  Ratio\n",
      "StreamingTV                            \n",
      "No                          2810 39.898\n",
      "Yes                         2707 38.435\n",
      "No internet service         1526 21.667\n",
      "##########################################\n",
      "                     StreamingMovies  Ratio\n",
      "StreamingMovies                            \n",
      "No                              2785 39.543\n",
      "Yes                             2732 38.790\n",
      "No internet service             1526 21.667\n",
      "##########################################\n",
      "                Contract  Ratio\n",
      "Contract                       \n",
      "Month-to-month      3875 55.019\n",
      "Two year            1695 24.066\n",
      "One year            1473 20.914\n",
      "##########################################\n",
      "                  PaperlessBilling  Ratio\n",
      "PaperlessBilling                         \n",
      "Yes                           4171 59.222\n",
      "No                            2872 40.778\n",
      "##########################################\n",
      "                           PaymentMethod  Ratio\n",
      "PaymentMethod                                  \n",
      "Electronic check                    2365 33.579\n",
      "Mailed check                        1612 22.888\n",
      "Bank transfer (automatic)           1544 21.922\n",
      "Credit card (automatic)             1522 21.610\n",
      "##########################################\n",
      "               SeniorCitizen  Ratio\n",
      "SeniorCitizen                      \n",
      "0                       5901 83.785\n",
      "1                       1142 16.215\n",
      "##########################################\n",
      "       Churn  Ratio\n",
      "Churn              \n",
      "0       5174 73.463\n",
      "1       1869 26.537\n",
      "##########################################\n"
     ]
    }
   ],
   "source": [
    "def cat_summary(dataframe, col_name, plot=False):\n",
    "    print(pd.DataFrame({col_name: dataframe[col_name].value_counts(),\n",
    "                        \"Ratio\": 100 * dataframe[col_name].value_counts() / len(dataframe)}))\n",
    "    print(\"##########################################\")\n",
    "    if plot:\n",
    "        sns.countplot(x=dataframe[col_name], data=dataframe)\n",
    "        plt.show()\n",
    "\n",
    "for col in cat_cols:\n",
    "    cat_summary(df, col)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "################################## ANALYSIS OF NUMERICAL VARIABLES ##############################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count   7043.000\n",
      "mean      32.371\n",
      "std       24.559\n",
      "min        0.000\n",
      "5%         1.000\n",
      "10%        2.000\n",
      "20%        6.000\n",
      "30%       12.000\n",
      "40%       20.000\n",
      "50%       29.000\n",
      "60%       40.000\n",
      "70%       50.000\n",
      "80%       60.000\n",
      "90%       69.000\n",
      "95%       72.000\n",
      "99%       72.000\n",
      "max       72.000\n",
      "Name: tenure, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count   7043.000\n",
      "mean      64.762\n",
      "std       30.090\n",
      "min       18.250\n",
      "5%        19.650\n",
      "10%       20.050\n",
      "20%       25.050\n",
      "30%       45.850\n",
      "40%       58.830\n",
      "50%       70.350\n",
      "60%       79.100\n",
      "70%       85.500\n",
      "80%       94.250\n",
      "90%      102.600\n",
      "95%      107.400\n",
      "99%      114.729\n",
      "max      118.750\n",
      "Name: MonthlyCharges, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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ABQUF1uu8vDxJv53WcrvdPtq7koq3VZ7bPB9R5/JBnX2PGpcP6lw+vFHn0i7r0wBz/PhxPfbYY+rdu7f1PKKcnBxFRER4TqJaNYWHhysnJ8caExsb6zEmMjLS6jtVgBk7dqxGjRpVoj09PV3BwcFe2Z+y+OORKPgGdS4f1Nn3qHH5oM7l41zqnJ+fX6pxPgswbrdbPXv2lDFG06dP99VmLMOGDdOQIUOs18VPs+zQoUO5P8wxIyND7du354FhPkSdywd19j1qXD6oc/nwRp2Lz6CciU8CTHF42bVrl1atWuURIKKiorR3716P8SdPntT+/fsVFRVljcnNzfUYU/y6eMwfOZ1OOZ3OEu0BAQEV8sNaUds931Dn8kGdfY8alw/qXD7Opc6lXc7rnwNTHF62b9+uFStWqHbt2h798fHxOnjwoDZs2GC1rVq1SkVFRWrTpo01Zu3atR7nwTIyMtSoUaNTnj4CAADnlzIfgTly5Ii+++476/WOHTu0adMmhYeHq06dOrrtttu0ceNGLV68WIWFhdZ1LeHh4QoMDFSTJk3UsWNH9evXTzNmzJDb7VZqaqp69eql6OhoSdIdd9yhUaNGqW/fvnrsscf09ddf66WXXtLEiRO9tNvnpsHj/z5tn9PfKK21dMnI5SoodJR53Tuf73IuUwMA4LxQ5gCzfv163XDDDdbr4utOkpOTNXLkSC1cuFCS1KJFC4/lVq9ereuvv16SNGfOHKWmpqpdu3by8/NTjx49NHnyZGtsaGio0tPTlZKSolatWumCCy7QiBEjuIUaAABIOosAc/3118sYc9r+P+srFh4errlz5/7pmEsvvVT/+c9/yjo9AABwHuBZSAAAwHYIMAAAwHYIMAAAwHYIMAAAwHYIMAAAwHYIMAAAwHYIMAAAwHYIMAAAwHYIMAAAwHYIMAAAwHYIMAAAwHYIMAAAwHYIMAAAwHYIMAAAwHYIMAAAwHYIMAAAwHYIMAAAwHYIMAAAwHYIMAAAwHYIMAAAwHYIMAAAwHYIMAAAwHYIMAAAwHYIMAAAwHYIMAAAwHYIMAAAwHYIMAAAwHYIMAAAwHYIMAAAwHYIMAAAwHYIMAAAwHYIMAAAwHYIMAAAwHYIMAAAwHYIMAAAwHYIMAAAwHYIMAAAwHYIMAAAwHYIMAAAwHYIMAAAwHYIMAAAwHYIMAAAwHYIMAAAwHYIMAAAwHYIMAAAwHYIMAAAwHYIMAAAwHYIMAAAwHbKHGDWrl2rrl27Kjo6Wg6HQwsWLPDoN8ZoxIgRqlOnjqpXr66EhARt377dY8z+/fuVlJQkl8ulsLAw9e3bV0eOHPEY8+WXX+qaa65RUFCQYmJilJaWVva9AwAAVVKZA8zRo0d12WWXadq0aafsT0tL0+TJkzVjxgxlZWUpJCREiYmJOn78uDUmKSlJmzdvVkZGhhYvXqy1a9fq/vvvt/rz8vLUoUMH1a9fXxs2bNALL7ygkSNH6tVXXz2LXQQAAFVNtbIu0KlTJ3Xq1OmUfcYYTZo0SU899ZRuvvlmSdJbb72lyMhILViwQL169dLWrVu1bNkyZWdnKy4uTpI0ZcoUde7cWS+++KKio6M1Z84cnThxQm+88YYCAwPVrFkzbdq0SRMmTPAIOgAA4PxU5gDzZ3bs2KGcnBwlJCRYbaGhoWrTpo0yMzPVq1cvZWZmKiwszAovkpSQkCA/Pz9lZWWpe/fuyszM1LXXXqvAwEBrTGJiosaNG6cDBw6oVq1aJbZdUFCggoIC63VeXp4kye12y+12e3M35fQ3p+/zMx7/lpW351pVFdeJevkWdfY9alw+qHP58EadS7usVwNMTk6OJCkyMtKjPTIy0urLyclRRESE5ySqVVN4eLjHmNjY2BLrKO47VYAZO3asRo0aVaI9PT1dwcHBZ7lHp5bW+sxjxsQVndW6lyxZclbLna8yMjIqegrnBerse9S4fFDn8nEudc7Pzy/VOK8GmIo0bNgwDRkyxHqdl5enmJgYdejQQS6Xy6vbumTk8tP2Of2MxsQVafh6PxUUOcq87q9HJp7L1M4bbrdbGRkZat++vQICAip6OlUWdfY9alw+qHP58Eadi8+gnIlXA0xUVJQkKTc3V3Xq1LHac3Nz1aJFC2vM3r17PZY7efKk9u/fby0fFRWl3NxcjzHFr4vH/JHT6ZTT6SzRHhAQ4PUf1oLCMweTgiJHqcb9Eb9YZeOL7y9Kos6+R43LB3UuH+dS59Iu59XPgYmNjVVUVJRWrlxpteXl5SkrK0vx8fGSpPj4eB08eFAbNmywxqxatUpFRUVq06aNNWbt2rUe58EyMjLUqFGjU54+AgAA55cyB5gjR45o06ZN2rRpk6TfLtzdtGmTdu/eLYfDoUGDBumZZ57RwoUL9dVXX6lPnz6Kjo7WLbfcIklq0qSJOnbsqH79+mndunX65JNPlJqaql69eik6OlqSdMcddygwMFB9+/bV5s2b9d577+mll17yOEUEAADOX2U+hbR+/XrdcMMN1uviUJGcnKxZs2bp0Ucf1dGjR3X//ffr4MGDuvrqq7Vs2TIFBQVZy8yZM0epqalq166d/Pz81KNHD02ePNnqDw0NVXp6ulJSUtSqVStdcMEFGjFiBLdQAwAASWcRYK6//noZc/pbhB0Oh0aPHq3Ro0efdkx4eLjmzp37p9u59NJL9Z///Kes0wMAAOcBnoUEAABshwADAABshwADAABshwADAABshwADAABshwADAABshwADAABshwADAABshwADAABshwADAABshwADAABshwADAABshwADAABshwADAABshwADAABshwADAABshwADAABshwADAABshwADAABshwADAABshwADAABshwADAABshwADAABshwADAABshwADAABshwADAABshwADAABshwADAABshwADAABshwADAABshwADAABshwADAABshwADAABshwADAABshwADAABshwADAABshwADAABshwADAABshwADAABshwADAABshwADAABshwADAABshwADAABshwADAABshwADAABshwADAABshwADAABshwADAABsx+sBprCwUMOHD1dsbKyqV6+uiy++WGPGjJExxhpjjNGIESNUp04dVa9eXQkJCdq+fbvHevbv36+kpCS5XC6FhYWpb9++OnLkiLenCwAAbMjrAWbcuHGaPn26pk6dqq1bt2rcuHFKS0vTlClTrDFpaWmaPHmyZsyYoaysLIWEhCgxMVHHjx+3xiQlJWnz5s3KyMjQ4sWLtXbtWt1///3eni4AALChat5e4aeffqqbb75ZXbp0kSQ1aNBA77zzjtatWyfpt6MvkyZN0lNPPaWbb75ZkvTWW28pMjJSCxYsUK9evbR161YtW7ZM2dnZiouLkyRNmTJFnTt31osvvqjo6GhvTxsAANiI1wNM27Zt9eqrr+rbb7/V3/72N33xxRf6+OOPNWHCBEnSjh07lJOTo4SEBGuZ0NBQtWnTRpmZmerVq5cyMzMVFhZmhRdJSkhIkJ+fn7KystS9e/cS2y0oKFBBQYH1Oi8vT5Lkdrvldru9uo9Of3P6Pj/j8W9ZeXuuVVVxnaiXb1Fn36PG5YM6lw9v1Lm0y3o9wDz++OPKy8tT48aN5e/vr8LCQj377LNKSkqSJOXk5EiSIiMjPZaLjIy0+nJychQREeE50WrVFB4ebo35o7Fjx2rUqFEl2tPT0xUcHHzO+/V7aa3PPGZMXNFZrXvJkiVntdz5KiMjo6KncF6gzr5HjcsHdS4f51Ln/Pz8Uo3zeoD5v//7P82ZM0dz585Vs2bNtGnTJg0aNEjR0dFKTk729uYsw4YN05AhQ6zXeXl5iomJUYcOHeRyuby6rUtGLj9tn9PPaExckYav91NBkaPM6/56ZOK5TO284Xa7lZGRofbt2ysgIKCip1NlUWffo8blgzqXD2/UufgMypl4PcAMHTpUjz/+uHr16iVJat68uXbt2qWxY8cqOTlZUVFRkqTc3FzVqVPHWi43N1ctWrSQJEVFRWnv3r0e6z158qT2799vLf9HTqdTTqezRHtAQIDXf1gLCs8cTAqKHKUa90f8YpWNL76/KIk6+x41Lh/UuXycS51Lu5zX70LKz8+Xn5/nav39/VVU9NspldjYWEVFRWnlypVWf15enrKyshQfHy9Jio+P18GDB7VhwwZrzKpVq1RUVKQ2bdp4e8oAAMBmvH4EpmvXrnr22WdVr149NWvWTJ9//rkmTJige++9V5LkcDg0aNAgPfPMM2rYsKFiY2M1fPhwRUdH65ZbbpEkNWnSRB07dlS/fv00Y8YMud1upaamqlevXtyBBAAAvB9gpkyZouHDh2vAgAHau3evoqOj9cADD2jEiBHWmEcffVRHjx7V/fffr4MHD+rqq6/WsmXLFBQUZI2ZM2eOUlNT1a5dO/n5+alHjx6aPHmyt6cLAABsyOsBpmbNmpo0aZImTZp02jEOh0OjR4/W6NGjTzsmPDxcc+fO9fb0AABAFcCzkAAAgO0QYAAAgO0QYAAAgO0QYAAAgO0QYAAAgO0QYAAAgO0QYAAAgO0QYAAAgO0QYAAAgO0QYAAAgO0QYAAAgO0QYAAAgO0QYAAAgO0QYAAAgO0QYAAAgO0QYAAAgO0QYAAAgO0QYAAAgO0QYAAAgO0QYAAAgO0QYAAAgO0QYAAAgO0QYAAAgO0QYAAAgO0QYAAAgO0QYAAAgO0QYAAAgO0QYAAAgO0QYAAAgO0QYAAAgO0QYAAAgO0QYAAAgO0QYAAAgO0QYAAAgO0QYAAAgO0QYAAAgO0QYAAAgO0QYAAAgO0QYAAAgO0QYAAAgO0QYAAAgO0QYAAAgO0QYAAAgO0QYAAAgO0QYAAAgO0QYAAAgO0QYAAAgO0QYAAAgO0QYAAAgO34JMD8/PPPuvPOO1W7dm1Vr15dzZs31/r1661+Y4xGjBihOnXqqHr16kpISND27ds91rF//34lJSXJ5XIpLCxMffv21ZEjR3wxXQAAYDNeDzAHDhzQVVddpYCAAC1dulRbtmzR+PHjVatWLWtMWlqaJk+erBkzZigrK0shISFKTEzU8ePHrTFJSUnavHmzMjIytHjxYq1du1b333+/t6cLAABsqJq3Vzhu3DjFxMRo5syZVltsbKz1f2OMJk2apKeeeko333yzJOmtt95SZGSkFixYoF69emnr1q1atmyZsrOzFRcXJ0maMmWKOnfurBdffFHR0dHenjYAALARrweYhQsXKjExUX//+9+1Zs0aXXTRRRowYID69esnSdqxY4dycnKUkJBgLRMaGqo2bdooMzNTvXr1UmZmpsLCwqzwIkkJCQny8/NTVlaWunfvXmK7BQUFKigosF7n5eVJktxut9xut1f30elvTt/nZzz+LStvz7WqKq4T9fIt6ux71Lh8UOfy4Y06l3ZZrweYH374QdOnT9eQIUP0xBNPKDs7WwMHDlRgYKCSk5OVk5MjSYqMjPRYLjIy0urLyclRRESE50SrVVN4eLg15o/Gjh2rUaNGlWhPT09XcHCwN3bNktb6zGPGxBWd1bqXLFlyVsudrzIyMip6CucF6ux71Lh8UOfycS51zs/PL9U4rweYoqIixcXF6bnnnpMktWzZUl9//bVmzJih5ORkb2/OMmzYMA0ZMsR6nZeXp5iYGHXo0EEul8ur27pk5PLT9jn9jMbEFWn4ej8VFDnKvO6vRyaey9TOG263WxkZGWrfvr0CAgIqejpVFnX2PWpcPqhz+fBGnYvPoJyJ1wNMnTp11LRpU4+2Jk2a6P3335ckRUVFSZJyc3NVp04da0xubq5atGhhjdm7d6/HOk6ePKn9+/dby/+R0+mU0+ks0R4QEOD1H9aCwjMHk4IiR6nG/RG/WGXji+8vSqLOvkeNywd1Lh/nUufSLuf1u5Cuuuoqbdu2zaPt22+/Vf369SX9dkFvVFSUVq5cafXn5eUpKytL8fHxkqT4+HgdPHhQGzZssMasWrVKRUVFatOmjbenDAAAbMbrR2AGDx6stm3b6rnnnlPPnj21bt06vfrqq3r11VclSQ6HQ4MGDdIzzzyjhg0bKjY2VsOHD1d0dLRuueUWSb8dsenYsaP69eunGTNmyO12KzU1Vb169eIOJAAA4P0Ac8UVV+iDDz7QsGHDNHr0aMXGxmrSpElKSkqyxjz66KM6evSo7r//fh08eFBXX321li1bpqCgIGvMnDlzlJqaqnbt2snPz089evTQ5MmTvT1dAABgQ14PMJJ000036aabbjptv8Ph0OjRozV69OjTjgkPD9fcuXN9MT0AAGBzPAsJAADYDgEGAADYDgEGAADYDgEGAADYDgEGAADYDgEGAADYDgEGAADYDgEGAADYDgEGAADYDgEGAADYDgEGAADYDgEGAADYDgEGAADYDgEGAADYDgEGAADYDgEGAADYDgEGAADYDgEGAADYDgEGAADYDgEGAADYDgEGAADYDgEGAADYTrWKngAAnC8aPP7vEm1Of6O01tIlI5eroNBxVuvd+XyXc50aYDscgQEAALZDgAEAALZDgAEAALZDgAEAALZDgAEAALZDgAEAALZDgAEAALZDgAEAALZDgAEAALZDgAEAALZDgAEAALbDs5AA4HdO9bwiAJUPR2AAAIDtEGAAAIDtcAoJAGzOl6e9dj7fxWfrBs4FR2AAAIDtcAQGAHBaHN1BZcURGAAAYDsEGAAAYDucQgJgS3xeC3B+4wgMAACwHQIMAACwHU4hAQAqhK9OA3J30/mBAFPJ8AsNAMCZcQoJAADYjs8DzPPPPy+Hw6FBgwZZbcePH1dKSopq166tGjVqqEePHsrNzfVYbvfu3erSpYuCg4MVERGhoUOH6uTJk76eLgAAsAGfBpjs7Gy98soruvTSSz3aBw8erEWLFmnevHlas2aN9uzZo1tvvdXqLywsVJcuXXTixAl9+umnevPNNzVr1iyNGDHCl9