Model_development.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c6708a0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "16b287fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\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>...</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>...</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>29.85</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>...</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>56.95</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>...</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>53.85</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>...</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>42.30</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>...</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>70.70</td>\n",
       "      <td>151.65</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   customerID  gender  SeniorCitizen Partner Dependents  tenure PhoneService  \\\n",
       "0  7590-VHVEG  Female              0     Yes         No       1           No   \n",
       "1  5575-GNVDE    Male              0      No         No      34          Yes   \n",
       "2  3668-QPYBK    Male              0      No         No       2          Yes   \n",
       "3  7795-CFOCW    Male              0      No         No      45           No   \n",
       "4  9237-HQITU  Female              0      No         No       2          Yes   \n",
       "\n",
       "      MultipleLines InternetService OnlineSecurity  ... DeviceProtection  \\\n",
       "0  No phone service             DSL             No  ...               No   \n",
       "1                No             DSL            Yes  ...              Yes   \n",
       "2                No             DSL            Yes  ...               No   \n",
       "3  No phone service             DSL            Yes  ...              Yes   \n",
       "4                No     Fiber optic             No  ...               No   \n",
       "\n",
       "  TechSupport StreamingTV StreamingMovies        Contract PaperlessBilling  \\\n",
       "0          No          No              No  Month-to-month              Yes   \n",
       "1          No          No              No        One year               No   \n",
       "2          No          No              No  Month-to-month              Yes   \n",
       "3         Yes          No              No        One year               No   \n",
       "4          No          No              No  Month-to-month              Yes   \n",
       "\n",
       "               PaymentMethod MonthlyCharges  TotalCharges Churn  \n",
       "0           Electronic check          29.85         29.85    No  \n",
       "1               Mailed check          56.95        1889.5    No  \n",
       "2               Mailed check          53.85        108.15   Yes  \n",
       "3  Bank transfer (automatic)          42.30       1840.75    No  \n",
       "4           Electronic check          70.70        151.65   Yes  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"WA_Fn-UseC_-Telco-Customer-Churn.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "98c8dddd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>customerID</th>\n",
       "      <td>7590-VHVEG</td>\n",
       "      <td>5575-GNVDE</td>\n",
       "      <td>3668-QPYBK</td>\n",
       "      <td>7795-CFOCW</td>\n",
       "      <td>9237-HQITU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <td>Female</td>\n",
       "      <td>Male</td>\n",
       "      <td>Male</td>\n",
       "      <td>Male</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Partner</th>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dependents</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tenure</th>\n",
       "      <td>1</td>\n",
       "      <td>34</td>\n",
       "      <td>2</td>\n",
       "      <td>45</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PhoneService</th>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MultipleLines</th>\n",
       "      <td>No phone service</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>InternetService</th>\n",
       "      <td>DSL</td>\n",
       "      <td>DSL</td>\n",
       "      <td>DSL</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Fiber optic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OnlineBackup</th>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DeviceProtection</th>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TechSupport</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StreamingTV</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StreamingMovies</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Contract</th>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>One year</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>One year</td>\n",
       "      <td>Month-to-month</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PaymentMethod</th>\n",
       "      <td>Electronic check</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>Electronic check</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <td>29.85</td>\n",
       "      <td>56.95</td>\n",
       "      <td>53.85</td>\n",
       "      <td>42.3</td>\n",
       "      <td>70.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TotalCharges</th>\n",
       "      <td>29.85</td>\n",
       "      <td>1889.5</td>\n",
       "      <td>108.15</td>\n",
       "      <td>1840.75</td>\n",
       "      <td>151.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Churn</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 0             1               2  \\\n",
       "customerID              7590-VHVEG    5575-GNVDE      3668-QPYBK   \n",
       "gender                      Female          Male            Male   \n",
       "SeniorCitizen                    0             0               0   \n",
       "Partner                        Yes            No              No   \n",
       "Dependents                      No            No              No   \n",
       "tenure                           1            34               2   \n",
       "PhoneService                    No           Yes             Yes   \n",
       "MultipleLines     No phone service            No              No   \n",
       "InternetService                DSL           DSL             DSL   \n",
       "OnlineSecurity                  No           Yes             Yes   \n",
       "OnlineBackup                   Yes            No             Yes   \n",
       "DeviceProtection                No           Yes              No   \n",
       "TechSupport                     No            No              No   \n",
       "StreamingTV                     No            No              No   \n",
       "StreamingMovies                 No            No              No   \n",
       "Contract            Month-to-month      One year  Month-to-month   \n",
       "PaperlessBilling               Yes            No             Yes   \n",
       "PaymentMethod     Electronic check  Mailed check    Mailed check   \n",
       "MonthlyCharges               29.85         56.95           53.85   \n",
       "TotalCharges                 29.85        1889.5          108.15   \n",
       "Churn                           No            No             Yes   \n",
       "\n",
       "                                          3                 4  \n",
       "customerID                       7795-CFOCW        9237-HQITU  \n",
       "gender                                 Male            Female  \n",
       "SeniorCitizen                             0                 0  \n",
       "Partner                                  No                No  \n",
       "Dependents                               No                No  \n",
       "tenure                                   45                 2  \n",
       "PhoneService                             No               Yes  \n",
       "MultipleLines              No phone service                No  \n",
       "InternetService                         DSL       Fiber optic  \n",
       "OnlineSecurity                          Yes                No  \n",
       "OnlineBackup                             No                No  \n",
       "DeviceProtection                        Yes                No  \n",
       "TechSupport                             Yes                No  \n",
       "StreamingTV                              No                No  \n",
       "StreamingMovies                          No                No  \n",
       "Contract                           One year    Month-to-month  \n",
       "PaperlessBilling                         No               Yes  \n",
       "PaymentMethod     Bank transfer (automatic)  Electronic check  \n",
       "MonthlyCharges                         42.3              70.7  \n",
       "TotalCharges                        1840.75            151.65  \n",
       "Churn                                    No               Yes  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "457e0535",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "824ea175",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['TotalCharges'] = pd.to_numeric(df['TotalCharges'], errors = 'coerce')\n",
    "df['TotalCharges'] = df['TotalCharges'].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3053de38",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns = df.columns.str.lower().str.replace(' ', '_')\n",
    "string_cols = list(df.dtypes[df.dtypes == 'object'].index)\n",
    "for col in string_cols:\n",
    "    df[col] = df[col].str.lower().str.replace(' ', '_')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f312517a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.churn = (df.churn== 'yes').astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b11ec3c2",
   "metadata": {},
   "outputs": [
    {
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       "      <th>customerid</th>\n",
       "      <td>7590-vhveg</td>\n",
       "      <td>5575-gnvde</td>\n",
       "      <td>3668-qpybk</td>\n",
       "      <td>7795-cfocw</td>\n",
       "      <td>9237-hqitu</td>\n",
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       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <td>female</td>\n",
       "      <td>male</td>\n",
       "      <td>male</td>\n",
