path: root/Data Prediction/Tele Churn/tele_churn.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "211755a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# pip install matplotlib pandas seaborn missingno plotly scikit-learn xgboost catboost lightgbm"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be0c7624-8210-4045-9033-2176cb5211ef",
   "metadata": {},
   "source": [
    "# Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "3ce7f5d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-2.27.0.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.simplefilter(action='ignore')\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import missingno as msno\n",
    "from plotly.offline import plot, iplot, init_notebook_mode\n",
    "init_notebook_mode(connected=True)\n",
    "import plotly.express as px\n",
    "import plotly.graph_objects as go\n",
    "from plotly.subplots import make_subplots\n",
    "from sklearn.preprocessing import MinMaxScaler, LabelEncoder, StandardScaler, RobustScaler\n",
    "from sklearn.model_selection import GridSearchCV, cross_validate\n",
    "from sklearn.metrics import roc_auc_score,roc_curve, classification_report, confusion_matrix, accuracy_score\n",
    "from sklearn.metrics import RocCurveDisplay\n",
    "from sklearn.model_selection import train_test_split, cross_validate\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, VotingClassifier, AdaBoostClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from xgboost import XGBClassifier\n",
    "from catboost import CatBoostClassifier\n",
    "from lightgbm import LGBMClassifier\n",
    "from sklearn.exceptions import ConvergenceWarning\n",
    "import tkinter\n",
    "from collections import Counter\n",
    "\n",
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.max_rows', None)\n",
    "pd.set_option('display.float_format', lambda x: '%.3f' % x)\n",
    "pd.set_option('display.width', 500)\n",
    "\n",
    "# %matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a35dd24-0ced-4d2d-930c-064a0be81b50",
   "metadata": {},
   "source": [
    "# Loading Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "fe7fdda7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.read_csv(\"WA_Fn-UseC_-Telco-Customer-Churn.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "147cc94e-f526-4f19-9101-caac1a7a8f4e",
   "metadata": {},
   "source": [
    "# Explorartory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "eccab7dd-6d55-4d84-9ef2-10c8b9fefc93",
   "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 StreamingTV StreamingMovies        Contract PaperlessBilling              PaymentMethod  MonthlyCharges TotalCharges Churn\n",
       "0  7590-VHVEG  Female              0     Yes         No       1           No  No phone service             DSL             No          Yes               No          No          No              No  Month-to-month              Yes           Electronic check          29.850        29.85    No\n",
       "1  5575-GNVDE    Male              0      No         No      34          Yes                No             DSL            Yes           No              Yes          No          No              No        One year               No               Mailed check          56.950       1889.5    No\n",
       "2  3668-QPYBK    Male              0      No         No       2          Yes                No             DSL            Yes          Yes               No          No          No              No  Month-to-month              Yes               Mailed check          53.850       108.15   Yes\n",
       "3  7795-CFOCW    Male              0      No         No      45           No  No phone service             DSL            Yes           No              Yes         Yes          No              No        One year               No  Bank transfer (automatic)          42.300      1840.75    No\n",
       "4  9237-HQITU  Female              0      No         No       2          Yes                No     Fiber optic             No           No               No          No          No              No  Month-to-month              Yes           Electronic check          70.700       151.65   Yes"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df1.copy()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "072cc34f-b29e-41ae-998a-3796be0dcc86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>7038</th>\n",
       "      <td>6840-RESVB</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>24</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>One year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>84.800</td>\n",
       "      <td>1990.5</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7039</th>\n",
       "      <td>2234-XADUH</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>72</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>One year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Credit card (automatic)</td>\n",
       "      <td>103.200</td>\n",
       "      <td>7362.9</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7040</th>\n",
       "      <td>4801-JZAZL</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>11</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>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>29.600</td>\n",
       "      <td>346.45</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7041</th>\n",
       "      <td>8361-LTMKD</td>\n",
       "      <td>Male</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</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>Mailed check</td>\n",
       "      <td>74.400</td>\n",
       "      <td>306.6</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7042</th>\n",
       "      <td>3186-AJIEK</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>66</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Two year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>105.650</td>\n",
       "      <td>6844.5</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      customerID  gender  SeniorCitizen Partner Dependents  tenure PhoneService     MultipleLines InternetService OnlineSecurity OnlineBackup DeviceProtection TechSupport StreamingTV StreamingMovies        Contract PaperlessBilling              PaymentMethod  MonthlyCharges TotalCharges Churn\n",
       "7038  6840-RESVB    Male              0     Yes        Yes      24          Yes               Yes             DSL            Yes           No              Yes         Yes         Yes             Yes        One year              Yes               Mailed check          84.800       1990.5    No\n",
       "7039  2234-XADUH  Female              0     Yes        Yes      72          Yes               Yes     Fiber optic             No          Yes              Yes          No         Yes             Yes        One year              Yes    Credit card (automatic)         103.200       7362.9    No\n",
       "7040  4801-JZAZL  Female              0     Yes        Yes      11           No  No phone service             DSL            Yes           No               No          No          No              No  Month-to-month              Yes           Electronic check          29.600       346.45    No\n",
       "7041  8361-LTMKD    Male              1     Yes         No       4          Yes               Yes     Fiber optic             No           No               No          No          No              No  Month-to-month              Yes               Mailed check          74.400        306.6   Yes\n",
       "7042  3186-AJIEK    Male              0      No         No      66          Yes                No     Fiber optic            Yes           No              Yes         Yes         Yes             Yes        Two year              Yes  Bank transfer (automatic)         105.650       6844.5    No"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "9c64a681",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>tenure</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7043.000</td>\n",
       "      <td>7043.000</td>\n",
       "      <td>7043.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.162</td>\n",
       "      <td>32.371</td>\n",
       "      <td>64.762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.369</td>\n",
       "      <td>24.559</td>\n",
       "      <td>30.090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>18.250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000</td>\n",
       "      <td>9.000</td>\n",
       "      <td>35.500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000</td>\n",
       "      <td>29.000</td>\n",
       "      <td>70.350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000</td>\n",
       "      <td>55.000</td>\n",
       "      <td>89.850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000</td>\n",
       "      <td>72.000</td>\n",
       "      <td>118.750</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       SeniorCitizen   tenure  MonthlyCharges\n",
       "count       7043.000 7043.000        7043.000\n",
       "mean           0.162   32.371          64.762\n",
       "std            0.369   24.559          30.090\n",
       "min            0.000    0.000          18.250\n",
       "25%            0.000    9.000          35.500\n",
       "50%            0.000   29.000          70.350\n",
       "75%            0.000   55.000          89.850\n",
       "max            1.000   72.000         118.750"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "0d330dc4",
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <td>7043.000</td>\n",
       "      <td>0.162</td>\n",
       "      <td>0.369</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tenure</th>\n",
       "      <td>7043.000</td>\n",
       "      <td>32.371</td>\n",
       "      <td>24.559</td>\n",
       "      <td>0.000</td>\n",
       "      <td>9.000</td>\n",
       "      <td>29.000</td>\n",
       "      <td>55.000</td>\n",
       "      <td>72.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <td>7043.000</td>\n",
       "      <td>64.762</td>\n",
       "      <td>30.090</td>\n",
       "      <td>18.250</td>\n",
       "      <td>35.500</td>\n",
       "      <td>70.350</td>\n",
       "      <td>89.850</td>\n",
       "      <td>118.750</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  count   mean    std    min    25%    50%    75%     max\n",
       "SeniorCitizen  7043.000  0.162  0.369  0.000  0.000  0.000  0.000   1.000\n",
       "tenure         7043.000 32.371 24.559  0.000  9.000 29.000 55.000  72.000\n",
       "MonthlyCharges 7043.000 64.762 30.090 18.250 35.500 70.350 89.850 118.750"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "a30694a2-5fec-4755-871a-ed37833b7c3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7043, 21)"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "f6e03abf-0d4e-4f82-b40d-a89f7cb5e102",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['customerID', 'gender', 'SeniorCitizen', 'Partner', 'Dependents', 'tenure', 'PhoneService', 'MultipleLines', 'InternetService', 'OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport', 'StreamingTV', 'StreamingMovies', 'Contract', 'PaperlessBilling', 'PaymentMethod', 'MonthlyCharges', 'TotalCharges', 'Churn'], dtype='object')"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "2962acc6-4b0b-4fab-96ba-1af16ddf8dcc",
   "metadata": {},
   "outputs": [
    {
     "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": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "76760a8d-b933-47d6-8357-de5fad75fe1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "09aeaead",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m******************** SHAPE ********************\u001b[0m\n",
      "(7043, 21)\n",
      "\u001b[1m******************** TYPES ********************\u001b[0m\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         object\n",
      "Churn                object\n",
      "dtype: object\n",
      "\u001b[1m******************** NA ********************\u001b[0m\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        0\n",
      "Churn               0\n",
      "dtype: int64\n",
      "\u001b[1m******************** DUPLICATED VALUE ********************\u001b[0m\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "def check_df(dataframe, head=10):\n",
    "    \n",
    "    print('\\033[1m' + 20*\"*\" + ' SHAPE ' + 20*\"*\" + '\\033[0m')\n",
    "    print(dataframe.shape)\n",
    "    \n",
    "    print('\\033[1m' + 20*\"*\" + ' TYPES ' + 20*\"*\" + '\\033[0m')\n",
    "    print(dataframe.dtypes)\n",
    "    \n",
    "    print('\\033[1m' + 20*\"*\" + ' NA ' + 20*\"*\" + '\\033[0m')\n",
    "    print(dataframe.isnull().sum())\n",
    "    \n",
    "    print('\\033[1m' + 20*\"*\" + ' DUPLICATED VALUE ' + 20*\"*\" + '\\033[0m')\n",
    "    print(dataframe.duplicated().sum())\n",
    "    \n",
    "check_df(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09adc81e-77f4-4ebe-8f85-e0ea6da42b7c",
   "metadata": {},
   "source": [
    "### Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "65f7a58b-4597-46e3-b3ab-87cd893aefd7",
   "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 StreamingTV StreamingMovies        Contract PaperlessBilling              PaymentMethod  MonthlyCharges TotalCharges Churn\n",
       "0  7590-VHVEG  Female              0     Yes         No       1           No  No phone service             DSL             No          Yes               No          No          No              No  Month-to-month              Yes           Electronic check          29.850        29.85    No\n",
       "1  5575-GNVDE    Male              0      No         No      34          Yes                No             DSL            Yes           No              Yes          No          No              No        One year               No               Mailed check          56.950       1889.5    No\n",
       "2  3668-QPYBK    Male              0      No         No       2          Yes                No             DSL            Yes          Yes               No          No          No              No  Month-to-month              Yes               Mailed check          53.850       108.15   Yes\n",
       "3  7795-CFOCW    Male              0      No         No      45           No  No phone service             DSL            Yes           No              Yes         Yes          No              No        One year               No  Bank transfer (automatic)          42.300      1840.75    No\n",
       "4  9237-HQITU  Female              0      No         No       2          Yes                No     Fiber optic             No           No               No          No          No              No  Month-to-month              Yes           Electronic check          70.700       151.65   Yes"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "b2f45ccf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Making the necessary arrangements\n",
    "# Since we do not need the CustomerID variable, we omitted it from the dataset.\n",
    "df = df.drop(['customerID'], axis = 1)\n",
    "\n",
    "# We converted the Churn variable as we wanted to see it as 1/0 instead of yes/no.\n",
    "df[\"Churn\"] = df[\"Churn\"].replace({\"Yes\":1, \"No\":0})\n",
    "\n",
    "# We converted the TotalCharges variable to a numeric variable.\n",
    "df.TotalCharges = pd.to_numeric(df.TotalCharges, errors='coerce')\n",
    "\n",
    "# SeniorCitizen variable should be object not integer, we changed that too.\n",
    "df[\"SeniorCitizen\"] = df[\"SeniorCitizen\"].astype(\"O\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "3974af26-4d84-44ce-b5e9-19421f4f6c92",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender SeniorCitizen Partner Dependents  tenure PhoneService     MultipleLines InternetService OnlineSecurity OnlineBackup DeviceProtection TechSupport StreamingTV StreamingMovies        Contract PaperlessBilling              PaymentMethod  MonthlyCharges  TotalCharges  Churn\n",
       "0  Female             0     Yes         No       1           No  No phone service             DSL             No          Yes               No          No          No              No  Month-to-month              Yes           Electronic check          29.850        29.850      0\n",
       "1    Male             0      No         No      34          Yes                No             DSL            Yes           No              Yes          No          No              No        One year               No               Mailed check          56.950      1889.500      0\n",
       "2    Male             0      No         No       2          Yes                No             DSL            Yes          Yes               No          No          No              No  Month-to-month              Yes               Mailed check          53.850       108.150      1\n",
       "3    Male             0      No         No      45           No  No phone service             DSL            Yes           No              Yes         Yes          No              No        One year               No  Bank transfer (automatic)          42.300      1840.750      0\n",
       "4  Female             0      No         No       2          Yes                No     Fiber optic             No           No               No          No          No              No  Month-to-month              Yes           Electronic check          70.700       151.650      1"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "9eae1591",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Churn\n",
       "0    5174\n",
       "1    1869\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Churn'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "63180e4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Churn\n",
       "0   73.463\n",
       "1   26.537\n",