MFAAA24bMAc+TIESUlJem1115TrVq1rPZDhw7pn//8pyZMmKAbb7xRrVq10syZM/Xpp5/qs88+kySlp6dry5Ytmj17tlq0aKFOnTppzJgxmjZtmk6cOOGrKQMAAJvw2UW8KSkp6tKlixISEvTMM89Y7Rs2bJDb7VZCQoLV1rhxY9WrV0+ZmZm68sorlZmZqebNmysyMtIak5iYqP79+2vz5s1q2bJlie0VFBSooKDAep2XlydJcrvdcrvdXt03p785fZ+f8fi3svB2DSpa8f5Utf2qbCpznf/s99BOKut7hp2d6ue1Mv8sVyXeqHNpl/VJgHn33Xe1ceNGZWdnl+jLyclRYGCgwsLCPNojIyOVk5Njjfl9eCnuL+47lbFjx2rUqFEl2tPT0xUcHHw2u3Faaa3PPGZMXJFXt3mulixZUtFT8ImMjIyKnsJ5oTLWuTS/h3ZS2d4z7OzP3u8q489yVXQudc7Pzy/VOK8HmB9//FEPPfSQMjIyFBQU5O3Vn9awYcM0ZMgQ63VeXp5iYmLUoUMHuVwur27rkpHLT9vn9DMaE1ek4ev9VFDk8Op2z8XXIxMregpe5Xa7lZGRofbt2ysgIKCip1NlVeY6/9nvoZ1U1vcMOzvV+11l/lmuSrxR5+IzKGfi9QCzYcMG7d27V5dffrnVVlhYqLVr12rq1Klavny5Tpw4oYMHD3ochcnNzVVUVJQkKSoqSuvWrfNYb/FdSsVj/sjpdMrpdJZoDwgI8PoPa0Hhmd9kCoocpRpXXqrqL6wvvr8oqTLWuTL9fnlDZXvPsLOGw9NLtDn9jdJaSy2fXXVOdeYztUrnXN4zSruc1y/ibdeunb766itt2rTJ+oqLi1NSUpL1/4CAAK1cudJaZtu2bdq9e7fi4+MlSfHx8frqq6+0d+9ea0xGRoZcLpeaNm3q7SkDAACb8foRmJo1a+qSSy7xaAsJCVHt2rWt9r59+2rIkCEKDw+Xy+XSgw8+qPj4eF155ZWSpA4dOqhp06a66667lJaWppycHD311FNKSUk55VEWAABwfqmQRwlMnDhRfn5+6tGjhwoKCpSYmKiXX37Z6vf399fixYvVv39/xcfHKyQkRMnJyRo9enRFTBcAAFQy5RJgPvroI4/XQUFBmjZtmqZNm3baZerXr19l75wBAADnhmchAQAA2yHAAAAA2yHAAAAA26mQi3gBALCjBo//2yfr5fNlyo4jMAAAwHYIMAAAwHYIMAAAwHYIMAAAwHYIMAAAwHYIMAAAwHYIMAAAwHYIMAAAwHYIMAAAwHYIMAAAwHZ4lABgE776CHOJjzEHYD8cgQEAALZDgAEAALZDgAEAALZDgAEAALZDgAEAALbDXUiAl/nybiEAwG84AgMAAGyHAAMAAGyHAAMAAGyHAAMAAGyHAAMAAGyHAAMAAGyHAAMAAGyHAAMAAGyHAAMAAGyHAAMAAGyHAAMAAGyHAAMAAGyHAAMAAGyHp1HjvMQTowHA3jgCAwAAbIcAAwAAbIcAAwAAbIdrYACc9pogp79RWmvpkpHLVVDoKPN6dz7f5VynBgCnxBEYAABgOwQYAABgOwQYAABgOwQYAABgOwQYAABgOwQYAABgO9xGDcBneGQDAF/hCAwAALAdAgwAALAdrweYsWPH6oorrlDNmjUVERGhW265Rdu2bfMYc/z4caWkpKh27dqqUaOGevToodzcXI8xu3fvVpcuXRQcHKyIiAgNHTpUJ0+e9PZ0AQCADXk9wKxZs0YpKSn67LPPlJGRIbfbrQ4dOujo0aPWmMGDB2vRokWaN2+e1qxZoz179ujWW2+1+gsLC9WlSxedOHFCn376qd58803NmjVLI0aM8PZ0AQCADXn9It5ly5Z5vJ41a5YiIiK0YcMGXXvttTp06JD++c9/au7cubrxxhslSTNnzlSTJk302Wef6corr1R6erq2bNmiFStWKDIyUi1atNCYMWP02GOPaeTIkQoMDPT2tAEAgI34/C6kQ4cOSZLCw8MlSRs2bJDb7VZCQoI1pnHjxqpXr54yMzN15ZVXKjMzU82bN1dkZKQ1JjExUf3799fmzZvVsmXLEtspKChQQUGB9TovL0+S5Ha75Xa7vbpPTn9z+j4/4/FvZeHtGlS04v052/36s+8h/qey/jxXJdS4fFT2OleV9+hzfW8uy7I+DTBFRUUaNGiQrrrqKl1yySWSpJycHAUGBiosLMxjbGRkpHJycqwxvw8vxf3FfacyduxYjRo1qkR7enq6goODz3VXPKS1PvOYMXFFXt3muVqyZElFT8EnMjIyzmq50nwP8T+V7ee5KqLG5aOy1rmqvUef7XuzJOXn55dqnE8DTEpKir7++mt9/PHHvtyMJGnYsGEaMmSI9TovL08xMTHq0KGDXC6XV7d1ycjlp+1z+hmNiSvS8PV+KihyeHW75+LrkYkVPQWvcrvdysjIUPv27RUQEFDm5f/se4j/qaw/z1UJNS4f53Ody/P9/1zfm6X/nUE5E58FmNTUVC1evFhr165V3bp1rfaoqCidOHFCBw8e9DgKk5ubq6ioKGvMunXrPNZXfJdS8Zg/cjqdcjqdJdoDAgLOuoinU1B45h/+giJHqcaVF2/XoLI42+9vZfre2EFl+3muiqhx+Tgf61wR7//n8re3tMt5/S4kY4xSU1P1wQcfaNWqVYqNjfXob9WqlQICArRy5Uqrbdu2bdq9e7fi4+MlSfHx8frqq6+0d+9ea0xGRoZcLpeaNm3q7SkDAACb8foRmJSUFM2dO1cffvihatasaV2zEhoaqurVqys0NFR9+/bVkCFDFB4eLpfLpQcffFDx8fG68sorJUkdOnRQ06ZNdddddyktLU05OTl66qmnlJKScsqjLAAA4Pzi9QAzffp0SdL111/v0T5z5kzdfffdkqSJEyfKz89PPXr0UEFBgRITE/Xyyy9bY/39/bV48WL1799f8fHxCgkJUXJyskaPHu3t6QIAABvyeoAx5sy3qAUFBWnatGmaNm3aacfUr1+/yl2VDQAAvINnIQEAANshwAAAANshwAAAANshwAAAANshwAAAANshwAAAANshwAAAANshwAAAANshwAAAANshwAAAANshwAAAANshwAAAANshwAAAANshwAAAANshwAAAANshwAAAANshwAAAANshwAAAANshwAAAANshwAAAANshwAAAANupVtETgP01ePzfPlv3zue7+GzdAAD7IsCgUjtdOHL6G6W1li4ZuVwFhY5ynhUAoKJxCgkAANgOR2AAAKjCfHWav6JP8XMEBgAA2A4BBgAA2A4BBgAA2A7XwJwnfHmrMwAA5Y0jMAAAwHYIMAAAwHYIMAAAwHYIMAAAwHYIMAAAwHYIMAAAwHYIMAAAwHYIMAAAwHYIMAAAwHYIMAAAwHYIMAAAwHYIMAAAwHYIMAAAwHYIMAAAwHYIMAAAwHYIMAAAwHYIMAAAwHYIMAAAwHYIMAAAwHYqdYCZNm2aGjRooKCgILVp00br1q2r6CkBAIBKoNIGmPfee09DhgzR008/rY0bN+qyyy5TYmKi9u7dW9FTAwAAFazSBpgJEyaoX79+uueee9S0aVPNmDFDwcHBeuONNyp6agAAoIJVygBz4sQJbdiwQQkJCVabn5+fEhISlJmZWYEzAwAAlUG1ip7Aqfz6668qLCxUZGSkR3tkZKS++eabUy5TUFCggoIC6/WhQ4ckSfv375fb7fbq/KqdPHr6viKj/PwiVXP7qbDI4dXt4n+oc/mgzr5HjcsHdfa+ffv2lWhzu93Kz8/Xvn37FBAQcFbrPXz4sCTJGPOn4yplgDkbY8eO1ahRo0q0x8bGlvtc7ij3LZ6fqHP5oM6+R43LB3X2rgvG+3b9hw8fVmho6Gn7K2WAueCCC+Tv76/c3FyP9tzcXEVFRZ1ymWHDhmnIkCHW66KiIu3fv1+1a9eWw1F+aTsvL08xMTH68ccf5XK5ym275xvqXD6os+9R4/JBncuHN+psjNHhw4cVHR39p+MqZYAJDAxUq1attHLlSt1yyy2SfgskK1euVGpq6imXcTqdcjqdHm1hYWE+nunpuVwufknKAXUuH9TZ96hx+aDO5eNc6/xnR16KVcoAI0lDhgxRcnKy4uLi1Lp1a02aNElHjx7VPffcU9FTAwAAFazSBpjbb79d//3vfzVixAjl5OSoRYsWWrZsWYkLewEAwPmn0gYYSUpNTT3tKaPKyul06umnny5xOgveRZ3LB3X2PWpcPqhz+SjPOjvMme5TAgAAqGQq5QfZAQAA/BkCDAAAsB0CDAAAsB0CDAAAsB0CzFkYO3asrrjiCtWsWVMRERG65ZZbtG3bNo8xx48fV0pKimrXrq0aNWqoR48eJT5ZGGXz/PPPy+FwaNCgQVYbdfaOn3/+WXfeeadq166t6tWrq3nz5lq/fr3Vb4zRiBEjVKdOHVWvXl0JCQnavn17Bc7YfgoLCzV8+HDFxsaqevXquvjiizVmzBiP571Q57Jbu3atunbtqujoaDkcDi1YsMCjvzQ13b9/v5KSkuRyuRQWFqa+ffvqyJEj5bgXlduf1djtduuxxx5T8+bNFRISoujoaPXp00d79uzxWIcvakyAOQtr1qxRSkqKPvvsM2VkZMjtdqtDhw46evR/D3kcPHiwFi1apHnz5mnNmjXas2ePbr311gqctb1lZ2frlVde0aWXXurRTp3P3YEDB3TVVVcpICBAS5cu1ZYtWzR+/HjVqlXLGpOWlqbJkydrxowZysrKUkhIiBITE3X8+PEKnLm9jBs3TtOnT9fUqVO1detWjRs3TmlpaZoyZYo1hjqX3dGjR3XZZZdp2rRpp+wvTU2TkpK0efNmZWRkaPHixVq7dq3uv//+8tqFSu/Papyfn6+NGzdq+PDh2rhxo+bPn69t27apW7duHuN8UmODc7Z3714jyaxZs8YYY8zBgwdNQECAmTdvnjVm69atRpLJzMysqGna1uHDh03Dhg1NRkaGue6668xDDz1kjKHO3vLYY4+Zq6+++rT9RUVFJioqyrzwwgtW28GDB43T6TTvvPNOeUyxSujSpYu59957PdpuvfVWk5SUZIyhzt4gyXzwwQfW69LUdMuWLUaSyc7OtsYsXbrUOBwO8/PPP5fb3O3ijzU+lXXr1hlJZteuXcYY39WYIzBecOjQIUlSeHi4JGnDhg1yu91KSEiwxjRu3Fj16tVTZmZmhczRzlJSUtSlSxePekrU2VsWLlyouLg4/f3vf1dERIRatmyp1157zerfsWOHcnJyPOocGhqqNm3aUOcyaNu2rVauXKlvv/1WkvTFF1/o448/VqdOnSRRZ18oTU0zMzMVFhamuLg4a0xCQoL8/PyUlZVV7nOuCg4dOiSHw2E9j9BXNa7Un8RrB0VFRRo0aJCuuuoqXXLJJZKknJwcBQYGlniYZGRkpHJycipglvb17rvvauPGjcrOzi7RR52944cfftD06dM1ZMgQPfHEE8rOztbAgQMVGBio5ORkq5Z/fIwHdS6bxx9/XHl5eWrcuLH8/f1VWFioZ599VklJSZJEnX2gNDXNyclRRESER3+1atUUHh5O3c/C8ePH9dhjj6l3797Wwxx9VWMCzDlKSUnR119/rY8//riip1Ll/Pjjj3rooYeUkZGhoKCgip5OlVVUVKS4uDg999xzkqSWLVvq66+/1owZM5ScnFzBs6s6/u///k9z5szR3Llz1axZM23atEmDBg1SdHQ0dUaV4Ha71bNnTxljNH36dJ9vj1NI5yA1NVWLFy/W6tWrVbduXas9KipKJ06c0MGDBz3G5+bmKioqqpxnaV8bNmzQ3r17dfnll6tatWqqVq2a1qxZo8mTJ6tatWqKjIykzl5Qp04dNW3a1KOtSZMm2r17tyRZtfzj3V3UuWyGDh2qxx9/XL169VLz5s111113afDgwRo7dqwk6uwLpalpVFSU9u7d69F/8uRJ7d+/n7qXQXF42bVrlzIyMqyjL5LvakyAOQvGGKWmpuqDDz7QqlWrFBsb69HfqlUrBQQEaOXKlVbbtm3btHv3bsXHx5f3dG2rXbt2+uqrr7Rp0ybrKy4uTklJSdb/qfO5u+qqq0p8DMC3336r+vXrS5JiY2MVFRXlUee8vDxlZWVR5zLIz8+Xn5/nW66/v7+KiookUWdfKE1N4+PjdfDgQW3YsMEas2rVKhUVFalNmzblPmc7Kg4v27dv14oVK1S7dm2Pfp/V+Kwv/z2P9e/f34SGhpqPPvrI/PLLL9ZXfn6+NeYf//iHqVevnlm1apVZv369iY+PN/Hx8RU466rh93chGUOdvWHdunWmWrVq5tlnnzXbt283c+bMMcHBwWb27NnWmOeff96EhYWZDz/80Hz55Zfm5ptvNrGxsebYsWMVOHN7SU5ONhdddJFZvHix2bFjh5k/f7654IILzKOPPmqNoc5ld/jwYfP555+bzz//3EgyEyZMMJ9//rl1B0xpatqxY0fTsmVLk5WVZT7++GPTsGFD07t374rapUrnz2p84sQJ061bN1O3bl2zadMmj7+JBQUF1jp8UWMCzFmQdMqvmTNnWmOOHTtmBgwYYGrVqmWCg4NN9+7dzS+//FJxk64i/hhgqLN3LFq0yFxyySXG6XSaxo0bm1dffdWjv6ioyAwfPtxERkYap9Np2rVrZ7Zt21ZBs7WnvLw889BDD5l69eqZoKAg85e//MU8+eSTHm/y1LnsVq9efcr34+TkZGNM6Wq6b98+07t3b1OjRg3jcrnMPffcYw4fPlwBe1M5/VmNd+zYcdq/iatXr7bW4YsaO4z53cdAAgAA2ADXwAAAANshwAAAANshwAAAANshwAAAANshwAAAANshwAAAANshwAAAANshwADwOYfDoQULFpy2/6OPPpLD4SjxXKtzNWvWrBJPKwdQNRBggCrs7rvvlsPh0D/+8Y8SfSkpKXI4HLr77ru9tr2RI0eqRYsWXlvfmaxevVqdO3dW7dq1FRwcrKZNm+rhhx/Wzz//XG5zAFAxCDBAFRcTE6N3331Xx44ds9qOHz+uuXPnql69ehU4s3PzyiuvKCEhQVFRUXr//fe1ZcsWzZgxQ4cOHdL48eN9um232+3T9QM4MwIMUMVdfvnliomJ0fz58622+fPnq169emrZsqXVVlBQoIEDByoiIkJBQUG6+uqrlZ2dbfUXn+ZZuXKl4uLiFBwcrLZt21pPsp41a5ZGjRqlL774Qg6HQw6HQ7NmzbKW//XXX9W9e3cFBwerYcOGWrhw4Snne/ToUblcLv3rX//yaF+wYIFCQkJ0+PBh/fTTTxo4cKAGDhyoN954Q9dff70aNGiga6+9Vq+//rpGjBjhsezy5cvVpEkT1ahRQx07dtQvv/xi9WVnZ6t9+/a64IILFBoaquuuu04bN270WN7hcGj69Onq1q2bQkJC9Oyzz0qSnnnmGUVERKhmzZq677779Pjjj5c4AvX666+rSZMmCgoKUuPGjfXyyy9bfSdOnFBqaqrq1KmjoKAg1a9fX2PHjj1lXQD8wTk9SQlApZacnGxuvvlmM2HCBNOuXTurvV27dmbixInm5ptvth56N3DgQBMdHW2WLFliNm/ebJKTk02tWrXMvn37jDH/e6BbmzZtzEcffWQ2b95srrnmGtO2bVtjjDH5+fnm4YcfNs2aNSvxhHZJpm7dumbu3Llm+/btZuDAgaZGjRol1n3gwAFjjDH9+vUznTt39tiXbt26mT59+hhjjJkwYYKRZPbs2fOn+z9z5kwTEBBgEhISTHZ2ttmwYYNp0qSJueOOO6wxK1euNG+//bbZunWr2bJli+nbt6+JjIw0eXl51hhJJiIiwrzxxhvm+++/N7t27TKzZ882QUFB5o033jDbtm0zo0aNMi6Xy1x22WXWcrNnzzZ16tQx77//vvnhhx/M+++/b8LDw82sWbOMMca88MILJiYmxqxdu9bs3LnT/Oc//zFz584t1fcWON8RYIAqrDjA7N271zidTrNz506zc+dOExQUZP773/9aAebIkSMmICDAzJkzx1r2xIkTJjo62qSlpRlj/hcyVqxYYY3597//bSSZY8eOGWOMefrppz3+gBeTZJ566inr9ZEjR4wks3TpUo91FweYrKws4+/vbwWU3NxcU61aNfPRRx8ZY4zp37+/cblcZ9z/mTNnGknmu+++s9qmTZtmIiMjT7tMYWGhqVmzplm0aJHH/AcNGuQxrk2bNiYlJcWj7aqrrvLY/4svvrhEIBkzZoyJj483xhjz4IMPmhtvvNEUFRWdcV8AeOIUEnAeuPDCC9WlSxfNmjVLM2fOVJcuXXTBBRdY/d9//73cbreuuuoqqy0gIECtW7fW1q1bPdZ16aWXWv+vU6eOJGnv3r1nnMPvlwsJCZHL5Trtcq1bt1azZs305ptvSpJmz56t+vXr69prr5UkGWPkcDjOuE1JCg4O1sUXX+wx599vNzc3V/369VPDhg0VGhoql8ulI0eOaPfu3R7riYuL83i9bds2tW7dusS8ix09elTff/+9+vbtqxo1alhfzzzzjL7//ntJv11kvWnTJjVq1EgDBw5Uenp6qfYJgFStoicAoHzce++9Sk1NlSRNmzbtrNcTEBBg/b84RBQVFZVpueJl/2y5++67T9OmTdPjjz+umTNn6p577rG297e//U2HDh3SL7/8YoWosmzXGGO9Tk5O1r59+/TSSy+pfv36cjqdio+P14kTJzyWCwkJOeM+/t6RI0ckSa+99pratGnj0efv7y/pt+uTduzYoaVLl2rFihXq2bOnEhISSlz/A6AkjsAA54mOHTvqxIkTcrvdSkxM9Oi7+OKLFRgYqE8++cRqc7vdys7OVtOmTUu9jcDAQBUWFnplvnfeead27dqlyZMna8uWLUpOTrb6brvtNgUGBiotLe2Uy5bl82Q++eQTDRw4UJ07d1azZs3kdDr166+/nnG5Ro0aeVzkLMnjdWRkpKKjo/XDDz/or3/9q8dXbGysNc7lcun222/Xa6+9pvfee0/vv/++9u/fX+r5A+crjsAA5wl/f3/rdFDxEYBiISEh6t+/v4YOHarw8HDVq1dPaWlpys/PV9++fUu9jQYNGmjHjh3atGmT6tatq5o1a8rpdJ7VfGvVqqVbb71VQ4cOVYcOHVS3bl2rLyYmRhMnTlRqaqry8vLUp08fNWjQQD/99JPeeust1ahRo9S3Ujds2FBvv/224uLilJeXp6FDh6p69epnXO7BBx9Uv379FBcXp7Zt2+q9997Tl19+qb/85S/WmFGjRmngwIEKDQ1Vx44dVVBQoPXr1+vAgQMaMmSIJkyYoDp16qhly5by8/PTvHnzFBUVxYfvAaXAERjgPOJyueRyuU7Z9/zzz6tHjx666667dPnll+u7777T8uXLVatWrVKvv0ePHurYsaNuuOEGXXjhhXrnnXfOab59+/bViRMndO+995boGzBggNLT0/Xzzz+re/fuaty4se677z65XC498sgjpd7GP//5Tx04cECXX3657rrrLutW8jNJSkrSsGHD9Mgjj1ingu6++24FBQVZY+677z69/vrrmjlzppo3b67rrrtOs2bNso7A1KxZU2lpaYqLi9MVV1yhnTt3asmSJfLz460ZOBOH+f3JYACoRN5++20NHjxYe/bsUWBgYEVP54zat2+vqKgovf322xU9FaDK4xQSgEonPz9fv/zyi55//nk98MADlTK85Ofna8aMGUpMTJS/v7/eeecdrVixQhkZGRU9NeC8wHFKAJVOWlqaGjdurKioKA0bNqyip3NKDodDS5Ys0bXXXqtWrVpp0aJFev/995WQkFDRUwPOC5xCAgAAtsMRGAAAYDsEGAAAYDsEGAAAYDsEGAAAYDsEGAAAYDsEGAAAYDsEGAAAYDsEGAAAYDsEGAAAYDv/DytBbvb80BE4AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count   7032.000\n",
      "mean    2283.300\n",
      "std     2266.771\n",
      "min       18.800\n",
      "5%        49.605\n",
      "10%       84.600\n",
      "20%      267.070\n",
      "30%      551.995\n",
      "40%      944.170\n",
      "50%     1397.475\n",
      "60%     2048.950\n",
      "70%     3141.130\n",
      "80%     4475.410\n",
      "90%     5976.640\n",
      "95%     6923.590\n",
      "99%     8039.883\n",
      "max     8684.800\n",