       "      <td>male</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seniorcitizen</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>partner</th>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dependents</th>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tenure</th>\n",
       "      <td>1</td>\n",
       "      <td>34</td>\n",
       "      <td>2</td>\n",
       "      <td>45</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>phoneservice</th>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>multiplelines</th>\n",
       "      <td>no_phone_service</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no_phone_service</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>internetservice</th>\n",
       "      <td>dsl</td>\n",
       "      <td>dsl</td>\n",
       "      <td>dsl</td>\n",
       "      <td>dsl</td>\n",
       "      <td>fiber_optic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>onlinesecurity</th>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>onlinebackup</th>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>deviceprotection</th>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>techsupport</th>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>streamingtv</th>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>streamingmovies</th>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>contract</th>\n",
       "      <td>month-to-month</td>\n",
       "      <td>one_year</td>\n",
       "      <td>month-to-month</td>\n",
       "      <td>one_year</td>\n",
       "      <td>month-to-month</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>paperlessbilling</th>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>paymentmethod</th>\n",
       "      <td>electronic_check</td>\n",
       "      <td>mailed_check</td>\n",
       "      <td>mailed_check</td>\n",
       "      <td>bank_transfer_(automatic)</td>\n",
       "      <td>electronic_check</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>monthlycharges</th>\n",
       "      <td>29.85</td>\n",
       "      <td>56.95</td>\n",
       "      <td>53.85</td>\n",
       "      <td>42.3</td>\n",
       "      <td>70.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>totalcharges</th>\n",
       "      <td>29.85</td>\n",
       "      <td>1889.5</td>\n",
       "      <td>108.15</td>\n",
       "      <td>1840.75</td>\n",
       "      <td>151.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>churn</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 0             1               2  \\\n",
       "customerid              7590-vhveg    5575-gnvde      3668-qpybk   \n",
       "gender                      female          male            male   \n",
       "seniorcitizen                    0             0               0   \n",
       "partner                        yes            no              no   \n",
       "dependents                      no            no              no   \n",
       "tenure                           1            34               2   \n",
       "phoneservice                    no           yes             yes   \n",
       "multiplelines     no_phone_service            no              no   \n",
       "internetservice                dsl           dsl             dsl   \n",
       "onlinesecurity                  no           yes             yes   \n",
       "onlinebackup                   yes            no             yes   \n",
       "deviceprotection                no           yes              no   \n",
       "techsupport                     no            no              no   \n",
       "streamingtv                     no            no              no   \n",
       "streamingmovies                 no            no              no   \n",
       "contract            month-to-month      one_year  month-to-month   \n",
       "paperlessbilling               yes            no             yes   \n",
       "paymentmethod     electronic_check  mailed_check    mailed_check   \n",
       "monthlycharges               29.85         56.95           53.85   \n",
       "totalcharges                 29.85        1889.5          108.15   \n",
       "churn                            0             0               1   \n",
       "\n",
       "                                          3                 4  \n",
       "customerid                       7795-cfocw        9237-hqitu  \n",
       "gender                                 male            female  \n",
       "seniorcitizen                             0                 0  \n",
       "partner                                  no                no  \n",
       "dependents                               no                no  \n",
       "tenure                                   45                 2  \n",
       "phoneservice                             no               yes  \n",
       "multiplelines              no_phone_service                no  \n",
       "internetservice                         dsl       fiber_optic  \n",
       "onlinesecurity                          yes                no  \n",
       "onlinebackup                             no                no  \n",
       "deviceprotection                        yes                no  \n",
       "techsupport                             yes                no  \n",
       "streamingtv                              no                no  \n",
       "streamingmovies                          no                no  \n",
       "contract                           one_year    month-to-month  \n",
       "paperlessbilling                         no               yes  \n",
       "paymentmethod     bank_transfer_(automatic)  electronic_check  \n",
       "monthlycharges                         42.3              70.7  \n",
       "totalcharges                        1840.75            151.65  \n",
       "churn                                     0                 1  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "64451aa4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "df_train_full, df_test = train_test_split(df, test_size = 0.2, random_state = 1)\n",
    "df_train, df_val = train_test_split(df_train_full, test_size = 0.33, random_state = 11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "503d10cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = df_train.churn.values\n",
    "y_val =df_val.churn.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3ed8130a",
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5805</th>\n",
       "      <td>1697-nvvgy</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>19</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>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>month-to-month</td>\n",
       "      <td>no</td>\n",
       "      <td>bank_transfer_(automatic)</td>\n",
       "      <td>66.40</td>\n",
       "      <td>1286.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3687</th>\n",
       "      <td>6849-oyamu</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>19</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>fiber_optic</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>one_year</td>\n",
       "      <td>no</td>\n",
       "      <td>bank_transfer_(automatic)</td>\n",
       "      <td>100.00</td>\n",
       "      <td>1888.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6900</th>\n",
       "      <td>7921-lmdfq</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>51</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>fiber_optic</td>\n",
       "      <td>no</td>\n",
       "      <td>no</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>bank_transfer_(automatic)</td>\n",
       "      <td>94.00</td>\n",
       "      <td>4905.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1644</th>\n",
       "      <td>3331-hqdtw</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>34</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>fiber_optic</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>month-to-month</td>\n",
       "      <td>yes</td>\n",
       "      <td>mailed_check</td>\n",
       "      <td>109.80</td>\n",
       "      <td>3587.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6975</th>\n",
       "      <td>1904-wajaa</td>\n",
       "      <td>female</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>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>two_year</td>\n",
       "      <td>no</td>\n",
       "      <td>electronic_check</td>\n",
       "      <td>64.35</td>\n",
       "      <td>1558.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4689</th>\n",
       "      <td>2832-scuco</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>no</td>\n",
       "      <td>no_internet_service</td>\n",
       "      <td>no_internet_service</td>\n",
       "      <td>no_internet_service</td>\n",
       "      <td>no_internet_service</td>\n",
       "      <td>no_internet_service</td>\n",
       "      <td>no_internet_service</td>\n",
       "      <td>month-to-month</td>\n",
       "      <td>no</td>\n",
       "      <td>electronic_check</td>\n",
       "      <td>19.90</td>\n",
       "      <td>57.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5203</th>\n",
       "      <td>0734-oxwbt</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>3</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>dsl</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>month-to-month</td>\n",
       "      <td>no</td>\n",
       "      <td>mailed_check</td>\n",
       "      <td>70.70</td>\n",
       "      <td>225.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1399</th>\n",
       "      <td>3131-nwvfj</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>14</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no_internet_service</td>\n",
       "      <td>no_internet_service</td>\n",
       "      <td>no_internet_service</td>\n",
       "      <td>no_internet_service</td>\n",
       "      <td>no_internet_service</td>\n",
       "      <td>no_internet_service</td>\n",
       "      <td>one_year</td>\n",
       "      <td>no</td>\n",
       "      <td>mailed_check</td>\n",
       "      <td>19.55</td>\n",
       "      <td>294.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>8266-vbfql</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>4</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>fiber_optic</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>month-to-month</td>\n",