       "Name: count, dtype: float64"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "100*df['Churn'].value_counts()/len(df['Churn'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "3048dfc6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7043 entries, 0 to 7042\n",
      "Data columns (total 20 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   gender            7043 non-null   object \n",
      " 1   SeniorCitizen     7043 non-null   object \n",
      " 2   Partner           7043 non-null   object \n",
      " 3   Dependents        7043 non-null   object \n",
      " 4   tenure            7043 non-null   int64  \n",
      " 5   PhoneService      7043 non-null   object \n",
      " 6   MultipleLines     7043 non-null   object \n",
      " 7   InternetService   7043 non-null   object \n",
      " 8   OnlineSecurity    7043 non-null   object \n",
      " 9   OnlineBackup      7043 non-null   object \n",
      " 10  DeviceProtection  7043 non-null   object \n",
      " 11  TechSupport       7043 non-null   object \n",
      " 12  StreamingTV       7043 non-null   object \n",
      " 13  StreamingMovies   7043 non-null   object \n",
      " 14  Contract          7043 non-null   object \n",
      " 15  PaperlessBilling  7043 non-null   object \n",
      " 16  PaymentMethod     7043 non-null   object \n",
      " 17  MonthlyCharges    7043 non-null   float64\n",
      " 18  TotalCharges      7032 non-null   float64\n",
      " 19  Churn             7043 non-null   int64  \n",
      "dtypes: float64(2), int64(2), object(16)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "54fbf0bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "c1c4087e-e2f5-46c9-9aac-d89ec3f03419",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "050e0244-8753-4930-b5cd-fb2a31dd0600",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "357b7883-24ef-41b6-84ef-b1250abd6c42",
   "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>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>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender SeniorCitizen Partner Dependents  tenure PhoneService     MultipleLines InternetService OnlineSecurity OnlineBackup DeviceProtection TechSupport StreamingTV StreamingMovies        Contract PaperlessBilling              PaymentMethod  MonthlyCharges  TotalCharges  Churn\n",
       "0  Female             0     Yes         No       1           No  No phone service             DSL             No          Yes               No          No          No              No  Month-to-month              Yes           Electronic check          29.850        29.850      0\n",
       "1    Male             0      No         No      34          Yes                No             DSL            Yes           No              Yes          No          No              No        One year               No               Mailed check          56.950      1889.500      0\n",
       "2    Male             0      No         No       2          Yes                No             DSL            Yes          Yes               No          No          No              No  Month-to-month              Yes               Mailed check          53.850       108.150      1\n",
       "3    Male             0      No         No      45           No  No phone service             DSL            Yes           No              Yes         Yes          No              No        One year               No  Bank transfer (automatic)          42.300      1840.750      0\n",
       "4  Female             0      No         No       2          Yes                No     Fiber optic             No           No               No          No          No              No  Month-to-month              Yes           Electronic check          70.700       151.650      1"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "8b1e531b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Observations: 7043\n",
      "Variables: 20\n",
      "cat_cols: 17\n",
      "num_cols: 3\n",
      "cat_but_car: 0\n",
      "num_but_cat: 1\n"
     ]
    }
   ],
   "source": [
    "def grab_col_names(dataframe, cat_th=10, car_th=20):\n",
    "    \"\"\"\n",
    "\n",
    "    It gives the names of categorical, numerical and categorical but cardinal variables in the data set.\n",
    "    Note: Categorical variables with numerical appearance are also included in categorical variables.\n",
    "\n",
    "    Parameters\n",
    "    ------\n",
    "            df: Dataframe\n",
    "                The dataframe from which variable names are to be retrieved\n",
    "        cat_th: int, optional\n",
    "                threshold value for numeric but categorical variables\n",
    "        car_th: int, optinal\n",
    "                threshold value 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",
    "                Categorical but cardinal variable list\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",
    "\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\n",
    "                   dataframe[col].dtypes != \"O\"]\n",
    "    cat_but_car = [col for col in dataframe.columns if dataframe[col].nunique() > car_th and\n",
    "                   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",
    "    return cat_cols, num_cols, cat_but_car\n",
    "\n",
    "cat_cols, num_cols, cat_but_car = grab_col_names(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "5831c338",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['tenure', 'MonthlyCharges', 'TotalCharges']"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "02d09051",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['gender',\n",
       " 'SeniorCitizen',\n",
       " 'Partner',\n",
       " 'Dependents',\n",
       " 'PhoneService',\n",
       " 'MultipleLines',\n",
       " 'InternetService',\n",
       " 'OnlineSecurity',\n",
       " 'OnlineBackup',\n",
       " 'DeviceProtection',\n",
       " 'TechSupport',\n",
       " 'StreamingTV',\n",
       " 'StreamingMovies',\n",
       " 'Contract',\n",
       " 'PaperlessBilling',\n",
       " 'PaymentMethod',\n",
       " 'Churn']"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "6128c410-ceb6-46e4-8380-0358f42d94a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tenure            0.240\n",
       "MonthlyCharges   -0.221\n",
       "TotalCharges      0.962\n",
       "Churn             1.063\n",
       "dtype: float64"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.skew(numeric_only=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "5716bd25-c35c-4c27-bdd9-9160811da1ea",
   "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",
       "      <th>Churn</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",
       "      <td>-0.352</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",
       "      <td>0.193</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",
       "      <td>-0.199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Churn</th>\n",
       "      <td>-0.352</td>\n",
       "      <td>0.193</td>\n",
       "      <td>-0.199</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                tenure  MonthlyCharges  TotalCharges  Churn\n",
       "tenure           1.000           0.248         0.826 -0.352\n",
       "MonthlyCharges   0.248           1.000         0.651  0.193\n",
       "TotalCharges     0.826           0.651         1.000 -0.199\n",
       "Churn           -0.352           0.193        -0.199  1.000"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr(numeric_only=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c805c38",
   "metadata": {},
   "source": [
    "# Data Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15cea021-3b0a-4461-aa2d-4a9ee370a482",
   "metadata": {},
   "source": [
    "### Feature distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "d7f21c97-7232-4241-b0f6-78b9e12d2dbd",
   "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>count</th>\n",
       "      <td>7043.000</td>\n",
       "      <td>7043.000</td>\n",
       "      <td>7032.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>32.371</td>\n",
       "      <td>64.762</td>\n",
       "      <td>2283.300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>24.559</td>\n",
       "      <td>30.090</td>\n",
       "      <td>2266.771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000</td>\n",
       "      <td>18.250</td>\n",
       "      <td>18.800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>9.000</td>\n",
       "      <td>35.500</td>\n",
       "      <td>401.450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>29.000</td>\n",
       "      <td>70.350</td>\n",
       "      <td>1397.475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>55.000</td>\n",
       "      <td>89.850</td>\n",
       "      <td>3794.738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>72.000</td>\n",
       "      <td>118.750</td>\n",
       "      <td>8684.800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        tenure  MonthlyCharges  TotalCharges\n",
       "count 7043.000        7043.000      7032.000\n",
       "mean    32.371          64.762      2283.300\n",
       "std     24.559          30.090      2266.771\n",
       "min      0.000          18.250        18.800\n",
       "25%      9.000          35.500       401.450\n",
       "50%     29.000          70.350      1397.475\n",
       "75%     55.000          89.850      3794.738\n",
       "max     72.000         118.750      8684.800"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[num_cols].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "4170fe4b-2082-47c1-aac3-89b3a83b0125",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<Axes: title={'center': 'tenure'}>,\n",
       "        <Axes: title={'center': 'MonthlyCharges'}>],\n",
       "       [<Axes: title={'center': 'TotalCharges'}>, <Axes: >]], dtype=object)"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[num_cols].hist(bins=20, figsize=(10,7))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "59124dd0-638a-47df-8d19-c4c44ebf2945",
   "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>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>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender SeniorCitizen Partner Dependents  tenure PhoneService     MultipleLines InternetService OnlineSecurity OnlineBackup DeviceProtection TechSupport StreamingTV StreamingMovies        Contract PaperlessBilling              PaymentMethod  MonthlyCharges  TotalCharges  Churn\n",
       "0  Female             0     Yes         No       1           No  No phone service             DSL             No          Yes               No          No          No              No  Month-to-month              Yes           Electronic check          29.850        29.850      0\n",
       "1    Male             0      No         No      34          Yes                No             DSL            Yes           No              Yes          No          No              No        One year               No               Mailed check          56.950      1889.500      0\n",
       "2    Male             0      No         No       2          Yes                No             DSL            Yes          Yes               No          No          No              No  Month-to-month              Yes               Mailed check          53.850       108.150      1\n",
       "3    Male             0      No         No      45           No  No phone service             DSL            Yes           No              Yes         Yes          No              No        One year               No  Bank transfer (automatic)          42.300      1840.750      0\n",
       "4  Female             0      No         No       2          Yes                No     Fiber optic             No           No               No          No          No              No  Month-to-month              Yes           Electronic check          70.700       151.650      1"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "153ebb4d",
   "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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ZvnbtWoWEhNh+ex07umyfU5LefNMn09rK5fJNdhP4a3Z/zS2R3R/5a26paWQ/depUvcbZXmAefPBBTZgwQampqZKknj176rPPPlNubq7S09MVFRUlSSotLVV0dLTneqWlperTp48kKSoqSmVlZV7znjlzRkePHvVc/4cmTpyonJwcz+WKigrFxsYqOTlZoaGhtuVzu91yuVw6cmSQLCvQtnlrjRpl+5S2qc0+aNAgBQban70p89fs/ppbIrs/ZvfX3FLTyl57BuWn2F5gTp06pYAA77fWtGjRQjU1NZKkuLg4RUVFad26dZ7CUlFRoaKiIo0dO1aSlJiYqPLychUXFys+Pl6StH79etXU1CghIeGst+t0OuV0OutsDwwM9MmDYVmBPikwJvyf8dV9agJ/ze6vuSWy+2N2f80tNY3s9b192wvMrbfeqkcffVSdOnXSFVdcoQ8++EBz5szRPffcI0lyOBzKzs7WzJkz1bVrV8XFxWny5MmKiYnRsGHDJEndu3fXzTffrDFjxigvL09ut1tZWVlKTU3lE0gAAMD+ArNgwQJNnjxZ9913n8rKyhQTE6Pf//73mjJlimfMQw89pJMnTyojI0Pl5eW69tprtWbNGrVq1cozZtmyZcrKytLAgQMVEBCgESNGaP78+XYvFwAAGMj2AtO2bVvNmzdP8+bNO+cYh8OhGTNmaMaMGeccEx4eruXLl9u9PAAA0AzwXUgAAMA4FBgAAGAcCgwAADAOBQYAABiHAgMAAIxDgQEAAMahwAAAAONQYAAAgHEoMAAAwDgUGAAAYBwKDAAAMI7t34UEAACajsWLf3qMwyFFREj5+ZJl1W/ejIyftayfjSMwAADAOBQYAABgHAoMAAAwDgUGAAAYhwIDAACMQ4EBAADGocAAAADjUGAAAIBxKDAAAMA4FBgAAGAcCgwAADAOBQYAABiHAgMAAIxDgQEAAMahwAAAAONQYAAAgHEoMAAAwDgUGAAAYBwKDAAAMA4FBgAAGIcCAwAAjEOBAQAAxqHAAAAA41BgAACAcSgwAADAOBQYAABgHAoMAAAwDgUGAAAYhwIDAACMQ4EBAADGocAAAADjUGAAAIBxKDAAAMA4FBgAAGAcCgwAADAOBQYAABiHAgMAAIxDgQEAAMahwAAAAONQYAAAgHEoMAAAwDgUGAAAYBwKDAAAMA4FBgAAGMcnBeaLL77QnXfeqQ4dOig4OFg9e/bU9u3bPfsty9KUKVMUHR2t4OBgJSUlaf/+/V5zHD16VGlpaQoNDVVYWJhGjx6tEydO+GK5AADAMLYXmG+++UYDBgxQYGCg3nrrLe3evVt/+tOf1L59e8+Y2bNna/78+crLy1NRUZFat26tlJQUnT592jMmLS1Nu3btksvl0urVq7Vp0yZlZGTYvVwAAGCglnZP+Pjjjys2NlZLlizxbIuLi/P827IszZs3T5MmTdLQoUMlSUuXLlVkZKRWrVql1NRU7dmzR2vWrNG2bdvUr18/SdKCBQt0yy236Mknn1RMTIzdywYAAAaxvcC8/vrrSklJ0e23366CggJdfPHFuu+++zRmzBhJ0oEDB1RSUqKkpCTPddq1a6eEhAQVFhYqNTVVhYWFCgsL85QXSUpKSlJAQICKiop022231bndyspKVVZWei5XVFRIktxut9xut235audyOOyb03t+n0xri9rsdt6fpvDX7P6aWyL79//2F801t8NRnzENf23z1d1U3/vf9gLz6aefatGiRcrJydEf//hHbdu2Tffff7+CgoKUnp6ukpISSVJkZKTX9SIjIz37SkpKFBER4b3Qli0VHh7uGfNDubm5mj59ep3ta9euVUhIiB3RvHTs6LJ9Tkl6802fTGsrl8s32U3gr9n9NbdEdn/U3HL/4OX0RzXktc1Xr1enTp2q1zjbC0xNTY369eunxx57TJLUt29fffzxx8rLy1N6errdN+cxceJE5eTkeC5XVFQoNjZWycnJCg0Nte123G63XC6XjhwZJMsKtG3eWqNG2T6lbWqzDxo0SIGB9mdvyvw1u7/mlsjuj9mba+78/J8e43C41bFjw17bfPV6VXsG5afYXmCio6PVo0cPr23du3fXX//6V0lSVFSUJKm0tFTR0dGeMaWlperTp49nTFlZmdccZ86c0dGjRz3X/yGn0ymn01lne2BgoE9+EC0r0CcFxoT/M766T03gr9n9NbdEdn/M3txyW1ZDxtb/tc1Xd1F973vbP4U0YMAA7d2712vbvn371LlzZ0nfvaE3KipK69at8+yvqKhQUVGREhMTJUmJiYkqLy9XcXGxZ8z69etVU1OjhIQEu5cMAAAMY/sRmPHjx+tXv/qVHnvsMY0cOVJbt27V4sWLtXjxYkmSw+FQdna2Zs6cqa5duyouLk6TJ09WTEyMhg0bJum7IzY333yzxowZo7y8PLndbmVlZSk1NZVPIAEAAPsLzNVXX62VK1dq4sSJmjFjhuLi4jRv3jylpaV5xjz00EM6efKkMjIyVF5ermuvvVZr1qxRq1atPGOWLVumrKwsDRw4UAEBARoxYoTmz59v93IBAICBbC8wkvSb3/xGv/nNb8653+FwaMaMGZoxY8Y5x4SHh2v58uW+WB4AADAc34UEAACMQ4EBAADGocAAAADjUGAAAIBxKDAAAMA4FBgAAGAcCgwAADAOBQYAABiHAgMAAIzjk9/Ei/P3f18ZZbuMDN/MCwBAY+AIDAAAMA4FBgAAGIcCAwAAjEOBAQAAxqHAAAAA41BgAACAcSgwAADAOBQYAABgHAoMAAAwDgUGAAAYhwIDAACMQ4EBAADGocAAAADjUGAAAIBxKDAAAMA4FBgAAGAcCgwAADAOBQYAABiHAgMAAIxDgQEAAMahwAAAAONQYAAAgHEoMAAAwDgUGAAAYBwKDAAAMA4FBgAAGIcCAwAAjEOBAQAAxqHAAAAA41BgAACAcSgwAADAOBQYAABgHAoMAAAwDgUGAAAYhwIDAACMQ4EBAADGocAAAADjUGAAAIBxKDAAAMA4FBgAAGAcCgwAADAOBQYAABiHAgMAAIxDgQEAAMahwAAAAOP4vMDMmjVLDodD2dnZnm2nT59WZmamOnTooDZt2mjEiBEqLS31ut6hQ4c0ZMgQhYSEKCIiQg8++KDOnDnj6+UCAAAD+LTAbNu2Tc8884x69erltX38+PF644039Oqrr6qgoEBffvmlhg8f7tlfXV2tIUOGqKqqSlu2bNELL7yg/Px8TZkyxZfLBQAAhvBZgTlx4oTS0tL07LPPqn379p7tx44d03PPPac5c+bopptuUnx8vJYsWaItW7bovffekyStXbtWu3fv1osvvqg+ffpo8ODBeuSRR7Rw4UJVVVX5askAAMAQLX01cWZmpoYMGaKkpCTNnDnTs724uFhut1tJSUmebd26dVOnTp1UWFio/v37q7CwUD179lRkZKRnTEpKisaOHatdu3apb9++dW6vsrJSlZWVnssVFRWSJLfbLbfbbVuu2rkcDvvmvBDsuAtqs9t5f5rCX7P7a26J7N//218019wOR33GNPy1zVd3U33vf58UmBUrVuj999/Xtm3b6uwrKSlRUFCQwsLCvLZHRkaqpKTEM+b75aV2f+2+s8nNzdX06dPrbF+7dq1CQkLOJ8aP6tjRZfucvvTmm/bN5XKZld1O/prdX3NLZPdHzS13RET9xzbktc3O15XvO3XqVL3G2V5gPv/8c40bN04ul0utWrWye/pzmjhxonJycjyXKyoqFBsbq+TkZIWGhtp2O263Wy6XS0eODJJlBdo2r6+NGvXz56jNPmjQIAUG/n/2/PyfP/e52LFuO5wre3Pnr7klsvtj9uaauz7P0Q6HWx07Nuy1zVfPz7VnUH6K7QWmuLhYZWVluuqqqzzbqqurtWnTJj399NN6++23VVVVpfLycq+jMKWlpYqKipIkRUVFaevWrV7z1n5KqXbMDzmdTjmdzjrbAwMDffKDaFmBRhUYO++CH96nlmXf3HVvy3dznw9f/Tw1df6aWyK7P2Zvbrkb8hzdkNc2X91F9b3vbX8T78CBA7Vz507t2LHD86dfv35KS0vz/DswMFDr1q3zXGfv3r06dOiQEhMTJUmJiYnauXOnysrKPGNcLpdCQ0PVo0cPu5cMAAAMY/sRmLZt2+rKK6/02ta6dWt16NDBs3306NHKyclReHi4QkND9Yc//EGJiYnq37+/JCk5OVk9evTQXXfdpdmzZ6ukpESTJk1SZmbmWY+yAAAA/+KzTyH9mLlz5yogIEAjRoxQZWWlUlJS9Oc//9mzv0WLFlq9erXGjh2rxMREtW7dWunp6ZoxY0ZjLBcAADQxF6TAbNy40etyq1attHDhQi1cuPCc1+ncubPe9NVbnAEAgNH4LiQAAGAcCgwAADAOBQYAABiHAgMAAIxDgQEAAMZplI9RAwCA/7d4cWOvwDwcgQEAAMbhCIyfsKPdOxzffatpfr5vv/8IAICfwhEYAABgHAoMAAAwDqeQAJv56s14GRm+mRcATMQRGAAAYBwKDAAAMA4FBgAAGIcCAwAAjEOBAQAAxqHAAAAA41BgAACAcSgwAADAOBQYAABgHAoMAAAwDgUGAAAYhwIDAACMQ4EBAADGocAAAADjtGzsBQCNYfHiho13OKSICCk/X7IsnywJANAAHIEBAADGocAAAADjUGAAAIBxKDAAAMA4FBgAAGAcCgwAADAOBQYAABiH3wMDAGhWGvp7nmrV5/c9ZWSc97JgMwoMgHM+4f/cX+DHkz1+zPkWDUDiFBIAADAQBQYAABiHAgMAAIzDe2DQpHGOHBda7c+cL77A01fvCbL7/8n3s48ZY+/cgF04AgMAAIxDgQEAAMahwAAAAONQYAAAgHEoMAAAwDgUGAAAYBwKDAAAMA4FBgAAGIcCAwAAjEOBAQAAxuGrBAAYia+ZAPwbR2AAAIBxKDAAAMA4nEICAKCeOHXZdFBgAADnxAs2mioKDACf4cUPgK/Y/h6Y3NxcXX311Wrbtq0iIiI0bNgw7d2712vM6dOnlZmZqQ4dOqhNmzYaMWKESktLvcYcOnRIQ4YMUUhIiCIiIvTggw/qzJkzdi8XAAAYyPYCU1BQoMzMTL333ntyuVxyu91KTk7WyZMnPWPGjx+vN954Q6+++qoKCgr05Zdfavjw4Z791dXVGjJkiKqqqrRlyxa98MILys/P15QpU+xeLgAAMJDtp5DWrFnjdTk/P18REREqLi7Wddddp2PHjum5557T8uXLddNNN0mSlixZou7du+u9995T//79tXbtWu3evVvvvPOOIiMj1adPHz3yyCN6+OGHNW3aNAUFBdm9bKDJ43QMAPw/n78H5tixY5Kk8PBwSVJxcbHcbreSkpI8Y7p166ZOnTqpsLBQ/fv3V2FhoXr27KnIyEjPmJSUFI0dO1a7du1S375969xOZWWlKisrPZcrKiokSW63W26327Y8tXM5HPbNaYrazGT3H/6aW/JNdhufirw4HHbP55+Pu7/mls4vu69+nuv7mu3TAlNTU6Ps7GwNGDBAV155pSSppKREQUFBCgsL8xobGRmpkpISz5jvl5fa/bX7ziY3N1fTp0+vs33t2rUKCQn5uVHq6NjRZfucpiC7//HX3JK92d9807apvERE+GZef33c/TW31LDsvvp5PnXqVL3G+bTAZGZm6uOPP9bmzZt9eTOSpIkTJyonJ8dzuaKiQrGxsUpOTlZoaKhtt+N2u+VyuXTkyCBZVqBt85rA4XCrY0ey+1N2f80