      "Name: TotalCharges, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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J0ZkzZ7R37141b95cTqdTubm5Hn2Kl89234zD4Sg1/AQGBnq90AVFNhUUVkyAqcq/lL+Xiqg5vIsa+Tbq49uqen3KemwV9jkwL7/8sjp16qR27dqdt292drb8/PwUFhYmSYqNjdWaNWs8roNlZGSoefPmpV4+AgAA1Uu5A8zJkyeVnZ2t7OxsSdKePXuUnZ2t/fv3u/vk5eVp8eLFuvvuu0tsn5mZqVmzZumLL77Qt99+q7S0NI0dO1a33367O5wMHDhQdrtdQ4cO1fbt2/Xmm29q9uzZHpeIAABA9VXuS0ibNm3Sdddd514uDhWJiYlasGCBJGnRokUyxui2224rsb3D4dCiRYuUkpKigoICRUdHa+zYsR7hJDQ0VOnp6UpKSlKnTp3UoEEDTZo0iUeoAQCApAsIMN26dZMx536MePjw4WcNGx07dtS6devOu5+2bdtq7dq15Z0eAACoBvguJAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDnlDjBr1qxR3759FRERIZvNpiVLlnisHzJkiGw2m8erZ8+eHn2OHDmiQYMGKSQkRHXq1NHQoUN18uRJjz5btmzRNddco6CgIEVGRmrGjBnlPzoAAFAllTvAnDp1Su3atdOcOXPO2qdnz546ePCg+/XGG294rB80aJC2b9+ujIwMLV26VGvWrNHw4cPd6/Py8hQfH6+oqChlZWXpySefVEpKiubOnVve6QIAgCoooLwb9OrVS7169TpnH4fDIafTWeq6L7/8UsuXL9fGjRvVuXNnSdKzzz6r3r1766mnnlJERITS0tJ0+vRpzZs3T3a7Xa1bt1Z2drZmzpzpEXQAAED1VO4AUxarV69WWFiY6tatqz/+8Y967LHHVL9+fUlSZmam6tSp4w4vkhQXFyc/Pz+tX79eN954ozIzM9W1a1fZ7XZ3n4SEBD3xxBM6evSo6tatW2KfBQUFKigocC/n5eVJklwul1wul1eOq3gch5/xynjn2gfKr/i94z30XdTIt1Ef31Zd6lPW4/N6gOnZs6duuukmRUdHa/fu3frrX/+qXr16KTMzU/7+/srJyVFYWJjnJAICVK9ePeXk5EiScnJyFB0d7dEnPDzcva60AJOamqrJkyeXaE9PT1dwcLC3Dk+SNLVzkVfH+7Vly5ZV2NjVRUZGRmVPAedBjXwb9fFtVb0++fn5Zern9QAzYMAA989t2rRR27Zt1bRpU61evVrdu3f39u7cJkyYoOTkZPdyXl6eIiMjFR8fr5CQEK/sw+VyKSMjQxM3+amgyOaVMX9rW0pChYxbHRTXp0ePHgoMDKzs6aAU1Mi3UR/fVl3qU3wF5Xwq5BLSr1188cVq0KCBdu3ape7du8vpdOrQoUMefc6cOaMjR46475txOp3Kzc316FO8fLZ7axwOhxwOR4n2wMBArxe6oMimgsKKCTBV+Zfy91IRNYd3USPfRn18W1WvT1mPrcI/B+b777/X4cOH1ahRI0lSbGysjh07pqysLHefjz76SEVFRYqJiXH3WbNmjcd1sIyMDDVv3rzUy0cAAKB6KXeAOXnypLKzs5WdnS1J2rNnj7Kzs7V//36dPHlS48aN07p167R3716tXLlS119/vS655BIlJPxyaaRly5bq2bOnhg0bpg0bNujTTz/VyJEjNWDAAEVEREiSBg4cKLvdrqFDh2r79u168803NXv2bI9LRAAAoPoqd4DZtGmTOnTooA4dOkiSkpOT1aFDB02aNEn+/v7asmWL+vXrp0svvVRDhw5Vp06dtHbtWo/LO2lpaWrRooW6d++u3r176+qrr/b4jJfQ0FClp6drz5496tSpk+6//35NmjSJR6gBAICkC7gHplu3bjLm7I8Rf/jhh+cdo169elq4cOE5+7Rt21Zr164t7/QAAEA1wHchAQAAyyHAAAAAyyHAAAAAyyHAAAAAyyHAAAAAyyHAAAAAyyHAAAAAyyHAAAAAyyHAAAAAyyHAAAAAyyHAAAAAyyHAAAAAyyHAAAAAyyHAAAAAyyHAAAAAyyHAAAAAyyHAAAAAyyHAAAAAyyHAAAAAyyHAAAAAyyHAAAAAyyHAAAAAyyHAAAAAyyHAAAAAyyHAAAAAyyHAAAAAyyHAAAAAyyHAAAAAyyHAAAAAyyHAAAAAyyHAAAAAyyHAAAAAyyHAAAAAyyHAAAAAyyHAAAAAyyHAAAAAyyl3gFmzZo369u2riIgI2Ww2LVmyxL3O5XJp/PjxatOmjWrWrKmIiAjdcccd+uGHHzzGaNKkiWw2m8dr+vTpHn22bNmia665RkFBQYqMjNSMGTMu7AgBAECVU+4Ac+rUKbVr105z5swpsS4/P1+bN2/WxIkTtXnzZr377rvauXOn+vXrV6LvlClTdPDgQfdr1KhR7nV5eXmKj49XVFSUsrKy9OSTTyolJUVz584t73QBAEAVFFDeDXr16qVevXqVui40NFQZGRkebX//+9/VpUsX7d+/X40bN3a3165dW06ns9Rx0tLSdPr0ac2bN092u12tW7dWdna2Zs6cqeHDh5d3ygAAoIopd4Apr+PHj8tms6lOnToe7dOnT9fUqVPVuHFjDRw4UGPHjlVAwC/TyczMVNeuXWW32939ExIS9MQTT+jo0aOqW7duif0UFBSooKDAvZyXlyfpl8taLpfLK8dSPI7Dz3hlvHPtA+VX/N7xHvouauTbqI9vqy71KevxVWiA+fnnnzV+/HjddtttCgkJcbePHj1aHTt2VL169fTZZ59pwoQJOnjwoGbOnClJysnJUXR0tMdY4eHh7nWlBZjU1FRNnjy5RHt6erqCg4O9eVia2rnIq+P92rJlyyps7Orit2cB4XuokW+jPr6tqtcnPz+/TP0qLMC4XC79+c9/ljFGzz//vMe65ORk989t27aV3W7XX/7yF6WmpsrhcFzQ/iZMmOAxbl5eniIjIxUfH+8Rnv4XLpdLGRkZmrjJTwVFNq+M+VvbUhIqZNzqoLg+PXr0UGBgYGVPB6WgRr6N+vi26lKf4iso51MhAaY4vOzbt08fffTReQNETEyMzpw5o71796p58+ZyOp3Kzc316FO8fLb7ZhwOR6nhJzAw0OuFLiiyqaCwYgJMVf6l/L1URM3hXdTIt1Ef31bV61PWY/P658AUh5dvvvlGK1asUP369c+7TXZ2tvz8/BQWFiZJio2N1Zo1azyug2VkZKh58+alXj4CAADVS7nPwJw8eVK7du1yL+/Zs0fZ2dmqV6+eGjVqpJtvvlmbN2/W0qVLVVhYqJycHElSvXr1ZLfblZmZqfXr1+u6665T7dq1lZmZqbFjx+r22293h5OBAwdq8uTJGjp0qMaPH69t27Zp9uzZevrpp7102AAAwMrKHWA2bdqk6667zr1cfN9JYmKiUlJS9P7770uS2rdv77HdqlWr1K1bNzkcDi1atEgpKSkqKChQdHS0xo4d63H/SmhoqNLT05WUlKROnTqpQYMGmjRpEo9QAwAASRcQYLp16yZjzv4Y8bnWSVLHjh21bt268+6nbdu2Wrt2bXmnBwAAqgG+CwkAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFhOuQPMmjVr1LdvX0VERMhms2nJkiUe640xmjRpkho1aqQaNWooLi5O33zzjUefI0eOaNCgQQoJCVGdOnU0dOhQnTx50qPPli1bdM011ygoKEiRkZGaMWNG+Y8OAABUSeUOMKdOnVK7du00Z86cUtfPmDFDzzzzjF544QWtX79eNWvWVEJCgn7++Wd3n0GDBmn79u3KyMjQ0qVLtWbNGg0fPty9Pi8vT/Hx8YqKilJWVpaefPJJpaSkaO7cuRdwiAAAoKoJKO8GvXr1Uq9evUpdZ4zRrFmz9Mgjj+j666+XJL366qsKDw/XkiVLNGDAAH355Zdavny5Nm7cqM6dO0uSnn32WfXu3VtPPfWUIiIilJaWptOnT2vevHmy2+1q3bq1srOzNXPmTI+gAwAAqiev3gOzZ88e5eTkKC4uzt0WGhqqmJgYZWZmSpIyMzNVp04dd3iRpLi4OPn5+Wn9+vXuPl27dpXdbnf3SUhI0M6dO3X06FFvThkAAFhQuc/AnEtOTo4kKTw83KM9PDzcvS4nJ0dhYWGekwgIUL169Tz6REdHlxijeF3dunVL7LugoEAFBQXu5by8PEmSy+WSy+X6Xw7LrXgch5/xynjn2gfKr/i94z30XdTIt1Ef31Zd6lPW4/NqgKlMqampmjx5con29PR0BQcHe3VfUzsXeXW8X1u2bFmFjV1dZGRkVPYUcB7UyLdRH99W1euTn59fpn5eDTBOp1OSlJubq0aNGrnbc3Nz1b59e3efQ4cOeWx35swZHTlyxL290+lUbm6uR5/i5eI+vzVhwgQlJye7l/Py8hQZGan4+HiFhIT8bwf2/1wulzIyMjRxk58KimxeGfO3tqUkVMi41UFxfXr06KHAwMDKng5KQY18G/XxbdWlPsVXUM7HqwEmOjpaTqdTK1eudAeWvLw8rV+/XiNGjJAkxcbG6tixY8rKylKnTp0kSR999JGKiooUExPj7vPwww/L5XK5i5SRkaHmzZuXevlIkhwOhxwOR4n2wMBArxe6oMimgsKKCTBV+Zfy91IRNYd3USPfRn18W1WvT1mPrdw38Z48eVLZ2dnKzs6W9MuNu9nZ2dq/f79sNpvGjBmjxx57TO+//762bt2qO+64QxEREbrhhhskSS1btlTPnj01bNgwbdiwQZ9++qlGjhypAQMGKCIiQpI0cOBA2e12DR06VNu3b9ebb76p2bNne5xhAQAA1Ve5z8Bs2rRJ1113nXu5OFQkJiZqwYIFevDBB3Xq1CkNHz5cx44d09VXX63ly5crKCjIvU1aWppGjhyp7t27y8/PT/3799czzzzjXh8aGqr09HQlJSWpU6dOatCggSZNmsQj1AAAQNIFBJhu3brJmLM/hWOz2TRlyhRNmTLlrH3q1aunhQsXnnM/bdu21dq1a8s7PQAAUA3wXUgAAMByCDAAAMByCDAAAMByCDAAAMByCDAAAMByCDAAAMByCDAAAMByCDAAAMByCDAAAMByCDAAAMByCDAAAMByCDAAAMByCDAAAMByCDAAAMByCDAAAMByCDAAAMByCDAAAMByCDAAAMByCDAAAMByCDAAAMByCDAAAMByCDAAAMByCDAAAMByCDAAAMByCDAAAMByCDAAAMByAip7AvDU5KF/V8i4e6f3qZBxAQCoDJyBAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAluP1ANOkSRPZbLYSr6SkJElSt27dSqy75557PMbYv3+/+vTpo+DgYIWFhWncuHE6c+aMt6cKAAAsyutfJbBx40YVFha6l7dt26YePXrolltucbcNGzZMU6ZMcS8HBwe7fy4sLFSfPn3kdDr12Wef6eDBg7rjjjsUGBioxx9/3NvTBQAAFuT1ANOwYUOP5enTp6tp06a69tpr3W3BwcFyOp2lbp+enq4dO3ZoxYoVCg8PV/v27TV16lSNHz9eKSkpstvt3p4yAACwmAr9MsfTp0/r9ddfV3Jysmw2m7s9LS1Nr7/+upxOp/r27auJEye6z8JkZmaqTZs2Cg8Pd/dPSEjQiBEjtH37dnXo0KHUfRUUFKigoMC9nJeXJ0lyuVxyuVxeOZ7icRx+xivj/Z689R74suJjrA7HalXUyLdRH99WXepT1uOr0ACzZMkSHTt2TEOGDHG3DRw4UFFRUYqIiNCWLVs0fvx47dy5U++++64kKScnxyO8SHIv5+TknHVfqampmjx5con29PR0j0tU3jC1c5FXx/s9LFu2rLKn8LvJyMio7CngPKiRb6M+vq2q1yc/P79M/So0wLz88svq1auXIiIi3G3Dhw93/9ymTRs1atRI3bt31+7du9W0adML3teECROUnJzsXs7Ly1NkZKTi4+MVEhJyweP+msvlUkZGhiZu8lNBke38G/iQbSkJlT2FCldcnx49eigwMLCyp4NSUCPfRn18W3WpT/EVlPOpsACzb98+rVixwn1m5WxiYmIkSbt27VLTpk3ldDq1YcMGjz65ubmSdNb7ZiTJ4XDI4XCUaA8MDPR6oQuKbCootFaAqcq/7L9VETWHd1Ej30Z9fFtVr09Zj63CPgdm/vz5CgsLU58+fc7ZLzs7W5LUqFEjSVJsbKy2bt2qQ4cOuftkZGQoJCRErVq1qqjpAgAAC6mQMzBFRUWaP3++EhMTFRDw313s3r1bCxcuVO/evVW/fn1t2bJFY8eOVdeuXdW2bVtJUnx8vFq1aqXBgwdrxowZysnJ0SOPPKKkpKRSz7AAAIDqp0ICzIoVK7R//37dddddHu12u10rVqzQrFmzdOrUKUVGRqp///565JFH3H38/f21dOlSjRgxQrGxsapZs6YSExM9PjcGAABUbxUSYOLj42VMyUeNIyMj9fHHH593+6ioqGr11AwAACgfvgsJAABYDgEGAABYDgEGAABYDgEGAABYDgEGAABYToV+lQB8R5OH/l1hY++dfu4PKwQAwNs4AwMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACwnoLInAOtr8tC/K2zsvdP7VNjYAADr4gwMAACwHAIMAACwHAIMAACwHK8HmJSUFNlsNo9XixYt3Ot//vlnJSUlqX79+qpVq5b69++v3NxcjzH279+vPn36KDg4WGFhYRo3bpzOnDnj7akCAACLqpCbeFu3bq0VK1b8dycB/93N2LFj9e9//1uLFy9WaGioRo4cqZtuukmffvqpJKmwsFB9+vSR0+nUZ599poMHD+qOO+5QYGCgHn/88YqYLgAAsJgKCTABAQFyOp0l2o8fP66XX35ZCxcu1B//+EdJ0vz589WyZUutW7dOV1xxhdLT07Vjxw6tWLFC4eHhat++vaZOnarx48crJSVFdru9IqYMAAAspEICzDfffKOIiAgFBQUpNjZWqampaty4sbKysuRyuRQXF+fu26JFCzVu3FiZmZm64oorlJmZqTZt2ig8PNzdJyEhQSNGjND27dvVoUOHUvdZUFCggoIC93JeXp4kyeVyyeVyeeW4isdx+BmvjIfzK0/tivt6q97wPmrk26iPb6su9Snr8Xk9wMTExGjBggVq3ry5Dh48qMmTJ+uaa67Rtm3blJOTI7vdrjp16nhsEx4erpycHElSTk6OR3gpXl+87mxSU1M1efLkEu3p6ekKDg7+H4/K09TORV4dD2e3bNmycm+TkZFRATOBN1Ej30Z9fFtVr09+fn6Z+nk9wPTq1cv9c9u2bRUTE6OoqCi99dZbqlGjhrd35zZhwgQlJye7l/Py8hQZGan4+HiFhIR4ZR8ul0sZGRmauMlPBUU2r4yJc9uWklDmvsX16dGjhwIDAytwVrhQ1Mi3UR/fVl3qU3wF5Xwq/JN469Spo0svvVS7du1Sjx49dPr0aR07dszjLExubq77nhmn06kNGzZ4jFH8lFJp99UUczgccjgcJdoDAwO9XuiCIpsKCgkwv4cLqV1F1BzeRY18G/XxbVW9PmU9tgr/HJiTJ09q9+7datSokTp16qTAwECtXLnSvX7nzp3av3+/YmNjJUmxsbHaunWrDh065O6TkZGhkJAQtWrVqqKnCwAALMDrZ2AeeOAB9e3bV1FRUfrhhx/06KOPyt/fX7fddptCQ0M1dOhQJScnq169egoJCdGoUaMUGxurK664QpIUHx+vVq1aafDgwZoxY4ZycnL0yCOPKCkpqdQzLAAAoPrxeoD5/vvvddttt+nw4cNq2LChrr76aq1bt04NGzaUJD399NPy8/NT//79VVBQoISEBD333HPu7f39/bV06VKNGDFCsbGxqlmzphITEzVlyhRvTxUAAFiU1wPMokWLzrk+KChIc+bM0Zw5c87aJyoq6oKePgEAANUD34UEAAAshwADAAAshwADAAAshwADAAAshwADAAAshwADAAAshwADAAAshwADAAAshwADAAAshwADAAAshwADAAAshwADAAAshwADAAAshwADAAAshwADAAAshwADAAAshwADAAAshwADAAAsJ6CyJwCcS5OH/l3mvg5/oxldpMtSPlRBoe2cffdO7/O/Tg0AUIk4AwMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyHAAMAACyH70JCtVSe71gqL75nCQAqHmdgAACA5RBgAACA5RBgAACA5RBgAACA5RBgAACA5Xg9wKSmpuryyy9X7dq1FRYWphtuuEE7d+706NOtWzfZbDaP1z333OPRZ//+/erTp4+Cg4MVFhamcePG6cyZM96eLgAAsCCvP0b98ccfKykpSZdffrnOnDmjv/71r4qPj9eOHTtUs2ZNd79hw4ZpypQp7uXg4GD3z4WFherTp4+cTqc+++wzHTx4UHfccYcCAwP1+OOPe3vKAADAYrweYJYvX+6xvGDBAoWFhSkrK0tdu3Z1twcHB8vpdJY6Rnp6unbs2KEVK1YoPDxc7du319SpUzV+/HilpKTIbrd7e9qA11TUZ8zw+TIA8F8V/kF2x48flyTVq1fPoz0tLU2vv/66nE6n+vbtq4kTJ7rPwmRmZqpNmzYKDw93909ISNCIESO0fft2dejQocR+CgoKVFBQ4F7Oy8uTJLlcLrlcLq8cS/E4Dj/jlfHgXcV1qar18dbvcWUqPoaqcCxVEfXxbdWlPmU9PpsxpsL+ti8qKlK/fv107NgxffLJJ+72uXPnKioqShEREdqyZYvGjx+vLl266N1335UkDR8+XPv27dOHH37o3iY/P181a9bUsmXL1KtXrxL7SklJ0eTJk0u0L1y40OPyFAAA8F35+fkaOHCgjh8/rpCQkLP2q9AzMElJSdq2bZtHeJF+CSjF2rRpo0aNGql79+7avXu3mjZtekH7mjBhgpKTk93LeXl5ioyMVHx8/DnfgPJwuVzKyMjQxE1+KiiyeWVMeI/Dz2hq5yLqcwG2pST8Lvsp/jPUo0cPBQYG/i77RNlRH99WXepTfAXlfCoswIwcOVJLly7VmjVrdNFFF52zb0xMjCRp165datq0qZxOpzZs2ODRJzc3V5LOet+Mw+GQw+Eo0R4YGOj1QhcU2VRQyD+Qvor6lN/v/ZdhRfy5hPdQH99W1etT1mPzeoAxxmjUqFH65z//qdWrVys6Ovq822RnZ0uSGjVqJEmKjY3VtGnTdOjQIYWFhUmSMjIyFBISolatWnl7ygAqyG9vaHb4G83oIl2W8uH/HDK5qRmo3rweYJKSkrRw4UK99957ql27tnJyciRJoaGhqlGjhnbv3q2FCxeqd+/eql+/vrZs2aKxY8eqa9euatu2rSQpPj5erVq10uDBgzVjxgzl5OTokUceUVJSUqlnWQAAQPXi9QDz/PPPS/rlw+p+bf78+RoyZIjsdrtWrFihWbNm6dSpU4qMjFT//v31yCOPuPv6+/tr6dKlGjFihGJjY1WzZk0lJiZ6fG4MAO+pqEe/AaCiVMglpHOJjIzUxx9/fN5xoqKitGzZMm9NCwAAVCF8FxIAALCcCv8gOwCoCFa87MWNx4D3EGAAwOK8FeZKe0qM0AVfRYABgN+JFc8aAb6Ke2AAAIDlEGAAAIDlEGAAAIDlEGAAAIDlEGAAAIDl8BQSAOCsKvLJKR7Rxv+CMzAAAMByCDAAAMByuIQEAKgUFXV5iktT1QNnYAAAgOUQYAAAgOUQYAAAgOVwDwwAoErh0e/qgTMwAADAcggwAADAcggwAADAcggwAADAcggwAADAcngKCQCAMuLTg30HZ2AAAIDlcAYGAIBKVpYzOw5/oxldpMtSPlRBoa3MY1fVszucgQEAAJZDgAEAAJZDgAEAAJZDgAEAAJZDgAEAAJZDgAEAAJZDgAEAAJbD58AAAFCFVdVPD+YMDAAAsBwCDAAAsByfDjBz5sxRkyZNFBQUpJiYGG3YsKGypwQAAHyAzwaYN998U8nJyXr00Ue1efNmtWvXTgkJCTp06FBlTw0AAFQynw0wM2fO1LBhw3TnnXeqVatWeuGFFxQcHKx58+ZV9tQAAEAl88kAc/r0aWVlZSkuLs7d5ufnp7i4OGVmZlbizAAAgC/wyceof/zxRxUWFio8PNyjPTw8XF999VWp2xQUFKigoMC9fPz4cUnSkSNH5HK5vDIvl8ul/Px8Bbj8VFhU9q8yx+8joMgoP7+I+vgwauTbqI9v87X6HD58uELGPXHihCTJGHPOfj4ZYC5EamqqJk+eXKI9Ojq6EmaDyjKwsieA86JGvo36+DZfqk+Dv1Xs+CdOnFBoaOhZ1/tkgGnQoIH8/f2Vm5vr0Z6bmyun01nqNhMmTFBycrJ7uaioSEeOHFH9+vVls3knqebl5SkyMlLfffedQkJCvDImvIf6+D5q5Nuoj2+rLvUxxujEiROKiIg4Zz+fDDB2u12dOnXSypUrdcMNN0j6JZCsXLlSI0eOLHUbh8Mhh8Ph0VanTp0KmV9ISEiV/uWxOurj+6iRb6M+vq061OdcZ16K+WSAkaTk5GQlJiaqc+fO6tKli2bNmqVTp07pzjvvrOypAQCASuazAebWW2/Vf/7zH02aNEk5OTlq3769li9fXuLGXgAAUP34bICRpJEjR571klFlcDgcevTRR0tcqoJvoD6+jxr5Nurj26iPJ5s533NKAAAAPsYnP8gOAADgXAgwAADAcggwAADAcggwAADAcggw5TBnzhw1adJEQUFBiomJ0YYNGyp7SlVOamqqLr/8ctWuXVthYWG64YYbtHPnTo8+P//8s5KSklS/fn3VqlVL/fv3L/Gpzfv371efPn0UHByssLAwjRs3TmfOnPHos3r1anXs2FEOh0OXXHKJFixYUNGHV+VMnz5dNptNY8aMcbdRn8p14MAB3X777apfv75q1KihNm3aaNOmTe71xhhNmjRJjRo1Uo0aNRQXF6dvvvnGY