       "      <td>yes</td>\n",
       "      <td>electronic_check</td>\n",
       "      <td>90.40</td>\n",
       "      <td>356.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>813</th>\n",
       "      <td>3551-gaegl</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>34</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>one_year</td>\n",
       "      <td>no</td>\n",
       "      <td>bank_transfer_(automatic)</td>\n",
       "      <td>30.40</td>\n",
       "      <td>938.65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1860 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      customerid  gender  seniorcitizen partner dependents  tenure  \\\n",
       "5805  1697-nvvgy    male              1     yes         no      19   \n",
       "3687  6849-oyamu    male              0     yes        yes      19   \n",
       "6900  7921-lmdfq    male              1      no         no      51   \n",
       "1644  3331-hqdtw  female              0      no         no      34   \n",
       "6975  1904-wajaa  female              0     yes        yes      24   \n",
       "...          ...     ...            ...     ...        ...     ...   \n",
       "4689  2832-scuco  female              0      no         no       2   \n",
       "5203  0734-oxwbt    male              0      no        yes       3   \n",
       "1399  3131-nwvfj  female              0     yes        yes      14   \n",
       "302   8266-vbfql    male              0      no         no       4   \n",
       "813   3551-gaegl    male              0     yes        yes      34   \n",
       "\n",
       "     phoneservice     multiplelines internetservice       onlinesecurity  \\\n",
       "5805          yes                no             dsl                  yes   \n",
       "3687          yes                no     fiber_optic                   no   \n",
       "6900          yes                no     fiber_optic                   no   \n",
       "1644          yes               yes     fiber_optic                  yes   \n",
       "6975          yes               yes             dsl                  yes   \n",
       "...           ...               ...             ...                  ...   \n",
       "4689          yes                no              no  no_internet_service   \n",
       "5203          yes                no             dsl                   no   \n",
       "1399          yes                no              no  no_internet_service   \n",
       "302           yes                no     fiber_optic                   no   \n",
       "813            no  no_phone_service             dsl                  yes   \n",
       "\n",
       "             onlinebackup     deviceprotection          techsupport  \\\n",
       "5805                   no                  yes                   no   \n",
       "3687                  yes                   no                  yes   \n",
       "6900                   no                  yes                   no   \n",
       "1644                  yes                  yes                   no   \n",
       "6975                  yes                  yes                   no   \n",
       "...                   ...                  ...                  ...   \n",
       "4689  no_internet_service  no_internet_service  no_internet_service   \n",
       "5203                   no                   no                  yes   \n",
       "1399  no_internet_service  no_internet_service  no_internet_service   \n",
       "302                    no                  yes                  yes   \n",
       "813                    no                   no                   no   \n",
       "\n",
       "              streamingtv      streamingmovies        contract  \\\n",
       "5805                  yes                   no  month-to-month   \n",
       "3687                  yes                  yes        one_year   \n",
       "6900                  yes                  yes        one_year   \n",
       "1644                  yes                  yes  month-to-month   \n",
       "6975                   no                   no        two_year   \n",
       "...                   ...                  ...             ...   \n",
       "4689  no_internet_service  no_internet_service  month-to-month   \n",
       "5203                  yes                  yes  month-to-month   \n",
       "1399  no_internet_service  no_internet_service        one_year   \n",
       "302                    no                  yes  month-to-month   \n",
       "813                    no                   no        one_year   \n",
       "\n",
       "     paperlessbilling              paymentmethod  monthlycharges  totalcharges  \n",
       "5805               no  bank_transfer_(automatic)           66.40       1286.05  \n",
       "3687               no  bank_transfer_(automatic)          100.00       1888.65  \n",
       "6900              yes  bank_transfer_(automatic)           94.00       4905.75  \n",
       "1644              yes               mailed_check          109.80       3587.25  \n",
       "6975               no           electronic_check           64.35       1558.65  \n",
       "...               ...                        ...             ...           ...  \n",
       "4689               no           electronic_check           19.90         57.40  \n",
       "5203               no               mailed_check           70.70        225.65  \n",
       "1399               no               mailed_check           19.55        294.50  \n",
       "302               yes           electronic_check           90.40        356.65  \n",
       "813                no  bank_transfer_(automatic)           30.40        938.65  \n",
       "\n",
       "[1860 rows x 20 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.drop(columns = ['churn'],  axis =1)\n",
    "df_val.drop(columns = ['churn'],  axis =1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "761199e4",
   "metadata": {},
   "source": [
    "## EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d16a8039",
   "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": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train_full.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a5636f35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4113\n",
       "1    1521\n",
       "Name: churn, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train_full.churn.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "16a7593a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data = df_train_full, x = 'churn')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "ebdae395",
   "metadata": {},
   "outputs": [],
   "source": [
    "global_mean = df_train_full.churn.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "4a012694",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.27"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(global_mean, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "831d8532",
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical = ['gender', 'seniorcitizen', 'partner', 'dependents',\n",
    "               'phoneservice', 'multiplelines', 'internetservice',\n",
    "               'onlinesecurity', 'onlinebackup', 'deviceprotection',\n",
    "               'techsupport', 'streamingtv', 'streamingmovies',\n",
    "               'contract', 'paperlessbilling', 'paymentmethod']\n",
    "numerical = ['tenure', 'monthlycharges', 'totalcharges']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "9d96fc05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender              2\n",
       "seniorcitizen       2\n",
       "partner             2\n",
       "dependents          2\n",
       "phoneservice        2\n",
       "multiplelines       3\n",
       "internetservice     3\n",
       "onlinesecurity      3\n",
       "onlinebackup        3\n",
       "deviceprotection    3\n",
       "techsupport         3\n",
       "streamingtv         3\n",
       "streamingmovies     3\n",
       "contract            3\n",
       "paperlessbilling    2\n",
       "paymentmethod       4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train_full[categorical].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "da44a2e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Now lets see categorical wise and calculate the churn mean in each case\n",
    "sns.countplot(x ='gender', hue = 'churn', data = df_train_full )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "5d1c6f8d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gender == female: 0.277\n",
      "gender == male:   0.263\n"
     ]
    }
   ],
   "source": [
    "female_mean = df_train_full[df_train_full.gender == 'female'].churn.mean()\n",
    "print('gender == female:', round(female_mean, 3))\n",
    "\n",
    "male_mean = df_train_full[df_train_full.gender == 'male'].churn.mean()\n",
    "print('gender == male:  ', round(male_mean, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "0791833f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0253955354648652"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "female_mean/ global_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "935e322b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9749802969838747"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "male_mean/global_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "1d6ae21a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x ='partner', hue = 'churn', data = df_train_full )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "55efdc30",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "partner == yes: 0.205\n",
      "partner == no : 0.33\n"
     ]
    }
   ],
   "source": [
    "partner_yes = df_train_full[df_train_full.partner == 'yes'].churn.mean()\n",
    "print('partner == yes:', round(partner_yes, 3))\n",
    "\n",
    "partner_no = df_train_full[df_train_full.partner == 'no'].churn.mean()\n",
    "print('partner == no :', round(partner_no, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "d0c007df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7594724924338315"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "partner_yes/global_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "517f7ef8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.2216593879412643"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "partner_no/global_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "3414496d",
   "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>mean</th>\n",
       "      <th>diff</th>\n",
       "      <th>risk</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>0.276824</td>\n",
       "      <td>0.006856</td>\n",
       "      <td>1.025396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.263214</td>\n",
       "      <td>-0.006755</td>\n",
       "      <td>0.974980</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            mean      diff      risk\n",
       "gender                              \n",
       "female  0.276824  0.006856  1.025396\n",
       "male    0.263214 -0.006755  0.974980"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_group = df_train_full.groupby(by='gender').churn.agg(['mean'])\n",
    "df_group['diff'] = df_group['mean'] - global_mean\n",
    "df_group['risk'] = df_group['mean'] / global_mean\n",
    "df_group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "1fe2f263",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6+OOPL/k/EQAAAFDkt89op6enX/K+Tp06JfZt2rSpvvjiC2VmZuqXX37RK6+8olOnTknSJb9UvnDhgvVM9H333aeIiAiH/XXq1FFYWJj1c8uWLa1JCUoaVULVUOags337drVq1UqtWrWSJMXExKhVq1aaNGmSJOn48eMO/6PXq1dPy5cv1zfffKObbrpJr776qt55552KnVr6OtCgQQO5urpqy5Yt1rZffvlFBw4cqMSqAAAAqoa2bdsqICBA0sVJBSTp2LFj2rx5sySpd+/ekqTIyEhFRkbqjTfesPpu3rzZmsH37Nmz1uQCrq6u6tu3r8N5Pv74Y+vfruPHj7+kjtmzZ+unn36yfv7hhx+sn38filC1lPkZnS5dulx2yHDBggXF9tm1a1dZT2U0b29vPfDAA3riiScUEBCgmjVrauLEiQ4PywEAAPxZubm5adq0aXrwwQe1ZMkS1a9fXydOnNDp06cVGBhozchW9PxN0cQFkvT8889r3bp1qlevnlJSUpSVdXG9w5dfflm1atVyOE/RrLddu3ZV69atL6njk08+0ZgxYxQaGqqAgADt379fFy5ckIuLi1UDqib+VV2JXn75ZXXu3FnR0dHq0aOHOnXqVOx/YAAAAH9Go0eP1ocffqiWLVvq2LFjstls6tu3rzZu3OhwO9nv3XrrrQoJCdHBgwd14cIFderUSZ999pnGjh3r0G7NmjXWjLfFjeZI0pgxYxQdHS1nZ2cdPHhQwcHBuuuuu7Rx40Z169at/C4W5c5m/6MnuqqA7Oxs+fn5KSsrS76+vg77zp07p6SkJNWrV49FmyoInzHKqvnC5pVdAn5nz7A9lV0CAADl4nLZ4LcY0QEAAABgHIIOAAAAAOMQdAAAAAAYp8yzrgEAAAAVjec9q5br8VlPRnQAAAAAGIegAwAAAMA4BB0AAAAAxiHoAAAAADAOQQcAAACAcQg6VYjNZtOyZcvKvS0AAADwZ2Ps9NLXekrC8phy7/jx4/L39y+HagAAAIA/N2ODzvUmPz9fISEhlV0GAAAAYARuXaskXbp00ZgxYzRu3DgFBgaqV69eDrej5efna8yYMQoNDZWHh4fq1q2r6dOnl3i8yZMnKzQ0VD/88MM1ugIAAACg6mJEpxItXLhQDz/8sDZs2CBJioyMtPa9/vrr+uKLL/Txxx+rTp06OnLkiI4cOXLJMex2ux577DF9+eWXWr9+vRo2bHjN6gcAAACqKoJOJWrUqJFeeumlYvelpKSoUaNG6tSpk2w2m+rWrXtJmwsXLuj//b//p127dun7779XrVq1KrpkAAAA4LpA0KlErVu3LnHf8OHDddttt+mGG25Q7969deedd6pnz54ObR5//HG5u7tr8+bNCgwMrOhyAQAAgOsGz+hUIi8vrxL3RUVFKSkpSc8995zOnj2r/v37q1+/fg5tbrvtNh09elRff/11RZcKAAAAXFcY0anCfH19NWDAAA0YMED9+vVT7969dfLkSdWoUUOSdNdddyk6OlqDBg2Ss7Oz/va3v1VyxQAAAEDVQNCpombOnKnQ0FC1atVKTk5O+uSTTxQSEqLq1as7tLvnnnv0wQcfaMiQIXJxcblk1AcAAAD4MyLoVFE+Pj566aWXdPDgQTk7O6tt27ZasWKFnJwuvduwX79+Kiws1JAhQ+Tk5KS+fftWQsUAAABA1WFs0NkzbE9ll3BZa9euvWSb3W633o8aNUqjRo0qsf9v20pS//791b9//3KrDwAAALieMRkBAAAAAOMQdAAAAAAYh6ADAAAAwDgEHQAAAADGIegAAAAAMI4xQef3s5Ch/PDZAgAA4Hpz3QcdZ2dnSVJ+fn4lV2KuM2fOSJJcXV0ruRIAAACgdK77dXRcXFxUrVo1ZWRkyNXVtdgFNXFl7Ha7zpw5o/T0dFWvXt0KlQAAAEBVd90HHZvNptDQUCUlJenw4cOVXY6RqlevrpCQkMouAwAAACi16z7oSJKbm5saNWrE7WsVwNXVlZEcAAAAXHeMCDqS5OTkJA8Pj8ouAwAAAEAVwAMtAAAAAIxD0AEAAABgHIIOAAAAAOMQdAAAAAAYh6ADAAAAwDgEHQAAAADGIegAAAAAMA5BBwAAAIBxCDoAAAAAjEPQAQAAAGAcgg4AAAAA4xB0AAAAABiHoAMAAADAOAQdAAAAAMYh6AAAAAAwDkEHAAAAgHEIOgAAAACMQ9ABAAAAYByCDgAAAADjEHQAAAAAGIegAwAAAMA4BB0AAAAAxiHoAAAAADAOQQcAAACAcQg6AAAAAIxD0AEAAABgHIIOAAAAAOMQdAAAAAAYh6ADAAAAwDgEHQAAAADGIegAAAAAMA5BBwAAAIBxCDoAAAAAjEPQAQAAAGAcgg4AAAAA4xB0AAAAABiHoAMAAADAOAQdAAAAAMYh6AAAAAAwDkEHAAAAgHEIOgAAAACMQ9ABAAAAYByCDgAAAADjEHQAAAAAGIegAwAAAMA4BB0AAAAAxiHoAAAAADAOQQcAAACAcQg6AAAAAIxD0AEAAABgnCsKOrNnz1ZERIQ8PDzUrl07bd269bLtZ82apRtuuEGenp4KDw/X448/rnPnzl1RwQAAAADwR8ocdBYvXqyYmBhNnjxZO3fu1E033aRevXopPT292PYfffSRnnrqKU2ePFn79u3T/PnztXjxYv3rX/+66uIBAAAAoDhlDjozZ87UqFGjNGLECDVt2lRz5sxRtWrV9O677xbbfuPGjbrllls0aNAgRUREqGfPnho4cOAfjgIBAAAAwJUqU9DJz8/Xjh071KNHj18P4OSkHj16aNOmTcX26dixo3bs2GEFm8TERK1YsUJ33HFHiefJy8tTdna2wwsAAAAASsulLI0zMzNVUFCg4OBgh+3BwcHav39/sX0GDRqkzMxMderUSXa7XRcuXNBDDz102VvXpk+frqlTp5alNAAAAACwVPisa2vXrtW0adP05ptvaufOnVq6dKmWL1+u5557rsQ+EyZMUFZWlvU6cuRIRZcJAAAAwCBlGtEJDAyUs7Oz0tLSHLanpaUpJCSk2D7PPPOMhgwZopEjR0qSmjdvrtzcXI0ePVoTJ06Uk9OlWcvd3V3u7u5lKQ0AAAAALGUa0XFzc1Pr1q0VGxtrbSssLFRsbKw6dOhQbJ8zZ85cEmacnZ0lSXa7vaz1AgAAAMAfKtOIjiTFxMRo2LBhatOmjW6++WbNmjVLubm5GjFihCRp6NChqlWrlqZPny5Jio6O1syZM9WqVSu1a9dOhw4d0jPPPKPo6Ggr8AAAAABAeSpz0BkwYIAyMjI0adIkpaamqmXLllq5cqU1QUFKSorDCM7TTz8tm82mp59+WkePHlVQUJCio6P1wgsvlN9VAAAAAMBv2OzXwf1j2dnZ8vPzU1ZWlnx9fSu7HAB/oPnC5pVdAn5nz7A9lV0CAJQJ3yVVS1X6HiltNqjwWdcAAAAA4Foj6AAAAAAwDkEHAAAAgHEIOgAAAACMQ9ABAAAAYByCDgAAAADjEHQAAAAAGIegAwAAAMA4BB0AAAAAxiHoAAAAADAOQQcAAACAcQg6AAAAAIxD0AEAAABgHIIOAAAAAOMQdAAAAAAYh6ADAAAAwDgEHQAAAADGIegAAAAAMA5BBwAAAIBxCDoAAAAAjEPQAQAAAGAcgg4AAAAA4xB0AAAAABiHoAMAAADAOAQdAAAAAMYh6AAAAAAwDkEHAAAAgHEIOgAAAACMQ9ABAAAAYByCDgAAAADjEHQAAAAAGIegAwAAAMA4BB0AAAAAxiHoAAAAADAOQQcAAACAcQg6AAAAAIxD0AEAAABgHIIOAAAAAOMQdAAAAAAYh6ADAAAAwDgEHQAAAADGIegAAAAAMA5BBwAAAIBxCDoAAAAAjEPQAQAAAGAcgg4AAAAA4xB0AAAAABiHoAMAAADAOAQdAAAAAMYh6AAAAAAwDkEHAAAAgHEIOgAAAACMQ9ABAAAAYByCDgAAAADjEHQAAAAAGIegAwAAAMA4BB0AAAAAxiHoAAAAADAOQQcAAACAcQg6AAAAAIxD0AEAAABgHIIOAAAAAOMQdAAAAAAYh6ADAAAAwDgEHQAAAADGIegAAAAAMA5BBwAAAIBxCDoAAAAAjEPQAQAAAGAcgg4AAAAA4xB0AAAAABiHoAMAAADAOAQdAAAAAMYh6AAAAAAwDkEHAAAAgHEIOgAAAACMQ9ABAAAAYByCDgAAAADjEHQAAAAAGIegAwAAAMA4BB0AAAAAxiHoAAAAADAOQQcAAACAcQg6AAAAAIxzRUFn9uzZioiIkIeHh9q1a6etW7detv2pU6f0yCOPKDQ0VO7u7mrcuLFWrFhxRQUDAAAAwB9xKWuHxYsXKyYmRnPmzFG7du00a9Ys9erVS/Hx8apZs+Yl7fPz83XbbbepZs2a+vTTT1WrVi0dPnxY1atXL4/6AQAAAOASZQ46M2fO1KhRozRixAhJ0pw5c7R8+XK9++67euqppy5p/+677+rkyZPauHGjXF1dJUkRERFXVzUAAAAAXEaZbl3Lz8/Xjh071KNHj18P4OSkHj16aNOmTcX2+eKLL9ShQwc98sgjCg4OVrNmzTRt2jQVFBSUeJ68vDxlZ2c7vAAAAACgtMoUdDIzM1VQUKDg4GCH7cHBwUpNTS22T2Jioj799FMVFBRoxYoVeuaZZ/Tqq6/q+eefL/E806dPl5+fn/UKDw8vS5kAAAAA/uQqfNa1wsJC1axZU3PnzlXr1q01YMAATZw4UXPmzCmxz4QJE5SVlWW9jhw5UtFlAgAAADBImZ7RCQwMlLOzs9LS0hy2p6WlKSQkpNg+oaGhcnV1lbOzs7WtSZMmSk1NVX5+vtzc3C7p4+7uLnd397KUBgAAAACWMo3ouLm5qXXr1oqNjbW2FRYWKjY2Vh06dCi2zy233KJDhw6psLDQ2nbgwAGFhoYWG3IAAAAA4GqV+da1mJgYzZs3TwsXLtS+ffv08MMPKzc315qFbejQoZowYYLV/uGHH9bJkyc1duxYHThwQMuXL9e0adP0yCOPlN9VAAAAAMBvlHl66QEDBigjI0OTJk1SamqqWrZsqZUrV1oTFKSkpMjJ6df8FB4erq+//lqPP/64WrRooVq1amns2LF68skny+8qAAAAAOA3bHa73V7ZRfyR7Oxs+fn5KSsrS76+vpVdDoA/0Hxh88ouAb+zZ9ieyi4BAMqE75KqpSp9j5Q2G1T4rGsAAAAAcK0RdAAAAAAYh6ADAAAAwDgEHQAAAADGIegAAAAAMA5BBwAAAIBxCDoAAAAAjEPQAQAAAGAcgg4AAAAA4xB0AAAAABiHoAMAAADAOAQdAAAAAMYh6AAAAAAwDkEHAAAAgHEIOgAAAACMQ9ABAAAAYByCDgAAAADjEHQAAAAAGIegAwAAAMA4BB0AAAAAxiHoAAAAADAOQQcAAACAcQg6AAAAAIxD0AEAAABgHIIOAAAAAOMQdAAAAAAYh6ADAAAAwDgEHQAAAADGIegAAAAAMA5BBwAAAIBxCDoAAAAAjEPQAQAAAGAcgg4AAAAA4xB0AAAAABiHoAMAAADAOAQdAAAAAMYh6AAAAAAwDkEHAAAAgHEIOgAAAACMQ9ABAAAAYByCDgAAAADjEHQAAAAAGIegAwAAAMA4BB0AAAAAxiHoAAAAADAOQQcAAACAcQg6AAAAAIxD0AEAAABgHIIOAAAAAOMQdAAAAAAYh6ADAAAAwDgEHQAAAADGIegAAAAAMA5BBwAAAIBxCDoAAAAAjEPQAQAAAGAcgg4AAAAA4xB0AAAAABiHoAMAAADAOAQdAAAAAMYh6AAAAAAwDkEHAAAAgHEIOgAAAACMQ9ABAAAAYByCDgAAAADjEHQAAAAAGIegAwAAAMA4BB0AAAAAxiHoAAAAADAOQQcAAACAcQg6AAAAAIxD0AEAAABgHIIOAAAAAOMQdAAAAAAYh6ADAAAAwDgEHQAAAADGIegAAAAAMA5BBwAAAIBxCDoAAAAAjEPQAQAAAGAcgg4AAAAA4xB0AAAAABiHoAMAAADAOAQdAAAAAMa5oqAze/ZsRUREyMPDQ+3atdPWrVtL1W/RokWy2Wy6++67r+S0AAAAAFAqZQ46ixcvVkxMjCZPnqydO3fqpptuUq9evZSenn7ZfsnJyRo/frw6d+58xcUCAAAAQGmUOejMnDlTo0aN0ogRI9S0aVPNmTNH1apV07vvvltin4KCAg0ePFhTp05V/fr1r6pgAAAAAPgjZQo6+fn52rFjh3r06PHrAZyc1KNHD23atKnEfs8++6xq1qypBx54oFTnycvLU3Z2tsMLAAAAAEqrTEEnMzNTBQUFCg4OdtgeHBys1NTUYvt8//33mj9/vubNm1fq80yfPl1+fn7WKzw8vCxlAgAAAPiTq9BZ106fPq0hQ4Zo3rx5CgwMLHW/CRMmKCsry3odOXKkAqsEAAAAYBqXsjQODAyUs7Oz0tLSHLanpaUpJCTkkvYJCQlKTk5WdHS0ta2wsPDiiV1cFB8frwYNGlzSz93dXe7u7mUpDQAAAAAsZRrRcXNzU+vWrRUbG2ttKywsVGxsrDp06HBJ+8jISO3Zs0e7d++2XnfddZe6du2q3bt3c0saAAAAgApRphEdSYqJidGwYcPUpk0b3XzzzZo1a5Zyc3M1YsQISdLQoUNVq1YtTZ8+XR4eHmrWrJlD/+rVq0vSJdsBAAAAoLyUOegMGDBAGRkZmjRpklJTU9WyZUutXLnSmqAgJSVFTk4V+ugPAAAAAFyWzW632yu7iD+SnZ0tPz8/ZWVlydfXt7LLAfAHmi9sXtkl4Hf2DNtT2SUAQJnwXVK1VKXvkdJmA4ZeAAAAABiHoAMAAADAOAQdAAAAAMYh6AAAAAAwDkEHAAAAgHEIOgAAAACMQ9ABAAAAYByCDgAAAADjEHQAAAAAGIegAwAAAMA4BB0AAAAAxiHoAAAAADAOQQcAAACAcQg6AAAAAIxD0AEAAABgHIIOAAAAAOMQdAAAAAAYh6ADAAAAwDgEHQAAAADGIegAAAAAMA5BBwAAAIBxCDoAAAAAjEPQAQAAAGAcgg4AAAAA4xB0AAAAABiHoAMAAADAOAQdAAAAAMYh6AAAAAAwDkEHAAAAgHEIOgAAAACMQ9ABAAAAYByCDgAAAADjEHQAAAAAGIegAwAAAMA4BB0AAAAAxiHoAAAAADAOQQcAAACAcQg6V2nRokWKioqSp6enatSooX79+ikhIeGyfZ566il16NBBNWvWlIeHh+rXr69HH31U6enpVpspU6bIZrOV+EpOTrbaXrhwQS+//LKaN28uDw8P+fn5qXXr1lq+fLnVJj09XQ8//LAiIiLk4eEhf39/3XzzzXr33XfL/TMBAAAAKptLZRdwPZs/f75GjhwpSapXr55OnDihJUuWaP369YqLi1NISEix/V588UU5OzurSZMmcnV1VVJSkt544w2tXbtWcXFxcnJyUu3atdWuXTuHfgcPHtTJkyfl7u4uf39/SZLdbte9996rL774QpLUoEEDeXt7KykpSbt27VKfPn0kSf3799e6devk7OysZs2a6fjx49q2bZu2bdumoKAgRUdHV9THBAAAAFxzjOhcofz8fD311FOSpHvvvVeJiYnat2+ffHx8lJ6ermnTppXYd+LEiTp+/Lj27NmjlJQU3XvvvZKkvXv3Ki4uTpI0cuRIbd682Xp9++23cnZ2liQNHTpUfn5+kqTFixfriy++kJeXlzZs2KBDhw5p9+7dOnHihMaNGyfpYhjauHGjJGnUqFHavXu3Nm/ebNVz+PDh8v1wAAAAgEpG0LlC27ZtU2ZmpiRZQSUsLEzt27eXJK1cubLEvs8//7yCgoIkSc7OzurYsaO1z93dvdg+CxcuVEZGhmw2m/7xj39Y2xcvXixJql+/viZOnCgfHx81aNBAU6ZMkZubmyTJZrPplltukSTNmzdPLVu2VPv27WWz2XTXXXdp+PDhV/IRAAAAAFUWQecKHTlyxHpfs2ZN631wcLAkKSUlpVTHyc3N1fvvvy9JuuWWW9S0adNL2hQWFmrmzJmSpOjoaN1www3Wvvj4eEnSnj17tHPnTtWqVUuJiYl69tlnFRMTY7X77LPP1KtXLxUUFCguLk7p6eny9vZWq1atVK1atdJeNgAAAHBdIOiUM7vdXuq2GRkZ6t69u+Li4hQZGalPPvmk2Haff/65Dh48KEl64oknHPZduHBB0sWRobi4OO3fv1/333+/JGnu3Lk6f/68JGnChAn6+uuv1a9fP2VlZWn9+vXKy8vT1KlT9frrr5f5OgEAAICqjKBzhcLDw633v50treh9nTp1Lts/Pj5e7du315YtW9S+fXutX79eoaGhxbZ95ZVXJEnt27dXp06dHPbVqlVLkhQUFKSIiAhJ0s033yxJOn/+vI4ePaqDBw9qzpw5kqRBgwbJ19dXnTp1UmRkpCRp9erVpbpmAAAA4HpB0LlCbdu2VUBAgCRpyZIlkqRjx45ZD/n37t1bkhQZGanIyEi98cYbVt/vvvtOHTt2VGJiovr166dvv/1WgYGBxZ5n48aN1kQC48ePv2R/jx49JF0cHSqaVGD79u2SJC8vL4WGhiorK8tqX7TvxIkT1hTVXl5eV/AJAADKQ0UtU1Dku+++U+/eveXv7y8PDw9FRERo7Nix1v7Vq1erc+fOCgoKkpubm2rWrKkuXbro888/v+JzAkBVYLOX5V6rSpKdnS0/Pz9lZWXJ19e3ssuxzJ07Vw8++KCkX6eXzs7OVmBgoOLi4hQWFiabzSZJmjx5sqZMmSLp4oQD+fn5stlsatu2rdVGkp555hlrSmhJ6tu3rz777DM1bNhQ8fHxcnJyzKanTp1Sy5YtdfjwYfn5+Sk0NFT79++XJD377LN65plndP78eTVp0sT64mzSpIlSU1P1yy+/SJK+/PJLh3MCV6v5wuaVXQJ+Z8+wPZVdAopR3DIF2dnZqlmz5mWXKbDZbNYyBSdPntSxY8ckSc2aNbOWKZCkjz/+WIMGDVJBQYECAgJUp04d/fLLL3JxcbFuiZ41a5amTp2q2rVry8XFRfv379e5c+fk5OSk9evXWxPmlPacQHnhu6RqqUrfI6XNBvytdBVGjx6tDz/8UC1bttSxY8dks9nUt29fbdy4UWFhYSX2y8/Pl3TxeZ6tW7dqy5Yt1isjI8Nqd+jQIes3ao8//nixXyLVq1fX+vXrNXDgQDk7O+vIkSOKiorSBx98oGeeeUaS5OrqqrVr1+qhhx5SvXr1lJSUJBcXF3Xp0kUrVqwg5ABAJajoZQpyc3P18MMPq6CgQP/85z+VmpqqnTt3KikpSTt37rSO9fDDD+uXX37Rnj17tGvXLn355ZeSLk6Es2nTpjKdEwCqEhYMvUqDBw/W4MGDS9xf3IBZaQfRGjZsqIKCgj9sFx4ero8++uiybWrXrq233nqrVOcFAFS8yy1T8M033/zhMgVFipYpKLqNumiZgtWrV+vkyZOSpLS0NNWuXVvnz5/XLbfcopkzZ8rHx8dqf/jwYQ0YMEB5eXnWbJ5OTk4Oyx+U5pwAUJUwogMAQCWo6GUKigKLJL3//vsKDAzU2bNn9b///U9dunRxeH7z7Nmz2rJli3bv3q2zZ8/Ky8tLixYtUocOHcp0TgCoSgg6AABUIeW1TEHR8gPSxWc29+7dq6+//lqSdPToUX322WfW/sjISNntdp04cUIzZsxQbm6uRo8e7XCLW2nOCQBVCUEHAIBKUNHLFBQtPyBdnClU+nX5AUnWzJu/VaNGDT355JPy9/fXqVOnrOUNSntOAKhKCDoAAFSCil6moFu3btYkNkVLCxT9KUmNGjWSJL3zzjvWszzSxWUNTp06JeniLWplOScAVCVML30FIp5aXtkl4HeSZzBzXFXClKBVT1WaFhS/quhlCsaOHavXX39dNptNN954oxITE3XmzBk1bdpUO3fulLu7uyIiIvTzzz+rXr16cnV11f79+63b5z799FNrooSyLI0AlAe+S6qWqvQ9UtpswKxrAABUktGjR8vLy0uvvPKK9u3bJw8PD/Xt21czZswo0zIFv/XbZQpee+01hYWF6Z133tGBAwdUq1Yt9enTR88++6w1U9rf/vY3LV++XCkpKcrNzVVAQIBat26tsWPH6vbbby/zOQGgqmBE5wowolP1MKJTtfBbuKqnKv0mDgBKg++SqqUqfY+wYCgAAACAPy2CDgAAAADjEHQAAAAAGIfJCAAAqAQ871m18KwnYB5GdAAAAAAYh6ADAAAAwDgEHQAAAADGIegAAAAAMA5BBwAAAIBxCDoAAAAAjEPQAQAAAGAcgg4AAAAA4xB0AAAAABiHoAMAAADAOAQdAAAAAMYh6AAAAAAwDkEHAAAAgHEIOgAAAACMQ9ABAAAAYByCDgAAAADjEHQAAAAAGIegAwAAAMA4BB0AAAAAxiHoAAAAADAOQQcAAACAcQg6AAAAAIxD0AEAAABgHIIOAAAAAOMQdAAAAAAY54qCzuzZsxURESEPDw+1a9dOW7duLbHtvHnz1LlzZ/n7+8vf3189evS4bHsAAAAAuFplDjqLFy9WTEyMJk+erJ07d+qmm25Sr169lJ6eXmz7tWvXauDAgfr222+1adMmhYeHq2fPnjp69OhVFw8AAAAAxSlz0Jk5c6ZGjRqlESNGqGnTppozZ46qVaumd999t9j2//3vf/X3v/9dLVu2VGRkpN555x0VFhYqNjb2qosHAAAAgOKUKejk5+drx44d6tGjx68HcHJSjx49tGnTplId48yZMzp//rxq1KhRYpu8vDxlZ2c7vAAAAACgtMoUdDIzM1VQUKDg4GCH7cHBwUpNTS3VMZ588kmFhYU5hKXfmz59uvz8/KxXeHh4WcoEAAAA8Cd3TWddmzFjhhYtWqTPPvtMHh4eJbabMGGCsrKyrNeRI0euYZUAAAAArncuZWkcGBgoZ2dnpaWlOWxPS0tTSEjIZfu+8sormjFjhlavXq0WLVpctq27u7vc3d3LUhoAAAAAWMo0ouPm5qbWrVs7TCRQNLFAhw4dSuz30ksv6bnnntPKlSvVpk2bK68WAAAAAEqhTCM6khQTE6Nhw4apTZs2uvnmmzVr1izl5uZqxIgRkqShQ4eqVq1amj59uiTpxRdf1KRJk/TRRx8pIiLCepbH29tb3t7e5XgpAAAAAHBRmYPOgAEDlJGRoUmTJik1NVUtW7bUypUrrQkKUlJS5OT060DRW2+9pfz8fPXr18/hOJMnT9aUKVOurnoAAAAAKEaZg44kjRkzRmPGjCl239q1ax1+Tk5OvpJTAAAAAMAVu6azrgEAAADAtUDQAQAAAGAcgg4AAAAA4xB0AAAAABiHoAMAAADAOAQdAAAAAMYh6AAAAAAwDkEHAAAAgHEIOgAAAACMQ9ABAAAAYByCDgAAAADjEHQAAAAAGIegAwAAAMA4BB0AAAAAxiHoAAAAADAOQQcAAACAcQg6AAAAAIxD0AEAAABgHIIOAAAAAOMQdAAAAAAYh6ADAAAAwDgEHQAAAADGIegAAAAAMA5BBwAAAIBxCDoAAAAAjEPQAQAAAGAcgg4AAAAA4xB0AAAAABiHoAMAAADAOAQdAAAAAMYh6AAAAAAwDkEHAAAAgHEIOgAAAACMQ9ABAAAAYByCDgAAAADjEHQAAAAAGIegAwAAAMA4BB0AAAAAxiHoAAAAADAOQQcAAACAcQg6AAAAAIxD0AEAAABgHIIOAAAAAOMQdAAAAAAYh6ADAAAAwDgEHQAAAADGIegAAAAAMA5BBwAAAIBxCDoAAAAAjEPQAQAAAGAcgg4AAAAA4xB0AAAAABiHoAMAAADAOAQdAAAAAMYh6AAAAAAwDkEHAAAAgHEIOgAAAACMQ9ABAAAAYByCDgAAAADjEHQAAAAAGIegAwAAAMA4BB0AAAAAxiHoAAAAADAOQQcAAACAcQg6AAAAAIxD0AEAAABgHIIOAAAAAOMQdAAAAAAYh6ADAAAAwDgEHQAAAADGIejAKIsWLVJUVJQ8PT1Vo0YN9evXTwkJCX/Y7z//+Y+aNm0qd3d31axZU/fff7/S0tIc2qSlpen+++9XzZo15e7urqZNm+qNN9645Fi5ubl6+umn1bhxY7m7u8vf318dO3bU1q1brTbJyckaPny46tatKw8PD91www166aWXVFhYePUfAgAAAORS2QUA5WX+/PkaOXKkJKlevXo6ceKElixZovXr1ysuLk4hISHF9nvmmWf0/PPPS5IaNWqkn3/+We+99542bdqkHTt2qFq1asrNzdWtt96q+Ph4eXp6qm7dutq3b58effRRpaen69lnn5UknTt3Tl27dtW2bdvk5OSkRo0ayc3NTXv37tWBAwd08803KyMjw/rT29tbkZGR2rt3r5588kkdO3ZMs2bNuiafFwAAgMkY0YER8vPz9dRTT0mS7r33XiUmJmrfvn3y8fFRenq6pk2bVmy/tLQ0vfjii5Kkf/zjHzpw4IA2b94sm82m/fv3a86cOZKkt99+W/Hx8bLZbNq8ebMOHDigmJgYSdKMGTOs0Z9Zs2Zp27ZtCg0N1U8//aT9+/frhx9+0C+//KK+fftKkj755BNlZGRIkjZv3qzdu3frrbfekiS98cYbOnLkSAV9SgAAAH8eBB0YYdu2bcrMzJR0MehIUlhYmNq3by9JWrlyZbH9Vq9erfPnzzv0a9GihRo2bOjQ76uvvpJ0ccSnRYsWDu3Pnz+v2NhYSdLixYslSfXr19eQIUPk5eWlJk2a6M0335SHh4ckOdye5uTk5PBnQUGBvv3226v4JAAAACARdGCI346C1KxZ03ofHBwsSUpJSbmqfkXtimvz23bx8fGSpA0bNigpKUnBwcHav3+/HnvsMc2cOVOSdMcdd8jb21uS1K5dO7Vs2VIPPfSQdayjR4+W7qIBAABQIoIOjGa32yusX3FtLly4IEmqUaOGDh06pISEBPXo0UOSrIkL6tevr1WrVqlr165ycnLSsWPHNHz4cNlsNkmSq6vrFdUMAACAXxF0YITw8HDrfXp6+iXv69Spc1X9itoV1+a37WrVqiVJaty4sfz8/GSz2dSmTRtJF0d9im5b69Chg9asWaNTp04pPT1d999/vxWcbrjhhtJfOAAAAIpF0IER2rZtq4CAAEnSkiVLJEnHjh3T5s2bJUm9e/eWJEVGRioyMtIaXenevbtcXFwc+v3www86dOiQQ7+iPw8ePKgffvjBob2rq6u6d+8uSdbozYEDB5SdnS273a4dO3ZIkho0aGA9i/P999+roKBAkvTLL79o/PjxkqTAwEDrWACAquH8+fOaOnWq6tevLzc3N9WuXVuPP/64cnJy/rDv6dOn9fjjj6t27dpyc3NTgwYNNHXqVOsOgCI7duxQ79695evrq2rVqqlTp05avXr1JcdLSkrS8OHDFRoaKjc3NwUHB6tPnz7Kysqy2sTGxuq2225TcHCw3N3dFRYWpn79+mnPnj1X/2EA1xGb/Urv7bmGsrOz5efnp6ysLPn6+lZ2OYp4anlll4DfSZ7RR3PnztWDDz4o6dfppbOzsxUYGKi4uDiFhYVZt4dNnjxZU6ZMkST961//0vTp0yVdHIk5cuSIzp49q0aNGmnXrl3y8vJSTk6OoqKidPDgQXl6eio8PFwHDhyw+r/wwguSLn4BRUVF6dSpUwoKCpKPj48SExMlSe+//76GDBkiSWrWrJmOHj2q8PBwJSQk6MyZM3J2dtbixYutSQ6uZ80XNq/sEvA7e4bxD5yqhu+SqiV5Rp8S9w0ZMkQffvihtWxAYmKizp8/r1tvvVVr1qyxfon1e4WFherWrZvWrVsnV1dX1a9fXwcPHlRhYaGGDBmi999/X9LFX7B16NBBZ86cUWBgoNzd3XX06FE5OztrxYoV6tmzp6SLv0Tr2LGjTpw4oWrVqqlhw4bKz89XQkKCEhMTVbt2bR04cEDNmzdXfn6+/P39FRERob179+r8+fMKCgrS8ePH5ezsXP4fYAXgu6RqqUrfI6XNBozowBijR4/Whx9+qJYtW+rYsWOy2Wzq27evNm7cqLCwsBL7vfDCC5o1a5YiIyOVlJQkLy8vDRs2TN999528vLwkSd7e3lq3bp2GDRsmLy8vJSUlKTIyUrNmzbJCjnQxYH3//fe68847lZeXp8zMTHXs2FFfffWVFXIkqWfPnvL19VV8fLxcXFzUs2dPrVmzxoiQAwAm2blzpz788ENJ0r///W/t37/fGtFft26dli1bVmLfZcuWad26dZKkpUuXav/+/dZaaR988IF27twpSXr66ad15swZRUREKDExUcnJyWrXrp0KCgqsEX9Jeuyxx3TixAl17dpVR48eVVxcnPbt26esrCxrrbitW7cqPz9f0sUZQ3fu3KkJEyZIkk6cOFGqUSjAFCwYCqMMHjxYgwcPLnF/cQOYNptNY8eO1dixYy977NDQUC1YsOAPa7jxxhv1v//977JtZs6cac3CBgCouoqWF5B+XVagT58+8vDw0Llz57Ry5UprnbSS+np6euqOO+6wjvHYY49JuriEQYsWLaxb1Hr27CkfHx9J0l133aUtW7Zoz549OnbsmDw9PbVq1SpJkr+/v9q0aaO0tDTdeOONeu6553TbbbdJujibp5ubm/Lz83XHHXeobt262rt3r/z8/PTCCy/Iz8+vvD8ioMpiRAcAAKAExS1D4OTkpMDAQEklL1/w274BAQHW7W2/X5ogMzNTZ8+edTh+ce0OHjxo/bJu6dKlKiwslIeHh7Zs2aLbb79dW7ZskXRxvbfVq1crKChIJ0+e1K5du3T+/HnVrl1bTZs2vfIPArgOEXQAAAB08Ta19u3bO7xKUpHLFxTX7reTF/To0UMJCQk6dOiQatSooYKCAr311luSLq7Fdv/99ysjI0OLFy9WTk6Oxo0bpx9//FF9+vTR8ePHr6hu4HpE0AEAANDFB5y3bNni8CpuGYLCwkKdOHFCUsnLF0i/Lk2QmZlpLS/w+6UJAgMD5enpecm+37crWr5Aktq0aSObzSY/Pz81btxYkpScnCxJevPNN3Xo0CH5+vqqf//+8vLy0tChQyVJZ8+e1YYNG8r4qQDXrysKOrNnz1ZERIQ8PDzUrl07bd269bLtP/nkE0VGRsrDw0PNmzfXihUrrqhYAACAitKlSxfZ7XaHV9HyAtKvywosX75c586dk/Tr8gNbt261ljAo+ndR0b5z585Z//YpOkbRfhcXF2tZgVWrVun06dO6cOGCvvjiC0lS8+bNFRYWprp166pRo0aSLk5FbbfblZ2dbc0AWrSvaJrp06dPW/u2b99unbNokh3gz6DMkxEsXrxYMTExmjNnjtq1a6dZs2apV69eio+Pd7i3tMjGjRs1cOBATZ8+XXfeeac++ugj3X333dq5c6eaNWtWLhcBaAoPV1Yp9Ur+DScAXE9at26tgQMH6v/+7/80duxYzZ49WwkJCZKkzp076+6775YknTlzRvHx8dZ7Sbr77rvVqVMnff/99+rbt68aNGhghY9BgwYpKipKkvT8888rNjZWycnJql+/vsP00i+99JJVy4wZM9SvXz998803atiwoU6fPq2TJ0/Ky8tLMTExkqR77rlHb775pux2u6KiolS/fn39+OOPkqS6deuqS5cuFf6ZAVVFmUd0Zs6cqVGjRmnEiBFq2rSp5syZo2rVqundd98ttv2///1v9e7dW0888YSaNGmi5557TlFRUdaCjQAAAFXZwoULNWnSJNWpU0cJCQkKCgrSY489puXLl5e4ho4kOTs7a/ny5XrssccUFBSkhIQE1alTR5MmTXKYxfOmm27SunXrdNttt+ncuXM6ceKEOnbsqBUrVjiMKPXt21fLli1T27ZtdezYMTk5Oenuu+/W9u3b1aRJE0kXF8JesWKFevToIW9vbx04cEB16tTRyJEjtX79eus2OeDPoEwLhubn56tatWr69NNPrd9gSNKwYcN06tQpff7555f0qVOnjmJiYjRu3Dhr2+TJk7Vs2TLFxcUVe568vDzl5eVZP2dnZys8PJwFQ1GiZI9BlV0CfqM5IzpVTlVa6A0X8V1StVxuwVBUDhYMrVqq0vdIaRcMLdOta5mZmSooKHCY8lC6OAXi/v37i+2TmppabPvU1NQSzzN9+nRNnTq1LKVdU/xlWBVlVXYB+I2q81chUHXxXVLFcAt0lbNnCt/tuDpVcta1CRMmKCsry3r9dg57AAAAAPgjZRrRCQwMlLOzs9LS0hy2p6WlKSQkpNg+ISEhZWovSe7u7nJ3dy9LaQAAAABgKVPQcXNzU+vWrRUbG2s9o1NYWKjY2FiNGTOm2D4dOnRQbGyswzM633zzjTp06HDFRQMAAJQrbpMCjFPm6aVjYmI0bNgwtWnTRjfffLNmzZql3NxcjRgxQpI0dOhQ1apVS9OnT5ckjR07VrfeeqteffVV9enTR4sWLdL27ds1d+7c8r0SAAAAAPj/lTnoDBgwQBkZGZo0aZJSU1PVsmVLrVy50ppwICUlxWGqxY4dO+qjjz7S008/rX/9619q1KiRli1bxho6AAAAACpMmaaXriylnUIOAAAAgNlKmw2q5KxrAAAAAHA1CDoAAAAAjEPQAQAAAGAcgg4AAAAA4xB0AAAAABiHoAMAAADAOAQdAAAAAMYh6AAAAAAwDkEHAAAAgHEIOgAAAACMQ9ABAAAAYByCDgAAAADjEHQAAAAAGIegAwAAAMA4BB0AAAAAxiHoAAAAADAOQQcAAACAcQg6AAAAAIxD0AEAAABgHIIOAAAAAOO4VHYBpWG32yVJ2dnZlVwJAAAAgMpUlAmKMkJJrougc/r0aUlSeHh4JVcCAAAAoCo4ffq0/Pz8Stxvs/9RFKoCCgsLdezYMfn4+Mhms1V2OUCFyM7OVnh4uI4cOSJfX9/KLgcAcB3iuwR/Bna7XadPn1ZYWJicnEp+Eue6GNFxcnJS7dq1K7sM4Jrw9fXlywkAcFX4LoHpLjeSU4TJCAAAAAAYh6ADAAAAwDgEHaCKcHd31+TJk+Xu7l7ZpQAArlN8lwC/ui4mIwAAAACAsmBEBwAAAIBxCDoAAAAAjEPQAQAAAGAcgg5wnYuIiNCsWbMquwwAQBWUnJwsm82m3bt3V3YpwDVH0AHKYPjw4bLZbJe8Dh06VNmlAQAMUfRd89BDD12y75FHHpHNZtPw4cOvfWHAdYagA5RR7969dfz4cYdXvXr1KrssAIBBwsPDtWjRIp09e9badu7cOX300UeqU6dOJVYGXD8IOkAZubu7KyQkxOHl7Oyszz//XFFRUfLw8FD9+vU1depUXbhwwepns9n09ttv684771S1atXUpEkTbdq0SYcOHVKXLl3k5eWljh07KiEhweqTkJCgv/71rwoODpa3t7fatm2r1atXX7a+U6dOaeTIkQoKCpKvr6+6deumuLi4Cvs8AADlLyoqSuHh4Vq6dKm1benSpapTp45atWplbVu5cqU6deqk6tWrKyAgQHfeeafD90hx9u7dq9tvv13e3t4KDg7WkCFDlJmZWWHXAlQWgg5QDtavX6+hQ4dq7Nix+umnn/T2229rwYIFeuGFFxzaPffccxo6dKh2796tyMhIDRo0SA8++KAmTJig7du3y263a8yYMVb7nJwc3XHHHYqNjdWuXbvUu3dvRUdHKyUlpcRa7rvvPqWnp+urr77Sjh07FBUVpe7du+vkyZMVdv0AgPJ3//3367333rN+fvfddzVixAiHNrm5uYqJidH27dsVGxsrJycn3XPPPSosLCz2mKdOnVK3bt3UqlUrbd++XStXrlRaWpr69+9fodcCVAo7gFIbNmyY3dnZ2e7l5WW9+vXrZ+/evbt92rRpDm0/+OADe2hoqPWzJPvTTz9t/bxp0ya7JPv8+fOtbf/3f/9n9/DwuGwNN954o/0///mP9XPdunXtr732mt1ut9vXr19v9/X1tZ87d86hT4MGDexvv/12ma8XAHDtDRs2zP7Xv/7Vnp6ebnd3d7cnJyfbk5OT7R4eHvaMjAz7X//6V/uwYcOK7ZuRkWGXZN+zZ4/dbrfbk5KS7JLsu3btstvtdvtzzz1n79mzp0OfI0eO2CXZ4+PjK/KygGvOpVJTFnAd6tq1q9566y3rZy8vL7Vo0UIbNmxwGMEpKCjQuXPndObMGVWrVk2S1KJFC2t/cHCwJKl58+YO286dO6fs7Gz5+voqJydHU6ZM0fLly3X8+HFduHBBZ8+eLXFEJy4uTjk5OQoICHDYfvbs2T+8lQEAULUEBQWpT58+WrBggex2u/r06aPAwECHNgcPHtSkSZO0ZcsWZWZmWiM5KSkpatas2SXHjIuL07fffitvb+9L9iUkJKhx48YVczFAJSDoAGXk5eWlhg0bOmzLycnR1KlT1bdv30vae3h4WO9dXV2t9zabrcRtRV9U48eP1zfffKNXXnlFDRs2lKenp/r166f8/Pxia8vJyVFoaKjWrl17yb7q1auX7gIBAFXG/fffb93SPHv27Ev2R0dHq27dupo3b57CwsJUWFioZs2aXfZ7Ijo6Wi+++OIl+0JDQ8u3eKCSEXSAchAVFaX4+PhLAtDV2rBhg4YPH6577rlH0sUvqOTk5MvWkZqaKhcXF0VERJRrLQCAa693797Kz8+XzWZTr169HPadOHFC8fHxmjdvnjp37ixJ+v777y97vKioKC1ZskQRERFyceGfgTAbkxEA5WDSpEl6//33NXXqVP3444/at2+fFi1apKeffvqqjtuoUSMtXbpUu3fvVlxcnAYNGlTiA6aS1KNHD3Xo0EF33323Vq1apeTkZG3cuFETJ07U9u3br6oWAMC15+zsrH379umnn36Ss7Ozwz5/f38FBARo7ty5OnTokNasWaOYmJjLHu+RRx7RyZMnNXDgQG3btk0JCQn6+uuvNWLECBUUFFTkpQDXHEEHKAe9evXSl19+qVWrVqlt27Zq3769XnvtNdWtW/eqjjtz5kz5+/urY8eOio6OVq9evRQVFVVie5vNphUrVugvf/mLRowYocaNG+tvf/ubDh8+bD0TBAC4vvj6+srX1/eS7U5OTlq0aJF27NihZs2a6fHHH9fLL7982WOFhYVpw4YNKigoUM+ePdW8eXONGzdO1atXl5MT/yyEWWx2u91e2UUAAAAAQHkiugMAAAAwDkEHAAAAgHEIOgAAAACMQ9ABAAAAYByCDgAAAADjEHQAAAAAGIegAwAAAMA4BB0AgDG6dOmicePGVXYZAIAqgKADAChXqampGjt2rBo2bCgPDw8FBwfrlltu0VtvvaUzZ85UdnkAgD8Jl8ouAABgjsTERN1yyy2qXr26pk2bpubNm8vd3V179uzR3LlzVatWLd11112VXWaJCgoKZLPZ5OTE7wEB4HrH3+QAgHLz97//XS4uLtq+fbv69++vJk2aqH79+vrrX/+q5cuXKzo6WpJ06tQpjRw5UkFBQfL19VW3bt0UFxdnHWfKlClq2bKlPvjgA0VERMjPz09/+9vfdPr0aatNbm6uhg4dKm9vb4WGhurVV1+9pJ68vDyNHz9etWrVkpeXl9q1a6e1a9da+xcsWKDq1avriy++UNOmTeXu7q6UlJSK+4AAANcMQQcAUC5OnDihVatW6ZFHHpGXl1exbWw2myTpvvvuU3p6ur766ivt2LFDUVFR6t69u06ePGm1TUhI0LJly/Tll1/qyy+/1Lp16zRjxgxr/xNPPKF169bp888/16pVq7R27Vrt3LnT4XxjxozRpk2btGjRIv3www+677771Lt3bx08eNBqc+bMGb344ot655139OOPP6pmzZrl+bEAACoJt64BAMrFoUOHZLfbdcMNNzhsDwwM1Llz5yRJjzzyiKKjo7V161alp6fL3d1dkvTKK69o2bJl+vTTTzV69GhJUmFhoRYsWCAfHx9J0pAhQxQbG6sXXnhBOTk5mj9/vj788EN1795dkrRw4ULVrl3bOm9KSoree+89paSkKCwsTJI0fvx4rVy5Uu+9956mTZsmSTp//rzefPNN3XTTTRX46QAArjWCDgCgQm3dulWFhYUaPHiw8vLyFBcXp5ycHAUEBDi0O3v2rBISEqyfIyIirJAjSaGhoUpPT5d0cbQnPz9f7dq1s/bXqFHDIWTt2bNHBQUFaty4scN58vLyHM7t5uamFi1alM/FAgCqDIIOAKBcNGzYUDabTfHx8Q7b69evL0ny9PSUJOXk5Cg0NNThWZki1atXt967uro67LPZbCosLCx1PTk5OXJ2dtaOHTvk7OzssM/b29t67+npad1SBwAwB0EHAFAuAgICdNttt+mNN97Qo48+WuJzOlFRUUpNTZWLi4siIiKu6FwNGjSQq6urtmzZojp16kiSfvnlFx04cEC33nqrJKlVq1YqKChQenq6OnfufEXnAQBcv5iMAABQbt58801duHBBbdq00eLFi7Vv3z7Fx8frww8/1P79++Xs7KwePXqoQ4cOuvvuu7Vq1SolJydr48aNmjhxorZv316q83h7e+uBBx7QE088oTVr1mjv3r0aPny4w7TQjRs31uDBgzV06FAtXbpUSUlJ2rp1q6ZPn67ly5dX1EcAAKgiGNEBAJSbBg0aaNeuXZo2bZomTJign3/+We7u7mratKnGjx+vv//977LZbFqxYoUmTpyoESNGKCMjQyEhIfrLX/6i4ODgUp/r5ZdfVk5OjqKjo+Xj46N//OMfysrKcmjz3nvv6fnnn9c//vEPHT16VIGBgWrfvr3uvPPO8r50AEAVY7Pb7fbKLgIAAAAAyhO3rgEAAAAwDkEHAAAAgHEIOgAAAACMQ9ABAAAAYByCDgAAAADjEHQAAAAAGIegAwAAAMA4BB0AAAAAxiHoAAAAADAOQQcAAACAcQg6AAAAAIxD0AEAAABgnP8Pe0oIap9vut4AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_labels = ['Female', 'Male']\n",