t+Sb7qFG2TFNHfr698/nr4+6vuaXzy+6rn+faMyg/xWcFJisrS6tXr9amTZt0ySWXeLZHRUWpqqpK5eXlXkdhSktLFRUV5RmzdetWr/lqP6VUO+aHnE6nnE5nne2BgYEKDLT/B9GyAv3uB7wW2f0vu7/mluzN7oOnIkmSZflqXv983P01t9Sw7L76ea7va7btn0KyLEtZWVlauXKl1q9fr7i4OK/98fHxCgwM1Lp16zzb9u7dq0OHDikxMVGSlJiYqJ07d6qsrMwzxuVyKTQ0VD169LB7yQAAwDC2H4HJzMzU8uXL9be//U1t27b1vGelXbt2Cg4OVrt27TR69Gjl5OQoPDxcoaGh+sMf/qDExET1799fkpScnKwePXrorrvu0uzZs1VSUqJJkyYpMzPzrEdZAACAf7G9wCxatEiSdMMNN3htX7JkiUb93wmzuXPnKiAgQCNGjFBlZaVSUlL05z//2TO2RYsWWr16tcaOHavExES1bt1a6enpmjFjht3LBYALho/CA/axvcBY9TgZ26pVKy1cuFALFy4855jOnTvrTV+9xRkAABjN9vfAAAAA+BoFBgAAGIcCAwAAjEOBAQAAxqHAAAAA41BgAACAcSgwAADAOBQYAABgHAoMAAAwDgUGAAAYhwIDAACMQ4EBAADGocAAAADjUGAAAIBxKDAAAMA4FBgAAGAcCgwAADAOBQYAABiHAgMAAIxDgQEAAMahwAAAAONQYAAAgHEoMAAAwDgUGAAAYBwKDAAAMA4FBgAAGIcCAwAAjEOBAQAAxqHAAAAA41BgAACAcSgwAADAOBQYAABgHAoMAAAwDgUGAAAYhwIDAACMQ4EBAADGocAAAADjUGAAAIBxKDAAAMA4FBgAAGAcCgwAADAOBQYAABiHAgMAAIxDgQEAAMahwAAAAONQYAAAgHEoMAAAwDgUGAAAYBwKDAAAMA4FBgAAGIcCAwAAjEOBAQAAxqHAAAAA41BgAACAcSgwAADAOE26wCxcuFBdunRRq1atlJCQoK1btzb2kgAAQBPQZAvMyy+/rJycHE2dOlXvv/++evfurZSUFJWVlTX20gAAQCNrsgVmzpw5GjNmjO6++2716NFDeXl5CgkJ0fPPP9/YSwMAAI2sSRaYqqoqFRcXKykpybMtICBASUlJKiwsbMSVAQCApqBlYy/gbL766itVV1crMjLSa3tkZKT++c9/nvU6lZWVqqys9Fw+duyYJOno0aNyu922rc3tduvUqVM6ffprWVagbfOawOEgu79l99fcEtn9Mbu/5pbOL/vXX/tmLcePH5ckWZb1o+OaZIE5H7m5uZo+fXqd7XFxcY2wGgAAmrdx43w7//Hjx9WuXbtz7m+SBeaiiy5SixYtVFpa6rW9tLRUUVFRZ73OxIkTlZOT47lcU1Ojo0ePqkOHDnI4HLatraKiQrGxsfr8888VGhpq27wmILv/ZffX3BLZ/TG7v+aWmlZ2y7J0/PhxxcTE/Oi4JllggoKCFB8fr3Xr1mnYsGGSvisk69atU1ZW1lmv43Q65XQ6vbaFhYX5bI2hoaGN/iA3FrL7X3Z/zS2R3R+z+2tuqelk/7EjL7WaZIGRpJycHKWnp6tfv3665pprNG/ePJ08eVJ33313Yy8NAAA0siZbYO644w4dOXJEU6ZMUUlJifr06aM1a9bUeWMvAADwP022wEhSVlbWOU8ZNRan06mpU6fWOV3lD8juf9n9NbdEdn/M7q+5JTOzO6yf+pwSAABAE9Mkf5EdAADAj6HAAAAA41BgAACAcSgwAADAOBSYBlq4cKG6dOmiVq1aKSEhQVu3bm3sJdlu06ZNuvXWWxUTEyOHw6FVq1Z57bcsS1OmTFF0dLSCg4OVlJSk/fv3N85ibZSbm6urr75abdu2VUREhIYNG6a9e/d6jTl9+rQyMzPVoUMHtWnTRiNGjKjzG6NNtGjRIvXq1cvzS6wSExP11ltvefY319w/NGvWLDkcDmVnZ3u2Ndfs06ZNk8Ph8PrTrVs3z/7mmluSvvjiC915553q0KGDgoOD1bNnT23fvt2zv7k+x3Xp0qXOY+5wOJSZmSnJvMecAtMAL7/8snJycjR16lS9//776t27t1JSUlRWVtbYS7PVyZMn1bt3by1cuPCs+2fPnq358+crLy9PRUVFat26tVJSUnT69OkLvFJ7FRQUKDMzU++9955cLpfcbreSk5N18uRJz5jx48frjTfe0KuvvqqCggJ9+eWXGj58eCOu2h6XXHKJZs2apeLiYm3fvl033XSThg4dql27dklqvrm/b9u2bXrmmWfUq1cvr+3NOfsVV1yhw4cPe/5s3rzZs6+55v7mm280YMAABQYG6q233tLu3bv1pz/9Se3bt/eMaa7Pcdu2bfN6vF0ulyTp9ttvl2TgY26h3q655horMzPTc7m6utqKiYmxcnNzG3FVviXJWrlypedyTU2NFRUVZT3xxBOebeXl5ZbT6bReeumlRlih75SVlVmSrIKCAsuyvssZGBhovfrqq54xe/bssSRZhYWFjbVMn2nfvr31l7/8xS9yHz9+3Oratavlcrms66+/3ho3bpxlWc37MZ86darVu3fvs+5rzrkffvhh69prrz3nfn96jhs3bpx12WWXWTU1NUY+5hyBqaeqqioVFxcrKSnJsy0gIEBJSUkqLCxsxJVdWAcOHFBJSYnX/dCuXTslJCQ0u/vh2LFjkqTw8HBJUnFxsdxut1f2bt26qVOnTs0qe3V1tVasWKGTJ08qMTHRL3JnZmZqyJAhXhml5v+Y79+/XzExMbr00kuVlpamQ4cOSWreuV9//XX169dPt99+uyIiItS3b189++yznv3+8hxXVVWlF198Uffcc48cDoeRjzkFpp6++uorVVdX1/kqg8jISJWUlDTSqi682qzN/X6oqalRdna2BgwYoCuvvFLSd9mDgoLqfEloc8m+c+dOtWnTRk6nU/fee69WrlypHj16NPvcK1as0Pvvv6/c3Nw6+5pz9oSEBOXn52vNmjVatGiRDhw4oF//+tc6fvx4s8796aefatGiReratavefvttjR07Vvfff79eeOEFSf7zHLdq1SqVl5dr1KhRksz8WW/SXyUANJbMzEx9/PHHXu8JaO5++ctfaseOHTp27Jhee+01paenq6CgoLGX5VOff/65xo0bJ5fLpVatWjX2ci6owYMHe/7dq1cvJSQkqHPnznrllVcUHBzciCvzrZqaGvXr10+PPfaYJKlv3776+OOPlZeXp/T09EZe3YXz3HPPafDgwYqJiWnspZw3jsDU00UXXaQWLVrUeUd2aWmpoqKiGmlVF15t1uZ8P2RlZWn16tXasGGDLrnkEs/2qKgoVVVVqby83Gt8c8keFBSkX/ziF4qPj1dubq569+6tp556qlnnLi4uVllZma666iq1bNlSLVu2VEFBgebPn6+WLVsqMjKy2Wb/obCwMF1++eX65JNPmvVjHh0drR49enht6969u+f0mT88x3322Wd655139Lvf/c6zzcTHnAJTT0FBQYqPj9e6des822pqarRu3TolJiY24sourLi4OEVFRXndDxUVFSoqKjL+frAsS1lZWVq5cqXWr1+vuLg4r/3x8fEKDAz0yr53714dOnTI+OxnU1NTo8rKymade+DAgdq5c6d27Njh+dOvXz+lpaV5/t1cs//QiRMn9K9//UvR0dHN+jEfMGBAnV+PsG/fPnXu3FlS836Oq7VkyRJFRERoyJAhnm1GPuaN/S5ik6xYscJyOp1Wfn6+tXv3bisjI8MKCwuzSkpKGntptjp+/Lj1wQcfWB988IElyZozZ471wQcfWJ999pllWZY1a9YsKywszPrb3/5mffTRR9bQoUOtuLg469tvv23klf88Y8eOtdq1a2dt3LjROnz4sOfPqVOnPGPuvfdeq1OnTtb69eut7du3W4mJiVZiYmIjrtoeEyZMsAoKCqwDBw5YH330kTVhwgTL4XBYa9eutSyr+eY+m+9/Csmymm/2//zP/7Q2btxoHThwwHr33XetpKQk66KLLrLKysosy2q+ubdu3Wq1bNnSevTRR639+/dby5Yts0JCQqwXX3zRM6a5PsdZ1nefnu3UqZP18MMP19ln2mNOgWmgBQsWWJ06dbKCgoKsa665xnrvvfcae0m227BhgyWpzp/09HTLsr77mOHkyZOtyMhIy+l0WgMHDrT27t3buIu2wdkyS7KWLFniGfPtt99a9913n9W+fXsrJCTEuu2226zDhw833qJtcs8991idO3e2goKCrI4dO1oDBw70lBfLar65z+aHBaa5Zr/jjjus6OhoKygoyLr44outO+64w/rkk088+5trbsuyrDfeeMO68sorLafTaXXr1s1avHix1/7m+hxnWZb19ttvW5LOmse0x9xhWZbVKId+AAAAzhPvgQEAAMahwAAAAONQYAAAgHEoMAAAwDgUGAAAYBwKDAAAMA4FBgAAGIcCAwAAjEOBAeBTN9xwg7Kzsxt7GQCaGQoMAL9gWZbOnDnT2MsAYBMKDACfGTVqlAoKCvTUU0/J4XDI4XDo4MGD+vjjjzV48GC1adNGkZGRuuuuu/TVV195rnfDDTfo/vvv10MPPaTw8HBFRUVp2rRpnv0HDx6Uw+HQjh07PNvKy8vlcDi0ceNGSdLGjRvlcDj01ltvKT4+Xk6nU5s3b1ZNTY1yc3MVFxen4OBg9e7dW6+99toFukcA2IUCA8BnnnrqKSUmJmrMmDE6fPiwDh8+rLZt2+qmm25S3759tX37dq1Zs0alpaUaOXKk13VfeOEFtW7dWkVFRZo9e7ZmzJghl8vV4DVMmDBBs2bN0p49e9SrVy/l5uZq6dKlysvL065duzR+/HjdeeedKigosCs2gAugZWMvAEDz1a5dOwUFBSkkJERRUVGSpJkzZ6pv37567LHHPOOef/55xcbGat++fbr88sslSb169dLUqVMlSV27dtXTTz+tdevWadCgQQ1aw4wZMzzXqays1GOPPaZ33nlHiYmJkqRLL71Umzdv1jPPPKPrr7/+Z2cGcGFQYABcUB9++KE2bNigNm3a1Nn3r3/9y6vAfF90dLTKysoafHv9+vXz/PuTTz7RqVOn6pSgqqoq9e3bt8FzA2g8FBgAF9SJEyd066236vHHH6+zLzo62vPvwMBAr30Oh0M1NTWSpICA785+W5bl2e92u896e61bt/a6bUn6+9//rosvvthrnNPpbEgMAI2MAgPAp4KCglRdXe25fNVVV+mvf/2runTpopYtz+8pqGPHjpKkw4cPe46cfP8NvefSo0cPOZ1OHTp0iNNFgOEoMAB8qkuXLioqKtLBgwfVpk0bZWZm6tlnn9W//du/eT5l9Mknn2jFihX6y1/+ohYtWvzknMHBwerfv79mzZqluLg4lZWVadKkST95vbZt2+qBBx7Q+PHjVVNTo2uvvVbHjh3Tu+++q9DQUKWnp9sRGcAFwKeQAPjUAw88oBYtWqhHjx7q2LGjqqqq9O6776q6ulrJycnq2bOnsrOzFRYW5jk1VB/PP/+8zpw5o/j4eGVnZ2vmzJn1ut4jjzyiyZMnKzc3V927d9fNN9+sv//974qLizvfiAAagcP6/klkAAAAA3AEBgAAGIcCAwAAjEOBAQAAxqHAAAAA41BgAACAcSgwAADAOBQYAABgHAoMAAAwDgUGAAAYhwIDAACMQ4EBAADGocAAAADj/C+STQ8TwsgEpwAAAABJRU5ErkJggg==",
      "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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",
      "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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",
      "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, alpha=0.4, color='b')\n",
    "        plt.xlabel(numerical_col)\n",
    "        plt.title(numerical_col)\n",
    "        plt.show(block=True)\n",
    "        \n",
    "for col in num_cols:\n",
    "    num_summary(df, col, plot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "98a13bb0-efa6-4224-bf10-840b08782536",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<Axes: title={'center': 'tenure'}>,\n",
       "       <Axes: title={'center': 'MonthlyCharges'}>,\n",
       "       <Axes: title={'center': 'TotalCharges'}>], dtype=object)"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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rnCTvVmF4U7ex+zfURnPqmkwOpaTYNGLECJnN5gu2648xnK+ur9U9vzyNlxSczxFv655drynj1VL98pa3z6e61YUAAAAA4Es+Ter06tVLCQkJ2rRpkyuJU1VVpR07dmjSpEmSJKvVqoqKCpWWlio1NVWS9Pbbb8vpdGrw4MEe2w0PD1d4eHi9crPZ7PHN4IU4nWZXUiekxulx+7nq6pnPrf7/j++s34wa6po3dRu7f0Nt+KZu/XEOvBga1y9faOh5GdzPkcbV9VTPm/FqqX55y9vnU1POUwAABIOtW7fqscceU2lpqQ4dOqTVq1fr1ltvdW03DEOzZ8/Wc889p4qKCg0ZMkRLlixR7969XXWOHj2qKVOmaM2aNTKZTMrOztZTTz2lqKioNoioYddvnuOxfPP1nssBwBe8TuocP35cn376qevxgQMHtHv3bsXGxqpnz56aOnWqHnroIfXu3dt1S/Pu3bu7Tt59+/bVDTfcoAkTJmjp0qVyOByaPHmyxowZ4zd3vmrohAwAAACg8U6cOKEBAwbonnvu0ejRo+ttX7BggZ5++mmtXLnS9d4hMzNT+/btU0REhCRp7NixOnTokGw2mxwOh+6++25NnDhRRUVFrR0OAPgdr5M67733nn74wx+6Htd9LSonJ0crVqzQ/fffrxMnTmjixImqqKjQ0KFDtX79etdJWZJefPFFTZ48WcOHD3dl259++mkfhOMfXEmhOeds4MuoQefsX2ndd48BoD3zNNUx/eFsdc+Hs6/ZMWtWm3YJLWjkyJEaOXKkx22GYejJJ5/UzJkzdcstt0iSnn/+ecXHx+v111/XmDFj9OGHH2r9+vXatWuX6+65ixYt0o033qjHH3/cbz4UBoC24nVS5/rrr5dhGA1uDwkJ0bx58zRv3rwG68TGxpJZBwAAANqxAwcOyG63Kz093VUWHR2twYMHq6SkRGPGjFFJSYliYmJcCR1JSk9Pl8lk0o4dO/SjH/2oLboOAH4jIO5+BbQmPmUGAABoeXa7XZIUHx/vVh4fH+/aZrfbFRcX57a9Q4cOio2NddXxpLq6WtXV1a7HdTctcDgccjgcjepfXT3DbGpU/YaYTI07nj+p63Pdv40csqBQ93tv7PMk2LTn+P0pdm/6QFIHQNAiGQcAQPtUUFCguXPn1isvLi5WZGSkV20dm5jSrL4M0Lpm7d+WUlJskqR1gRtCk9lstrbuQptqz/H7Q+wnT55sdF2SOgAAAABaXUJCgiSpvLxc3bp1c5WXl5e77qSbkJCgw4cPu+13+vRpHT161LW/J/n5+a5rf0pnVuokJiYqIyNDFoulUf1zOByy2Wzq/OwehTiafvvMvw7Nb/K+bcVkciglxaY9e0bI6TQrP/BCaLK63/uIESPa5R1M23P8/hR73erCxiCpAwAAAKDV9erVSwkJCdq0aZMriVNVVaUdO3Zo0qRJkiSr1aqKigqVlpYqNTVVkvT222/L6XRq8ODBDbYdHh6u8PDweuVms9nrN2shDqdCapqe1HE6A/eNsdNpltNpVjt7by+pac+VYNKe4/eH2L05PkkdAAAQkDzdgZA7KbUNrkeHhhw/flyffvqp6/GBAwe0e/duxcbGqmfPnpo6daoeeugh9e7d23VL8+7du+vWW2+VJPXt21c33HCDJkyYoKVLl8rhcGjy5MkaM2YMd74CAJHUaVGbN5/zeE5b9AIAgObhDTuApnrvvff0wx/+0PW47itROTk5WrFihe6//36dOHFCEydOVEVFhYYOHar169crIiLCtc+LL76oyZMna/jw4TKZTMrOztbTTz/d6rEAgD8iqePneNEMAED7c/1ZnwQZYSZVDRjQdp0BmuH666+XYRgNbg8JCdG8efM0b968BuvExsaqqKioJboHAAGPpA6AgEKiEwDat4bmAeYHBJLrG1jCv/l6z+VoOeeeO+q+ztuWfWioDPCEpA4AAEAjeH4T5qkMANqHlkhGkLgFvENSp53ipMgYAL60detWPfbYYyotLdWhQ4e0evVq10UuJckwDM2ePVvPPfecKioqNGTIEC1ZskS9e/d21Tl69KimTJmiNWvWuK6Z8NRTTykqKqoNIkJ7EUifjhYUSE4PN+Dx1/4COKOhVTmBhPMM4L9I6sBnyKoD7deJEyc0YMAA3XPPPRo9enS97QsWLNDTTz+tlStXuu5ukpmZqX379rkuhjl27FgdOnRINptNDodDd999tyZOnMh1FAAAAIAGkNTxUqvc0eqsLMj1Zx2P79gi0HH74eA1cuRIjRw50uM2wzD05JNPaubMmbrlllskSc8//7zi4+P1+uuva8yYMfrwww+1fv167dq1S4MGDZIkLVq0SDfeeKMef/xxblsLeKnhlQENlQNA4PHmw2NvP2j2tDqSD6vhj0jq+ImzTxBnJ3KA9oAJMrgdOHBAdrtd6enprrLo6GgNHjxYJSUlGjNmjEpKShQTE+NK6EhSenq6TCaTduzYoR/96Ece266urlZ1dbXrcVVVlSTJ4XDI4XB41U+T6ez/O9z+PZenps/e/0J121LduHgzPp5ia2h3b+o2dv+G2mjod9bc4zW0vxFWv7I34/iDbQUeyx2O/PMeyzCf+b83z0dPfT1Tt35lXzx3m/Ic8eXv7HzHawuN/Tvz9jwFBIKWTLTAO4H0FWM0HUkdAECLstvtkqT4+Hi38vj4eNc2u92uuLg4t+0dOnRQbGysq44nBQUFmjt3br3y4uJiRUZGetVPT3e6SEmxeay7bl3j9m+orj+w2TzH5omn2BqKy5u6jd2/oTYa+p0193gN7p9bv/I6bw7mYf8G2/BQ15vnozfH8sVztznPEV/8zs53vLZ0ob+zkydPtlJPEMi4UxaA8yGpAwAIWPn5+crLy3M9rqqqUmJiojIyMmSxWLxqq6BAuvbdMyspDLNJxyamqPOzexTicOqvQ91XUuTXX1ihAs+LMDzWbUsOh0M2m00jRoyQ2Wxu1D6eYmsoLm/qNnb/hto4u67J5FBKik179ozQjBmNi6uh4zXU33ez6lce+hfPlT21W/f8akwbZx+r7vm4Z88IOZ2Ni82bYzV1zC/kQs+RxvzOmnu8tvq7bOzfWd3qQgAX5g8rTLxZ+eIP/UX7QFIniHDiAOCPEhISJEnl5eXq1q2bq7y8vFwDBw501Tl8+LDbfqdPn9bRo0dd+3sSHh6u8PDweuVms7nRCYs6TqcUUuP+5fkQh1MhNc56b6Q9Ne3prkQN1fUH3oyRp9ga2tWbuo3dv6E2PNV1Or373XvT33OfH2fqeq7sqV1P+zfUhqe6Tqe50Ukdb47V3DFvSGOfI+f7nTX3eG39d3mhvzNvz1MA0BK47mVgI6kT4FiOCW+Q+ENb6NWrlxISErRp0yZXEqeqqko7duzQpEmTJElWq1UVFRUqLS1VamqqJOntt9+W0+nU4MGD26rrQLvW2DnDq4syz5njunagEWZS1YAB/39VEe8cgPaI16atjzEPPiR1AADNdvz4cX366aeuxwcOHNDu3bsVGxurnj17aurUqXrooYfUu3dv1y3Nu3fvrltvvVWS1LdvX91www2aMGGCli5dKofDocmTJ2vMmDHc+QoA0G40nCQFAM9I6gBoNjL+eO+99/TDH/7Q9bjuOjc5OTlasWKF7r//fp04cUITJ05URUWFhg4dqvXr1ysiIsK1z4svvqjJkydr+PDhMplMys7O1tNPP93qsQAAgODD61UEK5I6aHHt7QRaUODdNQCaq6XGt7393tA8119/vQzDaHB7SEiI5s2bp3nz5jVYJzY2VkVFRS3RPQAAgKDF6/b2jaQO2oVgONEFQwwAAP/D9fkAoOna22t07vblf0jqAACAoOLNLWcBAAgkzGc4F0mddsTTJ3F8CgcAaGnB/AI0mGMDAAD+j6QOAhYvpAEAAACg7Xnz3oz3cb5FUqcVBepKmaZc+DdQY20IJx4AgDe4LTGAtsA1suBLvAcKDCR12lggvehrjUmivZ042lu8AAB4wnwIAO0bF2BuOpI6AAAACHi88AcQTDinobFI6gAAgKDHi2Pf8IcVxsH2FW8AAJqDpE4z+cOLGwAAWlq9+W6OyJQAQBvifQgCFS8ffIukTgDhxA0A/oEXI+fHSgpInv9Ort/suS7PGQAAmoakDgAAAYaLCaKpPCZaWrsTAADAZ0jqIODx6R4AoD1jJW/L8pQII4EKnMHrcLQFb87B7eF8TVIHAAAgAFz7boFCapxt3Y1WQ7IK8B/8PaKttIekTHOR1AEAIIid+2LIZJIGDGiTrgB+g9U3QMOJmuautGmpdgFf8nTO/+1vW70bPkFSJ0iRTW97LEcFgKbxPId5KmsZDb25v74Vj9dSx/JGe3stQVIHway9/T0D7QlJHfil1vzkwJs2+eQBgC/46yoBf+gDAAB8OApf8ea1TUHBmdXMBQWS86xvO/v76yOSOgAAwO/56wsqPv0GgMYhUQO0DJI6QBBgkgQAAACA9oekDoCgQGILAPwLq5iAwHT95jkywkyqGjCgxe+65815gssgAJ6R1AEAAGgnSLQAAOAdf70WYh2SOu3c+V7ctVaGHoHFmxUxrJ4BAg9v+gEAAM6voaROWyR7SOogoPBmo/lItDAGgFT/zg5tgXP6GYwDAPiWL17refpgm9eL8EckddBqWvN7sL54gdwSL7J9MQb+2i+gvWn03+IcefzYpt7+dQ/9aT2vD5G4QGtjbgPah7aeX7w513BeQksgqYNma+6JtK1PxL7QmheUa02t+bth9QzgmS9yPGf/fZ19rtpyzazmN97G6mILtrgAAP7HFxd2BnyNpA7gBzjptz3vfgfe1AVajsfVPkG60gc4H+ZRABfiz6tkmnvNSm/44hsCzR0zf/5dBCKSOmj3gvWFYLDG5Q3GAP5g82apMU/FzZv//7+NqOsPGhuXv2OVIJrCny6QCaB5zp0H6lZ+trWWeh17oXbPXvna1DbOxpza8to0qVNYWKjHHntMdrtdAwYM0KJFi3T11Ve3ZZcANAHJE/gSc0N9/I0BvkMiL3AxPwBAfW2W1Pm///s/5eXlaenSpRo8eLCefPJJZWZmav/+/YqLi2urbgFoYZ6ufxEs1yBC8zE3BBaSTQBaC/MDEPxa83VFSyXzPa3YbOlVnG2W1HniiSc0YcIE3X333ZKkpUuX6i9/+Yv++Mc/6te//nVbdQuAH+ENY/vjr3NDsD4XAykuX1ycsrEv4AJpXOAbXN/B//nr/AAAba1Nkjo1NTUqLS1Vfn6+q8xkMik9PV0lJSX16ldXV6u6utr1uLKyUpJ09OhRORyORh/X4XDo5MmTqqk5IqfTLEk6bqppahhBz5BJJ0+elEk1CjGxkuJCGC/vBPJ4HTlyxKv6x44dkyQZhtES3Qka3s4Nku/mhzPH/3ZO8PXzs6am/nOmofln0NbfeKjbuOOs3Spt+835jxXIf3vn05i4mjO2bak9/87amqfnTEO2pf1SddND3WvOI0eOyGw2N7gP80PjtNX8UPd79OfnaEsJhL/PltKeY5d8H7+/zr2e+mV0MOl473666t25Cjn9bezb0n7ZrGN5+dZBkpfzg9EGvv76a0OSsW3bNrfy6dOnG1dffXW9+rNnzzYk8cMPP/wE9M+XX37ZWqfZgOTt3GAYzA/88MNPcPwwP5wf8wM//PDTXn8aMz8ExN2v8vPzlZeX53rsdDp19OhRdenSRSEhIY1up6qqSomJifryyy9lsVhaoqtBhfHyDuPlnfY0XoZh6NixY+revXtbdyXo+Gp+OFcwPz+DNbZgjUsK3tiCNS6p8bExP7QcX8wPwfwcvRBib5+xS+07fn+K3Zv5oU2SOhdffLFCQ0NVXl7uVl5eXq6EhIR69cPDwxUeHu5WFhMT0+TjWyyWNv8lBRLGyzuMl3fay3hFR0e3dRf8nrdzg+T7+eFcwfz8DNbYgjUuKXhjC9a4pMbFxvxwYW09PwTzc/RCiL19xi617/j9JfbGzg9t8m22sLAwpaamatOmTa4yp9OpTZs2yWq1tkWXAABtjLkBAOAJ8wMANKzNvn6Vl5ennJwcDRo0SFdffbWefPJJnThxwnVFewBA+8PcAADwhPkBADxrs6TO7bffrn//+9+aNWuW7Ha7Bg4cqPXr1ys+Pr7FjhkeHq7Zs2fXW4oJzxgv7zBe3mG84ElbzA2eBPPzM1hjC9a4pOCNLVjjkoI7trbCe4fWReztM3apfccfqLGHGAb3UAQAAAAAAAg0fnCHeAAAAAAAAHiLpA4AAAAAAEAAIqkDAAAAAAAQgEjqAAAAAAAABKB2k9QpLCzUpZdeqoiICA0ePFg7d+5s6y75hYKCAn3/+99X586dFRcXp1tvvVX79+93q3Pq1Cnl5uaqS5cuioqKUnZ2tsrLy9uox/7lkUceUUhIiKZOneoqY7zcff311/rZz36mLl26qGPHjkpJSdF7773n2m4YhmbNmqVu3bqpY8eOSk9P1yeffNKGPUZ70l7OgcF2rgrG80ptba0eeOAB9erVSx07dtRll12mBx98UGffzyJQ4tq6datGjRql7t27KyQkRK+//rrb9sbEcfToUY0dO1YWi0UxMTEaP368jh8/3opR1He+uBwOh2bMmKGUlBR16tRJ3bt315133qmDBw+6teGPccGzYHjv4Ks5rqysTFlZWYqMjFRcXJymT5+u06dPu9XZvHmzvve97yk8PFyXX365VqxY0dLhNVpT58BAjtsX82Rjzlf//Oc/de211yoiIkKJiYlasGBBq8TXEF/NpQEXu9EOvPTSS0ZYWJjxxz/+0di7d68xYcIEIyYmxigvL2/rrrW5zMxMY/ny5cYHH3xg7N6927jxxhuNnj17GsePH3fV+d///V8jMTHR2LRpk/Hee+8ZaWlpxjXXXNOGvfYPO3fuNC699FKjf//+xn333ecqZ7y+dfToUSMpKcm46667jB07dhifffaZsWHDBuPTTz911XnkkUeM6Oho4/XXXzf+8Y9/GDfffLPRq1cv45tvvmnDnqO9aA/nwGA7VwXreWX+/PlGly5djLVr1xoHDhwwVq1aZURFRRlPPfWUq06gxLVu3Trjt7/9rfHaa68ZkozVq1e7bW9MHDfccIMxYMAAY/v27cZf//pX4/LLLzfuuOOOVo7E3fniqqioMNLT043/+7//Mz766COjpKTEuPrqq43U1FS3NvwxLtQXLO8dfDHHnT592rjyyiuN9PR04/333zfWrVtnXHzxxUZ+fr6rzmeffWZERkYaeXl5xr59+4xFixYZoaGhxvr161s1Xk+aOgcGcty+micvdL6qrKw04uPjjbFjxxoffPCB8ec//9no2LGj8fvf/75V4z2br+bSQIu9XSR1rr76aiM3N9f1uLa21ujevbtRUFDQhr3yT4cPHzYkGVu2bDEM48yLFLPZbKxatcpV58MPPzQkGSUlJW3VzTZ37Ngxo3fv3obNZjN+8IMfuCYJxsvdjBkzjKFDhza43el0GgkJCcZjjz3mKquoqDDCw8ONP//5z63RRcBNsJ0Dg/FcFaznlaysLOOee+5xKxs9erQxduxYwzACN65zkx+NiWPfvn2GJGPXrl2uOm+99ZYREhJifP31163W9/PxlKw6186dOw1JxhdffGEYRmDEhTOC9b1DU+a4devWGSaTybDb7a46S5YsMSwWi1FdXW0YhmHcf//9Rr9+/dyOdfvttxuZmZktHdJ5NWcODOS4fTFPNuZ8tXjxYuOiiy5yjUfdsa+44gpfh9RovphLAzH2oP/6VU1NjUpLS5Wenu4qM5lMSk9PV0lJSRv2zD9VVlZKkmJjYyVJpaWlcjgcbuPXp08f9ezZs12PX25urrKystzGRWK8zvXmm29q0KBB+slPfqK4uDhdddVVeu6551zbDxw4ILvd7jZe0dHRGjx4cLscL7S9YDsHBuO5KljPK9dcc402bdqkjz/+WJL0j3/8Q++++65GjhwpKXDjOldj4igpKVFMTIwGDRrkqpOeni6TyaQdO3a0ep+bqrKyUiEhIYqJiZEUPHEFu2B+79CUOa6kpEQpKSmKj4931cnMzFRVVZX27t3rqnPuPJOZmdnm49WcOTCQ4/bFPNmY81VJSYmuu+46hYWFuepkZmZq//79+u9//9vSYXrki7k0EGPv0OpHbGX/+c9/VFtb6/YHKUnx8fH66KOP2qhX/snpdGrq1KkaMmSIrrzySkmS3W5XWFiY6wVJnfj4eNnt9jboZdt76aWX9Pe//127du2qt43xcvfZZ59pyZIlysvL029+8xvt2rVLv/jFLxQWFqacnBzXmHj6+2yP44W2FWznwGA9VwXreeXXv/61qqqq1KdPH4WGhqq2tlbz58/X2LFjJSlg4zpXY+Kw2+2Ki4tz296hQwfFxsYGTKynTp3SjBkzdMcdd8hisUgKjrjag2B979DUOc5ut3sci7pt56tTVVWlb775Rh07dmyJkM6ruXNgoMYt+WaebMz5ym63q1evXvXaqNt20UUXtUh85+OLuTQQYw/6pA4aLzc3Vx988IHefffdtu6K3/ryyy913333yWazKSIioq274/ecTqcGDRqkhx9+WJJ01VVX6YMPPtDSpUuVk5PTxr0D3AXTOTCYz1XBel55+eWX9eKLL6qoqEj9+vXT7t27NXXqVHXv3j2g42qPHA6HbrvtNhmGoSVLlrR1dwBJwTXHXUgwz4GNEazzZGO017k06L9+dfHFFys0NLTe1czLy8uVkJDQRr3yP5MnT9batWv1zjvvqEePHq7yhIQE1dTUqKKiwq1+ex2/0tJSHT58WN/73vfUoUMHdejQQVu2bNHTTz+tDh06KD4+nvE6S7du3ZScnOxW1rdvX5WVlUmSa0z4+0RbC7ZzYDCfq4L1vDJ9+nT9+te/1pgxY5SSkqJx48Zp2rRpKigokBS4cZ2rMXEkJCTo8OHDbttPnz6to0eP+n2sdQmdL774QjabzbVKRwrsuNqTYHzv0Jw5LiEhweNY1G07Xx2LxdImq1V8MQcGYtx1fDFPNuZ81Zgxam2+mEsDMfagT+qEhYUpNTVVmzZtcpU5nU5t2rRJVqu1DXvmHwzD0OTJk7V69Wq9/fbb9ZaRpaamymw2u43f/v37VVZW1i7Hb/jw4dqzZ492797t+hk0aJDGjh3r+j/j9a0hQ4bUu3Xmxx9/rKSkJElSr169lJCQ4DZeVVVV2rFjR7scL7S+YD0HBvO5KljPKydPnpTJ5P6yLDQ0VE6nU1LgxnWuxsRhtVpVUVGh0tJSV523335bTqdTgwcPbvU+N1ZdQueTTz7Rxo0b1aVLF7ftgRpXexNM7x18McdZrVbt2bPH7U1uXcKyLnFgtVrd2qir01bj5Ys5MBDjruOLebIx5yur1aqtW7fK4XC46thsNl1xxRVt8tUryTdzaUDG3iaXZ25lL730khEeHm6sWLHC2LdvnzFx4kQjJibG7Wrm7dWkSZOM6OhoY/PmzcahQ4dcPydPnnTV+d///V+jZ8+exttvv2289957htVqNaxWaxv22r+cfTV9w2C8zrZz506jQ4cOxvz5841PPvnEePHFF43IyEjjhRdecNV55JFHjJiYGOONN94w/vnPfxq33HKLX96iF8GpPZ0Dg+VcFaznlZycHOOSSy5x3Yb1tddeMy6++GLj/vvvd9UJlLiOHTtmvP/++8b7779vSDKeeOIJ4/3333fdBaoxcdxwww3GVVddZezYscN49913jd69e7f5rb/PF1dNTY1x8803Gz169DB2797tdj45++4o/hgX6guW9w6+mOPqbu2dkZFh7N6921i/fr3RtWtXj7f2nj59uvHhhx8ahYWFfnFr77N5OwcGcty+micvdL6qqKgw4uPjjXHjxhkffPCB8dJLLxmRkZFtektzX82lgRZ7u0jqGIZhLFq0yOjZs6cRFhZmXH311cb27dvbukt+QZLHn+XLl7vqfPPNN8a9995rXHTRRUZkZKTxox/9yDh06FDbddrPnDtJMF7u1qxZY1x55ZVGeHi40adPH+PZZ5912+50Oo0HHnjAiI+PN8LDw43hw4cb+/fvb6Peor1pT+fAYDpXBeN5paqqyrjvvvuMnj17GhEREcZ3vvMd47e//a1bQiBQ4nrnnXc8/l3l5OQYhtG4OI4cOWLccccdRlRUlGGxWIy7777bOHbsWBtE863zxXXgwIEGzyfvvPOOqw1/jAueBcN7B1/NcZ9//rkxcuRIo2PHjsbFF19s/PKXvzQcDodbnXfeeccYOHCgERYWZnznO99xO4Y/aMocGMhx+2KebMz56h//+IcxdOhQIzw83LjkkkuMRx55pMVjOx9fzaWBFnuIYRhGy64FAgAAAAAAgK8F/TV1AAAAAAAAghFJHQAAAAAAgABEUgcAAAAAACAAkdQBAAAAAAAIQCR1AAAAAAAAAhBJHQAAAAAAgABEUgcAAAAAACAAkdQBAAAAAAAIQCR1AAAAAAAAAhBJHQAAAAAAgABEUgcAAAAAACAAkdQBAAAAAAAIQP8P5P0+o2gHU8IAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1400x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 3, figsize=(14, 4))\n",
    "df[df.Churn == 0][num_cols].hist(bins=50, color=\"blue\", alpha=0.5, ax=ax)\n",
    "df[df.Churn == 1][num_cols].hist(bins=50, color=\"red\", alpha=0.5, ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8449cada-0706-4ca8-8936-754450d1e7eb",
   "metadata": {},
   "source": [
    "### Categorical feature distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "833ae457-7066-43f4-9c7e-9ecd75c20318",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<enumerate at 0x28f1acd10>"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(cat_cols)\n",