4wjR45o0KBBCgkJUZ06dTR06FCdPHnSo8+WLVt0zTXXKCgoSJGRkZoxY8bvcnxWVlhYqIkTJyo6Olo1atRQ06ZNNXXqVI/v/KE+5WBQJosWLTJ2u93MmzfPbN++3QwbNszUqVPH5ObmVvbUqpSEhAQzf/58s23bNpOdnW169+5tGjdubE6ePOnuc88995jIyEizcuVKs2nTJnPFFVeYK6+80r3+zJkz5rLLLjNxcXHm888/N8uWLTMNGjQwEyZMcPf59ttvTXBwsElOTjY7duwwzz77rPH39zfLly//XY/XyjZs2GCaNGli2rZta+677z53O/WpPEeOHDFRUVFmyJAhZv369ebbb781H374odm1a5e7z/Tp001oaKhZsmSJ+eKLL0y/fv1MdHS0+emnn9x9evbsadq1a2fWrVtn1q5day655BJz2223udcfP37chIeHm0GDBplt27aZN954w9SoUcO8+OKLv+vxWs20adNM/fr1zdKlS82ePXvM4sWLTa1atczs2bPdfahP2RFgyqhLly4mKSnJvVxYWGgiIiJMampqJc6q6jt06JCRZD7++GNjjDHHjh0zgYGBZvHixe4+X375pZFkMjMzjTHGLFu2zPj5+ZmcnBx3n+eff96EhISYgoICY4wxDz74oGndurXHvm699VaTkJBQ0YdUJZw4ccI0a9bMZGRkmGuvvdYdYKhP5Ro/fry5+uqrz7q+qKjIOJ1O8+STT7rbjh07ZhwOh3njjTeMMcbs2LHDSDIbN2509/nggw+MzWYzBw4cMMYY89xzz5m6deu661W87+bNm3v7kKqUPn36mLvuusuj7aabbjKDBg0yxlCf8uISUhmcPn1aWVlZiouLc7f5+fkpLi5OmZmZlTizqu/48eOSpHr16kmSsrKy5HK5PGrRokULNW7c2F2LzMxMtWnTxuNTmxMSEpSXl6ft27e7+/x6jOI+1LNskpKS1KdPnxLvIfWpXO+//746d+6sW265RWFhYerQoYNeeukl9/o9e/YoJyfH470NDQ1VTEyMR33q1Kmjzp07u/vExcXJz89P69evd/fp2rWr7Ha7u09CQoJ27typo0ePVvRhWtaVV16plStX6uuvv5YkffHFF/rkk0/Uq1cvSdSnvHz6k3h9xY8//qjCwsISX2MQHh6ur776qpJmVfUVFRVpzJgxuuqqq3TZZZdJknJycmS320t8UWd4eLhycnLcfUqrVfG6c/XJy8vTTz/9pBo1alTEIVUJixYt0ubNm7Vx48YS66hP5fr222/1/PPPKzk5WX/961+1ceNGjR49Wna7XYmJie73t7T39tfvfVhYmMf6gIAA1atXz6NPdHR0iTGK19WtW7dCjs/qHnroIeXl5alFixby9/dXYWGhpk2bpkGDBkkS9SknAgx8VlJSkrZt26ZPPvmksqeC//fdd9/pvvvuU0ZGhoKCgip7OviNoqIide7cWY8//rgkqUOHDtq2bZteeOEFJSYmVvLs8NZbbyktLU0LFy5U69atlZ2drTFjxigiIoL6XAAuIZVBgwYN5O/vX+JJitzcXDmdzkqaVdU2cuRILV26VKtWrdJFF13kbnc6nTp9+rSOHTvm0f/XtXA6naXWqnjdufqEhITwv/tzyMrK0qFDh9SxY0cFBAQoICBAH3/8sZ555hkFBAQoPDyc+lSiRo0aqVWrVh5tLVu21P79+yX99/09199lTqdThw4d8lh/5swZHTlypFw1REnjxo3TQw89pAEDBqhNmzYaPHiwxo4dq9TUVEnUp7wIMGVgt9vVqVMnrVy50t1WVFSklStXKjY2thJnVvUYYzRy5Ej985//1EcffVTiNGinTp0UGBjoUYudO3dq//797lrExsZq69atHn/IMzIyFBIS4v7LPTY21mOM4j7U89y6d++urVu3Kjs72/3q3LmzBg0a5P6Z+lSeq666qsTHDnz99deKioqSJEVHR8vpdHq8t3l5eVq/fr1HfY4dO6asrCx3n48++khFRUWKiYlx91mzZo1cLpe7T0ZGhpo3b15lLk9UhPz8fPn5ef6z6+/vr6KiIknUp9wq+y5iq1i0aJFxOBxmwYIFZseOHWb48OGmTp06Hk9S4H83YsQIExoaalavXm0OHjzofuXn57v73HPPPaZx48bmo48+Mps2bTKxsbEmNjbWvb74Md34+HiTnZ1tli9fbho2bFjqY7rjxo0zX375pZkzZw6P6V6gXz+FZAz1qUwbNmwwAQEBZtq0aeabb74xaWlpJjg42Lz++uvuPtOnTzd16tQx7733ntmyZYu5/vrrS31Mt0OHDmb9+vXmk08+Mc2aNfN4TPfYsWMmPDzcDB482Gzbts0sWrTIBAcHV7nHdL0tMTHR/OEPf3A/Rv3uu++aBg0amAcffNDdh/qUHQGmHJ599lnTuHFjY7fbTZcuXcy6desqe0pVjqRSX/Pnz3f3+emnn8y9995r6tata4KDg82NN95oDh486DHO3r17Ta9evUyNGjVMgwYNzP33329cLpdHn1WrVpn27dsbu91uLr74Yo99oOx+G2CoT+X617/+ZS677DLjcDhMixYtzNy5cz3WFxUVmYkTJ5rw8HDjcDhM9+7dzc6dOz36HD582Nx2222mVq1aJiQkxNx5553mxIkTHn2++OILc/XVVxuHw2H+8Ic/mOnTp1f4sVldXl6eue+++0zjxo1NUFCQufjii83DDz/s8bgz9Sk7mzG/+ghAAAAAC+AeGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGACVxmazacmSJeXaZsGCBSW+7RpA9UOAASCbzXbOV0pKylm33bt3r2w2m7Kzs70yl1WrVql3796qX7++goOD1apVK91///06cOCAV8YHUDUQYADo4MGD7tesWbMUEhLi0fbAAw/8LvN48cUXFRcXJ6fTqXfeeUc7duzQCy+8oOPHj+tvf/tbhe771198B8D3EWAAyOl0ul+hoaGy2Wzu5bCwMM2cOVMXXXSRHA6H2rdvr+XLl7u3Lf7G8A4dOshms6lbt26SpI0bN6pHjx5q0KCBQkNDde2112rz5s1nncP333+v0aNHa/To0Zo3b566deumJk2aqGvXrvrHP/6hSZMmefT/8MMP1bJlS9WqVUs9e/bUwYMH3evKsm+bzabnn39e/fr1U82aNTVt2jRJ0mOPPaawsDDVrl1bd999tx566CG1b9/eY9t//OMfatmypYKCgtSiRQs999xz7nWnT5/WyJEj1ahRIwUFBSkqKkqpqallLwaAMiHAADin2bNn629/+5ueeuopbdmyRQkJCerXr5+++eYbSdKGDRskSStWrNDBgwf17rvvSpJOnDihxMREffLJJ1q3bp2aNWum3r1768SJE6XuZ/HixTp9+rQefPDBUtf/+r6X/Px8PfXUU3rttde0Zs0a7d+/3+MsUVn3nZKSohtvvFFbt27VXXfdpbS0NE2bNk1PPPGEsrKy1LhxYz3//PMe26SlpWnSpEmaNm2avvzySz3++OOaOHGiXnnlFUnSM888o/fff19vvfWWdu7cqbS0NDVp0qTsbziAsqnsb5ME4Fvmz59vQkND3csRERFm2rRpHn0uv/xyc++99xpjjNmzZ4+RZD7//PNzjltYWGhq165t/vWvf7nbJJl//vOfxhhjRowYYUJCQso0P0lm165d7rY5c+aY8PDwcu97zJgxHv1iYmJMUlKSR9tVV11l2rVr515u2rSpWbhwoUefqVOnmtjYWGOMMaNGjTJ//OMfTVFR0XmPBcCF4wwMgLPKy8vTDz/8oKuuusqj/aqrrtKXX355zm1zc3M1bNgwNWvWTKGhoQoJCdHJkye1f//+UvsbY2Sz2co0r+DgYDVt2tS93KhRIx06dKjc++7cubPH8s6dO9WlSxePtl8vnzp1Srt379bQoUNVq1Yt9+uxxx7T7t27JUlDhgxRdna2mjdvrtGjRys9Pb1MxwSgfAIqewIAqqbExEQdPnxYs2fPVlRUlBwOh2JjY3X69OlS+1966aU6fvy4Dh48qEaNGp1z7MDAQI9lm80mY0y5912zZs1yHdPJkyclSS+99JJiYmI81vn7+0uSOnbsqD179uiDDz7QihUr9Oc//1lxcXF6++23y7UvAOfGGRgAZxUSEqKIiAh9+umnHu2ffvqpWrVqJUmy2+2SpMLCwhJ9Ro8erd69e6t169ZyOBz68ccfz7qvm2++WXa7XTNmzCh1/bFjx8o87/Luu1jz5s21ceNGj7ZfL4eHhysiIkLffvutLrnkEo9X8c3M0i/v26233qqXXnpJb775pt555x0dOXKkzPMHcH6cgQFwTuPGjdOjjz6qpk2bqn379po/f76ys7OVlpYmSQoLC1ONGjW0fPlyXXTRRQoKClJoaKiaNWum1157TZ07d1ZeXp7GjRunGjVqnHU/kZGRevrppzVy5Ejl5eXpjjvuUJMmTfT999/r1VdfVa1atcr8KHV5911s1KhRGjZsmDp37qwrr7xSb775prZs2aKLL77Y3Wfy5MkaPXq0QkND1bNnTxUUFGjTpk06evSokpOTNXPmTDVq1EgdOnSQn5+fFi9eLKfTyYfvAV7GGRgA5zR69GglJyfr/vvvV5s2bbR8+XK9//77atasmSQpICBAzzzzjF588UVFRETo+uuvlyS9/PLLOnr0qDp27KjBgwdr9OjRCgsLO+e+7r33XqWnp+vAgQO68cYb1aJFC919990KCQkp12fRXMi+JWnQoEGaMGGCHnjgAfeloCFDhigoKMjd5+6779Y//vEPzZ8/X23atNG1116rBQsWuM/A1K5dWzNmzFDnzp11+eWXa+/evVq2bJn8/PjrFvAmm/n1hWMAgIcePXrI6XTqtddeq+ypAPgVLiEBwP/Lz8/XCy+8oISEBPn7++uNN97QihUrlJGRUdlTA/AbnIEBgP/3008/qW/fvvr888/1888/q3nz5nrkkUd00003VfbUAPwGAQYAAFgOd5UBAADLIcAAAADLIcAAAADLIcAAAADLIcAAAADLIcAAAADLIcAAAADLIcAAAADLIcAAAADL+T9RMCL13+OuhQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def num_summary(dataframe, numerical_col, plot=False):\n",
    "    quantiles = [0.05, 0.10, 0.20, 0.30, 0.40, 0.50, 0.60, 0.70, 0.80, 0.90, 0.95, 0.99]\n",
    "    print(dataframe[numerical_col].describe(quantiles).T)\n",
    "\n",
    "    if plot:\n",
    "        dataframe[numerical_col].hist(bins=20)\n",
    "        plt.xlabel(numerical_col)\n",
    "        plt.title(numerical_col)\n",
    "        plt.show()\n",
    "\n",
    "for col in num_cols:\n",
    "    num_summary(df, col, plot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "######################## ANALYSIS OF NUMERICAL VARIABLES BY TARGET ########################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       tenure\n",
      "Churn        \n",
      "0      37.570\n",
      "1      17.979\n",
      "\n",
      "\n",
      "       MonthlyCharges\n",
      "Churn                \n",
      "0              61.265\n",
      "1              74.441\n",
      "\n",
      "\n",
      "       TotalCharges\n",
      "Churn              \n",
      "0          2555.344\n",
      "1          1531.796\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "def target_summary_with_num(dataframe, target, numerical_col):\n",
    "    print(dataframe.groupby(target).agg({numerical_col: \"mean\"}), end=\"\\n\\n\\n\")\n",
    "\n",
    "for col in num_cols:\n",
    "    target_summary_with_num(df, \"Churn\", col)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "######################## ANALYSIS OF CATEGORICAL VARIABLES BY TARGET ########################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gender\n",
      "        TARGET_MEAN  Count  Ratio\n",
      "gender                           \n",
      "Female        0.269   3488 49.524\n",
      "Male          0.262   3555 50.476\n",
      "\n",
      "\n",
      "Partner\n",
      "         TARGET_MEAN  Count  Ratio\n",
      "Partner                           \n",
      "No             0.330   3641 51.697\n",
      "Yes            0.197   3402 48.303\n",
      "\n",
      "\n",
      "Dependents\n",
      "            TARGET_MEAN  Count  Ratio\n",
      "Dependents                           \n",
      "No                0.313   4933 70.041\n",
      "Yes               0.155   2110 29.959\n",
      "\n",
      "\n",
      "PhoneService\n",
      "              TARGET_MEAN  Count  Ratio\n",
      "PhoneService                           \n",
      "No                  0.249    682  9.683\n",
      "Yes                 0.267   6361 90.317\n",
      "\n",
      "\n",
      "MultipleLines\n",
      "                  TARGET_MEAN  Count  Ratio\n",
      "MultipleLines                              \n",
      "No                      0.250   3390 48.133\n",
      "No phone service        0.249    682  9.683\n",
      "Yes                     0.286   2971 42.184\n",
      "\n",
      "\n",
      "InternetService\n",
      "                 TARGET_MEAN  Count  Ratio\n",
      "InternetService                           \n",
      "DSL                    0.190   2421 34.375\n",
      "Fiber optic            0.419   3096 43.959\n",
      "No                     0.074   1526 21.667\n",
      "\n",
      "\n",
      "OnlineSecurity\n",
      "                     TARGET_MEAN  Count  Ratio\n",
      "OnlineSecurity                                \n",
      "No                         0.418   3498 49.666\n",
      "No internet service        0.074   1526 21.667\n",
      "Yes                        0.146   2019 28.667\n",
      "\n",
      "\n",
      "OnlineBackup\n",
      "                     TARGET_MEAN  Count  Ratio\n",
      "OnlineBackup                                  \n",
      "No                         0.399   3088 43.845\n",
      "No internet service        0.074   1526 21.667\n",
      "Yes                        0.215   2429 34.488\n",
      "\n",
      "\n",
      "DeviceProtection\n",
      "                     TARGET_MEAN  Count  Ratio\n",
      "DeviceProtection                              \n",
      "No                         0.391   3095 43.944\n",
      "No internet service        0.074   1526 21.667\n",
      "Yes                        0.225   2422 34.389\n",
      "\n",
      "\n",
      "TechSupport\n",
      "                     TARGET_MEAN  Count  Ratio\n",
      "TechSupport                                   \n",
      "No                         0.416   3473 49.311\n",
      "No internet service        0.074   1526 21.667\n",
      "Yes                        0.152   2044 29.022\n",
      "\n",
      "\n",
      "StreamingTV\n",
      "                     TARGET_MEAN  Count  Ratio\n",
      "StreamingTV                                   \n",
      "No                         0.335   2810 39.898\n",
      "No internet service        0.074   1526 21.667\n",
      "Yes                        0.301   2707 38.435\n",
      "\n",
      "\n",
      "StreamingMovies\n",
      "                     TARGET_MEAN  Count  Ratio\n",
      "StreamingMovies                               \n",
      "No                         0.337   2785 39.543\n",
      "No internet service        0.074   1526 21.667\n",
      "Yes                        0.299   2732 38.790\n",
      "\n",
      "\n",
      "Contract\n",
      "                TARGET_MEAN  Count  Ratio\n",
      "Contract                                 \n",
      "Month-to-month        0.427   3875 55.019\n",
      "One year              0.113   1473 20.914\n",
      "Two year              0.028   1695 24.066\n",
      "\n",
      "\n",
      "PaperlessBilling\n",
      "                  TARGET_MEAN  Count  Ratio\n",
      "PaperlessBilling                           \n",