    "x = range(len(x_labels))\n",
    "width = 0.25\n",
    "\n",
    "fig, ax = plt.subplots(figsize = (10,7))\n",
    "ax.bar(x, df_group['mean'], width, label='mean')\n",
    "ax.bar([i + width for i in x], df_group['diff'], width, label='diff')\n",
    "ax.bar([i + width*2 for i in x], df_group['risk'], width, label='risk')\n",
    "\n",
    "ax.set_xticks([i + width for i in x])\n",
    "ax.set_xticklabels(x_labels)\n",
    "ax.set_xlabel('Gender')\n",
    "ax.legend()\n",
    "\n",
    "for i, v in enumerate(df_group['mean']):\n",
    "    ax.text(i - width/2, v + 0.01, str(round(v, 4)), color='black', fontweight='bold')\n",
    "for i, v in enumerate(df_group['diff']):\n",
    "    ax.text(i + width/2, v + 0.01, str(round(v, 4)), color='black', fontweight='bold')\n",
    "for i, v in enumerate(df_group['risk']):\n",
    "    ax.text(i + width*1.5, v + 0.01, str(round(v, 4)), color='black', fontweight='bold')\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "2d321b58",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.26996805111821087"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## lets not round off and see the difference and risk\n",
    "global_mean = df_train_full.churn.mean()\n",
    "global_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ba0240fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "from Ipython.display import display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "f07552f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>mean</th>\n",
       "      <th>diff</th>\n",
       "      <th>risk</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>0.276824</td>\n",
       "      <td>0.006856</td>\n",
       "      <td>1.025396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.263214</td>\n",
       "      <td>-0.006755</td>\n",
       "      <td>0.974980</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            mean      diff      risk\n",
       "gender                              \n",
       "female  0.276824  0.006856  1.025396\n",
       "male    0.263214 -0.006755  0.974980"
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       "      <th>risk</th>\n",
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       "    <tr>\n",
       "      <th>seniorcitizen</th>\n",
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       "      <td>0.897403</td>\n",
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       "      <th>1</th>\n",
       "      <td>0.413377</td>\n",
       "      <td>0.143409</td>\n",
       "      <td>1.531208</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "                   mean      diff      risk\n",
       "seniorcitizen                              \n",
       "0              0.242270 -0.027698  0.897403\n",
       "1              0.413377  0.143409  1.531208"
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       "      <td>0.059841</td>\n",
       "      <td>1.221659</td>\n",
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      "text/plain": [
       "             mean      diff      risk\n",
       "partner                              \n",
       "no       0.329809  0.059841  1.221659\n",
       "yes      0.205033 -0.064935  0.759472"
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       "      <td>0.043792</td>\n",
       "      <td>1.162212</td>\n",
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      "text/plain": [
       "                mean      diff      risk\n",
       "dependents                              \n",
       "no          0.313760  0.043792  1.162212\n",
       "yes         0.165666 -0.104302  0.613651"
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       "                  mean      diff      risk\n",
       "phoneservice                              \n",
       "no            0.241316 -0.028652  0.893870\n",
       "yes           0.273049  0.003081  1.011412"
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       "                      mean      diff      risk\n",
       "multiplelines                                 \n",
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       "      <td>0.192347</td>\n",
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       "    <tr>\n",
       "      <th>fiber_optic</th>\n",
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       "      <td>1.574895</td>\n",
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       "                     mean      diff      risk\n",
       "internetservice                              \n",
       "dsl              0.192347 -0.077621  0.712482\n",
       "fiber_optic      0.425171  0.155203  1.574895\n",
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       "                         mean      diff      risk\n",
       "onlinesecurity                                   \n",
       "no                   0.420921  0.150953  1.559152\n",
       "no_internet_service  0.077805 -0.192163  0.288201\n",
       "yes                  0.153226 -0.116742  0.567570"
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       "                         mean      diff      risk\n",
       "onlinebackup                                     \n",
       "no                   0.404323  0.134355  1.497672\n",
       "no_internet_service  0.077805 -0.192163  0.288201\n",
       "yes                  0.217232 -0.052736  0.804660"
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       "                         mean      diff      risk\n",
       "deviceprotection                                 \n",
       "no                   0.395875  0.125907  1.466379\n",
       "no_internet_service  0.077805 -0.192163  0.288201\n",
       "yes                  0.230412 -0.039556  0.853480"
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       "      <th>no</th>\n",
       "      <td>0.418914</td>\n",
       "      <td>0.148946</td>\n",
       "      <td>1.551717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>no_internet_service</th>\n",
       "      <td>0.077805</td>\n",
       "      <td>-0.192163</td>\n",
       "      <td>0.288201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yes</th>\n",
       "      <td>0.159926</td>\n",
       "      <td>-0.110042</td>\n",
       "      <td>0.592390</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         mean      diff      risk\n",
       "techsupport                                      \n",
       "no                   0.418914  0.148946  1.551717\n",
       "no_internet_service  0.077805 -0.192163  0.288201\n",
       "yes                  0.159926 -0.110042  0.592390"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>diff</th>\n",
       "      <th>risk</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>streamingtv</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>no</th>\n",
       "      <td>0.342832</td>\n",
       "      <td>0.072864</td>\n",
       "      <td>1.269897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>no_internet_service</th>\n",
       "      <td>0.077805</td>\n",
       "      <td>-0.192163</td>\n",
       "      <td>0.288201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yes</th>\n",
       "      <td>0.302723</td>\n",
       "      <td>0.032755</td>\n",
       "      <td>1.121328</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         mean      diff      risk\n",
       "streamingtv                                      \n",
       "no                   0.342832  0.072864  1.269897\n",
       "no_internet_service  0.077805 -0.192163  0.288201\n",
       "yes                  0.302723  0.032755  1.121328"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>diff</th>\n",
       "      <th>risk</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>streamingmovies</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>no</th>\n",
       "      <td>0.338906</td>\n",
       "      <td>0.068938</td>\n",
       "      <td>1.255358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>no_internet_service</th>\n",
       "      <td>0.077805</td>\n",
       "      <td>-0.192163</td>\n",
       "      <td>0.288201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yes</th>\n",
       "      <td>0.307273</td>\n",
       "      <td>0.037305</td>\n",
       "      <td>1.138182</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         mean      diff      risk\n",
       "streamingmovies                                  \n",
       "no                   0.338906  0.068938  1.255358\n",
       "no_internet_service  0.077805 -0.192163  0.288201\n",
       "yes                  0.307273  0.037305  1.138182"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "        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>mean</th>\n",
       "      <th>diff</th>\n",
       "      <th>risk</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>contract</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>month-to-month</th>\n",
       "      <td>0.431701</td>\n",
       "      <td>0.161733</td>\n",
       "      <td>1.599082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>one_year</th>\n",