    "enumerate(cat_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "60f6d566-13a4-4fe8-9f5f-dc11b70d5342",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x2000 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ROWS, COLS = 4, 4\n",
    "fig, ax = plt.subplots(ROWS,COLS, figsize=(20,20))\n",
    "row, col = 0, 0,\n",
    "for i, categorical_feature in enumerate(cat_cols[:-1]):\n",
    "    if col == COLS - 1:\n",
    "        row += 1\n",
    "    col = i % COLS\n",
    "    df[categorical_feature].value_counts().plot(kind='bar', ax=ax[row, col]).set_title(categorical_feature)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "6bb2c162",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "##################### gender ############################\n",
      "        gender  Ratio\n",
      "gender               \n",
      "Male      3555 50.476\n",
      "Female    3488 49.524\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": [
      "##################### SeniorCitizen ############################\n",
      "               SeniorCitizen  Ratio\n",
      "SeniorCitizen                      \n",
      "0                       5901 83.785\n",
      "1                       1142 16.215\n"
     ]
    },
    {
     "data": {
      "image/png": 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paWrZsqWcTqcmTJggl8ul3r17S5IGDBig+Ph4DRs2TLNnz5bb7dajjz6q1NRUBQYGSpIefvhhzZ8/X1OmTNGoUaO0YcMGLV++XGvWrKnfEwYAAA2WT2+q/jlz586Vn5+fUlJSVF5erqSkJL344ov2eKNGjbR69WqNGzdOLpdLTZs21YgRIzRz5kx7TmxsrNasWaNJkybphRde0NVXX61FixYpKSnJF6cEAAAaoAYVRBs3bvR6HBQUpAULFmjBggU/+ZyYmBi9//775z1u37599dlnn9XFEgEAwBXI599DBAAA4GsEEQAAMB5BBAAAjEcQAQAA4xFEAADAeAQRAAAwHkEEAACMRxABAADjEUQAAMB4BBEAADAeQQQAAIxHEAEAAOMRRAAAwHgEEQAAMB5BBAAAjEcQAQAA4xFEAADAeAQRAAAwHkEEAACMRxABAADjEUQAAMB4BBEAADAeQQQAAIxHEAEAAOMRRAAAwHgEEQAAMB5BBAAAjEcQAQAA4xFEAADAeAQRAAAwXq2CqH///iopKamx3+PxqH///he7JgAAgHpVqyDauHGjKioqauw/deqUPvroo4teFAAAQH3yv5DJX3zxhf3n3bt3y+1224/PnDmjtWvX6qqrrqq71QEAANSDCwqirl27yuFwyOFwnPOtseDgYP3lL3+ps8UBAADUhwsKooKCAlmWpWuuuUaffPKJ2rRpY48FBAQoLCxMjRo1qvNFAgAAXEoXFEQxMTGSpKqqqkuyGAAAAF+4oCA62759+/Thhx+quLi4RiBNmzbtohcGAABQX2oVRK+++qrGjRun1q1bKyIiQg6Hwx5zOBwEEQAAuKzUKoiefPJJ/elPf9LUqVPrej0AAAD1rlbfQ3Ts2DE98MADdb0WAAAAn6hVED3wwANav359Xa8FAADAJ2r1lln79u312GOPaevWrercubMaN27sNf6b3/ymThYHAABQH2oVRK+88oqaNWum7OxsZWdne405HA6CCAAAXFZqFUQFBQV1vQ4AAACfqdU9RAAAAFeSWl0hGjVq1HnHX3/99VotBgAAwBdqFUTHjh3zelxZWakvv/xSJSUl5/ylrwAAAA1ZrYJo5cqVNfZVVVVp3Lhxuvbaay96UQAAAPWpzu4h8vPzU1pamubOnVtXhwQAAKgXdXpT9f79+3X69OlfPP+ll17SjTfeKKfTKafTKZfLpQ8++MAeP3XqlFJTU9WqVSs1a9ZMKSkpKioq8jpGYWGhkpOT1aRJE4WFhWny5Mk11rBx40Z1795dgYGBat++vTIyMi7qPAEAwJWlVm+ZpaWleT22LEuHDh3SmjVrNGLEiF98nKuvvlpPP/20rrvuOlmWpSVLluiee+7RZ599pk6dOmnSpElas2aNVqxYoZCQEI0fP1733XefNm/eLEk6c+aMkpOTFRERoS1btujQoUMaPny4GjdurKeeekrSD18RkJycrIcfflhLly5VVlaWxowZo8jISCUlJdXm9AEAwBXGYVmWdaFP6tevn9djPz8/tWnTRv3799eoUaPk71+rzpIktWzZUs8++6zuv/9+tWnTRsuWLdP9998vSdq7d6/i4uKUk5Oj3r1764MPPtBdd92lgwcPKjw8XJK0cOFCTZ06VYcPH1ZAQICmTp2qNWvW6Msvv7RfY/DgwSopKdHatWt/0Zo8Ho9CQkJUWloqp9NZ63M7n08//VQ9evTQyk3/T526drwkrwFcjnbl7dW9fYYpNzdX3bt39/VyAFxGLuTf71qVy4cfflirhZ3PmTNntGLFCh0/flwul0u5ubmqrKxUYmKiPadjx45q27atHUQ5OTnq3LmzHUOSlJSUpHHjxmnXrl3q1q2bcnJyvI5RPWfixIk/uZby8nKVl5fbjz0eT92dKAAAaHBqfylH0uHDh5Wfny9J6tChg9q0aXPBx9i5c6dcLpdOnTqlZs2aaeXKlYqPj1deXp4CAgIUGhrqNT88PFxut1uS5Ha7vWKoerx67HxzPB6PTp48qeDg4BprmjVrlh5//PELPhcAAHB5qtVN1cePH9eoUaMUGRmpPn36qE+fPoqKitLo0aN14sSJCzpWhw4dlJeXp23btmncuHEaMWKEdu/eXZtl1Zn09HSVlpba24EDB3y6HgAAcGnVKojS0tKUnZ2t9957TyUlJSopKdE777yj7Oxs/e53v7ugYwUEBKh9+/bq0aOHZs2apS5duuiFF15QRESEKioqVFJS4jW/qKhIERERkqSIiIganzqrfvxzc5xO5zmvDklSYGCg/cm36g0AAFy5ahVEf/vb3/Taa69p4MCBdjDceeedevXVV/XXv/71ohZUVVWl8vJy9ejRQ40bN1ZWVpY9lp+fr8LCQrlcLkmSy+XSzp07VVxcbM/JzMyU0+lUfHy8PefsY1TPqT4GAABAre4hOnHiRI37ciQpLCzsgt4yS09P18CBA9W2bVt9//33WrZsmTZu3Kh169YpJCREo0ePVlpamlq2bCmn06kJEybI5XKpd+/ekqQBAwYoPj5ew4YN0+zZs+V2u/Xoo48qNTVVgYGBkqSHH35Y8+fP15QpUzRq1Cht2LBBy5cv15o1a2pz6gAA4ApUqytELpdL06dP16lTp+x9J0+e1OOPP35BV16Ki4s1fPhwdejQQbfddpu2b9+udevW6fbbb5ckzZ07V3fddZdSUlLUp08fRURE6O9//7v9/EaNGmn16tVq1KiRXC6Xhg4dquHDh2vmzJn2nNjYWK1Zs0aZmZnq0qWL5syZo0WLFvEdRAAAwFar7yHauXOn7rjjDpWXl6tLly6SpM8//1yBgYFav369OnXqVOcL9SW+hwjwHb6HCEBtXfLvIercubP27dunpUuXau/evZKkBx98UEOGDPnJG5UBAAAaqloF0axZsxQeHq6xY8d67X/99dd1+PBhTZ06tU4WBwAAUB9qdQ/Ryy+/rI4da76t06lTJy1cuPCiFwUAAFCfahVEbrdbkZGRNfa3adNGhw4duuhFAQAA1KdaBVF0dLT9G+fPtnnzZkVFRV30ogAAAOpTre4hGjt2rCZOnKjKykr1799fkpSVlaUpU6Zc8DdVAwAA+Fqtgmjy5Mn67rvv9Mgjj6iiokKSFBQUpKlTpyo9Pb1OFwgAAHCp1SqIHA6HnnnmGT322GPas2ePgoODdd1119nfDg0AAHA5qVUQVWvWrJl69epVV2sBAADwiVrdVA0AAHAlIYgAAIDxCCIAAGA8gggAABiPIAIAAMYjiAAAgPEIIgAAYDyCCAAAGI8gAgAAxiOIAACA8QgiAABgPIIIAAAYjyACAADGI4gAAIDxCCIAAGA8gggAABiPIAIAAMYjiAAAgPEIIgAAYDyCCAAAGI8gAgAAxiOIAACA8QgiAABgPIIIAAAYjyACAADGI4gAAIDxCCIAAGA8gggAABiPIAIAAMYjiAAAgPEIIgAAYDyCCAAAGI8gAgAAxiOIAACA8QgiAABgPIIIAAAYjyACAADGI4gAAIDxCCIAAGA8gggAABjPp0E0a9Ys9erVS82bN1dYWJgGDRqk/Px8rzmnTp1SamqqWrVqpWbNmiklJUVFRUVecwoLC5WcnKwmTZooLCxMkydP1unTp73mbNy4Ud27d1dgYKDat2+vjIyMS316AADgMuHTIMrOzlZqaqq2bt2qzMxMVVZWasCAATp+/Lg9Z9KkSXrvvfe0YsUKZWdn6+DBg7rvvvvs8TNnzig5OVkVFRXasmWLlixZooyMDE2bNs2eU1BQoOTkZPXr1095eXmaOHGixowZo3Xr1tXr+QIAgIbJ35cvvnbtWq/HGRkZCgsLU25urvr06aPS0lK99tprWrZsmfr37y9JWrx4seLi4rR161b17t1b69ev1+7du/WPf/xD4eHh6tq1q5544glNnTpVM2bMUEBAgBYuXKjY2FjNmTNHkhQXF6ePP/5Yc+fOVVJSUo11lZeXq7y83H7s8Xgu4U8BAAD4WoO6h6i0tFSS1LJlS0lSbm6uKisrlZiYaM/p2LGj2rZtq5ycHElSTk6OOnfurPDwcHtOUlKSPB6Pdu3aZc85+xjVc6qP8WOzZs1SSEiIvUVHR9fdSQIAgAanwQRRVVWVJk6cqJtvvlk33HCDJMntdisgIEChoaFec8PDw+V2u+05Z8dQ9Xj12PnmeDwenTx5ssZa0tPTVVpaam8HDhyok3MEAAANk0/fMjtbamqqvvzyS3388ce+XooCAwMVGBjo62UAAIB60iCuEI0fP16rV6/Whx9+qKuvvtreHxERoYqKCpWUlHjNLyoqUkREhD3nx586q378c3OcTqeCg4Pr+nQAAMBlxqdBZFmWxo8fr5UrV2rDhg2KjY31Gu/Ro4caN26srKwse19+fr4KCwvlcrkkSS6XSzt37lRxcbE9JzMzU06nU/Hx8facs49RPaf6GAAAwGw+fcssNTVVy5Yt0zvvvKPmzZvb9/yEhIQoODhYISEhGj16tNLS0tSyZUs5nU5NmDBBLpdLvXv3liQNGDBA8fHxGjZsmGbPni23261HH31Uqamp9tteDz/8sObPn68pU6Zo1KhR2rBhg5YvX641a9b47NwBAEDD4dMrRC+99JJKS0vVt29fRUZG2tvbb79tz5k7d67uuusupaSkqE+fPoqIiNDf//53e7xRo0ZavXq1GjVqJJfLpaFDh2r48OGaOXOmPSc2NlZr1qxRZmamunTpojlz5mjRokXn/Mg9AAAwj0+vEFmW9bNzgoKCtGDBAi1YsOAn58TExOj9998/73H69u2rzz777ILXCAAArnwN4qZqAAAAXyKIAACA8QgiAABgPIIIAAAYjyACAADGI4gAAIDxCCIAAGA8gggAABiPIAIAAMYjiAAAgPEIIgAAYDyCCAAAGI8gAgAAxiOIAACA8QgiAABgPIIIAAAYjyACAADGI4gAAIDxCCIAAGA8gggAABiPIAIAAMYjiAAAgPEIIgAAYDyCCAAAGI8gAgAAxiOIAACA8QgiAABgPIIIAAAYjyACAADGI4gAAIDxCCIAAGA8gggAABiPIAIAAMYjiAAAgPEIIgAAYDyCCAAAGI8gAgAAxiOIAACA8QgiAABgPIIIAAAYjyACAADGI4gAAIDxCCIAAGA8f18vAABMUFhYqCNHjvh6GUCD07p1a7Vt29bXyyCIAOBSKywsVFxcnE6cOOHrpQANTpMmTbRnzx6fRxFBBACX2JEjR3TixAn9+dWZurZDrK+XAzQY+/ML9Pux03TkyBGCCABMcW2HWHXq2tHXywBwDtxUDQAAjOfTINq0aZPuvvtuRUVFyeFwaNWqVV7jlmVp2rRpioyMVHBwsBITE7Vv3z6vOUePHtWQIUPkdDoVGhqq0aNHq6yszGvOF198oVtvvVVBQUGKjo7W7NmzL/WpAQCAy4hPg+j48ePq0qWLFixYcM7x2bNna968eVq4cKG2bdumpk2bKikpSadOnbLnDBkyRLt27VJmZqZWr16tTZs26aGHHrLHPR6PBgwYoJiYGOXm5urZZ5/VjBkz9Morr1zy8wMAAJcHn95DNHDgQA0cOPCcY5Zl6fnnn9ejjz6qe+65R5L0xhtvKDw8XKtWrdLgwYO1Z88erV27Vtu3b1fPnj0lSX/5y19055136s9//rOioqK0dOlSVVRU6PXXX1dAQIA6deqkvLw8Pffcc17hBAAAzNVg7yEqKCiQ2+1WYmKivS8kJEQJCQnKycmRJOXk5Cg0NNSOIUlKTEyUn5+ftm3bZs/p06ePAgIC7DlJSUnKz8/XsWPHzvna5eXl8ng8XhsAALhyNdggcrvdkqTw8HCv/eHh4faY2+1WWFiY17i/v79atmzpNedcxzj7NX5s1qxZCgkJsbfo6OiLPyEAANBgNdgg8qX09HSVlpba24EDB3y9JAAAcAk12CCKiIiQJBUVFXntLyoqssciIiJUXFzsNX769GkdPXrUa865jnH2a/xYYGCgnE6n1wYAAK5cDTaIYmNjFRERoaysLHufx+PRtm3b5HK5JEkul0slJSXKzc2152zYsEFVVVVKSEiw52zatEmVlZX2nMzMTHXo0EEtWrSop7MBAAANmU+DqKysTHl5ecrLy5P0w43UeXl5KiwslMPh0MSJE/Xkk0/q3Xff1c6dOzV8+HBFRUVp0KBBkqS4uDjdcccdGjt2rD755BNt3rxZ48eP1+DBgxUVFSVJ+vWvf62AgACNHj1au3bt0ttvv60XXnhBaWlpPjprAADQ0Pj0Y/c7duxQv3797MfVkTJixAhlZGRoypQpOn78uB566CGVlJTolltu0dq1axUUFGQ/Z+nSpRo/frxuu+02+fn5KSUlRfPmzbPHQ0JCtH79eqWmpqpHjx5q3bq1pk2bxkfuAQCAzadB1LdvX1mW9ZPjDodDM2fO1MyZM39yTsuWLbVs2bLzvs6NN96ojz76qNbrBAAAV7YGew8RAABAfSGIAACA8QgiAABgPIIIAAAYjyACAADGI4gAAIDxCCIAAGA8gggAABiPIAIAAMYjiAAAgPEIIgAAYDyCCAAAGI8gAgAAxiOIAACA8QgiAABgPIIIAAAYjyACAADGI4gAAIDxCCIAAGA8gggAABiPIAIAAMYjiAAAgPEIIgAAYDyCCAAAGI8gAgAAxiOIAACA8QgiAABgPIIIAAAYjyACAADGI4gAAIDxCCIAAGA8gggAABiPIAIAAMYjiAAAgPEIIgAAYDyCCAAAGI8gAgAAxiOIAACA8QgiAABgPIIIAAAYjyACAADGI4gAAIDxCCIAAGA8gggAABiPIAIAAMYjiAAAgPEIIgAAYDyCCAAAGI8gAgAAxjMqiBYsWKB27dopKChICQkJ+uSTT3y9JAAA0AAYE0Rvv/220tLSNH36dH366afq0qWLkpKSVFxc7OulAQAAHzMmiJ577jmNHTtWI0eOVHx8vBYuXKgmTZro9ddf9/XSAACAj/n7egH1oaKiQrm5uUpPT7f3+fn5KTExUTk5OTXml5eXq7y83H5cWloqSfJ4PJdsjWVlZZKkXXl7dOL4yUv2OsDlpmDfN5J++DtyKf8OXkr8/QbO7VL//a4+pmVZPz/ZMsC3335rSbK2bNnitX/y5MnWTTfdVGP+9OnTLUlsbGxsbGxsV8B24MCBn20FI64QXaj09HSlpaXZj6uqqnT06FG1atVKDofDhytDffB4PIqOjtaBAwfkdDp9vRwAdYi/32axLEvff/+9oqKifnauEUHUunVrNWrUSEVFRV77i4qKFBERUWN+YGCgAgMDvfaFhoZeyiWiAXI6nfwHE7hC8ffbHCEhIb9onhE3VQcEBKhHjx7Kysqy91VVVSkrK0sul8uHKwMAAA2BEVeIJCktLU0jRoxQz549ddNNN+n555/X8ePHNXLkSF8vDQAA+JgxQfSrX/1Khw8f1rRp0+R2u9W1a1etXbtW4eHhvl4aGpjAwEBNnz69xtumAC5//P3GT3FY1i/5LBoAAMCVy4h7iAAAAM6HIAIAAMYjiAAAgPEIIgAAYDyCCPiRBQsWqF27dgoKClJCQoI++eQTXy8JQB3YtGmT7r77bkVFRcnhcGjVqlW+XhIaEIIIOMvbb7+ttLQ0TZ8+XZ9++qm6dOmipKQkFRcX+3ppAC7S8ePH1aVLFy1YsMDXS0EDxMfugbMkJCSoV69emj9/vqQfvtE8OjpaEyZM0B/+8Acfrw5AXXE4HFq5cqUGDRrk66WggeAKEfBvFRUVys3NVWJior3Pz89PiYmJysnJ8eHKAACXGkEE/NuRI0d05syZGt9eHh4eLrfb7aNVAQDqA0EEAACMRxAB/9a6dWs1atRIRUVFXvuLiooUERHho1UBAOoDQQT8W0BAgHr06KGsrCx7X1VVlbKysuRyuXy4MgDApWbMb7sHfom0tDSNGDFCPXv21E033aTnn39ex48f18iRI329NAAXqaysTF9//bX9uKCgQHl5eWrZsqXatm3rw5WhIeBj98CPzJ8/X88++6zcbre6du2qefPmKSEhwdfLAnCRNm7cqH79+tXYP2LECGVkZNT/gtCgEEQAAMB43EMEAACMRxABAADjEUQAAMB4BBEAADAeQQQAAIxHEAEAAOMRRAAAwHgEEQAAMB5BBOCy1LdvX02cOLHeXq9du3Z6/vnnzztnxowZ6tq1a72sB0Dd4puqAdS5w4cPa9q0aVqzZo2KiorUokULdenSRdOmTdPNN99cJ69x9OhRNW7cWM2bN7/oY3k8Hj3zzDP629/+pm+++UahoaG64YYb9Mgjj+jee++Vw+HQ4cOH1bRpUzVp0kSS5HA4tHLlSg0aNMg+TllZmcrLy9WqVauLXhOA+sUvdwVQ51JSUlRRUaElS5bommuuUVFRkbKysvTdd9/V2Wu0bNnyop5/5swZORwOeTwe3XLLLSotLdWTTz6pXr16yd/fX9nZ2ZoyZYr69++v0NBQtWnT5meP2axZMzVr1uyi1gXARywAqEPHjh2zJFkbN24875zRo0dbrVu3tpo3b27169fPysvLs8enT59udenSxXrjjTesmJgYy+l0Wr/61a8sj8djz/nP//xP67e//a39+OjRo9awYcOs0NBQKzg42Lrjjjusr776yh5fvHixFRISYr3zzjtWXFyc1ahRI6ugoMAaN26c1bRpU+vbb7+tsc7vv//eqqystCzLsmJiYqy5c+faf5ZkbzExMV7rrnb2nB/PtSzL2rlzp3XHHXdYTZs2tcLCwqyhQ4dahw8f9jrHCRMmWJMnT7ZatGhhhYeHW9OnTz/fjx9ALXEPEYA6VX2VZNWqVSovLz/nnAceeEDFxcX64IMPlJubq+7du+u2227T0aNH7Tn79+/XqlWrtHr1aq1evVrZ2dl6+umnf/J1//u//1s7duzQu+++q5ycHFmWpTvvvFOVlZX2nBMnTuiZZ57RokWLtGvXLoWFhemtt97SkCFDFBUVdc5z8feveSF9+/btkqTFixfr0KFD9uMfO3TokL19/fXXat++vfr06SNJKikpUf/+/dWtWzft2LFDa9euVVFRkf7rv/7L6xhLlixR06ZNtW3bNs2ePVszZ85UZmbmT/4cANSSr4sMwJXnr3/9q9WiRQsrKCjI+o//+A8rPT3d+vzzzy3LsqyPPvrIcjqd1qlTp7yec+2111ovv/yyZVk/XGlp0qSJ1xWhyZMnWwkJCfbjs68QffXVV5Yka/Pmzfb4kSNHrODgYGv58uWWZf1whUiS15WooqIiS5L13HPP/ew5nX2FyLJ+uPqzcuVKrzk/vkJUraqqyrr33nutHj16WCdOnLAsy7KeeOIJa8CAAV7zDhw4YEmy8vPz7XO85ZZbvOb06tXLmjp16s+uF8CF4R4iAHUuJSVFycnJ+uijj7R161Z98MEHmj17thYtWqTjx4+rrKysxo3HJ0+e1P79++3H7dq187phOjIyUsXFxed8vT179sjf318JCQn2vlatWqlDhw7as2ePvS8gIEA33nij/diqp8+U/M///I9ycnK0Y8cOBQcHS5I+//xzffjhh+e852j//v26/vrrJclrvdL5fw4Aao8gAnBJBAUF6fbbb9ftt9+uxx57TGPGjNH06dP1yCOPKDIyUhs3bqzxnNDQUPvPjRs39hpzOByqqqq6qDUFBwfL4XDYj9u0aaPQ0FDt3bv3oo57Pm+++abmzp2rjRs36qqrrrL3l5WV6e6779YzzzxT4zmRkZH2ny/FzwFATdxDBKBexMfH6/jx4+revbvcbrf8/f3Vvn17r61169a1OnZcXJxOnz6tbdu22fu+++475efnKz4+/ief5+fnp8GDB2vp0qU6ePBgjfGysjKdPn36nM9t3Lixzpw5c9515eTkaMyYMXr55ZfVu3dvr7Hu3btr165dateuXY2fQ9OmTc97XAB1jyACUKe+++479e/fX2+++aa++OILFRQUaMWKFZo9e7buueceJSYmyuVyadCgQVq/fr2++eYbbdmyRX/84x+1Y8eOWr3mddddp3vuuUdjx47Vxx9/rM8//1xDhw7VVVddpXvuuee8z/3Tn/6k6OhoJSQk6I033tDu3bu1b98+vf766+rWrZvKysrO+bx27dopKytLbrdbx44dqzHudrt17733avDgwUpKSpLb7Zbb7dbhw4clSampqTp69KgefPBBbd++Xfv379e6des0cuTInw0tAHWPIAJQp5o1a6aEhATNnTtXffr00Q033KDHHntMY8eO1fz58+VwOPT++++rT58+GjlypK6//noNHjxY//d//6fw8PBav+7ixYvVo0cP3XXXXXK5XLIsS++//36Nt5x+rGXLltq6dauGDh2qJ598Ut26ddOtt96q//3f/9Wzzz6rkJCQcz5vzpw5yszMVHR0tLp161ZjfO/evSoqKtKSJUsUGRlpb7169ZIkRUVFafPmzTpz5owGDBigzp07a+LEiQoNDZWfH/9pBuob31QNAACMx/+GAAAA4xFEAADAeAQRAAAwHkEEAACMRxABAADjEUQAAMB4BBEAADAeQQQAAIxHEAEAAOMRRAAAwHgEEQAAMN7/B5rXDDbo9ImBAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "##################### Partner ############################\n",
      "         Partner  Ratio\n",
      "Partner                \n",
      "No          3641 51.697\n",
      "Yes         3402 48.303\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": [
      "##################### Dependents ############################\n",
      "            Dependents  Ratio\n",
      "Dependents                   \n",
      "No                4933 70.041\n",
      "Yes               2110 29.959\n"
     ]
    },
    {
     "data": {
      "image/png": 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/mVZWViaXy6Xg4OALrsnpdMrpdDbI+QEAgObP51eIfujs2bOqrq5W79691aJFC23YsMEeKywsVHFxsdxutyTJ7XZr9+7dKi8vt+fk5OTI5XIpPj7ennPuMerm1B0DAADAp1eIpk6dqqFDh6pz58767rvvtHz5cm3evFnr1q1TaGioxo0bp/T0dLVt21Yul0uPPvqo3G63+vbtK0kaMmSI4uPjNWbMGM2ePVulpaWaNm2a0tLS7Cs8Dz30kBYsWKDJkydr7Nix2rhxo1asWKGsrCxfnjoAAGhGfBpE5eXluu+++3T48GGFhoaqR48eWrdunX7zm99IkubOnSs/Pz+NHDlS1dXVSk5O1iuvvGI/39/fX2vWrNHDDz8st9utli1bKjU1VTNnzrTndOnSRVlZWZo4caLmzZunTp06acmSJUpOTm7y8wUAAM2TT4PojTfeuOh4UFCQFi5cqIULF/7onJiYGH300UcXPc6AAQO0c+fOeq0RAABc/ZrdPUQAAABNjSACAADGI4gAAIDxCCIAAGA8gggAABivXkE0aNAgVVRUnLff4/Fo0KBBP3dNAAAATapeQbR582bV1NSct//UqVP6+OOPf/aiAAAAmtJl/R6iL774wv7zl19+aX+jvCTV1tYqOztb11xzTcOtDgAAoAlcVhD16tVLDodDDofjgm+NBQcHa/78+Q22OAAAgKZwWUFUVFQky7J03XXX6ZNPPlGHDh3sscDAQIWHh8vf37/BFwkAANCYLiuIYmJiJH3/jfQAAABXi3p/l9n+/fu1adMmlZeXnxdIGRkZP3thAAAATaVeQfT666/r4YcfVvv27RUZGSmHw2GPORwOgggAAFxR6hVEzzzzjP74xz9qypQpDb0eAACAJlev30N07Ngx3XnnnQ29FgAAAJ+oVxDdeeedWr9+fUOvBQAAwCfq9ZZZ165d9eSTT2r79u1KSEhQixYtvMYfe+yxBlkcAABAU6hXEL322mtq1aqVcnNzlZub6zXmcDgIIgAAcEWpVxAVFRU19DoAAAB8pl73EAEAAFxN6nWFaOzYsRcdX7p0ab0WAwAA4Av1CqJjx455PT59+rT27NmjioqKC37pKwAAQHNWryBatWrVefvOnj2rhx9+WNdff/3PXhQAAEBTarB7iPz8/JSenq65c+c21CEBAACaRIPeVH3gwAGdOXOmIQ8JAADQ6Or1lll6errXY8uydPjwYWVlZSk1NbVBFgYAANBU6hVEO3fu9Hrs5+enDh06aM6cOT/5CTQAAIDmpl5BtGnTpoZeBwAAgM/UK4jqHDlyRIWFhZKkbt26qUOHDg2yKAAAgKZUr5uqjx8/rrFjx6pjx47q37+/+vfvr6ioKI0bN04nTpxo6DUCAAA0qnoFUXp6unJzc/Xhhx+qoqJCFRUV+uCDD5Sbm6vHH3+8odcIAADQqOr1ltnKlSv13nvvacCAAfa+W2+9VcHBwbrrrrv06quvNtT6AAAAGl29rhCdOHFCERER5+0PDw/nLTMAAHDFqVcQud1uTZ8+XadOnbL3nTx5Uk899ZTcbneDLQ4AAKAp1Osts5deekm33HKLOnXqpJ49e0qSdu3aJafTqfXr1zfoAgEAABpbvYIoISFB+/fv11tvvaWvvvpKknTvvfdq9OjRCg4ObtAFAgAANLZ6BdGsWbMUERGh8ePHe+1funSpjhw5oilTpjTI4gAAAJpCve4hWrx4sWJjY8/b3717dy1atOhnLwoAAKAp1SuISktL1bFjx/P2d+jQQYcPH/7ZiwIAAGhK9Qqi6Ohobd269bz9W7duVVRU1M9eFAAAQFOq1z1E48eP14QJE3T69GkNGjRIkrRhwwZNnjyZ31QNAACuOPUKokmTJunbb7/V73//e9XU1EiSgoKCNGXKFE2dOrVBFwgAANDY6hVEDodDzz//vJ588knt27dPwcHB+sUvfiGn09nQ6wMAAGh09QqiOq1atdKvfvWrhloLAACAT9TrpmoAAICrCUEEAACMRxABAADjEUQAAMB4BBEAADAeQQQAAIxHEAEAAOMRRAAAwHgEEQAAMB5BBAAAjEcQAQAA4/2s7zIDAFya4uJiffPNN75eBtDstG/fXp07d/b1MggiAGhsxcXFiouL04kTJ3y9FKDZCQkJ0b59+3weRQQRADSyb775RidOnNALr8/U9d26+Ho5QLNxoLBIT4zP0DfffEMQAYApru/WRd17xfp6GQAugJuqAQCA8XwaRLNmzdKvfvUrtW7dWuHh4brjjjtUWFjoNefUqVNKS0tTu3bt1KpVK40cOVJlZWVec4qLi5WSkqKQkBCFh4dr0qRJOnPmjNeczZs3KzExUU6nU127dlVmZmZjnx4AALhC+DSIcnNzlZaWpu3btysnJ0enT5/WkCFDdPz4cXvOxIkT9eGHH+rdd99Vbm6uSkpKNGLECHu8trZWKSkpqqmp0bZt27Rs2TJlZmYqIyPDnlNUVKSUlBQNHDhQBQUFmjBhgh588EGtW7euSc8XAAA0Tz69hyg7O9vrcWZmpsLDw5Wfn6/+/fursrJSb7zxhpYvX65BgwZJkt58803FxcVp+/bt6tu3r9avX68vv/xS//M//6OIiAj16tVLTz/9tKZMmaIZM2YoMDBQixYtUpcuXTRnzhxJUlxcnP76179q7ty5Sk5ObvLzBgAAzUuzuoeosrJSktS2bVtJUn5+vk6fPq2kpCR7TmxsrDp37qy8vDxJUl5enhISEhQREWHPSU5Olsfj0d69e+055x6jbk7dMX6ourpaHo/HawMAAFevZhNEZ8+e1YQJE9SvXz/deOONkqTS0lIFBgYqLCzMa25ERIRKS0vtOefGUN143djF5ng8Hp08efK8tcyaNUuhoaH2Fh0d3SDnCAAAmqdmE0RpaWnas2eP3n77bV8vRVOnTlVlZaW9HTp0yNdLAgAAjahZ/B6iRx55RGvWrNGWLVvUqVMne39kZKRqampUUVHhdZWorKxMkZGR9pxPPvnE63h1n0I7d84PP5lWVlYml8ul4ODg89bjdDrldDob5NwAAEDz59MrRJZl6ZFHHtGqVau0ceNGdeni/Rtce/furRYtWmjDhg32vsLCQhUXF8vtdkuS3G63du/erfLycntOTk6OXC6X4uPj7TnnHqNuTt0xAACA2Xx6hSgtLU3Lly/XBx98oNatW9v3/ISGhio4OFihoaEaN26c0tPT1bZtW7lcLj366KNyu93q27evJGnIkCGKj4/XmDFjNHv2bJWWlmratGlKS0uzr/I89NBDWrBggSZPnqyxY8dq48aNWrFihbKysnx27gAAoPnw6RWiV199VZWVlRowYIA6duxob++88449Z+7cuRo2bJhGjhyp/v37KzIyUu+//7497u/vrzVr1sjf319ut1v/9m//pvvuu08zZ86053Tp0kVZWVnKyclRz549NWfOHC1ZsoSP3AMAAEk+vkJkWdZPzgkKCtLChQu1cOHCH50TExOjjz766KLHGTBggHbu3HnZawQAAFe/ZvMpMwAAAF8hiAAAgPEIIgAAYDyCCAAAGI8gAgAAxiOIAACA8QgiAABgPIIIAAAYjyACAADGI4gAAIDxCCIAAGA8gggAABiPIAIAAMYjiAAAgPEIIgAAYDyCCAAAGI8gAgAAxiOIAACA8QgiAABgPIIIAAAYjyACAADGI4gAAIDxCCIAAGA8gggAABiPIAIAAMYjiAAAgPEIIgAAYDyCCAAAGI8gAgAAxiOIAACA8QgiAABgPIIIAAAYjyACAADGI4gAAIDxCCIAAGA8gggAABiPIAIAAMYjiAAAgPEIIgAAYDyCCAAAGI8gAgAAxiOIAACA8QgiAABgPIIIAAAYjyACAADGI4gAAIDxCCIAAGA8gggAABiPIAIAAMYjiAAAgPEIIgAAYDyCCAAAGI8gAgAAxiOIAACA8QgiAABgPIIIAAAYjyACAADGI4gAAIDxCCIAAGA8gggAABiPIAIAAMbzaRBt2bJFt912m6KiouRwOLR69WqvccuylJGRoY4dOyo4OFhJSUnav3+/15yjR49q9OjRcrlcCgsL07hx41RVVeU154svvtCvf/1rBQUFKTo6WrNnz27sUwMAAFcQnwbR8ePH1bNnTy1cuPCC47Nnz9bLL7+sRYsWaceOHWrZsqWSk5N16tQpe87o0aO1d+9e5eTkaM2aNdqyZYt+97vf2eMej0dDhgxRTEyM8vPz9ac//UkzZszQa6+91ujnBwAArgwBvnzxoUOHaujQoRccsyxLL730kqZNm6bbb79dkvRf//VfioiI0OrVq3XPPfdo3759ys7O1qeffqqbbrpJkjR//nzdeuuteuGFFxQVFaW33npLNTU1Wrp0qQIDA9W9e3cVFBToxRdf9Aqnc1VXV6u6utp+7PF4GvjMAQBAc9Js7yEqKipSaWmpkpKS7H2hoaHq06eP8vLyJEl5eXkKCwuzY0iSkpKS5Ofnpx07dthz+vfvr8DAQHtOcnKyCgsLdezYsQu+9qxZsxQaGmpv0dHRjXGKAACgmWi2QVRaWipJioiI8NofERFhj5WWlio8PNxrPCAgQG3btvWac6FjnPsaPzR16lRVVlba26FDh37+CQEAgGbLp2+ZNVdOp1NOp9PXywAAAE2k2V4hioyMlCSVlZV57S8rK7PHIiMjVV5e7jV+5swZHT161GvOhY5x7msAAACzNdsg6tKliyIjI7VhwwZ7n8fj0Y4dO+R2uyVJbrdbFRUVys/Pt+ds3LhRZ8+eVZ8+few5W7Zs0enTp+05OTk56tatm9q0adNEZwMAAJoznwZRVVWVCgoKVFBQIOn7G6kLCgpUXFwsh8OhCRMm6JlnntFf/vIX7d69W/fdd5+ioqJ0xx13SJLi4uJ0yy23aPz48frkk0+0detWPfLII7rnnnsUFRUlSfrtb3+rwMBAjRs3Tnv37tU777yjefPmKT093UdnDQAAmhuf3kP02WefaeDAgfbjukhJTU1VZmamJk+erOPHj+t3v/udKioq9K//+q/Kzs5WUFCQ/Zy33npLjzzyiAYPHiw/Pz+NHDlSL7/8sj0eGhqq9evXKy0tTb1791b79u2VkZHxox+5BwAA5vFpEA0YMECWZf3ouMPh0MyZMzVz5swfndO2bVstX778oq/To0cPffzxx/VeJwAAuLo123uIAAAAmgpBBAAAjEcQAQAA4xFEAADAeAQRAAAwHkEEAACMRxABAADjEUQAAMB4BBEAADAeQQQAAIxHEAEAAOMRRAAAwHgEEQAAMB5BBAAAjEcQAQAA4xFEAADAeAQRAAAwHkEEAACMRxABAADjEUQAAMB4BBEAADAeQQQAAIxHEAEAAOMRRAAAwHgEEQAAMB5BBAAAjEcQAQAA4xFEAADAeAQRAAAwHkEEAACMRxABAADjEUQAAMB4BBEAADAeQQQAAIxHEAEAAOMRRAAAwHgEEQAAMB5BBAAAjEcQAQAA4xFEAADAeAQRAAAwHkEEAACMRxABAADjEUQAAMB4BBEAADAeQQQAAIxHEAEAAOMRRAAAwHgEEQAAMB5BBAAAjEcQAQAA4xFEAADAeAQRAAAwHkEEAACMRxABAADjEUQAAMB4BBEAADAeQQQAAIxHEAEAAOMRRAAAwHhGBdHChQt17bXXKigoSH369NEnn3zi6yUBAIBmwJggeuedd5Senq7p06fr888/V8+ePZWcnKzy8nJfLw0AAPiYMUH04osvavz48XrggQcUHx+vRYsWKSQkREuXLvX10gAAgI8F+HoBTaGmpkb5+fmaOnWqvc/Pz09JSUnKy8s7b351dbWqq6vtx5WVlZIkj8fTaGusqqqSJO0t2KcTx0822usAV5qi/Qclff8z0pg/g42Jn2/gwhr757vumJZl/fRkywD/+7//a0mytm3b5rV/0qRJ1s0333ze/OnTp1uS2NjY2NjY2K6C7dChQz/ZCkZcIbpcU6dOVXp6uv347NmzOnr0qNq1ayeHw+HDlaEpeDweRUdH69ChQ3K5XL5eDoAGxM+3WSzL0nfffaeoqKifnGtEELVv317+/v4qKyvz2l9WVqbIyMjz5judTjmdTq99YWFhjblENEMul4v/YQJXKX6+zREaGnpJ84y4qTowMFC9e/fWhg0b7H1nz57Vhg0b5Ha7fbgyAADQHBhxhUiS0tPTlZqaqptuukk333yzXnrpJR0/flwPPPCAr5cGAAB8zJgguvvuu3XkyBFlZGSotLRUvXr1UnZ2tiIiIny9NDQzTqdT06dPP+9tUwBXPn6+8WMclnUpn0UDAAC4ehlxDxEAAMDFEEQAAMB4BBEAADAeQQQAAIxHEMFI999/vxwOh5577jmv/atXr+a3kQNXIMuylJSUpOTk5PPGXnnlFYWFhenrr7/2wcpwpSCIYKygoCA9//zzOnbsmK+XAuBncjgcevPNN7Vjxw4tXrzY3l9UVKTJkydr/vz56tSpkw9XiOaOIIKxkpKSFBkZqVmzZv3onJUrV6p79+5yOp269tprNWfOnCZcIYDLER0drXnz5umJJ55QUVGRLMvSuHHjNGTIEP3yl7/U0KFD1apVK0VERGjMmDH65ptv7Oe+9957SkhIUHBwsNq1a6ekpCQdP37ch2eDpkYQwVj+/v569tlnNX/+/AteSs/Pz9ddd92le+65R7t379aMGTP05JNPKjMzs+kXC+CSpKamavDgwRo7dqwWLFigPXv2aPHixRo0aJB++ctf6rPPPlN2drbKysp01113SZIOHz6se++9V2PHjtW+ffu0efNmjRgxQvyaPrPwixlhpPvvv18VFRVavXq13G634uPj9cYbb2j16tUaPny4LMvS6NGjdeTIEa1fv95+3uTJk5WVlaW9e/f6cPUALqa8vFzdu3fX0aNHtXLlSu3Zs0cff/yx1q1bZ8/5+uuvFR0drcLCQlVVVal37946ePCgYmJifLhy+BJXiGC8559/XsuWLdO+ffu89u/bt0/9+vXz2tevXz/t379ftbW1TblEAJchPDxc//7v/664uDjdcccd2rVrlzZt2qRWrVrZW2xsrCTpwIED6tmzpwYPHqyEhATdeeedev3117m30EAEEYzXv39/JScna+rUqb5eCoAGEhAQoICA77+us6qqSrfddpsKCgq8tv3796t///7y9/dXTk6O1q5dq/j4eM2fP1/dunVTUVGRj88CTcmYL3cFLua5555Tr1691K1bN3tfXFyctm7d6jVv69atuuGGG+Tv79/USwRQT4mJiVq5cqWuvfZaO5J+yOFwqF+/furXr58yMjIUExOjVatWKT09vYlXC1/hChEgKSEhQaNHj9bLL79s73v88ce1YcMGPf300/rb3/6mZcuWacGCBXriiSd8uFIAlystLU1Hjx7Vvffeq08//VQHDhzQunXr9MADD6i2tlY7duzQs88+q88++0zFxcV6//33deTIEcXFxfl66WhCBBHw/82cOVNnz561HycmJmrFihV6++23deONNyojI0MzZ87U/fff77tFArhsUVFR2rp1q2prazVkyBAlJCRowoQJCgsLk5+fn1wul7Zs2aJbb71VN9xwg6ZNm6Y5c+Zo6NChvl46mhCfMgMAAMbjChEAADAeQQQAAIxHEAEAAOMRRAAAwHgEEQAAMB5BBAAAjEcQAQAA4xFEAADAeAQRAFyCzMxMhYWF+XoZABoJQQSg0d1///1yOBxyOBxq0aKFIiIi9Jvf/EZLly71+roU02zevFkOh0MVFRW+XgpgPIIIQJO45ZZbdPjwYR08eFBr167VwIED9Yc//EHDhg3TmTNnfL08AIYjiAA0CafTqcjISF1zzTVKTEzUf/zHf+iDDz7Q2rVrlZmZKUmqqKjQgw8+qA4dOsjlcmnQoEHatWuXfYwZM2aoV69eWrx4saKjoxUSEqK77rpLlZWVXq+1ZMkSxcXFKSgoSLGxsXrllVfssYMHD8rhcOj999/XwIEDFRISop49eyovL8/rGJmZmercubNCQkI0fPhwffvtt+ed0wcffKDExEQFBQXpuuuu01NPPeUVdw6HQ0uWLNHw4cMVEhKiX/ziF/rLX/5ir2PgwIGSpDZt2sjhcNhfHPzee+8pISFBwcHBateunZKSknT8+PH6/+UD+GkWADSy1NRU6/bbb7/gWM+ePa2hQ4dalmVZSUlJ1m233WZ9+umn1t/+9jfr8ccft9q1a2d9++23lmVZ1vTp062WLVtagwYNsnbu3Gnl5uZaXbt2tX7729/ax/vzn/9sdezY0Vq5cqX1j3/8w1q5cqXVtm1bKzMz07IsyyoqKrIkWbGxsdaaNWuswsJCa9SoUVZMTIx1+vRpy7Isa/v27Zafn5/1/PPPW4WFhda8efOssLAwKzQ01H6dLVu2WC6Xy8rMzLQOHDhgrV+/3rr22mutGTNm2HMkWZ06dbKWL19u7d+/33rsscesVq1aWd9++6115swZa+XKlZYkq7Cw0Dp8+LBVUVFhlZSUWAEBAdaLL75oFRUVWV988YW1cOFC67vvvmvI/yQAfoAgAtDoLhZEd999txUXF2d9/PHHlsvlsk6dOuU1fv3111uLFy+2LOv7IPL397e+/vpre3zt2rWWn5+fdfjwYXv+8uXLvY7x9NNPW26327Ks/wuiJUuW2ON79+61JFn79u2zLMuy7r33XuvWW289b53nBtHgwYOtZ5991mvOf//3f1sdO3a0H0uypk2bZj+uqqqyJFlr1661LMuyNm3aZEmyjh07Zs/Jz8+3JFkHDx684N8XgMYR4LNLUwAgybIsORwO7dq1S1VVVWrXrp3X+MmTJ3XgwAH7cefOnXXNNdfYj91ut86ePavCwkK1bt1aBw4c0Lhx4zR+/Hh7zpkzZxQaGup13B49eth/7tixoySpvLxcsbGx2rdvn4YPH+413+12Kzs72368a9cubd26VX/84x/tfbW1tTp16pROnDihkJCQ816nZcuWcrlcKi8v/9G/j549e2rw4MFKSEhQcnKyhgwZolGjRqlNmzY/+hwAPx9BBMCn9u3bpy5duqiqqkodO3bU5s2bz5tzqR93r6qqkiS9/vrr6tOnj9eYv7+/1+MWLVrYf3Y4HJJ0WZ94q6qq0lNPPaURI0acNxYUFHTB16l7rYu9jr+/v3JycrRt2zatX79e8+fP13/+539qx44d6tKlyyWvD8DlIYgA+MzGjRu1e/duTZw4UZ06dVJpaakCAgJ07bXX/uhziouLVVJSoqioKEnS9u3b5efnp27duikiIkJRUVH6xz/+odGjR9d7XXFxcdqxY4fXvu3bt3s9TkxMVGFhobp27Vrv1wkMDJT0/ZWlczkcDvXr10/9+vVTRkaGYmJitGrVKqWnp9f7tQBcHEEEoElUV1ertLRUtbW1KisrU3Z2tmbNmqVhw4bpvvvuk5+fn9xut+644w7Nnj1bN9xwg0pKSpSVlaXhw4frpptukvT91ZfU1FS98MIL8ng8euyxx3TXXXcpMjJSkvTUU0/pscceU2hoqG655RZVV1frs88+07Fjxy45KB577DH169dPL7zwgm6//XatW7fO6+0yScrIyNCwYcPUuXNnjRo1Sn5+ftq1a5f27NmjZ5555pJeJyYmRg6HQ2vWrNGtt96q4OBg7d27Vxs2bNCQIUMUHh6uHTt26MiRI4qLi7uMv20Al83XNzEBuPqlpqZakixJVkBAgNWhQwcrKSnJWrp0qVVbW2vP83g81qOPPmpFRUVZLVq0sKKjo63Ro0dbxcXFlmV9f1N1z549rVdeecWKioqygoKCrFGjRllHjx71er233nrL6tWrlxUYGGi1adPG6t+/v/X+++9blvV/N1Xv3LnTnn/s2DFLkrVp0yZ73xtvvGF16tTJCg4Otm677TbrhRde8Lqp2rIsKzs72/qXf/kXKzg42HK5XNbNN99svfbaa/a4JGvVqlVezwkNDbXefPNN+/HMmTOtyMhIy+FwWKmpqdaXX35pJScnWx06dLCcTqd1ww03WPPnz6/H3zqAy+GwLMvyaZEBwCWaMWOGVq9erYKCAl8vBcBVhl/MCAAAjEcQAQAA4/GWGQAAMB5XiAAAgPEIIgAAYDyCCAAAGI8gAgAAxiOIAACA8QgiAABgPIIIAAAYjyACAADG+39pXNAuoLoV1QAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "##################### PhoneService ############################\n",
      "              PhoneService  Ratio\n",
      "PhoneService                     \n",
      "Yes                   6361 90.317\n",
      "No                     682  9.683\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": [
      "##################### MultipleLines ############################\n",
      "                  MultipleLines  Ratio\n",
      "MultipleLines                         \n",
      "No                         3390 48.133\n",
      "Yes                        2971 42.184\n",
      "No phone service            682  9.683\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": [
      "##################### InternetService ############################\n",
      "                 InternetService  Ratio\n",
      "InternetService                        \n",
      "Fiber optic                 3096 43.959\n",
      "DSL                         2421 34.375\n",
      "No                          1526 21.667\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": [
      "##################### OnlineSecurity ############################\n",
      "                     OnlineSecurity  Ratio\n",
      "OnlineSecurity                            \n",
      "No                             3498 49.666\n",
      "Yes                            2019 28.667\n",
      "No internet service            1526 21.667\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": [
      "##################### OnlineBackup ############################\n",
      "                     OnlineBackup  Ratio\n",
      "OnlineBackup                            \n",
      "No                           3088 43.845\n",
      "Yes                          2429 34.488\n",
      "No internet service          1526 21.667\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": [
      "##################### DeviceProtection ############################\n",
      "                     DeviceProtection  Ratio\n",
      "DeviceProtection                            \n",
      "No                               3095 43.944\n",
      "Yes                              2422 34.389\n",
      "No internet service              1526 21.667\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": [
      "##################### TechSupport ############################\n",
      "                     TechSupport  Ratio\n",
      "TechSupport                            \n",
      "No                          3473 49.311\n",
      "Yes                         2044 29.022\n",
      "No internet service         1526 21.667\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": [
      "##################### StreamingTV ############################\n",
      "                     StreamingTV  Ratio\n",
      "StreamingTV                            \n",
      "No                          2810 39.898\n",
      "Yes                         2707 38.435\n",
      "No internet service         1526 21.667\n"
     ]
    },
    {
     "data": {
      "image/png": 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rDRs2TPfdd99lx3Tv3l0pKSn2uqenp1P/wIEDlZubq7S0NJ07d04PPfSQRowYofnz50v6+XUi3bp1U0xMjGbPnq1vvvlGw4YNk5+fn0aMGHE9pw0AqGA5OTkKDw/X6dOnXV0KqoiaNWtq586dFR6KyhSIXn/9dXl7e2v16tVavXq1U5/D4bjmQNSjRw/16NHjqmM8PT0VFBR02b6dO3dq2bJl2rx5s2677TZJ0ksvvaSePXtq2rRpCgkJ0bx581RcXKw5c+bIw8NDLVu2VGZmpmbMmEEgAoAq5vDhwzp9+rSmvZGsJs0au7ocuFjWrmyNHp6kw4cPV81AlJ2dXd51XNGqVasUEBCgOnXqqEuXLnrmmWdUr149SdKGDRvk5+dnhyFJiomJkZubmzZt2qR7771XGzZsUFRUlDw8POwxsbGxmjJlio4dO6Y6deqUOmZRUZGKiors9YKCggo8QwDALzVp1lgt2zR3dRkwSJkmVVeW7t276+2331Z6erqmTJmi1atXq0ePHrpw4YIkKS8vTwEBAU7bVKtWTXXr1rXfs5aXl6fAwECnMRfXL30X26UmT54sX19fewkNDS3vUwMAAFVIme4QDRs27Kr9c+bMKVMxv9S/f3/754iICLVq1UpNmjTRqlWr1LVr13I5xuUkJiYqISHBXi8oKCAUAQDwO1amQHTs2DGn9XPnzunbb7/V8ePHL/vS1/Jy0003qX79+tq7d6+6du2qoKAgHTx40GnM+fPndfToUXveUVBQkPLz853GXFy/0twkT0/PUpO3AQDA71eZAtGiRYtKtZWUlOjRRx9VkyZNbrioK/nxxx915MgRBQcHS5IiIyN1/PhxZWRkqH379pKkzz77TCUlJerQoYM95p///KfOnTtnfxsuLS1NzZo1u+z8IQAAYJ5ym0Pk5uamhIQEzZw585q3KSwsVGZmpjIzMyX9PFk7MzNTOTk5Kiws1JgxY7Rx40bt27dP6enp6t27t5o2barY2FhJUnh4uLp3767hw4fryy+/1Lp16zRy5Ej1799fISEhkqQBAwbIw8NDcXFx2r59uxYsWKAXXnjB6ZEYAAAwW7lOqs7KytL58+evefxXX32ltm3bqm3btpKkhIQEtW3bVklJSXJ3d9e2bdv0l7/8Rbfccovi4uLUvn17rV271ulx1rx589S8eXN17dpVPXv21J133qnXX3/d7vf19dWKFSuUnZ2t9u3b68knn1RSUhJfuQcAALYyPTL75d0Vy7KUm5urTz75REOGDLnm/URHR8uyrCv2L1++/Ff3UbduXfuPMF5Jq1attHbt2muuCwAAmKVMgWjLli1O625ubvL399f06dN/9RtoAAAAVU2ZAtHnn39e3nUAAAC4TJkC0UWHDh3Srl27JEnNmjWTv79/uRQFAABQmco0qfrUqVMaNmyYgoODFRUVpaioKIWEhCguLo4X8gEAgN+cMgWihIQErV69Wh9//LGOHz+u48eP66OPPtLq1av15JNPlneNAAAAFapMj8wWLlyoDz74QNHR0XZbz5495eXlpb/97W969dVXy6s+AACAClemO0SnT58u9cJUSQoICOCRGQAA+M0pUyCKjIzUhAkTdPbsWbvtzJkzevrppxUZGVluxQEAAFSGMj0ye/7559W9e3c1aNBArVu3liRt3bpVnp6eWrFiRbkWCAAAUNHKFIgiIiK0Z88ezZs3T999950k6YEHHtDAgQPl5eVVrgUCAABUtDIFosmTJyswMFDDhw93ap8zZ44OHTqkcePGlUtxAAAAlaFMc4hee+01NW/evFR7y5YtNXv27BsuCgAAoDKVKRDl5eUpODi4VLu/v79yc3NvuCgAAIDKVKZAFBoaqnXr1pVqX7dunUJCQm64KAAAgMpUpjlEw4cP16hRo3Tu3Dl16dJFkpSenq6xY8fyl6oBAMBvTpkC0ZgxY3TkyBH913/9l4qLiyVJNWrU0Lhx45SYmFiuBQIAAFS0MgUih8OhKVOmaPz48dq5c6e8vLx08803y9PTs7zrAwAAqHBlCkQXeXt76/bbby+vWgAAAFyiTJOqAQAAfk8IRAAAwHgEIgAAYDwCEQAAMB6BCAAAGI9ABAAAjEcgAgAAxiMQAQAA4xGIAACA8QhEAADAeAQiAABgPAIRAAAwHoEIAAAYj0AEAACMRyACAADGIxABAADjEYgAAIDxCEQAAMB4BCIAAGA8AhEAADAegQgAABiPQAQAAIxHIAIAAMYjEAEAAOMRiAAAgPEIRAAAwHgEIgAAYDwCEQAAMB6BCAAAGI9ABAAAjEcgAgAAxiMQAQAA4xGIAACA8QhEAADAeC4NRGvWrNE999yjkJAQORwOLV682KnfsiwlJSUpODhYXl5eiomJ0Z49e5zGHD16VAMHDpSPj4/8/PwUFxenwsJCpzHbtm1T586dVaNGDYWGhmrq1KkVfWoAAOA3xKWB6NSpU2rdurVmzZp12f6pU6fqxRdf1OzZs7Vp0ybVqlVLsbGxOnv2rD1m4MCB2r59u9LS0rR06VKtWbNGI0aMsPsLCgrUrVs3hYWFKSMjQ//+9781ceJEvf766xV+fgAA4LehmisP3qNHD/Xo0eOyfZZl6fnnn9dTTz2l3r17S5LefvttBQYGavHixerfv7927typZcuWafPmzbrtttskSS+99JJ69uypadOmKSQkRPPmzVNxcbHmzJkjDw8PtWzZUpmZmZoxY4ZTcLpUUVGRioqK7PWCgoJyPnMAAFCVVNk5RNnZ2crLy1NMTIzd5uvrqw4dOmjDhg2SpA0bNsjPz88OQ5IUExMjNzc3bdq0yR4TFRUlDw8Pe0xsbKx27dqlY8eOXfbYkydPlq+vr72EhoZWxCkCAIAqosoGory8PElSYGCgU3tgYKDdl5eXp4CAAKf+atWqqW7duk5jLrePS4/xS4mJiTpx4oS97N+//8ZPCAAAVFkufWRWVXl6esrT09PVZQAAgEpSZe8QBQUFSZLy8/Od2vPz8+2+oKAgHTx40Kn//PnzOnr0qNOYy+3j0mMAAACzVdlA1LhxYwUFBSk9Pd1uKygo0KZNmxQZGSlJioyM1PHjx5WRkWGP+eyzz1RSUqIOHTrYY9asWaNz587ZY9LS0tSsWTPVqVOnks4GAABUZS4NRIWFhcrMzFRmZqaknydSZ2ZmKicnRw6HQ6NGjdIzzzyjJUuW6JtvvtHgwYMVEhKiPn36SJLCw8PVvXt3DR8+XF9++aXWrVunkSNHqn///goJCZEkDRgwQB4eHoqLi9P27du1YMECvfDCC0pISHDRWQMAgKrGpXOIvvrqK9111132+sWQMmTIEKWmpmrs2LE6deqURowYoePHj+vOO+/UsmXLVKNGDXubefPmaeTIkeratavc3NzUt29fvfjii3a/r6+vVqxYofj4eLVv317169dXUlLSFb9yDwAAzOPSQBQdHS3Lsq7Y73A4lJycrOTk5CuOqVu3rubPn3/V47Rq1Upr164tc50AAOD3rcrOIQIAAKgsBCIAAGA8AhEAADAegQgAABiPQAQAAIxHIAIAAMYjEAEAAOMRiAAAgPEIRAAAwHgEIgAAYDwCEQAAMB6BCAAAGI9ABAAAjEcgAgAAxiMQAQAA4xGIAACA8QhEAADAeAQiAABgPAIRAAAwHoEIAAAYj0AEAACMRyACAADGIxABAADjEYgAAIDxCEQAAMB4BCIAAGA8AhEAADAegQgAABiPQAQAAIxHIAIAAMYjEAEAAOMRiAAAgPEIRAAAwHgEIgAAYDwCEQAAMB6BCAAAGI9ABAAAjEcgAgAAxiMQAQAA4xGIAACA8QhEAADAeAQiAABgPAIRAAAwHoEIAAAYj0AEAACMRyACAADGIxABAADjEYgAAIDxCEQAAMB4BCIAAGA8AhEAADBelQ5EEydOlMPhcFqaN29u9589e1bx8fGqV6+evL291bdvX+Xn5zvtIycnR7169VLNmjUVEBCgMWPG6Pz585V9KgAAoAqr5uoCfk3Lli21cuVKe71atf9f8hNPPKFPPvlE77//vnx9fTVy5Ejdd999WrdunSTpwoUL6tWrl4KCgrR+/Xrl5uZq8ODBql69uv71r39V+rkAAICqqcoHomrVqikoKKhU+4kTJ/TWW29p/vz56tKliyQpJSVF4eHh2rhxozp27KgVK1Zox44dWrlypQIDA9WmTRtNmjRJ48aN08SJE+Xh4VHZpwMAAKqgKv3ITJL27NmjkJAQ3XTTTRo4cKBycnIkSRkZGTp37pxiYmLssc2bN1fDhg21YcMGSdKGDRsUERGhwMBAe0xsbKwKCgq0ffv2Kx6zqKhIBQUFTgsAAPj9qtKBqEOHDkpNTdWyZcv06quvKjs7W507d9bJkyeVl5cnDw8P+fn5OW0TGBiovLw8SVJeXp5TGLrYf7HvSiZPnixfX197CQ0NLd8TAwAAVUqVfmTWo0cP++dWrVqpQ4cOCgsL03vvvScvL68KO25iYqISEhLs9YKCAkIRAAC/Y1X6DtEv+fn56ZZbbtHevXsVFBSk4uJiHT9+3GlMfn6+PecoKCio1LfOLq5fbl7SRZ6envLx8XFaAADA79dvKhAVFhYqKytLwcHBat++vapXr6709HS7f9euXcrJyVFkZKQkKTIyUt98840OHjxoj0lLS5OPj49atGhR6fUDAICqqUo/Mhs9erTuuecehYWF6cCBA5owYYLc3d31wAMPyNfXV3FxcUpISFDdunXl4+Ojxx57TJGRkerYsaMkqVu3bmrRooUGDRqkqVOnKi8vT0899ZTi4+Pl6enp4rMDAABVRZUORD/++KMeeOABHTlyRP7+/rrzzju1ceNG+fv7S5JmzpwpNzc39e3bV0VFRYqNjdUrr7xib+/u7q6lS5fq0UcfVWRkpGrVqqUhQ4YoOTnZVacEAACqoCodiN59992r9teoUUOzZs3SrFmzrjgmLCxMn376aXmXBgAAfkd+U3OIAAAAKgKBCAAAGI9ABAAAjEcgAgAAxiMQAQAA4xGIAACA8QhEAADAeAQiAABgPAIRAAAwHoEIAAAYj0AEAACMRyACAADGIxABAADjEYgAAIDxCEQAAMB4BCIAAGA8AhEAADAegQgAABiPQAQAAIxHIAIAAMYjEAEAAOMRiAAAgPEIRAAAwHgEIgAAYDwCEQAAMB6BCAAAGI9ABAAAjEcgAgAAxiMQAQAA4xGIAACA8QhEAADAeAQiAABgPAIRAAAwHoEIAAAYj0AEAACMRyACAADGIxABAADjEYgAAIDxCEQAAMB4BCIAAGA8AhEAADAegQgAABiPQAQAAIxHIAIAAMYjEAEAAOMRiAAAgPEIRAAAwHgEIgAAYDwCEQAAMB6BCAAAGI9ABAAAjEcgAgAAxjMqEM2aNUuNGjVSjRo11KFDB3355ZeuLgkAAFQBxgSiBQsWKCEhQRMmTNDXX3+t1q1bKzY2VgcPHnR1aQAAwMWMCUQzZszQ8OHD9dBDD6lFixaaPXu2atasqTlz5ri6NAAA4GLVXF1AZSguLlZGRoYSExPtNjc3N8XExGjDhg2lxhcVFamoqMheP3HihCSpoKCgzDUUFhZKkrZn7tTpU2fKvB/8PmTv2Sfp58/FjXyubgSfSfwSn0tUNTf6mby4jWVZvz7YMsBPP/1kSbLWr1/v1D5mzBjrjjvuKDV+woQJliQWFhYWFhaW38Gyf//+X80KRtwhul6JiYlKSEiw10tKSnT06FHVq1dPDofDhZX99hUUFCg0NFT79++Xj4+Pq8sB+EyiSuJzWT4sy9LJkycVEhLyq2ONCET169eXu7u78vPzndrz8/MVFBRUarynp6c8PT2d2vz8/CqyROP4+PjwjxxVCp9JVEV8Lm+cr6/vNY0zYlK1h4eH2rdvr/T0dLutpKRE6enpioyMdGFlAACgKjDiDpEkJSQkaMiQIbrtttt0xx136Pnnn9epU6f00EMPubo0AADgYsYEovvvv1+HDh1SUlKS8vLy1KZNGy1btkyBgYGuLs0onp6emjBhQqlHkoCr8JlEVcTnsvI5LOtavosGAADw+2XEHCIAAICrIRABAADjEYgAAIDxCEQAAKNMnDhRbdq0cXUZv2tDhw5Vnz59XF3GdSEQodwNHTpUDodDzz33nFP74sWL+UvfqDSWZSkmJkaxsbGl+l555RX5+fnpxx9/dEFluBYVeR0ZPXq009+luxaNGjXS888/f0PHLU/79u2Tw+FQZmamq0u5rBdeeEGpqamuLuO6EIhQIWrUqKEpU6bo2LFjri4FhnI4HEpJSdGmTZv02muv2e3Z2dkaO3asXnrpJTVo0MCFFeLXVNR1xNvbW/Xq1SvXfV6r4uJilxy3vFxr/b6+vr+5NzwQiFAhYmJiFBQUpMmTJ19xzMKFC9WyZUt5enqqUaNGmj59eiVWCBOEhobqhRde0OjRo5WdnS3LshQXF6du3bqpbdu26tGjh7y9vRUYGKhBgwbp8OHD9rYffPCBIiIi5OXlpXr16ikmJkanTp1y4dmY51quI9L1X0t++cjs4uOdadOmKTg4WPXq1VN8fLzOnTsnSYqOjtYPP/ygJ554Qg6Hw+kO1RdffKHOnTvLy8tLoaGhevzxx50+J40aNdKkSZM0ePBg+fj4aMSIEUpNTZWfn5+WL1+u8PBweXt7q3v37srNzXWq880331R4eLhq1Kih5s2b65VXXrH7GjduLElq27atHA6HoqOjL3uux44d08CBA+Xv7y8vLy/dfPPNSklJsfv379+vv/3tb/Lz81PdunXVu3dv7du3r9Tv5tlnn1VISIiaNWumf/zjH+rQoUOpY7Vu3VrJyclO211UUlKiqVOnqmnTpvL09FTDhg317LPPXnMdlaI83iYPXGrIkCFW7969rQ8//NCqUaOG/ZbhRYsWWRc/cl999ZXl5uZmJScnW7t27bJSUlIsLy8vKyUlxYWV4/eqd+/eVnR0tPXiiy9a/v7+1sGDBy1/f38rMTHR2rlzp/X1119bd999t3XXXXdZlmVZBw4csKpVq2bNmDHDys7OtrZt22bNmjXLOnnypIvPxBzXch2xrLJdSyZMmGC1bt3a6Vg+Pj7WI488Yu3cudP6+OOPrZo1a1qvv/66ZVmWdeTIEatBgwZWcnKylZuba+Xm5lqWZVl79+61atWqZc2cOdPavXu3tW7dOqtt27bW0KFD7X2HhYVZPj4+1rRp06y9e/dae/futVJSUqzq1atbMTEx1ubNm62MjAwrPDzcGjBggL3dO++8YwUHB1sLFy60vv/+e2vhwoVW3bp1rdTUVMuyLOvLL7+0JFkrV660cnNzrSNHjlz2XOPj4602bdpYmzdvtrKzs620tDRryZIllmVZVnFxsRUeHm4NGzbM2rZtm7Vjxw5rwIABVrNmzayioiL7d+Pt7W0NGjTI+vbbb+1FkrV37177OBfb9uzZ4/Tf76KxY8daderUsVJTU629e/daa9eutd54441rrqMyEIhQ7i79h9CxY0dr2LBhlmU5X8gGDBhg3X333U7bjRkzxmrRokWl1goz5OfnW/Xr17fc3NysRYsWWZMmTbK6devmNGb//v2WJGvXrl1WRkaGJcnat2+fiyrGtVxHLKts15LLBaKwsDDr/Pnzdlu/fv2s+++/314PCwuzZs6c6bSfuLg4a8SIEU5ta9eutdzc3KwzZ87Y2/Xp08dpTEpKSqlAMWvWLCswMNBeb9KkiTV//nyn7SZNmmRFRkZalmVZ2dnZliRry5YtVzxPy7Kse+65x3rooYcu2/ef//zHatasmVVSUmK3FRUVWV5eXtby5csty/r5dxMYGFgqmLRu3dpKTk621xMTE60OHTrY65f+9ysoKLA8PT3tAFSWOioDj8xQoaZMmaK5c+dq586dTu07d+5Up06dnNo6deqkPXv26MKFC5VZIgwQEBCgv//97woPD1efPn20detWff755/L29raX5s2bS5KysrLUunVrde3aVREREerXr5/eeOMN5sO50JWuI1L5XUtatmwpd3d3ez04OFgHDx686jZbt25Vamqq0+coNjZWJSUlys7OtsfddtttpbatWbOmmjRpctnjnTp1SllZWYqLi3Pa9zPPPKOsrKxrPidJevTRR/Xuu++qTZs2Gjt2rNavX+9U/969e1W7dm37GHXr1tXZs2edjhMRESEPDw+n/Q4cOFDz58+X9PMXGP7nf/5HAwcOvGwNO3fuVFFRkbp27XrZ/muto6IZ8y4zuEZUVJRiY2OVmJiooUOHurocGKxatWqqVu3nS15hYaHuueceTZkypdS44OBgubu7Ky0tTevXr9eKFSv00ksv6Z///Kc2bdpkz91A5amM60j16tWd1h0Oh0pKSq66TWFhof7+97/r8ccfL9XXsGFD++datWpd0/Gs//cmrcLCQknSG2+8UWquzqWh7Vr06NFDP/zwgz799FOlpaWpa9euio+P17Rp01RYWKj27dtr3rx5pbbz9/e/av0PPPCAxo0bp6+//lpnzpzR/v37df/991+2Bi8vr6vWeK11VDQCESrcc889pzZt2qhZs2Z2W3h4uNatW+c0bt26dbrllluu+x88cL3atWunhQsXqlGjRnZI+iWHw6FOnTqpU6dOSkpKUlhYmBYtWqSEhIRKrhbS5a8jUuVdSzw8PErdcWrXrp127Nihpk2blttxJCkwMFAhISH6/vvvr3jX5eIdm2u5C+bv768hQ4ZoyJAh6ty5s8aMGaNp06apXbt2WrBggQICAuTj43NdNTZo0EB/+tOfNG/ePJ05c0Z33323AgICLjv25ptvlpeXl9LT0/Xwww+X6r+ROsoTj8xQ4SIiIjRw4EC9+OKLdtuTTz6p9PR0TZo0Sbt379bcuXP18ssva/To0S6sFKaIj4/X0aNH9cADD2jz5s3KysrS8uXL9dBDD+nChQvatGmT/vWvf+mrr75STk6OPvzwQx06dEjh4eGuLt1Yl7uOSJV3LWnUqJHWrFmjn376yf424rhx47R+/XqNHDlSmZmZ2rNnjz766CONHDnyho/39NNPa/LkyXrxxRe1e/duffPNN0pJSdGMGTMk/fwY2MvLS8uWLVN+fr5OnDhx2f0kJSXpo48+0t69e7V9+3YtXbrU/hwPHDhQ9evXV+/evbV27VplZ2dr1apVevzxx6/pb3QNHDhQ7777rt5///0rBjfp5z+fMG7cOI0dO1Zvv/22srKytHHjRr311lvlUkd5IRChUiQnJzvdfm7Xrp3ee+89vfvuu7r11luVlJSk5ORkHquhUoSEhGjdunW6cOGCunXrpoiICI0aNUp+fn5yc3OTj4+P1qxZo549e+qWW27RU089penTp6tHjx6uLt1ov7yOSJV3LUlOTta+ffvUpEkT+zFOq1attHr1au3evVudO3dW27ZtlZSUpJCQkBs+3sMPP6w333xTKSkpioiI0J/+9Celpqbaj2yrVaumF198Ua+99ppCQkLUu3fvy+7Hw8NDiYmJatWqlaKiouTu7q53331X0s/zmNasWaOGDRvqvvvuU3h4uOLi4nT27NlrulPz17/+VUeOHNHp06d/9a9Sjx8/Xk8++aSSkpIUHh6u+++/354zdaN1lBeHdfGhJQAAgKG4QwQAAIxHIAIAAMYjEAEAAOMRiAAAgPEIRAAAwHgEIgAAYDwCEQAAMB6BCAAAGI9ABABXkJqaKj8/P1eXAaASEIgAVIhDhw7p0UcfVcOGDeXp6amgoCDFxsbaL+J0OBxavHixa4v8Fffff792795dbvvbt2+fHA7HVZfp06fL3d1dP/3002X3cfPNN/OCWaAC8LZ7ABWib9++Ki4u1ty5c3XTTTcpPz9f6enpOnLkyDXvo7i42H6rtyt4eXnJy8ur3PYXGhqq3Nxce33atGlatmyZVq5cabfVqlVLU6ZM0dy5c/WPf/zDafs1a9Zo7969iouLK7eaAPyMO0QAyt3x48e1du1aTZkyRXfddZfCwsJ0xx13KDExUX/5y1/UqFEjSdK9994rh8Nhr0+cOFFt2rTRm2++qcaNG6tGjRr2/h5++GH5+/vLx8dHXbp00datW+3jZWVlqXfv3goMDJS3t7duv/12p5Ah/fy28meeeUaDBw+Wt7e3wsLCtGTJEh06dEi9e/eWt7e3WrVqpa+++sre5pePzC7W95///EeNGjWSr6+v+vfvr5MnT9pjTp48qYEDB6pWrVoKDg7WzJkzFR0drVGjRsnd3V1BQUH24u3trWrVqjm11a5dW4MGDVJqamqp3+ucOXPUoUMHtWzZ8gb/CwH4JQIRgHLn7e0tb29vLV68WEVFRaX6N2/eLElKSUlRbm6uvS5Je/fu1cKFC/Xhhx8qMzNTktSvXz8dPHhQ//u//6uMjAy1a9dOXbt21dGjRyVJhYWF6tmzp9LT07VlyxZ1795d99xzj3JycpyOO3PmTHXq1ElbtmxRr169NGjQIA0ePFgPPvigvv76azVp0kSDBw/W1d55nZWVpcWLF2vp0qVaunSpVq9ereeee87uT0hI0Lp167RkyRKlpaVp7dq1+vrrr6/r9xcXF6c9e/ZozZo1dlthYaE++OAD7g4BFcUCgArwwQcfWHXq1LFq1Khh/fGPf7QSExOtrVu32v2SrEWLFjltM2HCBKt69erWwYMH7ba1a9daPj4+1tmzZ53GNmnSxHrttdeuePyWLVtaL730kr0eFhZmPfjgg/Z6bm6uJckaP3683bZhwwZLkpWbm2tZlmWlpKRYvr6+TvXVrFnTKigosNvGjBljdejQwbIsyyooKLCqV69uvf/++3b/8ePHrZo1a1r//d//XarGCRMmWK1bt75s/R07drSGDBlir7/11luljg2g/HCHCECF6Nu3rw4cOKAlS5aoe/fuWrVqldq1a3fZR0GXCgsLk7+/v72+detWFRYWql69evadJ29vb2VnZysrK0vSz3dPRo8erfDwcPn5+cnb21s7d+4sdYeoVatW9s+BgYGSpIiIiFJtBw8evGJ9jRo1Uu3ate314OBge/z333+vc+fO6Y477rD7fX191axZs6ue8+UMGzZMH3zwgf04bs6cOerXr5/TsQGUHyZVA6gwNWrU0N133627775b48eP18MPP6wJEyZo6NChV9ymVq1aTuuFhYUKDg7WqlWrSo29OL9n9OjRSktL07Rp09S0aVN5eXnpr3/9q4qLi53GV69e3f7Z4XBcsa2kpOSK9V06/uI2VxtfVv3799cTTzyh9957T1FRUVq3bp0mT55c7scB8DMCEYBK06JFC/ur9tWrV9eFCxd+dZt27dopLy9P1apVsydf/9K6des0dOhQ3XvvvZJ+DlH79u0rp6qv3U033aTq1atr8+bNatiwoSTpxIkT2r17t6Kioq5rX7Vr11a/fv00Z84cZWVl6ZZbblHnzp0romwAYlI1gApw5MgRdenSRe+88462bdum7Oxsvf/++5o6dap69+4t6edHT+np6crLy9OxY8euuK+YmBhFRkaqT58+WrFihfbt26f169frn//8p/2NsJtvvtmehL1161YNGDCgQu7a/JratWtryJAhGjNmjD7//HNt375dcXFxcnNzs+8+XY+4uDitX79es2fP1rBhwyqgYgAXEYgAlDtvb2916NBBM2fOVFRUlG699VaNHz9ew4cP18svvyxJmj59utLS0hQaGqq2bdtecV8Oh0OffvqpoqKi9NBDD+mWW25R//799cMPP9hzfmbMmKE6deroj3/8o+655x7FxsaqXbt2lXKuvzRjxgxFRkbqz3/+s2JiYtSpUyeFh4fbf0Lgetx5551q1qyZCgoKNHjw4AqoFsBFDsu6yvdLAQA35NSpU/rDH/6g6dOn85V5oApjDhEAlKMtW7bou+++0x133KETJ04oOTlZkuxHhQCqJgIRAJSzadOmadeuXfLw8FD79u21du1a1a9f39VlAbgKHpkBAADjMakaAAAYj0AEAACMRyACAADGIxABAADjEYgAAIDxCEQAAMB4BCIAAGA8AhEAADDe/wHepJwITOTaAAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "##################### StreamingMovies ############################\n",
