Churn Analysis Part-2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing required packages\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Reading the data\n",
    "df=pd.read_csv('WA_Fn-UseC_-Telco-Customer-Churn.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7043, 21)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7043 entries, 0 to 7042\n",
      "Data columns (total 21 columns):\n",
      "customerID          7043 non-null object\n",
      "gender              7043 non-null object\n",
      "SeniorCitizen       7043 non-null int64\n",
      "Partner             7043 non-null object\n",
      "Dependents          7043 non-null object\n",
      "tenure              7043 non-null int64\n",
      "PhoneService        7043 non-null object\n",
      "MultipleLines       7043 non-null object\n",
      "InternetService     7043 non-null object\n",
      "OnlineSecurity      7043 non-null object\n",
      "OnlineBackup        7043 non-null object\n",
      "DeviceProtection    7043 non-null object\n",
      "TechSupport         7043 non-null object\n",
      "StreamingTV         7043 non-null object\n",
      "StreamingMovies     7043 non-null object\n",
      "Contract            7043 non-null object\n",
      "PaperlessBilling    7043 non-null object\n",
      "PaymentMethod       7043 non-null object\n",
      "MonthlyCharges      7043 non-null float64\n",
      "TotalCharges        7043 non-null object\n",
      "Churn               7043 non-null object\n",
      "dtypes: float64(1), int64(2), object(18)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#No missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check for Nans\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Converting type of Total Charges column from object to float to \n",
    "df['TotalCharges']=pd.to_numeric(df['TotalCharges'],errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7043 entries, 0 to 7042\n",
      "Data columns (total 21 columns):\n",
      "customerID          7043 non-null object\n",
      "gender              7043 non-null object\n",
      "SeniorCitizen       7043 non-null int64\n",
      "Partner             7043 non-null object\n",
      "Dependents          7043 non-null object\n",
      "tenure              7043 non-null int64\n",
      "PhoneService        7043 non-null object\n",
      "MultipleLines       7043 non-null object\n",
      "InternetService     7043 non-null object\n",
      "OnlineSecurity      7043 non-null object\n",
      "OnlineBackup        7043 non-null object\n",
      "DeviceProtection    7043 non-null object\n",
      "TechSupport         7043 non-null object\n",
      "StreamingTV         7043 non-null object\n",
      "StreamingMovies     7043 non-null object\n",
      "Contract            7043 non-null object\n",
      "PaperlessBilling    7043 non-null object\n",
      "PaymentMethod       7043 non-null object\n",
      "MonthlyCharges      7043 non-null float64\n",
      "TotalCharges        7032 non-null float64\n",
      "Churn               7043 non-null object\n",
      "dtypes: float64(2), int64(2), object(17)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Replacing Nans by deleting rows including null values\n",
    "df.dropna(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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>1889.50</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       1889.50     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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Customer ID is not required for analysis \n",
    "df=df.iloc[:,1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>tenure</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>InternetService</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>OnlineBackup</th>\n",
       "      <th>DeviceProtection</th>\n",
       "      <th>TechSupport</th>\n",
       "      <th>StreamingTV</th>\n",
       "      <th>StreamingMovies</th>\n",
       "      <th>Contract</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>PaymentMethod</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>29.85</td>\n",
       "      <td>29.85</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>34</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>56.95</td>\n",
       "      <td>1889.50</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>53.85</td>\n",
       "      <td>108.15</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>45</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>42.30</td>\n",
       "      <td>1840.75</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>70.70</td>\n",
       "      <td>151.65</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender  SeniorCitizen Partner Dependents  tenure PhoneService  \\\n",
       "0  Female              0     Yes         No       1           No   \n",
       "1    Male              0      No         No      34          Yes   \n",
       "2    Male              0      No         No       2          Yes   \n",
       "3    Male              0      No         No      45           No   \n",
       "4  Female              0      No         No       2          Yes   \n",
       "\n",
       "      MultipleLines InternetService OnlineSecurity OnlineBackup  \\\n",
       "0  No phone service             DSL             No          Yes   \n",
       "1                No             DSL            Yes           No   \n",
       "2                No             DSL            Yes          Yes   \n",
       "3  No phone service             DSL            Yes           No   \n",
       "4                No     Fiber optic             No           No   \n",