      "No                      0.163   2872 40.778\n",
      "Yes                     0.336   4171 59.222\n",
      "\n",
      "\n",
      "PaymentMethod\n",
      "                           TARGET_MEAN  Count  Ratio\n",
      "PaymentMethod                                       \n",
      "Bank transfer (automatic)        0.167   1544 21.922\n",
      "Credit card (automatic)          0.152   1522 21.610\n",
      "Electronic check                 0.453   2365 33.579\n",
      "Mailed check                     0.191   1612 22.888\n",
      "\n",
      "\n",
      "SeniorCitizen\n",
      "               TARGET_MEAN  Count  Ratio\n",
      "SeniorCitizen                           \n",
      "0                    0.236   5901 83.785\n",
      "1                    0.417   1142 16.215\n",
      "\n",
      "\n",
      "Churn\n",
      "       TARGET_MEAN  Count  Ratio\n",
      "Churn                           \n",
      "0            0.000   5174 73.463\n",
      "1            1.000   1869 26.537\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "def target_summary_with_cat(dataframe, target, categorical_col):\n",
    "    print(categorical_col)\n",
    "    print(pd.DataFrame({\"TARGET_MEAN\": dataframe.groupby(categorical_col)[target].mean(),\n",
    "                        \"Count\": dataframe[categorical_col].value_counts(),\n",
    "                        \"Ratio\": 100 * dataframe[categorical_col].value_counts() / len(dataframe)}), end=\"\\n\\n\\n\")\n",
    "\n",
    "for col in cat_cols:\n",
    "    target_summary_with_cat(df, \"Churn\", col)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "################################## CORELATION ##################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Churn             1.000\n",
      "MonthlyCharges    0.193\n",
      "SeniorCitizen     0.151\n",
      "TotalCharges     -0.199\n",
      "tenure           -0.352\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Ensure 'Churn' is a numeric column. If not, you need to convert or handle it appropriately.\n",
    "# Assuming 'Churn' is already numeric and you want to compute correlations:\n",
    "numeric_cols = df.select_dtypes(include=[np.number])  # Select only numeric columns\n",
    "\n",
    "# If 'Churn' is not in numeric_cols, ensure it's added or checked.\n",
    "if 'Churn' not in numeric_cols:\n",
    "    print(\"Churn is not numeric. Please check the data type.\")\n",
    "else:\n",
    "    correlation_results = numeric_cols.corrwith(df[\"Churn\"]).sort_values(ascending=False)\n",
    "    print(correlation_results)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['tenure', 'MonthlyCharges', 'TotalCharges']"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>tenure</th>\n",
       "      <td>1.000</td>\n",
       "      <td>0.248</td>\n",
       "      <td>0.826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <td>0.248</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TotalCharges</th>\n",
       "      <td>0.826</td>\n",
       "      <td>0.651</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                tenure  MonthlyCharges  TotalCharges\n",
       "tenure           1.000           0.248         0.826\n",
       "MonthlyCharges   0.248           1.000         0.651\n",
       "TotalCharges     0.826           0.651         1.000"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[num_cols].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x1300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Correlation Matrix\n",
    "f, ax = plt.subplots(figsize=[18, 13])\n",
    "sns.heatmap(df[num_cols].corr(), annot=True, fmt=\".2f\", ax=ax, cmap=\"magma\")\n",
    "ax.set_title(\"Correlation Matrix\", fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Churn             1.000\n",
      "MonthlyCharges    0.193\n",
      "SeniorCitizen     0.151\n",
      "TotalCharges     -0.199\n",
      "tenure           -0.352\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# TotalChargers is highly correlated with monthly wages and tenure\n",
    "\n",
    "# Filter only numeric columns from DataFrame\n",
    "numeric_cols = df.select_dtypes(include=[np.number])\n",
    "\n",
    "# Check if the numeric column 'Churn' is included\n",
    "if 'Churn' in numeric_cols:\n",
    "    # Calculate correlation\n",
    "    correlation_results = numeric_cols.corrwith(df['Churn']).sort_values(ascending=False)\n",
    "    print(correlation_results)\n",
    "else:\n",
    "    print(\"'Churn' sayısal bir sütun değil veya listede yok.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##################################\n",
    "### TASK 2: FEATURE ENGINEERING\n",
    "##################################"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "################################## MISSING VALUE ANALYSIS ##################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              n_miss  ratio\n",
      "TotalCharges      11  0.160\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "customerID          0\n",
       "gender              0\n",
       "SeniorCitizen       0\n",
       "Partner             0\n",
       "Dependents          0\n",
       "tenure              0\n",
       "PhoneService        0\n",
       "MultipleLines       0\n",
       "InternetService     0\n",
       "OnlineSecurity      0\n",
       "OnlineBackup        0\n",
       "DeviceProtection    0\n",
       "TechSupport         0\n",
       "StreamingTV         0\n",
       "StreamingMovies     0\n",
       "Contract            0\n",
       "PaperlessBilling    0\n",
       "PaymentMethod       0\n",
       "MonthlyCharges      0\n",
       "TotalCharges        0\n",
       "Churn               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()\n",
    "\n",
    "def missing_values_table(dataframe, na_name=False):\n",
    "    na_columns = [col for col in dataframe.columns if dataframe[col].isnull().sum() > 0]\n",
    "    n_miss = dataframe[na_columns].isnull().sum().sort_values(ascending=False)\n",
    "    ratio = (dataframe[na_columns].isnull().sum() / dataframe.shape[0] * 100).sort_values(ascending=False)\n",
    "    missing_df = pd.concat([n_miss, np.round(ratio, 2)], axis=1, keys=['n_miss', 'ratio'])\n",
    "    print(missing_df, end=\"\\n\")\n",
    "    if na_name:\n",
    "        return na_columns\n",
    "\n",
    "na_columns = missing_values_table(df, na_name=True)\n",
    "\n",
    "df[\"TotalCharges\"].fillna(df[\"TotalCharges\"].median(), inplace=True)  # aylık ödenecek miktarlarıyla totalcharge doldurulailir (daya iyi olur denensin)  veya 11 değişken drop edilebilir\n",
    "\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "################################## BASE MODEL SETUP ##################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "dff = df.copy()\n",
    "cat_cols = [col for col in cat_cols if col not in [\"Churn\"]]\n",
    "cat_cols\n",
    "\n",
    "def one_hot_encoder(dataframe, categorical_cols, drop_first=False):\n",
    "    # Get dummies and apply one-hot encoding\n",
    "    dataframe = pd.get_dummies(dataframe, columns=categorical_cols, drop_first=drop_first)\n",
    "    # Convert all boolean columns to int\n",
    "    for col in dataframe.columns:\n",
    "        if dataframe[col].dtype == 'bool':\n",
    "            dataframe[col] = dataframe[col].astype(int)\n",
    "    return dataframe\n",
    "dff = one_hot_encoder(dff, cat_cols, drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>customerID</th>\n",
       "      <th>tenure</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>Churn</th>\n",
       "      <th>gender_Male</th>\n",
       "      <th>Partner_Yes</th>\n",
       "      <th>Dependents_Yes</th>\n",
       "      <th>PhoneService_Yes</th>\n",
       "      <th>MultipleLines_No phone service</th>\n",
       "      <th>MultipleLines_Yes</th>\n",
       "      <th>InternetService_Fiber optic</th>\n",
       "      <th>InternetService_No</th>\n",
       "      <th>OnlineSecurity_No internet service</th>\n",
       "      <th>OnlineSecurity_Yes</th>\n",
       "      <th>OnlineBackup_No internet service</th>\n",
       "      <th>OnlineBackup_Yes</th>\n",
       "      <th>DeviceProtection_No internet service</th>\n",
       "      <th>DeviceProtection_Yes</th>\n",
       "      <th>TechSupport_No internet service</th>\n",
       "      <th>TechSupport_Yes</th>\n",
       "      <th>StreamingTV_No internet service</th>\n",
       "      <th>StreamingTV_Yes</th>\n",
       "      <th>StreamingMovies_No internet service</th>\n",
       "      <th>StreamingMovies_Yes</th>\n",
       "      <th>Contract_One year</th>\n",
       "      <th>Contract_Two year</th>\n",
       "      <th>PaperlessBilling_Yes</th>\n",
       "      <th>PaymentMethod_Credit card (automatic)</th>\n",
       "      <th>PaymentMethod_Electronic check</th>\n",
       "      <th>PaymentMethod_Mailed check</th>\n",
       "      <th>SeniorCitizen_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7590-VHVEG</td>\n",
       "      <td>1</td>\n",
       "      <td>29.850</td>\n",
       "      <td>29.850</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5575-GNVDE</td>\n",
       "      <td>34</td>\n",
       "      <td>56.950</td>\n",
       "      <td>1889.500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3668-QPYBK</td>\n",
       "      <td>2</td>\n",
       "      <td>53.850</td>\n",
       "      <td>108.150</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7795-CFOCW</td>\n",
       "      <td>45</td>\n",
       "      <td>42.300</td>\n",
       "      <td>1840.750</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9237-HQITU</td>\n",
       "      <td>2</td>\n",
       "      <td>70.700</td>\n",
       "      <td>151.650</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   customerID  tenure  MonthlyCharges  TotalCharges  Churn  gender_Male  Partner_Yes  Dependents_Yes  PhoneService_Yes  MultipleLines_No phone service  \\\n",
       "0  7590-VHVEG       1          29.850        29.850      0            0            1               0                 0                               1   \n",
       "1  5575-GNVDE      34          56.950      1889.500      0            1            0               0                 1                               0   \n",
       "2  3668-QPYBK       2          53.850       108.150      1            1            0               0                 1                               0   \n",
       "3  7795-CFOCW      45          42.300      1840.750      0            1            0               0                 0                               1   \n",
       "4  9237-HQITU       2          70.700       151.650      1            0            0               0                 1                               0   \n",