      "                     StreamingMovies  Ratio\n",
      "StreamingMovies                            \n",
      "No                              2785 39.543\n",
      "Yes                             2732 38.790\n",
      "No internet service             1526 21.667\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": [
      "##################### Contract ############################\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"
     ]
    },
    {
     "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": [
      "##################### PaperlessBilling ############################\n",
      "                  PaperlessBilling  Ratio\n",
      "PaperlessBilling                         \n",
      "Yes                           4171 59.222\n",
      "No                            2872 40.778\n"
     ]
    },
    {
     "data": {
      "image/png": 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ECRM0fPhwbdiwQcuXL9eqVau8uXQAAOBDvBqIKioqNHToUB0+fFhhYWHq2LGj1q5dq1/96leSpFmzZsnPz08DBw5UdXW1kpOTNX/+fOP1/v7+ysnJ0ejRo+VwOBQaGqphw4YpMzPTqGndurVWrVqlcePGafbs2WrZsqXeeOMNJScnN/h6AQCAb/JqIHrzzTcvOx4UFKR58+Zp3rx5l6yJjY3V6tWrLztP7969tWPHjnr1CAAAbn4+9wwRAABAQyMQAQAA0/P62+4BwAzKysp09OhRb7cB+JzmzZurVatW3m6DQAQA11tZWZni4uJ06tQpb7cC+JyQkBDt27fP66GIQAQA19nRo0d16tQpvfx6pm5v29rb7QA+40BJqZ4dmaGjR48SiADALG5v21rtO7fzdhsALoKHqgEAgOkRiAAAgOkRiAAAgOkRiAAAgOkRiAAAgOkRiAAAgOkRiAAAgOkRiAAAgOkRiAAAgOkRiAAAgOkRiAAAgOkRiAAAgOkRiAAAgOkRiAAAgOkRiAAAgOkRiAAAgOkRiAAAgOkRiAAAgOkRiAAAgOkRiAAAgOkRiAAAgOkRiAAAgOkRiAAAgOkRiAAAgOkRiAAAgOl5NRBlZWXp7rvvVpMmTRQREaGHHnpIJSUlHjW9e/eWxWLx2EaNGuVRU1ZWppSUFIWEhCgiIkLjx4/X2bNnPWry8/PVtWtXWa1WtWnTRtnZ2dd7eQAA4Abh1UBUUFCg1NRUbd26Vbm5uaqtrVVSUpJOnjzpUTdy5EgdPnzY2KZPn26MnTt3TikpKaqpqdGWLVu0ePFiZWdnKyMjw6gpLS1VSkqK7rvvPhUXF2vs2LF68skntXbt2gZbKwAA8F0B3jz5mjVrPPazs7MVERGhoqIi9erVyzgeEhIiu91+0TnWrVunvXv3av369YqMjFTnzp01depUTZw4UVOmTFFgYKAWLlyo1q1ba8aMGZKkuLg4ffLJJ5o1a5aSk5Ov3wIBAMANwaeeIaqqqpIkNW3a1OP4kiVL1Lx5c3Xo0EHp6ek6deqUMVZYWKiEhARFRkYax5KTk+VyubRnzx6jJjEx0WPO5ORkFRYWXrSP6upquVwujw0AANy8vHqF6Ifq6uo0duxY9ezZUx06dDCOP/7444qNjVV0dLR27typiRMnqqSkRO+//74kyel0eoQhSca+0+m8bI3L5dLp06cVHBzsMZaVlaXnnnvumq8RAAD4Jp8JRKmpqdq9e7c++eQTj+NPPfWU8XNCQoKioqLUt29fHThwQLfffvt16SU9PV1paWnGvsvlUkxMzHU5FwAA8D6fuGU2ZswY5eTk6OOPP1bLli0vW9u9e3dJ0ldffSVJstvtKi8v96g5v3/+uaNL1dhstguuDkmS1WqVzWbz2AAAwM3Lq4HI7XZrzJgxWrFihTZs2KDWrVv/5GuKi4slSVFRUZIkh8OhXbt2qaKiwqjJzc2VzWZTfHy8UZOXl+cxT25urhwOxzVaCQAAuJF5NRClpqbqn//8p5YuXaomTZrI6XTK6XTq9OnTkqQDBw5o6tSpKioq0tdff60PPvhAQ4cOVa9evdSxY0dJUlJSkuLj4zVkyBB98cUXWrt2rSZNmqTU1FRZrVZJ0qhRo/Tf//5XEyZM0Jdffqn58+dr+fLlGjdunNfWDgAAfIdXA9GCBQtUVVWl3r17KyoqytiWLVsmSQoMDNT69euVlJSkdu3a6ZlnntHAgQP14YcfGnP4+/srJydH/v7+cjgc+r//+z8NHTpUmZmZRk3r1q21atUq5ebmqlOnTpoxY4beeOMN3nIPAAAkefmharfbfdnxmJgYFRQU/OQ8sbGxWr169WVrevfurR07dlxVfwAAwBx84qFqAAAAbyIQAQAA06tXIOrTp48qKysvOO5yudSnT5+f2xMAAECDqlcgys/PV01NzQXHz5w5o02bNv3spgAAABrSVT1UvXPnTuPnvXv3Gl+NIX3/rfNr1qzRL37xi2vXHQAAQAO4qkDUuXNnWSwWWSyWi94aCw4O1pw5c65ZcwAAAA3hqgJRaWmp3G63brvtNn366adq0aKFMRYYGKiIiAj5+/tf8yYBAACup6sKRLGxsZK+/2Z6AACAm0W9P5hx//79+vjjj1VRUXFBQMrIyPjZjQEAADSUegWi119/XaNHj1bz5s1lt9tlsViMMYvFQiACAAA3lHoFor/+9a96/vnnNXHixGvdDwAAQIOr1+cQfffdd3r00UevdS8AAABeUa9A9Oijj2rdunXXuhcAAACvqNctszZt2ugvf/mLtm7dqoSEBDVq1Mhj/E9/+tM1aQ4AAKAh1CsQ/e1vf1Pjxo1VUFCggoICjzGLxUIgAgAAN5R6BaLS0tJr3QcAAIDX1OsZIgAAgJtJva4QDR8+/LLjb731Vr2aAQAA8IZ6BaLvvvvOY7+2tla7d+9WZWXlRb/0FQAAwJfVKxCtWLHigmN1dXUaPXq0br/99p/dFAAAQEO6Zs8Q+fn5KS0tTbNmzbpWUwIAADSIa/pQ9YEDB3T27NlrOSUAAMB1V69bZmlpaR77brdbhw8f1qpVqzRs2LBr0hgAAEBDqVcg2rFjh8e+n5+fWrRooRkzZvzkO9AAAAB8Tb0C0ccff3yt+wAAAPCaegWi844cOaKSkhJJUtu2bdWiRYtr0hQAAEBDqtdD1SdPntTw4cMVFRWlXr16qVevXoqOjtaIESN06tSpa90jAADAdVWvQJSWlqaCggJ9+OGHqqysVGVlpf7973+roKBAzzzzzLXuEQAA4Lqq1y2z9957T++++6569+5tHBswYICCg4P129/+VgsWLLhW/QEAAFx39bpCdOrUKUVGRl5wPCIigltmAADghlOvQORwODR58mSdOXPGOHb69Gk999xzcjgc16w5AACAhlCvQPTKK69o8+bNatmypfr27au+ffsqJiZGmzdv1uzZs694nqysLN19991q0qSJIiIi9NBDDxnvWjvvzJkzSk1NVbNmzdS4cWMNHDhQ5eXlHjVlZWVKSUlRSEiIIiIiNH78+As+MTs/P19du3aV1WpVmzZtlJ2dXZ+lAwCAm1C9AlFCQoL279+vrKwsde7cWZ07d9a0adP01VdfqX379lc8T0FBgVJTU7V161bl5uaqtrZWSUlJOnnypFEzbtw4ffjhh3rnnXdUUFCgQ4cO6eGHHzbGz507p5SUFNXU1GjLli1avHixsrOzlZGRYdSUlpYqJSVF9913n4qLizV27Fg9+eSTWrt2bX2WDwAAbjL1eqg6KytLkZGRGjlypMfxt956S0eOHNHEiROvaJ41a9Z47GdnZysiIkJFRUXq1auXqqqq9Oabb2rp0qXq06ePJGnRokWKi4vT1q1b1aNHD61bt0579+7V+vXrFRkZqc6dO2vq1KmaOHGipkyZosDAQC1cuFCtW7fWjBkzJElxcXH65JNPNGvWLCUnJ9fnVwAAAG4i9bpC9Nprr6ldu3YXHG/fvr0WLlxY72aqqqokSU2bNpUkFRUVqba2VomJiUZNu3bt1KpVKxUWFkqSCgsLlZCQ4PGQd3Jyslwul/bs2WPU/HCO8zXn5/ix6upquVwujw0AANy86hWInE6noqKiLjjeokULHT58uF6N1NXVaezYserZs6c6dOhgnCcwMFDh4eEetZGRkXI6nUbNj9/xdn7/p2pcLpdOnz59QS9ZWVkKCwsztpiYmHqtCQAA3BjqFYjOP0D9Y5s3b1Z0dHS9GklNTdXu3bv19ttv1+v111J6erqqqqqM7eDBg95uCQAAXEf1eoZo5MiRGjt2rGpra41ne/Ly8jRhwoR6fVL1mDFjlJOTo40bN6ply5bGcbvdrpqaGlVWVnpcJSovL5fdbjdqPv30U4/5zr8L7Yc1P35nWnl5uWw2m4KDgy/ox2q1ymq1XvU6AADAjalegWj8+PH69ttv9cc//lE1NTWSpKCgIE2cOFHp6elXPI/b7dbTTz+tFStWKD8/X61bt/YY79atmxo1aqS8vDwNHDhQklRSUqKysjLj844cDoeef/55VVRUKCIiQpKUm5srm82m+Ph4o2b16tUec+fm5vKZSQAAQFI9A5HFYtGLL76ov/zlL9q3b5+Cg4N1xx13XPVVldTUVC1dulT//ve/1aRJE+OZn7CwMAUHByssLEwjRoxQWlqamjZtKpvNpqeffloOh0M9evSQJCUlJSk+Pl5DhgzR9OnT5XQ6NWnSJKWmphr9jBo1SnPnztWECRM0fPhwbdiwQcuXL9eqVavqs3wAAHCTqVcgOq9x48a6++676/3689959sPvRJO+f2v9E088IUmaNWuW/Pz8NHDgQFVXVys5OVnz5883av39/ZWTk6PRo0fL4XAoNDRUw4YNU2ZmplHTunVrrVq1SuPGjdPs2bPVsmVLvfHGG7zlHgAASPqZgejncrvdP1kTFBSkefPmad68eZesiY2NveCW2I/17t1bO3bsuOoeAQDAza9e7zIDAAC4mRCIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6Xk1EG3cuFEPPPCAoqOjZbFYtHLlSo/xJ554QhaLxWPr16+fR82xY8c0ePBg2Ww2hYeHa8SIETpx4oRHzc6dO3XvvfcqKChIMTExmj59+vVeGgAAuIF4NRCdPHlSnTp10rx58y5Z069fPx0+fNjY/vWvf3mMDx48WHv27FFubq5ycnK0ceNGPfXUU8a4y+VSUlKSYmNjVVRUpJdeeklTpkzR3/72t+u2LgAAcGMJ8ObJ+/fvr/79+1+2xmq1ym63X3Rs3759WrNmjT777DPdddddkqQ5c+ZowIABevnllxUdHa0lS5aopqZGb731lgIDA9W+fXsVFxdr5syZHsHph6qrq1VdXW3su1yueq4QAADcCHz+GaL8/HxFRESobdu2Gj16tL799ltjrLCwUOHh4UYYkqTExET5+flp27ZtRk2vXr0UGBho1CQnJ6ukpETffffdRc+ZlZWlsLAwY4uJiblOqwMAAL7ApwNRv3799Pe//115eXl68cUXVVBQoP79++vcuXOSJKfTqYiICI/XBAQEqGnTpnI6nUZNZGSkR835/fM1P5aenq6qqipjO3jw4LVeGgAA8CFevWX2UwYNGmT8nJCQoI4dO+r2229Xfn6++vbte93Oa7VaZbVar9v8AADAt/j0FaIfu+2229S8eXN99dVXkiS73a6KigqPmrNnz+rYsWPGc0d2u13l5eUeNef3L/VsEgAAMJcbKhB98803+vbbbxUVFSVJcjgcqqysVFFRkVGzYcMG1dXVqXv37kbNxo0bVVtba9Tk5uaqbdu2uuWWWxp2AQAAwCd5NRCdOHFCxcXFKi4uliSVlpaquLhYZWVlOnHihMaPH6+tW7fq66+/Vl5enh588EG1adNGycnJkqS4uDj169dPI0eO1KeffqrNmzdrzJgxGjRokKKjoyVJjz/+uAIDAzVixAjt2bNHy5Yt0+zZs5WWluatZQMAAB/j1UC0fft2denSRV26dJEkpaWlqUuXLsrIyJC/v7927typX//617rzzjs1YsQIdevWTZs2bfJ4vmfJkiVq166d+vbtqwEDBuiXv/ylx2cMhYWFad26dSotLVW3bt30zDPPKCMj45JvuQcAAObj1Yeqe/fuLbfbfcnxtWvX/uQcTZs21dKlSy9b07FjR23atOmq+wMAAOZwQz1DBAAAcD0QiAAAgOkRiAAAgOkRiAAAgOkRiAAAgOkRiAAAgOkRiAAAgOkRiAAAgOkRiAAAgOkRiAAAgOkRiAAAgOkRiAAAgOkRiAAAgOkRiAAAgOkRiAAAgOkRiAAAgOkRiAAAgOkRiAAAgOkRiAAAgOkRiAAAgOkRiAAAgOkRiAAAgOkRiAAAgOkRiAAAgOkRiAAAgOkRiAAAgOkRiAAAgOkRiAAAgOkRiAAAgOkRiAAAgOkRiAAAgOl5NRBt3LhRDzzwgKKjo2WxWLRy5UqPcbfbrYyMDEVFRSk4OFiJiYnav3+/R82xY8c0ePBg2Ww2hYeHa8SIETpx4oRHzc6dO3XvvfcqKChIMTExmj59+vVeGgAAuIF4NRCdPHlSnTp10rx58y46Pn36dL366qtauHChtm3bptDQUCUnJ+vMmTNGzeDBg7Vnzx7l5uYqJydHGzdu1FNPPWWMu1wuJSUlKTY2VkVFRXrppZc0ZcoU/e1vf7vu6wMAADeGAG+evH///urfv/9Fx9xut1555RVNmjRJDz74oCTp73//uyIjI7Vy5UoNGjRI+/bt05o1a/TZZ5/prrvukiTNmTNHAwYM0Msvv6zo6GgtWbJENTU1euuttxQYGKj27duruLhYM2fO9AhOP1RdXa3q6mpj3+VyXeOVAwAAX+KzzxCVlpbK6XQqMTHROBYWFqbu3bursLBQklRYWKjw8HAjDElSYmKi/Pz8tG3bNqOmV69eCgwMNGqSk5NVUlKi77777qLnzsrKUlhYmLHFxMRcjyUCAAAf4bOByOl0SpIiIyM9jkdGRhpjTqdTERERHuMBAQFq2rSpR83F5vjhOX4sPT1dVVVVxnbw4MGfvyAAAOCzvHrLzFdZrVZZrVZvtwEAABqIz14hstvtkqTy8nKP4+Xl5caY3W5XRUWFx/jZs2d17Ngxj5qLzfHDcwAAAHPz2UDUunVr2e125eXlGcdcLpe2bdsmh8MhSXI4HKqsrFRRUZFRs2HDBtXV1al79+5GzcaNG1VbW2vU5Obmqm3btrrlllsaaDUAAMCXeTUQnThxQsXFxSouLpb0/YPUxcXFKisrk8Vi0dixY/XXv/5VH3zwgXbt2qWhQ4cqOjpaDz30kCQpLi5O/fr108iRI/Xpp59q8+bNGjNmjAYNGqTo6GhJ0uOPP67AwECNGDFCe/bs0bJlyzR79mylpaV5adUAAMDXePUZou3bt+u+++4z9s+HlGHDhik7O1sTJkzQyZMn9dRTT6myslK//OUvtWbNGgUFBRmvWbJkicaMGaO+ffvKz89PAwcO1KuvvmqMh4WFad26dUpNTVW3bt3UvHlzZWRkXPIt9wAAwHy8Goh69+4tt9t9yXGLxaLMzExlZmZesqZp06ZaunTpZc/TsWNHbdq0qd59AgCAm5vPPkMEAADQUAhEAADA9AhEAADA9AhEAADA9AhEAADA9AhEAADA9AhEAADA9AhEAADA9AhEAADA9AhEAADA9AhEAADA9AhEAADA9AhEAADA9AhEAADA9AhEAADA9AhEAADA9AhEAADA9AhEAADA9AhEAADA9AhEAADA9AhEAADA9AhEAADA9AhEAADA9AhEAADA9AhEAADA9AhEAADA9AhEAADA9AhEAADA9AhEAADA9AhEAADA9AhEAADA9Hw6EE2ZMkUWi8Vja9eunTF+5swZpaamqlmzZmrcuLEGDhyo8vJyjznKysqUkpKikJAQRUREaPz48Tp79mxDLwUAAPiwAG838FPat2+v9evXG/sBAf9redy4cVq1apXeeecdhYWFacyYMXr44Ye1efNmSdK5c+eUkpIiu92uLVu26PDhwxo6dKgaNWqkF154ocHXAgAAfJPPB6KAgADZ7fYLjldVVenNN9/U0qVL1adPH0nSokWLFBcXp61bt6pHjx5at26d9u7dq/Xr1ysyMlKdO3fW1KlTNXHiRE2ZMkWBgYENvRwAAOCDfPqWmSTt379f0dHRuu222zR48GCVlZVJkoqKilRbW6vExESjtl27dmrVqpUKCwslSYWFhUpISFBkZKRRk5ycLJfLpT179lzynNXV1XK5XB4bAAC4efl0IOrevbuys7O1Zs0aLViwQKWlpbr33nt1/PhxOZ1OBQYGKjw83OM1kZGRcjqdkiSn0+kRhs6Pnx+7lKysLIWFhRlbTEzMtV0YAADwKT59y6x///7Gzx07dlT37t0VGxur5cuXKzg4+LqdNz09XWlpaca+y+UiFAEAcBPz6StEPxYeHq4777xTX331lex2u2pqalRZWelRU15ebjxzZLfbL3jX2fn9iz2XdJ7VapXNZvPYAADAzeuGCkQnTpzQgQMHFBUVpW7duqlRo0bKy8szxktKSlRWViaHwyFJcjgc2rVrlyoqKoya3Nxc2Ww2xcfHN3j/AADAN/n0LbNnn31WDzzwgGJjY3Xo0CFNnjxZ/v7+euyxxxQWFqYRI0YoLS1NTZs2lc1m09NPPy2Hw6EePXpIkpKSkhQfH68hQ4Zo+vTpcjqdmjRpklJTU2W1Wr28OgAA4Ct8OhB98803euyxx/Ttt9+qRYsW+uUvf6mtW7eqRYsWkqRZs2bJz89PAwcOVHV1tZKTkzV//nzj9f7+/srJydHo0aPlcDgUGhqqYcOGKTMz01tLAgAAPsinA9Hbb7992fGgoCDNmzdP8+bNu2RNbGysVq9efa1bAwAAN5Eb6hkiAACA64FABAAATI9ABAAATI9ABAAATI9ABAAATI9ABAAATI9ABAAATI9ABAAATI9ABAAATI9ABAAATI9ABAAATI9ABAAATI9ABAAATI9ABAAATI9ABAAATI9ABAAATI9ABAAATI9ABAAATI9ABAAATI9ABAAATI9ABAAATI9ABAAATI9ABAAATI9ABAAATI9ABAAATI9ABAAATI9ABAAATI9ABAAATI9ABAAATI9ABAAATI9ABAAATM9UgWjevHm69dZbFRQUpO7du+vTTz/1dksAAMAHmCYQLVu2TGlpaZo8ebI+//xzderUScnJyaqoqPB2awAAwMtME4hmzpypkSNH6ve//73i4+O1cOFChYSE6K233vJ2awAAwMsCvN1AQ6ipqVFRUZHS09ONY35+fkpMTFRhYeEF9dXV1aqurjb2q6qqJEkul+u69XjixAlJ0p7ifTp18vR1Ow9woynd/7Wk7/9Gruff4PXE3zdwcdf77/v8nG63+6eL3Sbw//7f/3NLcm/ZssXj+Pjx49333HPPBfWTJ092S2JjY2NjY2O7CbaDBw/+ZFYwxRWiq5Wenq60tDRjv66uTseOHVOzZs1ksVi82BkagsvlUkxMjA4ePCibzebtdgBcQ/x9m4vb7dbx48cVHR39k7WmCETNmzeXv7+/ysvLPY6Xl5fLbrdfUG+1WmW1Wj2OhYeHX88W4YNsNhv/YAI3Kf6+zSMsLOyK6kzxUHVgYKC6deumvLw841hdXZ3y8vLkcDi82BkAAPAFprhCJElpaWkaNmyY7rrrLt1zzz165ZVXdPLkSf3+97/3dmsAAMDLTBOIfve73+nIkSPKyMiQ0+lU586dtWbNGkVGRnq7NfgYq9WqyZMnX3DbFMCNj79vXIrF7b6S96IBAADcvEzxDBEAAMDlEIgAAIDpEYgAAIDpEYgAAIDpEYhgGm63W4mJiUpOTr5gbP78+QoPD9c333zjhc4AXEtPPPGELBaLpk2b5nF85cqVfNsALolABNOwWCxatGiRtm3bptdee804XlpaqgkTJmjOnDlq2bKlFzsEcK0EBQXpxRdf1HfffeftVnCDIBDBVGJiYjR79mw9++yzKi0tldvt1ogRI5SUlKQuXbqof//+aty4sSIjIzVkyBAdPXrUeO27776rhIQEBQcHq1mzZkpMTNTJkye9uBoAl5KYmCi73a6srKxL1rz33ntq3769rFarbr31Vs2YMaMBO4SvIRDBdIYNG6a+fftq+PDhmjt3rnbv3q3XXntNffr0UZcuXbR9+3atWbNG5eXl+u1vfytJOnz4sB577DENHz5c+/btU35+vh5++GHxMV6Ab/L399cLL7ygOXPmXPRWeFFRkX77299q0KBB2rVrl6ZMmaK//OUvys7Obvhm4RP4YEaYUkVFhdq3b69jx47pvffe0+7du7Vp0yatXbvWqPnmm28UExOjkpISnThxQt26ddPXX3+t2NhYL3YO4Kc88cQTqqys1MqVK+VwOBQfH68333xTK1eu1G9+8xu53W4NHjxYR44c0bp164zXTZgwQatWrdKePXu82D28hStEMKWIiAj94Q9/UFxcnB566CF98cUX+vjjj9W4cWNja9eunSTpwIED6tSpk/r27auEhAQ9+uijev3113k2AbgBvPjii1q8eLH27dvncXzfvn3q2bOnx7GePXtq//79OnfuXEO2CB9BIIJpBQQEKCDg+6/zO3HihB544AEVFxd7bPv371evXr3k7++v3NxcffTRR4qPj9ecOXPUtm1blZaWenkVAC6nV69eSk5OVnp6urdbgY8zzZe7ApfTtWtXvffee7r11luNkPRjFotFPXv2VM+ePZWRkaHY2FitWLFCaWlpDdwtgKsxbdo0de7cWW3btjWOxcXFafPmzR51mzdv1p133il/f/+GbhE+gCtEgKTU1FQdO3ZMjz32mD777DMdOHBAa9eu1e9//3udO3dO27Zt0wsvvKDt27errKxM77//vo4cOaK4uDhvtw7gJyQkJGjw4MF69dVXjWPPPPOM8vLyNHXqVP3nP//R4sWLNXfuXD377LNe7BTeRCACJEVHR2vz5s06d+6ckpKSlJCQoLFjxyo8PFx+fn6y2WzauHGjBgwYoDvvvFOTJk3SjBkz1L9/f2+3DuAKZGZmqq6uztjv2rWrli9frrffflsdOnRQRkaGMjMz9cQTT3ivSXgV7zIDAACmxxUiAABgegQiAABgegQiAABgegQiAABgegQiAABgegQiAABgegQiAABgegQiAABgegQiADeF/Px8WSwWVVZWersVg8Vi0cqVKyVJX3/9tSwWi4qLiyVd2G92drbCw8O90icAAhGAq/TEE0/IYrHIYrEoMDBQbdq0UWZmps6ePevt1hrMlClTjN+BxWJRWFiY7r33XhUUFHjUHT58+Iq/3uV3v/ud/vOf/1yPdgFcAQIRgKvWr18/HT58WPv379czzzyjKVOm6KWXXvJaP7W1tQ1+zvbt2+vw4cM6fPiwCgsLdccdd+j+++9XVVWVUWO322W1Wq9ovuDgYEVERFyvdgH8BAIRgKtmtVplt9sVGxur0aNHKzExUR988IFmzpyphIQEhYaGKiYmRn/84x914sQJ43XnbwutXLlSd9xxh4KCgpScnKyDBw96zP/vf/9bXbt2VVBQkG677TY999xzHlegLBaLFixYoF//+tcKDQ3V888/f9E+P/nkE917770KDg5WTEyM/vSnP+nkyZPG+Pz5840+IiMj9cgjjxhj7777rhISEhQcHKxmzZopMTHR47UBAQGy2+2y2+2Kj49XZmamTpw44XGV54e3zH7Kj2+ZTZkyRZ07d9Y//vEP3XrrrQoLC9OgQYN0/Phxo+b48eMaPHiwQkNDFRUVpVmzZql3794aO3bsFZ0TwP8QiAD8bMHBwaqpqZGfn59effVV7dmzR4sXL9aGDRs0YcIEj9pTp07p+eef19///ndt3rxZlZWVGjRokDG+adMmDR06VH/+85+1d+9evfbaa8rOzr4g9EyZMkW/+c1vtGvXLg0fPvyCng4cOKB+/fpp4MCB2rlzp5YtW6ZPPvlEY8aMkSRt375df/rTn5SZmamSkhKtWbNGvXr1kvT9ra7HHntMw4cP1759+5Sfn6+HH35Yl/ou7Orqai1atEjh4eFq27btz/pd/ngNK1euVE5OjnJyclRQUKBp06YZ42lpadq8ebM++OAD5ebmatOmTfr888+v2fkBU3EDwFUYNmyY+8EHH3S73W53XV2dOzc31221Wt3PPvvsBbXvvPOOu1mzZsb+okWL3JLcW7duNY7t27fPLcm9bds2t9vtdvft29f9wgsveMzzj3/8wx0VFWXsS3KPHTvWo+bjjz92S3J/9913brfb7R4xYoT7qaee8qjZtGmT28/Pz3369Gn3e++957bZbG6Xy3VB30VFRW5J7q+//vqiv4PJkye7/fz83KGhoe7Q0FC3xWJx22w290cffeRRJ8m9YsUKt9vtdpeWlroluXfs2HHRfhctWuQOCwvzOEdISIhHf+PHj3d3797d7Xa73S6Xy92oUSP3O++8Y4xXVla6Q0JC3H/+858v2jeASwvwXhQDcKPKyclR48aNVVtbq7q6Oj3++OOaMmWK1q9fr6ysLH355ZdyuVw6e/aszpw5o1OnTikkJETS97ea7r77bmOudu3aKTw8XPv27dM999yjL774Qps3b/a4InTu3LkL5rnrrrsu2+MXX3yhnTt3asmSJcYxt9uturo6lZaW6le/+pViY2N12223qV+/furXr59+85vfKCQkRJ06dVLfvn2VkJCg5ORkJSUl6ZFHHtEtt9xizNW2bVt98MEHkr6/dbVs2TI9+uij+vjjj3+ytyt16623qkmTJsZ+VFSUKioqJEn//e9/VVtbq3vuuccYDwsLu6ZXqAAz4ZYZgKt23333qbi4WPv379fp06e1ePFiHTlyRPfff786duyo9957T0VFRZo3b54kqaam5ornPnHihJ577jkVFxcb265du7R//34FBQUZdaGhoT85zx/+8AePeb744gvt379ft99+u5o0aaLPP/9c//rXvxQVFaWMjAx16tRJlZWV8vf3V25urj766CPFx8drzpw5atu2rUpLS435z7/Drk2bNurSpYumTZumX/ziF3rllVeu7pd5GY0aNfLYt1gsqquru2bzA/gfAhGAqxYaGqo2bdqoVatWCgj4/kJzUVGR6urqNGPGDPXo0UN33nmnDh06dMFrz549q+3btxv7JSUlqqysVFxcnCSpa9euKikpMcLGDzc/vyv/J6tr167au3fvRecJDAyU9P3VqsTERE2fPl07d+7U119/rQ0bNkj6Pnz07NlTzz33nHbs2KHAwECtWLHisuf09/fX6dOnr7jHn+O2225To0aN9NlnnxnHqqqqeOs+UE/cMgNwTbRp00a1tbWaM2eOHnjgAW3evFkLFy68oK5Ro0Z6+umn9eqrryogIEBjxoxRjx49jFs/GRkZuv/++9WqVSs98sgj8vPz0xdffKHdu3frr3/96xX3M3HiRPXo0UNjxozRk08+qdDQUO3du1e5ubmaO3eucnJy9N///le9evXSLbfcotWrV6uurk5t27bVtm3blJeXp6SkJEVERGjbtm06cuSIEdqk74Od0+mU9L9bZnv37tXEiRN/5m/yyjRp0kTDhg3T+PHj1bRpU0VERGjy5Mny8/OTxWJpkB6AmwlXiABcE506ddLMmTP14osvqkOHDlqyZImysrIuqAsJCdHEiRP1+OOPq2fPnmrcuLGWLVtmjCcnJysnJ0fr1q3T3XffrR49emjWrFmKjY29qn46duyogoIC/ec//9G9996rLl26KCMjQ9HR0ZKk8PBwvf/+++rTp4/i4uK0cOFC/etf/1L79u1ls9m0ceNGDRgwQHfeeacmTZqkGTNmeHzI4p49exQVFaWoqCh17txZy5cv14IFCzR06NB6/gav3syZM+VwOHT//fcrMTFRPXv2VFxcnMetRQBXxuJ2X+J9pABwjWVnZ2vs2LE+9fUaN5OTJ0/qF7/4hWbMmKERI0Z4ux3ghsItMwC4Qe3YsUNffvml7rnnHlVVVSkzM1OS9OCDD3q5M+DGQyACgBvYyy+/rJKSEgUGBqpbt27atGmTmjdv7u22gBsOt8wAAIDp8VA1AAAwPQIRAAAwPQIRAAAwPQIRAAAwPQIRAAAwPQIRAAAwPQIRAAAwPQIRAAAwvf8POjq/PuGy2bsAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "##################### PaymentMethod ############################\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"
     ]
    },
    {
     "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": [
      "##################### Churn ############################\n",
      "       Churn  Ratio\n",
      "Churn              \n",
      "0       5174 73.463\n",
      "1       1869 26.537\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def cat_summary(dataframe, col_name, plot = False):\n",
    "    print('#####################',col_name,'############################')\n",
    "    print(pd.DataFrame({col_name: dataframe[col_name].value_counts(),\n",
    "                       'Ratio':100 * dataframe[col_name].value_counts()/len(dataframe)}))\n",
    "    \n",
    "    if plot:\n",
    "        sns.countplot(x = dataframe[col_name], data = dataframe, edgecolor='black', color='#D9F9C4')\n",
    "        plt.show(block = True)\n",
    "        \n",
    "for col in cat_cols:\n",