       "      <td>0.120573</td>\n",
       "      <td>-0.149395</td>\n",
       "      <td>0.446621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two_year</th>\n",
       "      <td>0.028274</td>\n",
       "      <td>-0.241694</td>\n",
       "      <td>0.104730</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    mean      diff      risk\n",
       "contract                                    \n",
       "month-to-month  0.431701  0.161733  1.599082\n",
       "one_year        0.120573 -0.149395  0.446621\n",
       "two_year        0.028274 -0.241694  0.104730"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>diff</th>\n",
       "      <th>risk</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>paperlessbilling</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>no</th>\n",
       "      <td>0.172071</td>\n",
       "      <td>-0.097897</td>\n",
       "      <td>0.637375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yes</th>\n",
       "      <td>0.338151</td>\n",
       "      <td>0.068183</td>\n",
       "      <td>1.252560</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      mean      diff      risk\n",
       "paperlessbilling                              \n",
       "no                0.172071 -0.097897  0.637375\n",
       "yes               0.338151  0.068183  1.252560"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "\n",
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       "        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>mean</th>\n",
       "      <th>diff</th>\n",
       "      <th>risk</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>paymentmethod</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bank_transfer_(automatic)</th>\n",
       "      <td>0.168171</td>\n",
       "      <td>-0.101797</td>\n",
       "      <td>0.622928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>credit_card_(automatic)</th>\n",
       "      <td>0.164339</td>\n",
       "      <td>-0.105630</td>\n",
       "      <td>0.608733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>electronic_check</th>\n",
       "      <td>0.455890</td>\n",
       "      <td>0.185922</td>\n",
       "      <td>1.688682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mailed_check</th>\n",
       "      <td>0.193870</td>\n",
       "      <td>-0.076098</td>\n",
       "      <td>0.718121</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               mean      diff      risk\n",
       "paymentmethod                                          \n",
       "bank_transfer_(automatic)  0.168171 -0.101797  0.622928\n",
       "credit_card_(automatic)    0.164339 -0.105630  0.608733\n",
       "electronic_check           0.455890  0.185922  1.688682\n",
       "mailed_check               0.193870 -0.076098  0.718121"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for col in categorical:\n",
    "    df_group = df_train_full.groupby(by=col).churn.agg(['mean'])\n",
    "    df_group['diff'] = df_group['mean'] - global_mean\n",
    "    df_group['risk'] = df_group['mean'] / global_mean\n",
    "    display(df_group)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "825d938f",
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in categorical:\n",
    "    x_labels = ['Female', 'Male']\n",
    "    x = range(len(x_labels))\n",
    "    width = 0.25\n",
    "\n",
    "    fig, ax = plt.subplots(figsize = (10,7))\n",
    "    ax.bar(x, df_group['mean'], width, label='mean')\n",
    "    ax.bar([i + width for i in x], df_group['diff'], width, label='diff')\n",
    "    ax.bar([i + width*2 for i in x], df_group['risk'], width, label='risk')\n",
    "\n",
    "    ax.set_xticks([i + width for i in x])\n",
    "    ax.set_xticklabels(x_labels)\n",
    "    ax.set_xlabel('Gender')\n",
    "    ax.legend()\n",
    "\n",
    "    for i, v in enumerate(df_group['mean']):\n",
    "        ax.text(i - width/2, v + 0.01, str(round(v, 4)), color='black', fontweight='bold')\n",
    "    for i, v in enumerate(df_group['diff']):\n",
    "        ax.text(i + width/2, v + 0.01, str(round(v, 4)), color='black', fontweight='bold')\n",
    "    for i, v in enumerate(df_group['risk']):\n",
    "        ax.text(i + width*1.5, v + 0.01, str(round(v, 4)), color='black', fontweight='bold')\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "aa51e9bd",
   "metadata": {},
   "outputs": [],
   "source": [
    " from sklearn.metrics import mutual_info_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "35b07aaf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>Mututal_Info</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>contract</th>\n",
       "      <td>0.098320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>onlinesecurity</th>\n",
       "      <td>0.063085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>techsupport</th>\n",
       "      <td>0.061032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>internetservice</th>\n",
       "      <td>0.055868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>onlinebackup</th>\n",
       "      <td>0.046923</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Mututal_Info\n",
       "contract             0.098320\n",
       "onlinesecurity       0.063085\n",
       "techsupport          0.061032\n",
       "internetservice      0.055868\n",
       "onlinebackup         0.046923"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>Mututal_Info</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>partner</th>\n",
       "      <td>0.009968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seniorcitizen</th>\n",
       "      <td>0.009410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>multiplelines</th>\n",
       "      <td>0.000857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>phoneservice</th>\n",
       "      <td>0.000229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <td>0.000117</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Mututal_Info\n",
       "partner            0.009968\n",
       "seniorcitizen      0.009410\n",
       "multiplelines      0.000857\n",
       "phoneservice       0.000229\n",
       "gender             0.000117"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def calculate_mi(series):\n",
    "    return mutual_info_score(series, df_train_full.churn)\n",
    "\n",
    "df_mi = df_train_full[categorical].apply(calculate_mi)\n",
    "df_mi = df_mi.sort_values(ascending=False).to_frame(name='Mututal_Info')\n",
    "\n",
    "\n",
    "display(df_mi.head())\n",
    "display(df_mi.tail())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "1429cf33",
   "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>correlation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>tenure</th>\n",
       "      <td>-0.351885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>monthlycharges</th>\n",
       "      <td>0.196805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>totalcharges</th>\n",
       "      <td>-0.196353</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                correlation\n",
       "tenure            -0.351885\n",
       "monthlycharges     0.196805\n",
       "totalcharges      -0.196353"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train_full[numerical].corrwith(df_train_full.churn).to_frame('correlation')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "3696ccde",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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",
       "    <tr>\n",
       "      <th>churn</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>37.531972</td>\n",
       "      <td>61.176477</td>\n",
       "      <td>2548.021627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18.070348</td>\n",
       "      <td>74.521203</td>\n",
       "      <td>1545.689415</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          tenure  monthlycharges  totalcharges\n",
       "churn                                         \n",
       "0      37.531972       61.176477   2548.021627\n",
       "1      18.070348       74.521203   1545.689415"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train_full.groupby(by='churn')[numerical].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "73121af8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction import DictVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "38d3033d",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dict = df_train[categorical + numerical].to_dict(orient='records')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "00099cb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'gender': 'male',\n",
       " 'seniorcitizen': 0,\n",
       " 'partner': 'yes',\n",
       " 'dependents': 'no',\n",
       " 'phoneservice': 'yes',\n",
       " 'multiplelines': 'no',\n",
       " 'internetservice': 'dsl',\n",
       " 'onlinesecurity': 'yes',\n",
       " 'onlinebackup': 'yes',\n",
       " 'deviceprotection': 'yes',\n",
       " 'techsupport': 'yes',\n",
       " 'streamingtv': 'yes',\n",
       " 'streamingmovies': 'yes',\n",
       " 'contract': 'two_year',\n",
       " 'paperlessbilling': 'yes',\n",
       " 'paymentmethod': 'bank_transfer_(automatic)',\n",
       " 'tenure': 71,\n",
       " 'monthlycharges': 86.1,\n",
       " 'totalcharges': 6045.9}"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_dict[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "786d13e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DictVectorizer(sparse=False)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DictVectorizer</label><div class=\"sk-toggleable__content\"><pre>DictVectorizer(sparse=False)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DictVectorizer(sparse=False)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dv = DictVectorizer(sparse = False)\n",
    "dv.fit(train_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "2115d00c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3774, 45)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = dv.transform(train_dict)\n",
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "dfbe58a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['contract=month-to-month', 'contract=one_year',\n",
       "       'contract=two_year', 'dependents=no', 'dependents=yes',\n",
       "       'deviceprotection=no', 'deviceprotection=no_internet_service',\n",