       "\n",
       "   MultipleLines_Yes  InternetService_Fiber optic  InternetService_No  OnlineSecurity_No internet service  OnlineSecurity_Yes  OnlineBackup_No internet service  \\\n",
       "0                  0                            0                   0                                   0                   0                                 0   \n",
       "1                  0                            0                   0                                   0                   1                                 0   \n",
       "2                  0                            0                   0                                   0                   1                                 0   \n",
       "3                  0                            0                   0                                   0                   1                                 0   \n",
       "4                  0                            1                   0                                   0                   0                                 0   \n",
       "\n",
       "   OnlineBackup_Yes  DeviceProtection_No internet service  DeviceProtection_Yes  TechSupport_No internet service  TechSupport_Yes  StreamingTV_No internet service  \\\n",
       "0                 1                                     0                     0                                0                0                                0   \n",
       "1                 0                                     0                     1                                0                0                                0   \n",
       "2                 1                                     0                     0                                0                0                                0   \n",
       "3                 0                                     0                     1                                0                1                                0   \n",
       "4                 0                                     0                     0                                0                0                                0   \n",
       "\n",
       "   StreamingTV_Yes  StreamingMovies_No internet service  StreamingMovies_Yes  Contract_One year  Contract_Two year  PaperlessBilling_Yes  \\\n",
       "0                0                                    0                    0                  0                  0                     1   \n",
       "1                0                                    0                    0                  1                  0                     0   \n",
       "2                0                                    0                    0                  0                  0                     1   \n",
       "3                0                                    0                    0                  1                  0                     0   \n",
       "4                0                                    0                    0                  0                  0                     1   \n",
       "\n",
       "   PaymentMethod_Credit card (automatic)  PaymentMethod_Electronic check  PaymentMethod_Mailed check  SeniorCitizen_1  \n",
       "0                                      0                               1                           0                0  \n",
       "1                                      0                               0                           1                0  \n",
       "2                                      0                               0                           1                0  \n",
       "3                                      0                               0                           0                0  \n",
       "4                                      0                               1                           0                0  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dff.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.7847\n",
      "Recall: 0.6331\n",
      "Precision: 0.493\n",
      "F1: 0.5544\n",
      "Auc: 0.7292\n"
     ]
    }
   ],
   "source": [
    "y = dff[\"Churn\"]\n",
    "X = dff.drop([\"Churn\",\"customerID\"], axis=1)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=17)\n",
    "\n",
    "catboost_model = CatBoostClassifier(verbose=False, random_state=12345).fit(X_train, y_train)\n",
    "y_pred = catboost_model.predict(X_test)\n",
    "\n",
    "\n",
    "print(f\"Accuracy: {round(accuracy_score(y_pred, y_test), 4)}\")\n",
    "print(f\"Recall: {round(recall_score(y_pred,y_test),4)}\")\n",
    "print(f\"Precision: {round(precision_score(y_pred,y_test), 4)}\")\n",
    "print(f\"F1: {round(f1_score(y_pred,y_test), 4)}\")\n",
    "print(f\"Auc: {round(roc_auc_score(y_pred,y_test), 4)}\")\n",
    "\n",
    "# Accuracy: 0.7847\n",
    "# Recall: 0.6331\n",
    "# Precision: 0.493\n",
    "# F1: 0.5544\n",
    "# Auc: 0.7292"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "################################## OUTLIER ANALYSIS ##################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def outlier_thresholds(dataframe, col_name, q1=0.05, q3=0.95):\n",
    "    quartile1 = dataframe[col_name].quantile(q1)\n",
    "    quartile3 = dataframe[col_name].quantile(q3)\n",
    "    interquantile_range = quartile3 - quartile1\n",
    "    up_limit = quartile3 + 1.5 * interquantile_range\n",
    "    low_limit = quartile1 - 1.5 * interquantile_range\n",
    "    return low_limit, up_limit\n",
    "\n",
    "def check_outlier(dataframe, col_name):\n",
    "    low_limit, up_limit = outlier_thresholds(dataframe, col_name)\n",
    "    if dataframe[(dataframe[col_name] > up_limit) | (dataframe[col_name] < low_limit)].any(axis=None):\n",
    "        return True\n",
    "    else:\n",
    "        return False\n",
    "\n",
    "def replace_with_thresholds(dataframe, variable, q1=0.05, q3=0.95):\n",
    "    low_limit, up_limit = outlier_thresholds(dataframe, variable, q1=0.05, q3=0.95)\n",
    "    dataframe.loc[(dataframe[variable] < low_limit), variable] = low_limit\n",
    "    dataframe.loc[(dataframe[variable] > up_limit), variable] = up_limit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tenure False\n",
      "MonthlyCharges False\n",
      "TotalCharges False\n"
     ]
    }
   ],
   "source": [
    "# Outlier Analysis and Suppression\n",
    "for col in num_cols:\n",
    "    print(col, check_outlier(df, col))\n",
    "    if check_outlier(df, col):\n",
    "        replace_with_thresholds(df, col)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "################################## FEATURE EXTRACTION ##################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create an annual categorical variable from the variable Tenure\n",
    "df.loc[(df[\"tenure\"]>=0) & (df[\"tenure\"]<=12),\"NEW_TENURE_YEAR\"] = \"0-1 Year\"\n",
    "df.loc[(df[\"tenure\"]>12) & (df[\"tenure\"]<=24),\"NEW_TENURE_YEAR\"] = \"1-2 Year\"\n",
    "df.loc[(df[\"tenure\"]>24) & (df[\"tenure\"]<=36),\"NEW_TENURE_YEAR\"] = \"2-3 Year\"\n",
    "df.loc[(df[\"tenure\"]>36) & (df[\"tenure\"]<=48),\"NEW_TENURE_YEAR\"] = \"3-4 Year\"\n",
    "df.loc[(df[\"tenure\"]>48) & (df[\"tenure\"]<=60),\"NEW_TENURE_YEAR\"] = \"4-5 Year\"\n",
    "df.loc[(df[\"tenure\"]>60) & (df[\"tenure\"]<=72),\"NEW_TENURE_YEAR\"] = \"5-6 Year\"\n",
    "\n",
    "\n",
    "# Specify customers with a 1 or 2 year contract as Engaged\n",
    "df[\"NEW_Engaged\"] = df[\"Contract\"].apply(lambda x: 1 if x in [\"One year\",\"Two year\"] else 0)\n",
    "\n",
    "# People who do not receive any support, backup or protection\n",
    "df[\"NEW_noProt\"] = df.apply(lambda x: 1 if (x[\"OnlineBackup\"] != \"Yes\") or (x[\"DeviceProtection\"] != \"Yes\") or (x[\"TechSupport\"] != \"Yes\") else 0, axis=1)\n",
    "\n",
    "# Young customers with a monthly contract\n",
    "df[\"NEW_Young_Not_Engaged\"] = df.apply(lambda x: 1 if (x[\"NEW_Engaged\"] == 0) and (x[\"SeniorCitizen\"] == 0) else 0, axis=1)\n",
    "\n",
    "\n",
    "# Total number of services received by the person\n",
    "df['NEW_TotalServices'] = (df[['PhoneService', 'InternetService', 'OnlineSecurity',\n",
    "                                       'OnlineBackup', 'DeviceProtection', 'TechSupport',\n",
    "                                       'StreamingTV', 'StreamingMovies']]== 'Yes').sum(axis=1)\n",
    "\n",
    "\n",
    "# People who receive any streaming service\n",
    "df[\"NEW_FLAG_ANY_STREAMING\"] = df.apply(lambda x: 1 if (x[\"StreamingTV\"] == \"Yes\") or (x[\"StreamingMovies\"] == \"Yes\") else 0, axis=1)\n",
    "\n",
    "# Does the person make automatic payments?\n",
    "df[\"NEW_FLAG_AutoPayment\"] = df[\"PaymentMethod\"].apply(lambda x: 1 if x in [\"Bank transfer (automatic)\",\"Credit card (automatic)\"] else 0)\n",
    "\n",
    "# average monthly payment\n",
    "df[\"NEW_AVG_Charges\"] = df[\"TotalCharges\"] / (df[\"tenure\"] + 1)\n",
    "\n",
    "# Increase in current price compared to average price\n",
    "df[\"NEW_Increase\"] = df[\"NEW_AVG_Charges\"] / df[\"MonthlyCharges\"]\n",
    "\n",
    "# Fee per service\n",
    "df[\"NEW_AVG_Service_Fee\"] = df[\"MonthlyCharges\"] / (df['NEW_TotalServices'] + 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>customerID</th>\n",
       "      <th>gender</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>tenure</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>InternetService</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>OnlineBackup</th>\n",
       "      <th>DeviceProtection</th>\n",
       "      <th>TechSupport</th>\n",
       "      <th>StreamingTV</th>\n",
       "      <th>StreamingMovies</th>\n",
       "      <th>Contract</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>PaymentMethod</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>Churn</th>\n",
       "      <th>NEW_TENURE_YEAR</th>\n",
       "      <th>NEW_Engaged</th>\n",
       "      <th>NEW_noProt</th>\n",
       "      <th>NEW_Young_Not_Engaged</th>\n",
       "      <th>NEW_TotalServices</th>\n",
       "      <th>NEW_FLAG_ANY_STREAMING</th>\n",
       "      <th>NEW_FLAG_AutoPayment</th>\n",
       "      <th>NEW_AVG_Charges</th>\n",
       "      <th>NEW_Increase</th>\n",
       "      <th>NEW_AVG_Service_Fee</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7590-VHVEG</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>29.850</td>\n",
       "      <td>29.850</td>\n",
       "      <td>0</td>\n",
       "      <td>0-1 Year</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14.925</td>\n",
       "      <td>0.500</td>\n",
       "      <td>14.925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5575-GNVDE</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>34</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>56.950</td>\n",