    "    cat_summary(df, col, plot = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "ca62bf62-3100-4a01-9f7c-801fb4d1933c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'churned')"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature = 'Contract'\n",
    "fig, ax = plt.subplots(1, 2, figsize=(12, 4))\n",
    "df[df.Churn == 0][feature].value_counts().plot(kind='bar', ax=ax[0]).set_title('not churned')\n",
    "df[df.Churn == 1][feature].value_counts().plot(kind='bar', ax=ax[1]).set_title('churned')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "03a89b6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from plotly.subplots import make_subplots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "884f9b09",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g_labels = ['Male', 'Female']\n",
    "c_labels = ['No', 'Yes']\n",
    "# Create subplots: use 'domain' type for Pie subplot\n",
    "fig = make_subplots(rows=1, cols=2, specs=[[{'type':'domain'}, {'type':'domain'}]])\n",
    "fig.add_trace(go.Pie(labels=g_labels, values=df['gender'].value_counts(), name=\"Gender\"),\n",
    "              1, 1)\n",
    "fig.add_trace(go.Pie(labels=c_labels, values=df['Churn'].value_counts(), name=\"Churn\"),\n",
    "              1, 2)\n",
    "\n",
    "# Use `hole` to create a donut-like pie chart\n",
    "fig.update_traces(hole=.4, hoverinfo=\"label+percent+name\", textfont_size=16)\n",
    "\n",
    "fig.update_layout(\n",
    "    title_text=\"Gender and Churn Distributions\",\n",
    "    # Add annotations in the center of the donut pies.\n",
    "    annotations=[dict(text='Gender', x=0.16, y=0.5, font_size=20, showarrow=False),\n",
    "                 dict(text='Churn', x=0.84, y=0.5, font_size=20, showarrow=False)])\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "ff85677a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        CHURN_MEAN\n",
      "gender            \n",
      "Female       0.269\n",
      "Male         0.262\n",
      "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
      "               CHURN_MEAN\n",
      "SeniorCitizen            \n",
      "0                   0.236\n",
      "1                   0.417\n",
      "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
      "         CHURN_MEAN\n",
      "Partner            \n",
      "No            0.330\n",
      "Yes           0.197\n",
      "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
      "            CHURN_MEAN\n",
      "Dependents            \n",
      "No               0.313\n",
      "Yes              0.155\n",
      "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
      "              CHURN_MEAN\n",
      "PhoneService            \n",
      "No                 0.249\n",
      "Yes                0.267\n",
      "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
      "                  CHURN_MEAN\n",
      "MultipleLines               \n",
      "No                     0.250\n",
      "No phone service       0.249\n",
      "Yes                    0.286\n",
      "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
      "                 CHURN_MEAN\n",
      "InternetService            \n",
      "DSL                   0.190\n",
      "Fiber optic           0.419\n",
      "No                    0.074\n",
      "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
      "                     CHURN_MEAN\n",
      "OnlineSecurity                 \n",
      "No                        0.418\n",
      "No internet service       0.074\n",
      "Yes                       0.146\n",
      "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
      "                     CHURN_MEAN\n",
      "OnlineBackup                   \n",
      "No                        0.399\n",
      "No internet service       0.074\n",
      "Yes                       0.215\n",
      "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
      "                     CHURN_MEAN\n",
      "DeviceProtection               \n",
      "No                        0.391\n",
      "No internet service       0.074\n",
      "Yes                       0.225\n",
      "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
      "                     CHURN_MEAN\n",
      "TechSupport                    \n",
      "No                        0.416\n",
      "No internet service       0.074\n",
      "Yes                       0.152\n",
      "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
      "                     CHURN_MEAN\n",
      "StreamingTV                    \n",
      "No                        0.335\n",
      "No internet service       0.074\n",
      "Yes                       0.301\n",
      "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
      "                     CHURN_MEAN\n",
      "StreamingMovies                \n",
      "No                        0.337\n",
      "No internet service       0.074\n",
      "Yes                       0.299\n",
      "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
      "                CHURN_MEAN\n",
      "Contract                  \n",
      "Month-to-month       0.427\n",
      "One year             0.113\n",
      "Two year             0.028\n",
      "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
      "                  CHURN_MEAN\n",
      "PaperlessBilling            \n",
      "No                     0.163\n",
      "Yes                    0.336\n",
      "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
      "                           CHURN_MEAN\n",
      "PaymentMethod                        \n",
      "Bank transfer (automatic)       0.167\n",
      "Credit card (automatic)         0.152\n",
      "Electronic check                0.453\n",
      "Mailed check                    0.191\n",
      "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
      "       CHURN_MEAN\n",
      "Churn            \n",
      "0           0.000\n",
      "1           1.000\n",
      "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n"
     ]
    }
   ],
   "source": [
    "def target_summary_with_cat(dataframe,target,categorical_col):\n",
    "    print(pd.DataFrame({\"CHURN_MEAN\": dataframe.groupby(categorical_col)[target].mean()}))\n",
    "    print(\"~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\")\n",
    "\n",
    "for col in cat_cols:\n",
    "    target_summary_with_cat(df,\"Churn\",col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "cd0bef79",
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "3a1bd489",
   "metadata": {},
   "outputs": [],
   "source": [
    "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "id": "4b42d8eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tenure False\n",
      "MonthlyCharges False\n",
      "TotalCharges False\n"
     ]
    }
   ],
   "source": [
    "for col in num_cols:\n",
    "    print(col, check_outlier(df, col))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "0e5ca3eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              n_miss  ratio\n",
      "TotalCharges      11  0.160\n"
     ]
    }
   ],
   "source": [
    "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",
    "    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",
    "\n",
    "    if na_name:\n",
    "        return na_columns\n",
    "\n",
    "missing_values_table(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "6f73eb78",
   "metadata": {},
   "outputs": [
    {
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      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filling in missing values with the median\n",
    "\n",
    "df[\"TotalCharges\"].fillna(df[\"TotalCharges\"].median(), inplace=True)\n",
    "\n",
    "df['TotalCharges'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "137afb24",
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    {
     "data": {
      "text/plain": [
       "<Axes: >"
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     "execution_count": 209,
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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df[['tenure', 'MonthlyCharges', 'TotalCharges','Churn']].corr())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d5436d7-9833-4e97-a620-0dbbcd219b58",
   "metadata": {},
   "source": [
    "IT IS LOGICAL THAT THERE IS A HIGH CORRELATION BETWEEN TENURE AND TOTALCHARGES BECAUSE AS THE TENURE, I.e., TOTAL SERVICE PROVIDED MONTH INCREASES, THE AMOUNT COLLECTED ALSO INCREASES."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "2aa3d5f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[((df[\"gender\"] == \"Male\") & (df[\"SeniorCitizen\"]== 1)),\"SENIOR/YOUNG_GENDER\"] = \"senior_male\"\n",
    "df.loc[((df[\"gender\"] == \"Female\") & (df[\"SeniorCitizen\"]== 0)),\"SENIOR/YOUNG_GENDER\"] =\"young_male\"\n",
    "df.loc[((df[\"gender\"] == \"Male\") & (df[\"SeniorCitizen\"]== 1)),\"SENIOR/YOUNG_GENDER\"] =\"senior_female\"\n",
    "df.loc[((df[\"gender\"] == \"Female\") & (df[\"SeniorCitizen\"]== 0)),\"SENIOR/YOUNG_GENDER\"] =\"young_female\"\n",
    "\n",
    "\n",
    "df.loc[((df[\"gender\"] == \"Male\") & (df[\"TechSupport\"] == \"No\")),\"GENDER_SUPPORT\"] = \"no_sup_male\"\n",
    "df.loc[((df[\"gender\"] == \"Female\") & (df[\"TechSupport\"] == \"No\")),\"GENDER_SUPPORT\"] = \"no_sup_female\"\n",
    "\n",
    "\n",
    "df.loc[((df[\"Contract\"] == \"Month-to-month\")\n",
    "       & (df[\"PaymentMethod\"] == \"Electronic check\")\n",
    "       & (df[\"gender\"] == \"Male\")),\"GENDER_EC_MONTH\"] = \"male_ec_month\"\n",
    "\n",
    "df.loc[((df[\"Contract\"] == \"Month-to-month\")\n",
    "       & (df[\"PaymentMethod\"] == \"Electronic check\")\n",
    "       & (df[\"gender\"] == \"Female\")),\"GENDER_EC_MONTH\"] = \"female_ec_month\"\n",
    "\n",
    "\n",
    "df.loc[((df[\"OnlineSecurity\"] == \"No\") & (df[\"gender\"] == \"Female\")), \"GENDER_SECURITY\"] = \"no_sec_female\"\n",
    "df.loc[((df[\"OnlineSecurity\"] == \"Yes\") & (df[\"gender\"] == \"Female\")),\"GENDER_SECURITY\"] = \"yes_sec_female\"\n",
    "df.loc[((df[\"OnlineSecurity\"] == \"No\") & (df[\"gender\"] == \"Male\")),\"GENDER_SECURITY\"] = \"no_sec_male\"\n",
    "df.loc[((df[\"OnlineSecurity\"] == \"Yes\") & (df[\"gender\"] == \"Male\")),\"GENDER_SECURITY\"] = \"yes_sec_male\"\n",
    "\n",
    "\n",
    "df.loc[((df[\"InternetService\"] == \"Fiber optic\")\n",
    "       & (df[\"gender\"] == \"Male\")\n",
    "       & (df[\"Dependents\"] == \"No\")),\"GENDER_FIB_DEP\"] = \"male_fib_dep_no\"\n",
    "\n",
    "df.loc[((df[\"InternetService\"] == \"Fiber optic\")\n",
    "       & (df[\"gender\"] == \"Female\")\n",
    "       & (df[\"Dependents\"] == \"No\")),\"GENDER_FIB_DEP\"] = \"female_fib_dep_no\"\n",
    "\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",
    "df.loc[((df[\"Partner\"] == \"No\") & (df[\"Contract\"] == \"Month-to-month\")),\"PARTNER_CONTR\"] = \"no_partner_month\"\n",
    "df.loc[((df[\"Partner\"] == \"Yes\") & (df[\"Contract\"] == \"Month-to-month\")),\"PARTNER_CONTR\"] = \"yes_partner_month\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "id": "39b74b87",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Observations: 7043\n",
      "Variables: 27\n",
      "cat_cols: 24\n",
      "num_cols: 3\n",
      "cat_but_car: 0\n",
      "num_but_cat: 1\n"
     ]
    }
   ],
   "source": [
    "cat_cols, num_cols, cat_but_car = grab_col_names(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "ed4b3bb8",
   "metadata": {},
   "outputs": [],
   "source": [
    "le = LabelEncoder()\n",
    "\n",
    "binary_cols = [col for col in df.columns if df[col].dtype not in [int, float]\n",
    "               and df[col].nunique() == 2]\n",
    "\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",
    "\n",
    "for col in binary_cols:\n",
    "    df = label_encoder(df, col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "id": "2ef6b00b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def one_hot_encoder(dataframe, categorical_cols, drop_first=True):\n",
    "    dataframe = pd.get_dummies(dataframe, columns=categorical_cols, drop_first=drop_first)\n",
    "    return dataframe\n",
    "\n",
    "ohe_cols = [col for col in df.columns if 30 >= df[col].nunique() > 2]\n",
    "df = one_hot_encoder(df, ohe_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "id": "27af7c78",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tenure</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.277</td>\n",
       "      <td>-1.160</td>\n",
       "      <td>-0.994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.066</td>\n",
       "      <td>-0.260</td>\n",
       "      <td>-0.173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.237</td>\n",
       "      <td>-0.363</td>\n",
       "      <td>-0.960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.514</td>\n",
       "      <td>-0.747</td>\n",
       "      <td>-0.195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.237</td>\n",
       "      <td>0.197</td>\n",
       "      <td>-0.940</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   tenure  MonthlyCharges  TotalCharges\n",
       "0  -1.277          -1.160        -0.994\n",
       "1   0.066          -0.260        -0.173\n",
       "2  -1.237          -0.363        -0.960\n",
       "3   0.514          -0.747        -0.195\n",
       "4  -1.237           0.197        -0.940"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = StandardScaler()\n",
    "df[num_cols] = scaler.fit_transform(df[num_cols])\n",
    "\n",
    "df[num_cols].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "id": "d2dd691f",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df[\"Churn\"]\n",
    "X = df.drop([\"Churn\"], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "id": "7036f544",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "id": "ce93de41-cf13-4b1f-bbc6-cf35402a3720",
   "metadata": {},
   "outputs": [],
   "source": [
    "def base_models(X, y, scoring=\"roc_auc\"):\n",
    "    print(\"Base Models....\")\n",
    "    classifiers = [('LR', LogisticRegression()),\n",
    "                   ('KNN', KNeighborsClassifier()),\n",
    "                   (\"CART\", DecisionTreeClassifier()),\n",
    "                   (\"RF\", RandomForestClassifier()),\n",
    "                   ('Adaboost', AdaBoostClassifier()),\n",
    "                   ('GBM', GradientBoostingClassifier()),\n",
    "                   ('XGBoost', XGBClassifier(use_label_encoder=False, eval_metric='logloss')),\n",
    "                   ('LightGBM', LGBMClassifier()),\n",
    "                   ]\n",
    "\n",
    "    for name, classifier in classifiers:\n",
    "        cv_results = cross_validate(classifier, X, y, cv=3, scoring=scoring)\n",
    "        print(f\"{scoring}: {round(cv_results['test_score'].mean(), 4)} ({name}) \")\n",
    "\n",
    "base_models(X, y, scoring=\"accuracy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "id": "e412726e",
   "metadata": {},
   "outputs": [],
   "source": [
    "knn_params = {\"n_neighbors\": range(2, 50)}\n",
    "\n",
    "cart_params = {'max_depth': range(1, 20),\n",
    "               \"min_samples_split\": range(2, 30)}\n",
    "\n",
    "rf_params = {\"max_depth\": [8, 15, None],\n",
    "             \"max_features\": [5, 7, \"auto\"],\n",
    "             \"min_samples_split\": [15, 20],\n",
    "             \"n_estimators\": [200, 300]}\n",
    "\n",
    "xgboost_params = {\"learning_rate\": [0.1, 0.01],\n",
    "                  \"max_depth\": [5, 8],\n",
    "                  \"n_estimators\": [100, 200]}\n",
    "\n",
    "lightgbm_params = {\"learning_rate\": [0.01, 0.1],\n",
    "                   \"n_estimators\": [300, 500]}\n",
    "\n",
    "\n",
    "classifiers = [('KNN', KNeighborsClassifier(), knn_params),\n",
    "               (\"CART\", DecisionTreeClassifier(), cart_params),\n",
    "               (\"RF\", RandomForestClassifier(), rf_params),\n",
    "               ('XGBoost', XGBClassifier(use_label_encoder=False, eval_metric='logloss'), xgboost_params),\n",
    "               ('LightGBM', LGBMClassifier(), lightgbm_params)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "id": "6568b4b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def hyperparameter_optimization(X, y, cv=3, scoring=\"roc_auc\"):\n",
    "    print(\"Hyperparameter Optimization....\")\n",
    "    best_models = {}\n",
    "    for name, classifier, params in classifiers:\n",
    "        print(f\"########## {name} ##########\")\n",
    "        cv_results = cross_validate(classifier, X, y, cv=cv, scoring=scoring)\n",
    "        print(f\"{scoring} (Before): {round(cv_results['test_score'].mean(), 4)}\")\n",
    "\n",
    "        gs_best = GridSearchCV(classifier, params, cv=cv, n_jobs=-1, verbose=False).fit(X, y)\n",
    "        final_model = classifier.set_params(**gs_best.best_params_)\n",
    "\n",
    "        cv_results = cross_validate(final_model, X, y, cv=cv, scoring=scoring)\n",
    "        print(f\"{scoring} (After): {round(cv_results['test_score'].mean(), 4)}\")\n",
    "        print(f\"{name} best params: {gs_best.best_params_}\", end=\"\\n\\n\")\n",
    "        best_models[name] = final_model\n",
    "    return best_models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "id": "23ff2b58",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Base Models....\n",
      "roc_auc: 0.8403 (LR) \n",
      "roc_auc: 0.7827 (KNN) \n",
      "roc_auc: 0.6498 (CART) \n",
      "roc_auc: 0.8182 (RF) \n",
      "roc_auc: 0.8371 (Adaboost) \n",
      "roc_auc: 0.8383 (GBM) \n",
      "roc_auc: 0.8114 (XGBoost) \n",
      "roc_auc: 0.8356 (CatBoost) \n",
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] Number of positive: 998, number of negative: 2758\n",
      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000621 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 676\n",