       "\n",
       "  DeviceProtection TechSupport StreamingTV StreamingMovies        Contract  \\\n",
       "0               No          No          No              No  Month-to-month   \n",
       "1              Yes          No          No              No        One year   \n",
       "2               No          No          No              No  Month-to-month   \n",
       "3              Yes         Yes          No              No        One year   \n",
       "4               No          No          No              No  Month-to-month   \n",
       "\n",
       "  PaperlessBilling              PaymentMethod  MonthlyCharges  TotalCharges  \\\n",
       "0              Yes           Electronic check           29.85         29.85   \n",
       "1               No               Mailed check           56.95       1889.50   \n",
       "2              Yes               Mailed check           53.85        108.15   \n",
       "3               No  Bank transfer (automatic)           42.30       1840.75   \n",
       "4              Yes           Electronic check           70.70        151.65   \n",
       "\n",
       "  Churn  \n",
       "0    No  \n",
       "1    No  \n",
       "2   Yes  \n",
       "3    No  \n",
       "4   Yes  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Male      3549\n",
       "Female    3483\n",
       "Name: gender, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.gender.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Converting few text columns to binary format\n",
    "\n",
    "df.gender = [1 if each == \"Male\" else 0 for each in df.gender]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1, ..., 0, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.gender.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns_to_convert = ['Partner', \n",
    "                      'Dependents', \n",
    "                      'PhoneService', \n",
    "                      'MultipleLines',\n",
    "                      'OnlineSecurity',\n",
    "                      'OnlineBackup',\n",
    "                      'DeviceProtection',\n",
    "                      'TechSupport',\n",
    "                      'StreamingTV',\n",
    "                      'StreamingMovies',\n",
    "                      'PaperlessBilling', \n",
    "                      'Churn']\n",
    "for item in columns_to_convert:\n",
    "    df[item] = [1 if each == \"Yes\" else 0 if each == \"No\" else -1 for each in df[item]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>tenure</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>InternetService</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>OnlineBackup</th>\n",
       "      <th>DeviceProtection</th>\n",
       "      <th>TechSupport</th>\n",
       "      <th>StreamingTV</th>\n",
       "      <th>StreamingMovies</th>\n",
       "      <th>Contract</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>PaymentMethod</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>DSL</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>1</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>29.85</td>\n",
       "      <td>29.85</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>DSL</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>One year</td>\n",
       "      <td>0</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>56.95</td>\n",
       "      <td>1889.50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>DSL</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>1</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>53.85</td>\n",
       "      <td>108.15</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender  SeniorCitizen  Partner  Dependents  tenure  PhoneService  \\\n",
       "0       0              0        1           0       1             0   \n",
       "1       1              0        0           0      34             1   \n",
       "2       1              0        0           0       2             1   \n",
       "\n",
       "   MultipleLines InternetService  OnlineSecurity  OnlineBackup  \\\n",
       "0             -1             DSL               0             1   \n",
       "1              0             DSL               1             0   \n",
       "2              0             DSL               1             1   \n",
       "\n",
       "   DeviceProtection  TechSupport  StreamingTV  StreamingMovies  \\\n",
       "0                 0            0            0                0   \n",
       "1                 1            0            0                0   \n",
       "2                 0            0            0                0   \n",
       "\n",
       "         Contract  PaperlessBilling     PaymentMethod  MonthlyCharges  \\\n",
       "0  Month-to-month                 1  Electronic check           29.85   \n",
       "1        One year                 0      Mailed check           56.95   \n",
       "2  Month-to-month                 1      Mailed check           53.85   \n",
       "\n",
       "   TotalCharges  Churn  \n",
       "0         29.85      0  \n",
       "1       1889.50      0  \n",
       "2        108.15      1  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f6685a8a58>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(df.Churn,data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Data is imbalanced"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1f668140160>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 582.375x540 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(df,vars = ['tenure','MonthlyCharges','TotalCharges'], hue=\"Churn\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1f669f8c588>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"whitegrid\")\n",