       "      <td>1889.500</td>\n",
       "      <td>0</td>\n",
       "      <td>2-3 Year</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>53.986</td>\n",
       "      <td>0.948</td>\n",
       "      <td>14.238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3668-QPYBK</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>53.850</td>\n",
       "      <td>108.150</td>\n",
       "      <td>1</td>\n",
       "      <td>0-1 Year</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>36.050</td>\n",
       "      <td>0.669</td>\n",
       "      <td>13.463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7795-CFOCW</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>45</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>42.300</td>\n",
       "      <td>1840.750</td>\n",
       "      <td>0</td>\n",
       "      <td>3-4 Year</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>40.016</td>\n",
       "      <td>0.946</td>\n",
       "      <td>10.575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9237-HQITU</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>70.700</td>\n",
       "      <td>151.650</td>\n",
       "      <td>1</td>\n",
       "      <td>0-1 Year</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50.550</td>\n",
       "      <td>0.715</td>\n",
       "      <td>35.350</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   customerID  gender  SeniorCitizen Partner Dependents  tenure PhoneService     MultipleLines InternetService OnlineSecurity OnlineBackup DeviceProtection TechSupport  \\\n",
       "0  7590-VHVEG  Female              0     Yes         No       1           No  No phone service             DSL             No          Yes               No          No   \n",
       "1  5575-GNVDE    Male              0      No         No      34          Yes                No             DSL            Yes           No              Yes          No   \n",
       "2  3668-QPYBK    Male              0      No         No       2          Yes                No             DSL            Yes          Yes               No          No   \n",
       "3  7795-CFOCW    Male              0      No         No      45           No  No phone service             DSL            Yes           No              Yes         Yes   \n",
       "4  9237-HQITU  Female              0      No         No       2          Yes                No     Fiber optic             No           No               No          No   \n",
       "\n",
       "  StreamingTV StreamingMovies        Contract PaperlessBilling              PaymentMethod  MonthlyCharges  TotalCharges  Churn NEW_TENURE_YEAR  NEW_Engaged  NEW_noProt  \\\n",
       "0          No              No  Month-to-month              Yes           Electronic check          29.850        29.850      0        0-1 Year            0           1   \n",
       "1          No              No        One year               No               Mailed check          56.950      1889.500      0        2-3 Year            1           1   \n",
       "2          No              No  Month-to-month              Yes               Mailed check          53.850       108.150      1        0-1 Year            0           1   \n",
       "3          No              No        One year               No  Bank transfer (automatic)          42.300      1840.750      0        3-4 Year            1           1   \n",
       "4          No              No  Month-to-month              Yes           Electronic check          70.700       151.650      1        0-1 Year            0           1   \n",
       "\n",
       "   NEW_Young_Not_Engaged  NEW_TotalServices  NEW_FLAG_ANY_STREAMING  NEW_FLAG_AutoPayment  NEW_AVG_Charges  NEW_Increase  NEW_AVG_Service_Fee  \n",
       "0                      1                  1                       0                     0           14.925         0.500               14.925  \n",
       "1                      0                  3                       0                     0           53.986         0.948               14.238  \n",
       "2                      1                  3                       0                     0           36.050         0.669               13.463  \n",
       "3                      0                  3                       0                     1           40.016         0.946               10.575  \n",
       "4                      1                  1                       0                     0           50.550         0.715               35.350  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7043, 31)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "################################## ENCODING ##################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Observations: 7043\n",
      "Variables: 31\n",
      "cat_cols: 24\n",
      "num_cols: 6\n",
      "cat_but_car: 1\n",
      "num_but_cat: 8\n"
     ]
    }
   ],
   "source": [
    "# Separation of variables by type\n",
    "cat_cols, num_cols, cat_but_car = grab_col_names(df)\n",
    "\n",
    "# ETIKET KODLAMASI\n",
    "def label_encoder(dataframe, binary_col):\n",
    "    labelencoder = LabelEncoder()\n",
    "    dataframe[binary_col] = labelencoder.fit_transform(dataframe[binary_col])\n",
    "    return dataframe\n",
    "\n",
    "binary_cols = [col for col in df.columns if df[col].dtypes == \"O\" and df[col].nunique() == 2]\n",
    "binary_cols\n",
    "\n",
    "for col in binary_cols:\n",
    "    df = label_encoder(df, col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>customerID</th>\n",
       "      <th>gender</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>tenure</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>Churn</th>\n",
       "      <th>NEW_TotalServices</th>\n",
       "      <th>NEW_AVG_Charges</th>\n",
       "      <th>NEW_Increase</th>\n",
       "      <th>NEW_AVG_Service_Fee</th>\n",
       "      <th>MultipleLines_No phone service</th>\n",
       "      <th>MultipleLines_Yes</th>\n",
       "      <th>InternetService_Fiber optic</th>\n",
       "      <th>InternetService_No</th>\n",
       "      <th>OnlineSecurity_No internet service</th>\n",
       "      <th>OnlineSecurity_Yes</th>\n",
       "      <th>OnlineBackup_No internet service</th>\n",
       "      <th>OnlineBackup_Yes</th>\n",
       "      <th>DeviceProtection_No internet service</th>\n",
       "      <th>DeviceProtection_Yes</th>\n",
       "      <th>TechSupport_No internet service</th>\n",
       "      <th>TechSupport_Yes</th>\n",
       "      <th>StreamingTV_No internet service</th>\n",
       "      <th>StreamingTV_Yes</th>\n",
       "      <th>StreamingMovies_No internet service</th>\n",
       "      <th>StreamingMovies_Yes</th>\n",
       "      <th>Contract_One year</th>\n",
       "      <th>Contract_Two year</th>\n",
       "      <th>PaymentMethod_Credit card (automatic)</th>\n",
       "      <th>PaymentMethod_Electronic check</th>\n",
       "      <th>PaymentMethod_Mailed check</th>\n",
       "      <th>NEW_TENURE_YEAR_1-2 Year</th>\n",
       "      <th>NEW_TENURE_YEAR_2-3 Year</th>\n",
       "      <th>NEW_TENURE_YEAR_3-4 Year</th>\n",
       "      <th>NEW_TENURE_YEAR_4-5 Year</th>\n",
       "      <th>NEW_TENURE_YEAR_5-6 Year</th>\n",
       "      <th>SeniorCitizen_1</th>\n",
       "      <th>NEW_Engaged_1</th>\n",
       "      <th>NEW_noProt_1</th>\n",
       "      <th>NEW_Young_Not_Engaged_1</th>\n",
       "      <th>NEW_FLAG_ANY_STREAMING_1</th>\n",
       "      <th>NEW_FLAG_AutoPayment_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7590-VHVEG</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>29.850</td>\n",
       "      <td>29.850</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>14.925</td>\n",
       "      <td>0.500</td>\n",
       "      <td>14.925</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5575-GNVDE</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>56.950</td>\n",
       "      <td>1889.500</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>53.986</td>\n",
       "      <td>0.948</td>\n",
       "      <td>14.238</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3668-QPYBK</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>53.850</td>\n",
       "      <td>108.150</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>36.050</td>\n",
       "      <td>0.669</td>\n",
       "      <td>13.463</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7795-CFOCW</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>42.300</td>\n",
       "      <td>1840.750</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>40.016</td>\n",
       "      <td>0.946</td>\n",
       "      <td>10.575</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9237-HQITU</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>70.700</td>\n",
       "      <td>151.650</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>50.550</td>\n",
       "      <td>0.715</td>\n",
       "      <td>35.350</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   customerID  gender  Partner  Dependents  tenure  PhoneService  PaperlessBilling  MonthlyCharges  TotalCharges  Churn  NEW_TotalServices  NEW_AVG_Charges  \\\n",
       "0  7590-VHVEG       0        1           0       1             0                 1          29.850        29.850      0                  1           14.925   \n",
       "1  5575-GNVDE       1        0           0      34             1                 0          56.950      1889.500      0                  3           53.986   \n",
       "2  3668-QPYBK       1        0           0       2             1                 1          53.850       108.150      1                  3           36.050   \n",
       "3  7795-CFOCW       1        0           0      45             0                 0          42.300      1840.750      0                  3           40.016   \n",
       "4  9237-HQITU       0        0           0       2             1                 1          70.700       151.650      1                  1           50.550   \n",
       "\n",
       "   NEW_Increase  NEW_AVG_Service_Fee  MultipleLines_No phone service  MultipleLines_Yes  InternetService_Fiber optic  InternetService_No  \\\n",
       "0         0.500               14.925                               1                  0                            0                   0   \n",
       "1         0.948               14.238                               0                  0                            0                   0   \n",
       "2         0.669               13.463                               0                  0                            0                   0   \n",