       "       'deviceprotection=yes', 'gender=female', 'gender=male',\n",
       "       'internetservice=dsl', 'internetservice=fiber_optic',\n",
       "       'internetservice=no', 'monthlycharges', 'multiplelines=no',\n",
       "       'multiplelines=no_phone_service', 'multiplelines=yes',\n",
       "       'onlinebackup=no', 'onlinebackup=no_internet_service',\n",
       "       'onlinebackup=yes', 'onlinesecurity=no',\n",
       "       'onlinesecurity=no_internet_service', 'onlinesecurity=yes',\n",
       "       'paperlessbilling=no', 'paperlessbilling=yes', 'partner=no',\n",
       "       'partner=yes', 'paymentmethod=bank_transfer_(automatic)',\n",
       "       'paymentmethod=credit_card_(automatic)',\n",
       "       'paymentmethod=electronic_check', 'paymentmethod=mailed_check',\n",
       "       'phoneservice=no', 'phoneservice=yes', 'seniorcitizen',\n",
       "       'streamingmovies=no', 'streamingmovies=no_internet_service',\n",
       "       'streamingmovies=yes', 'streamingtv=no',\n",
       "       'streamingtv=no_internet_service', 'streamingtv=yes',\n",
       "       'techsupport=no', 'techsupport=no_internet_service',\n",
       "       'techsupport=yes', 'tenure', 'totalcharges'], dtype=object)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dv.get_feature_names_out()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "06a91686",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "01559880",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(random_state=1, solver=&#x27;liblinear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(random_state=1, solver=&#x27;liblinear&#x27;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(random_state=1, solver='liblinear')"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LogisticRegression(solver = 'liblinear', random_state = 1)\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "6cadbb02",
   "metadata": {},
   "outputs": [],
   "source": [
    "val_dict = df_val[categorical + numerical].to_dict(orient='records')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "8d1b01d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_val = dv.transform(val_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "3f3d4752",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.76508784, 0.23491216],\n",
       "       [0.73113015, 0.26886985],\n",
       "       [0.68054704, 0.31945296],\n",
       "       ...,\n",
       "       [0.94274614, 0.05725386],\n",
       "       [0.38476895, 0.61523105],\n",
       "       [0.93872763, 0.06127237]])"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict_proba(X_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "95bcc4b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.23491216, 0.26886985, 0.31945296, ..., 0.05725386, 0.61523105,\n",
       "       0.06127237])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = model.predict_proba(X_val)[:, 1]\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "029adf1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8016129032258065"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "churn = y_pred > 0.5\n",
    "(y_val == churn).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "9efdaf99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.12198896163042137"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "model.intercept_[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "91b24907",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'contract=month-to-month': 0.563,\n",
       " 'contract=one_year': -0.086,\n",
       " 'contract=two_year': -0.599,\n",
       " 'dependents=no': -0.03,\n",
       " 'dependents=yes': -0.092,\n",
       " 'deviceprotection=no': 0.1,\n",
       " 'deviceprotection=no_internet_service': -0.116,\n",
       " 'deviceprotection=yes': -0.106,\n",
       " 'gender=female': -0.027,\n",
       " 'gender=male': -0.095,\n",
       " 'internetservice=dsl': -0.323,\n",
       " 'internetservice=fiber_optic': 0.317,\n",
       " 'internetservice=no': -0.116,\n",
       " 'monthlycharges': 0.001,\n",
       " 'multiplelines=no': -0.168,\n",
       " 'multiplelines=no_phone_service': 0.127,\n",
       " 'multiplelines=yes': -0.081,\n",
       " 'onlinebackup=no': 0.136,\n",
       " 'onlinebackup=no_internet_service': -0.116,\n",
       " 'onlinebackup=yes': -0.142,\n",
       " 'onlinesecurity=no': 0.258,\n",
       " 'onlinesecurity=no_internet_service': -0.116,\n",
       " 'onlinesecurity=yes': -0.264,\n",
       " 'paperlessbilling=no': -0.213,\n",
       " 'paperlessbilling=yes': 0.091,\n",
       " 'partner=no': -0.048,\n",
       " 'partner=yes': -0.074,\n",
       " 'paymentmethod=bank_transfer_(automatic)': -0.027,\n",
       " 'paymentmethod=credit_card_(automatic)': -0.136,\n",
       " 'paymentmethod=electronic_check': 0.175,\n",
       " 'paymentmethod=mailed_check': -0.134,\n",
       " 'phoneservice=no': 0.127,\n",
       " 'phoneservice=yes': -0.249,\n",
       " 'seniorcitizen': 0.297,\n",
       " 'streamingmovies=no': -0.085,\n",
       " 'streamingmovies=no_internet_service': -0.116,\n",
       " 'streamingmovies=yes': 0.079,\n",
       " 'streamingtv=no': -0.099,\n",
       " 'streamingtv=no_internet_service': -0.116,\n",
       " 'streamingtv=yes': 0.093,\n",
       " 'techsupport=no': 0.178,\n",
       " 'techsupport=no_internet_service': -0.116,\n",
       " 'techsupport=yes': -0.184,\n",
       " 'tenure': -0.069,\n",
       " 'totalcharges': 0.0}"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict(zip(dv.get_feature_names_out(), model.coef_[0].round(3)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "6ccc03b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['contract=month-to-month', 'contract=one_year',\n",
       "       'contract=two_year', 'tenure', 'totalcharges'], dtype=object)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "subset = ['contract', 'tenure', 'totalcharges']\n",
    "train_dict_small = df_train[subset].to_dict(orient='records')\n",
    "dv_small = DictVectorizer(sparse=False)\n",
    "dv_small.fit(train_dict_small)\n",
    "\n",
    "X_small_train = dv_small.transform(train_dict_small)\n",
    "\n",
    "dv_small.get_feature_names_out()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "d57dcaff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(random_state=1, solver=&#x27;liblinear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(random_state=1, solver=&#x27;liblinear&#x27;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(random_state=1, solver='liblinear')"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_small = LogisticRegression(solver='liblinear', random_state=1)\n",
    "model_small.fit(X_small_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "c3557088",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.6387618613273348"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_small.intercept_[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "14ebd42c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'contract=month-to-month': 0.91,\n",
       " 'contract=one_year': -0.144,\n",
       " 'contract=two_year': -1.404,\n",
       " 'tenure': -0.097,\n",
       " 'totalcharges': 0.001}"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict(zip(dv_small.get_feature_names_out(), model_small.coef_[0].round(3)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "08eebc0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "val_dict_small = df_val[subset].to_dict(orient='records')\n",
    "X_small_val = dv_small.transform(val_dict_small)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "9fcb23de",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_small = model_small.predict_proba(X_small_val)[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "275103a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7672043010752688"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "churn = y_pred_small > 0.5\n",
    "(y_val == churn).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "165a210e",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Model Test\n",
    "\n",
    "customer = {\n",
    "    'customerid': '8879-zkjof',\n",
    "    'gender': 'female',\n",
    "    'seniorcitizen': 0,\n",
    "    'partner': 'no',\n",
    "    'dependents': 'no',\n",
    "    'tenure': 41,\n",
    "    'phoneservice': 'yes',\n",
    "    'multiplelines': 'no',\n",
    "    'internetservice': 'dsl',\n",
    "    'onlinesecurity': 'yes',\n",
    "    'onlinebackup': 'no',\n",
    "    'deviceprotection': 'yes',\n",
    "    'techsupport': 'yes',\n",
    "    'streamingtv': 'yes',\n",
    "    'streamingmovies': 'yes',\n",
    "    'contract': 'one_year',\n",
    "    'paperlessbilling': 'yes',\n",
    "    'paymentmethod': 'bank_transfer_(automatic)',\n",
    "    'monthlycharges': 79.85,\n",
    "    'totalcharges': 3320.75,\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "e457fe84",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.07332111084949638"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test = dv.transform([customer])\n",
    "model.predict_proba(X_test)[0, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "4b53833c",
   "metadata": {},
   "outputs": [],
   "source": [
    "customer = {\n",
    "    'gender': 'female',\n",
    "    'seniorcitizen': 1,\n",
    "    'partner': 'no',\n",
    "    'dependents': 'no',\n",
    "    'phoneservice': 'yes',\n",
    "    'multiplelines': 'yes',\n",
    "    'internetservice': 'fiber_optic',\n",
    "    'onlinesecurity': 'no',\n",
    "    'onlinebackup': 'no',\n",
    "    'deviceprotection': 'no',\n",
    "    'techsupport': 'no',\n",
    "    'streamingtv': 'yes',\n",
    "    'streamingmovies': 'no',\n",
    "    'contract': 'month-to-month',\n",
    "    'paperlessbilling': 'yes',\n",
    "    'paymentmethod': 'electronic_check',\n",
    "    'tenure': 1,\n",
    "    'monthlycharges': 85.7,\n",
    "    'totalcharges': 85.7\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "f986d4d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8321656556055403"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test = dv.transform([customer])\n",
    "model.predict_proba(X_test)[0, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "abf935d2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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