      "[LightGBM] [Info] Number of data points in the train set: 3756, number of used features: 49\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.265708 -> initscore=-1.016508\n",
      "[LightGBM] [Info] Start training from score -1.016508\n",
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] Number of positive: 997, number of negative: 2759\n",
      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000553 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 676\n",
      "[LightGBM] [Info] Number of data points in the train set: 3756, number of used features: 49\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.265442 -> initscore=-1.017873\n",
      "[LightGBM] [Info] Start training from score -1.017873\n",
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] Number of positive: 997, number of negative: 2759\n",
      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000501 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 676\n",
      "[LightGBM] [Info] Number of data points in the train set: 3756, number of used features: 49\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.265442 -> initscore=-1.017873\n",
      "[LightGBM] [Info] Start training from score -1.017873\n",
      "roc_auc: 0.8262 (LightGBM) \n",
      "Hyperparameter Optimization....\n",
      "########## KNN ##########\n",
      "roc_auc (Before): 0.7827\n",
      "roc_auc (After): 0.8246\n",
      "KNN best params: {'n_neighbors': 26}\n",
      "\n",
      "########## CART ##########\n",
      "roc_auc (Before): 0.6471\n",
      "roc_auc (After): 0.8081\n",
      "CART best params: {'max_depth': 4, 'min_samples_split': 2}\n",
      "\n",
      "########## RF ##########\n",
      "roc_auc (Before): 0.816\n",
      "roc_auc (After): 0.8391\n",
      "RF best params: {'max_depth': 15, 'max_features': 5, 'min_samples_split': 20, 'n_estimators': 300}\n",
      "\n",
      "########## XGBoost ##########\n",
      "roc_auc (Before): 0.8114\n",
      "roc_auc (After): 0.8362\n",
      "XGBoost best params: {'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 100}\n",
      "\n",
      "########## LightGBM ##########\n",
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] Number of positive: 998, number of negative: 2758\n",
      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001389 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 676\n",
      "[LightGBM] [Info] Number of data points in the train set: 3756, number of used features: 49\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.265708 -> initscore=-1.016508\n",
      "[LightGBM] [Info] Start training from score -1.016508\n",
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] Number of positive: 997, number of negative: 2759\n",
      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000611 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 676\n",
      "[LightGBM] [Info] Number of data points in the train set: 3756, number of used features: 49\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.265442 -> initscore=-1.017873\n",
      "[LightGBM] [Info] Start training from score -1.017873\n",
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] Number of positive: 997, number of negative: 2759\n",
      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000637 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 676\n",
      "[LightGBM] [Info] Number of data points in the train set: 3756, number of used features: 49\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.265442 -> initscore=-1.017873\n",
      "[LightGBM] [Info] Start training from score -1.017873\n",
      "roc_auc (Before): 0.8262\n",
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] Number of positive: 1496, number of negative: 4138\n",
      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000679 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 676\n",
      "[LightGBM] [Info] Number of data points in the train set: 5634, number of used features: 49\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.265531 -> initscore=-1.017418\n",
      "[LightGBM] [Info] Start training from score -1.017418\n",
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] Number of positive: 998, number of negative: 2758\n",
      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000629 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 676\n",
      "[LightGBM] [Info] Number of data points in the train set: 3756, number of used features: 49\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.265708 -> initscore=-1.016508\n",
      "[LightGBM] [Info] Start training from score -1.016508\n",
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] Number of positive: 997, number of negative: 2759\n",
      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000625 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 676\n",
      "[LightGBM] [Info] Number of data points in the train set: 3756, number of used features: 49\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.265442 -> initscore=-1.017873\n",
      "[LightGBM] [Info] Start training from score -1.017873\n",
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] Number of positive: 997, number of negative: 2759\n",
      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000640 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 676\n",
      "[LightGBM] [Info] Number of data points in the train set: 3756, number of used features: 49\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.265442 -> initscore=-1.017873\n",
      "[LightGBM] [Info] Start training from score -1.017873\n",
      "roc_auc (After): 0.8388\n",
      "LightGBM best params: {'learning_rate': 0.01, 'n_estimators': 300}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "def fit_models(X,y):\n",
    "    base_models(X, y)\n",
    "    best_models = hyperparameter_optimization(X, y)\n",
    "    return best_models\n",
    "\n",
    "best_models = fit_models(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "id": "4b9313a7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] Number of positive: 1496, number of negative: 4138\n",
      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001857 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 676\n",
      "[LightGBM] [Info] Number of data points in the train set: 5634, number of used features: 49\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.265531 -> initscore=-1.017418\n",
      "[LightGBM] [Info] Start training from score -1.017418\n"
     ]
    }
   ],
   "source": [
    "lgbm_model = best_models['LightGBM'].fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "id": "b1554003",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.84      0.92      0.88      1036\n",
      "           1       0.68      0.51      0.58       373\n",
      "\n",
      "    accuracy                           0.81      1409\n",
      "   macro avg       0.76      0.71      0.73      1409\n",
      "weighted avg       0.80      0.81      0.80      1409\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred = lgbm_model.predict(X_test)\n",
    "y_prob = lgbm_model.predict_proba(X_test)[:, 1]\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "id": "d3d93aca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lgbm_roc_auc = roc_auc_score(y_test, y_prob)\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_prob)\n",
    "plt.figure()\n",
    "\n",
    "plt.plot([0,1],[0,1],'r--')\n",
    "plt.plot(fpr, tpr, marker='.', label='LGBM')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title(\"LGBM ROC\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "id": "e4af4a2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1])"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.columns\n",
    "random_user = X.sample(1, random_state = 42)\n",
    "lgbm_model.predict(random_user)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "id": "4d3cba12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Observing the variables that affect the success of our model\n",
    "\n",
    "def plot_importance(model, features, num=len(X), save=False):\n",
    "    feature_imp = pd.DataFrame({'Value': model.feature_importances_, 'Feature': features.columns})\n",
    "    plt.figure(figsize=(10, 10))\n",
    "    sns.set(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(lgbm_model, X_train, num=35)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "id": "36ff6f7c",
   "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>gender</th>\n",
       "      <th>SeniorCitizen</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>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>SENIOR/YOUNG_GENDER_senior_female</th>\n",
       "      <th>SENIOR/YOUNG_GENDER_young_female</th>\n",
       "      <th>GENDER_SUPPORT_no_sup_female</th>\n",
       "      <th>GENDER_SUPPORT_no_sup_male</th>\n",
       "      <th>GENDER_EC_MONTH_male_ec_month</th>\n",
       "      <th>GENDER_EC_MONTH_nan</th>\n",
       "      <th>GENDER_SECURITY_no_sec_female</th>\n",
       "      <th>GENDER_SECURITY_no_sec_male</th>\n",
       "      <th>GENDER_SECURITY_yes_sec_female</th>\n",
       "      <th>GENDER_SECURITY_yes_sec_male</th>\n",
       "      <th>GENDER_FIB_DEP_male_fib_dep_no</th>\n",
       "      <th>GENDER_FIB_DEP_nan</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>PARTNER_CONTR_no_partner_month</th>\n",
       "      <th>PARTNER_CONTR_yes_partner_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.277</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.160</td>\n",
       "      <td>-0.994</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.066</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.260</td>\n",
       "      <td>-0.173</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.237</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.363</td>\n",
       "      <td>-0.960</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.514</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.747</td>\n",
       "      <td>-0.195</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.237</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.197</td>\n",
       "      <td>-0.940</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender  SeniorCitizen  Partner  Dependents  tenure  PhoneService  PaperlessBilling  MonthlyCharges  TotalCharges  Churn  MultipleLines_No phone service  MultipleLines_Yes  InternetService_Fiber optic  InternetService_No  OnlineSecurity_No internet service  OnlineSecurity_Yes  OnlineBackup_No internet service  OnlineBackup_Yes  DeviceProtection_No internet service  DeviceProtection_Yes  TechSupport_No internet service  TechSupport_Yes  StreamingTV_No internet service  StreamingTV_Yes  \\\n",
       "0       0              0        1           0  -1.277             0                 1          -1.160        -0.994      0                            True              False                        False               False                               False               False                             False              True                                 False                 False                            False            False                            False            False   \n",
       "1       1              0        0           0   0.066             1                 0          -0.260        -0.173      0                           False              False                        False               False                               False                True                             False             False                                 False                  True                            False            False                            False            False   \n",
       "2       1              0        0           0  -1.237             1                 1          -0.363        -0.960      1                           False              False                        False               False                               False                True                             False              True                                 False                 False                            False            False                            False            False   \n",
       "3       1              0        0           0   0.514             0                 0          -0.747        -0.195      0                            True              False                        False               False                               False                True                             False             False                                 False                  True                            False             True                            False            False   \n",
       "4       0              0        0           0  -1.237             1                 1           0.197        -0.940      1                           False              False                         True               False                               False               False                             False             False                                 False                 False                            False            False                            False            False   \n",
       "\n",
       "   StreamingMovies_No internet service  StreamingMovies_Yes  Contract_One year  Contract_Two year  PaymentMethod_Credit card (automatic)  PaymentMethod_Electronic check  PaymentMethod_Mailed check  SENIOR/YOUNG_GENDER_senior_female  SENIOR/YOUNG_GENDER_young_female  GENDER_SUPPORT_no_sup_female  GENDER_SUPPORT_no_sup_male  GENDER_EC_MONTH_male_ec_month  GENDER_EC_MONTH_nan  GENDER_SECURITY_no_sec_female  GENDER_SECURITY_no_sec_male  GENDER_SECURITY_yes_sec_female  GENDER_SECURITY_yes_sec_male  \\\n",
       "0                                False                False              False              False                                  False                            True                       False                              False                              True                          True                       False                          False                False                           True                        False                           False                         False   \n",
       "1                                False                False               True              False                                  False                           False                        True                              False                             False                         False                        True                          False                 True                          False                        False                           False                          True   \n",
       "2                                False                False              False              False                                  False                           False                        True                              False                             False                         False                        True                          False                 True                          False                        False                           False                          True   \n",
       "3                                False                False               True              False                                  False                           False                       False                              False                             False                         False                       False                          False                 True                          False                        False                           False                          True   \n",
       "4                                False                False              False              False                                  False                            True                       False                              False                              True                          True                       False                          False                False                           True                        False                           False                         False   \n",
       "\n",
       "   GENDER_FIB_DEP_male_fib_dep_no  GENDER_FIB_DEP_nan  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  PARTNER_CONTR_no_partner_month  PARTNER_CONTR_yes_partner_month  \n",
       "0                           False                True                     False                     False                     False                     False                     False                           False                             True  \n",
       "1                           False                True                     False                      True                     False                     False                     False                           False                            False  \n",
       "2                           False                True                     False                     False                     False                     False                     False                            True                            False  \n",
       "3                           False                True                     False                     False                      True                     False                     False                           False                            False  \n",
       "4                           False               False                     False                     False                     False                     False                     False                            True                            False  "
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  }
 ],
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