       "3         0.946               10.575                               1                  0                            0                   0   \n",
       "4         0.715               35.350                               0                  0                            1                   0   \n",
       "\n",
       "   OnlineSecurity_No internet service  OnlineSecurity_Yes  OnlineBackup_No internet service  OnlineBackup_Yes  DeviceProtection_No internet service  \\\n",
       "0                                   0                   0                                 0                 1                                     0   \n",
       "1                                   0                   1                                 0                 0                                     0   \n",
       "2                                   0                   1                                 0                 1                                     0   \n",
       "3                                   0                   1                                 0                 0                                     0   \n",
       "4                                   0                   0                                 0                 0                                     0   \n",
       "\n",
       "   DeviceProtection_Yes  TechSupport_No internet service  TechSupport_Yes  StreamingTV_No internet service  StreamingTV_Yes  StreamingMovies_No internet service  \\\n",
       "0                     0                                0                0                                0                0                                    0   \n",
       "1                     1                                0                0                                0                0                                    0   \n",
       "2                     0                                0                0                                0                0                                    0   \n",
       "3                     1                                0                1                                0                0                                    0   \n",
       "4                     0                                0                0                                0                0                                    0   \n",
       "\n",
       "   StreamingMovies_Yes  Contract_One year  Contract_Two year  PaymentMethod_Credit card (automatic)  PaymentMethod_Electronic check  PaymentMethod_Mailed check  \\\n",
       "0                    0                  0                  0                                      0                               1                           0   \n",
       "1                    0                  1                  0                                      0                               0                           1   \n",
       "2                    0                  0                  0                                      0                               0                           1   \n",
       "3                    0                  1                  0                                      0                               0                           0   \n",
       "4                    0                  0                  0                                      0                               1                           0   \n",
       "\n",
       "   NEW_TENURE_YEAR_1-2 Year  NEW_TENURE_YEAR_2-3 Year  NEW_TENURE_YEAR_3-4 Year  NEW_TENURE_YEAR_4-5 Year  NEW_TENURE_YEAR_5-6 Year  SeniorCitizen_1  NEW_Engaged_1  \\\n",
       "0                         0                         0                         0                         0                         0                0              0   \n",
       "1                         0                         1                         0                         0                         0                0              1   \n",
       "2                         0                         0                         0                         0                         0                0              0   \n",
       "3                         0                         0                         1                         0                         0                0              1   \n",
       "4                         0                         0                         0                         0                         0                0              0   \n",
       "\n",
       "   NEW_noProt_1  NEW_Young_Not_Engaged_1  NEW_FLAG_ANY_STREAMING_1  NEW_FLAG_AutoPayment_1  \n",
       "0             1                        1                         0                       0  \n",
       "1             1                        0                         0                       0  \n",
       "2             1                        1                         0                       0  \n",
       "3             1                        0                         0                       1  \n",
       "4             1                        1                         0                       0  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# One-Hot Encoding Process\n",
    "# updating the cat_cols list\n",
    "cat_cols = [col for col in cat_cols if col not in binary_cols and col not in [\"Churn\", \"NEW_TotalServices\"]]\n",
    "cat_cols\n",
    "\n",
    "def one_hot_encoder(dataframe, categorical_cols, drop_first=False):\n",
    "    # Get the dummies and the one-off coding app\n",
    "    dataframe = pd.get_dummies(dataframe, columns=categorical_cols, drop_first=drop_first)\n",
    "    # Convert all boolean columns to int\n",
    "    for col in dataframe.columns:\n",
    "        if dataframe[col].dtype == 'bool':\n",
    "            dataframe[col] = dataframe[col].astype(int)\n",
    "    return dataframe\n",
    "\n",
    "df = one_hot_encoder(df, cat_cols, drop_first=True)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "################################## MODELING ##################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.79\n",
      "Recall: 0.65\n",
      "Precision: 0.5\n",
      "F1: 0.56\n",
      "Auc: 0.74\n"
     ]
    }
   ],
   "source": [
    "y = df[\"Churn\"]\n",
    "X = df.drop([\"Churn\",\"customerID\"], axis=1)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=17)\n",
    "\n",
    "catboost_model = CatBoostClassifier(verbose=False, random_state=12345).fit(X_train, y_train)\n",
    "y_pred = catboost_model.predict(X_test)\n",
    "\n",
    "print(f\"Accuracy: {round(accuracy_score(y_pred, y_test), 2)}\")\n",
    "print(f\"Recall: {round(recall_score(y_pred,y_test),2)}\")\n",
    "print(f\"Precision: {round(precision_score(y_pred,y_test), 2)}\")\n",
    "print(f\"F1: {round(f1_score(y_pred,y_test), 2)}\")\n",
    "print(f\"Auc: {round(roc_auc_score(y_pred,y_test), 2)}\")\n",
    "\n",
    "# Accuracy: 0.79\n",
    "# Recall: 0.65\n",
    "# Precision: 0.5\n",
    "# F1: 0.56\n",
    "# Auc: 0.74\n",
    "\n",
    "# BASE Model\n",
    "# Accuracy: 0.7847\n",
    "# Recall: 0.6331\n",
    "# Precision: 0.493\n",
    "# F1: 0.5544\n",
    "# Auc: 0.7292"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Value                                Feature\n",
      "10  9.659                           NEW_Increase\n",
      "11  9.069                    NEW_AVG_Service_Fee\n",
      "7   8.978                           TotalCharges\n",
      "3   8.942                                 tenure\n",
      "6   7.916                         MonthlyCharges\n",
      "9   6.689                        NEW_AVG_Charges\n",
      "39  5.055                          NEW_Engaged_1\n",
      "29  4.784                      Contract_Two year\n",
      "5   3.809                       PaperlessBilling\n",
      "14  2.759            InternetService_Fiber optic\n",
      "0   2.337                                 gender\n",
      "13  1.771                      MultipleLines_Yes\n",
      "38  1.741                        SeniorCitizen_1\n",
      "21  1.677                   DeviceProtection_Yes\n",
      "2   1.587                             Dependents\n",
      "8   1.538                      NEW_TotalServices\n",
      "31  1.501         PaymentMethod_Electronic check\n",
      "28  1.348                      Contract_One year\n",
      "17  1.292                     OnlineSecurity_Yes\n",
      "25  1.259                        StreamingTV_Yes\n",
      "19  1.233                       OnlineBackup_Yes\n",
      "23  1.231                        TechSupport_Yes\n",
      "34  1.186               NEW_TENURE_YEAR_2-3 Year\n",
      "32  1.087             PaymentMethod_Mailed check\n",
      "41  1.057                NEW_Young_Not_Engaged_1\n",
      "37  1.043               NEW_TENURE_YEAR_5-6 Year\n",
      "30  0.978  PaymentMethod_Credit card (automatic)\n",
      "1   0.965                                Partner\n",
      "42  0.879               NEW_FLAG_ANY_STREAMING_1\n",
      "35  0.849               NEW_TENURE_YEAR_3-4 Year\n",
      "12  0.848         MultipleLines_No phone service\n",
      "43  0.805                 NEW_FLAG_AutoPayment_1\n",
      "27  0.748                    StreamingMovies_Yes\n",
      "4   0.576                           PhoneService\n",
      "20  0.506   DeviceProtection_No internet service\n",
      "22  0.436        TechSupport_No internet service\n",
      "16  0.403     OnlineSecurity_No internet service\n",
      "33  0.398               NEW_TENURE_YEAR_1-2 Year\n",
      "36  0.284               NEW_TENURE_YEAR_4-5 Year\n",
      "24  0.250        StreamingTV_No internet service\n",
      "15  0.181                     InternetService_No\n",
      "40  0.137                           NEW_noProt_1\n",
      "26  0.105    StreamingMovies_No internet service\n",
      "18  0.104       OnlineBackup_No internet service\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_importance(model, features, num=len(X), save=False):\n",
    "    feature_imp = pd.DataFrame({'Value': model.feature_importances_, 'Feature': features.columns})\n",
    "    print(feature_imp.sort_values(\"Value\",ascending=False))\n",
    "    plt.figure(figsize=(10, 10))\n",
    "    sns.set_theme(font_scale=1)\n",
    "    sns.barplot(x=\"Value\", y=\"Feature\", data=feature_imp.sort_values(by=\"Value\",\n",
    "                                                                     ascending=False)[0:num])\n",
    "    plt.title('Features')\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "    if save:\n",
    "        plt.savefig('importances.png')\n",
    "\n",
    "plot_importance(catboost_model, X)"
   ]
  }
 ],
 "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": "3.10.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}