    "g1=sns.catplot(x=\"Contract\", y=\"Churn\", data=df,kind=\"bar\")\n",
    "g1.set_ylabels(\"Churn Probability\")\n",
    "\n",
    "g2=sns.catplot(x=\"InternetService\", y=\"Churn\", data=df,kind=\"bar\")\n",
    "g2.set_ylabels(\"Churn Probability\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#People having lower tenure and higher monthly charges are tend to churn more. \n",
    "#having month-to-month contract and fiber obtic internet have a really huge effect on churn probability."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data can be explored further by examining each column/factor further."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>tenure</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>OnlineBackup</th>\n",
       "      <th>DeviceProtection</th>\n",
       "      <th>...</th>\n",
       "      <th>InternetService_DSL</th>\n",
       "      <th>InternetService_Fiber optic</th>\n",
       "      <th>InternetService_No</th>\n",
       "      <th>Contract_Month-to-month</th>\n",
       "      <th>Contract_One year</th>\n",
       "      <th>Contract_Two year</th>\n",
       "      <th>PaymentMethod_Bank transfer (automatic)</th>\n",
       "      <th>PaymentMethod_Credit card (automatic)</th>\n",
       "      <th>PaymentMethod_Electronic check</th>\n",
       "      <th>PaymentMethod_Mailed check</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender  SeniorCitizen  Partner  Dependents  tenure  PhoneService  \\\n",
       "0       0              0        1           0       1             0   \n",
       "1       1              0        0           0      34             1   \n",
       "2       1              0        0           0       2             1   \n",
       "3       1              0        0           0      45             0   \n",
       "4       0              0        0           0       2             1   \n",
       "\n",
       "   MultipleLines  OnlineSecurity  OnlineBackup  DeviceProtection  ...  \\\n",
       "0             -1               0             1                 0  ...   \n",
       "1              0               1             0                 1  ...   \n",
       "2              0               1             1                 0  ...   \n",
       "3             -1               1             0                 1  ...   \n",
       "4              0               0             0                 0  ...   \n",
       "\n",
       "   InternetService_DSL  InternetService_Fiber optic  InternetService_No  \\\n",
       "0                    1                            0                   0   \n",
       "1                    1                            0                   0   \n",
       "2                    1                            0                   0   \n",
       "3                    1                            0                   0   \n",
       "4                    0                            1                   0   \n",
       "\n",
       "   Contract_Month-to-month  Contract_One year  Contract_Two year  \\\n",
       "0                        1                  0                  0   \n",
       "1                        0                  1                  0   \n",
       "2                        1                  0                  0   \n",
       "3                        0                  1                  0   \n",
       "4                        1                  0                  0   \n",
       "\n",
       "   PaymentMethod_Bank transfer (automatic)  \\\n",
       "0                                        0   \n",
       "1                                        0   \n",
       "2                                        0   \n",
       "3                                        1   \n",
       "4                                        0   \n",
       "\n",
       "   PaymentMethod_Credit card (automatic)  PaymentMethod_Electronic check  \\\n",
       "0                                      0                               1   \n",
       "1                                      0                               0   \n",
       "2                                      0                               0   \n",
       "3                                      0                               0   \n",
       "4                                      0                               1   \n",
       "\n",
       "   PaymentMethod_Mailed check  \n",
       "0                           0  \n",
       "1                           1  \n",
       "2                           1  \n",
       "3                           0  \n",
       "4                           0  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.get_dummies(data=df)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tenure                                    -0.354049\n",
       "Contract_Two year                         -0.301552\n",
       "InternetService_No                        -0.227578\n",
       "TotalCharges                              -0.199484\n",
       "Contract_One year                         -0.178225\n",
       "Dependents                                -0.163128\n",
       "Partner                                   -0.149982\n",
       "PaymentMethod_Credit card (automatic)     -0.134687\n",
       "InternetService_DSL                       -0.124141\n",
       "PaymentMethod_Bank transfer (automatic)   -0.118136\n",
       "PaymentMethod_Mailed check                -0.090773\n",
       "gender                                    -0.008545\n",
       "PhoneService                               0.011691\n",
       "OnlineSecurity                             0.023014\n",
       "TechSupport                                0.026744\n",
       "MultipleLines                              0.036148\n",
       "OnlineBackup                               0.073934\n",
       "DeviceProtection                           0.084402\n",
       "SeniorCitizen                              0.150541\n",
       "StreamingMovies                            0.162672\n",
       "StreamingTV                                0.164509\n",
       "PaperlessBilling                           0.191454\n",
       "MonthlyCharges                             0.192858\n",
       "PaymentMethod_Electronic check             0.301455\n",
       "InternetService_Fiber optic                0.307463\n",
       "Contract_Month-to-month                    0.404565\n",
       "Churn                                      1.000000\n",
       "Name: Churn, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.corr()['Churn'].sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfc=data.corr()['Churn']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f66a2d07f0>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(data.corr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 1, ..., 0, 1, 0], dtype=int64)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#assign Class_att column as y attribute\n",
    "y = data.Churn.values\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop Class_att column, remain only numerical columns\n",
    "new_data = data.drop([\"Churn\"],axis=1)\n",
    "\n",
    "#Normalize values to fit between 0 and 1. \n",
    "x = (new_data-np.min(new_data))/(np.max(new_data)-np.min(new_data)).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>tenure</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>OnlineBackup</th>\n",
       "      <th>DeviceProtection</th>\n",
       "      <th>...</th>\n",
       "      <th>InternetService_DSL</th>\n",
       "      <th>InternetService_Fiber optic</th>\n",
       "      <th>InternetService_No</th>\n",
       "      <th>Contract_Month-to-month</th>\n",
       "      <th>Contract_One year</th>\n",
       "      <th>Contract_Two year</th>\n",
       "      <th>PaymentMethod_Bank transfer (automatic)</th>\n",
       "      <th>PaymentMethod_Credit card (automatic)</th>\n",
       "      <th>PaymentMethod_Electronic check</th>\n",
       "      <th>PaymentMethod_Mailed check</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.464789</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.014085</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender  SeniorCitizen  Partner  Dependents    tenure  PhoneService  \\\n",
       "0     0.0            0.0      1.0         0.0  0.000000           0.0   \n",
       "1     1.0            0.0      0.0         0.0  0.464789           1.0   \n",
       "2     1.0            0.0      0.0         0.0  0.014085           1.0   \n",
       "\n",
       "   MultipleLines  OnlineSecurity  OnlineBackup  DeviceProtection  ...  \\\n",
       "0            0.0             0.5           1.0               0.5  ...   \n",
       "1            0.5             1.0           0.5               1.0  ...   \n",
       "2            0.5             1.0           1.0               0.5  ...   \n",
       "\n",
       "   InternetService_DSL  InternetService_Fiber optic  InternetService_No  \\\n",
       "0                  1.0                          0.0                 0.0   \n",
       "1                  1.0                          0.0                 0.0   \n",
       "2                  1.0                          0.0                 0.0   \n",
       "\n",
       "   Contract_Month-to-month  Contract_One year  Contract_Two year  \\\n",
       "0                      1.0                0.0                0.0   \n",
       "1                      0.0                1.0                0.0   \n",
       "2                      1.0                0.0                0.0   \n",
       "\n",
       "   PaymentMethod_Bank transfer (automatic)  \\\n",
       "0                                      0.0   \n",
       "1                                      0.0   \n",
       "2                                      0.0   \n",
       "\n",
       "   PaymentMethod_Credit card (automatic)  PaymentMethod_Electronic check  \\\n",
       "0                                    0.0                             1.0   \n",
       "1                                    0.0                             0.0   \n",
       "2                                    0.0                             0.0   \n",
       "\n",
       "   PaymentMethod_Mailed check  \n",
       "0                         0.0  \n",
       "1                         1.0  \n",
       "2                         1.0  \n",
       "\n",
       "[3 rows x 26 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Split data into Train and Test \n",
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2,random_state =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "score_array = []\n",
    "for each in range(1,25):\n",
    "    knn_loop = KNeighborsClassifier(n_neighbors = each) #set K neighbor as 3\n",
    "    knn_loop.fit(x_train,y_train)\n",
    "    score_array.append(knn_loop.score(x_test,y_test))\n",
    "    \n",
    "plt.plot(range(1,25),score_array)\n",
    "plt.xlabel(\"Range\")\n",
    "plt.ylabel(\"Score\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN accuracy according to K=11 is : 0.7874911158493249\n"
     ]
    }
   ],
   "source": [
    "knn_model = KNeighborsClassifier(n_neighbors = 11) #set K neighbor as 11\n",
    "knn_model.fit(x_train,y_train)\n",
    "predicted_y = knn_model.predict(x_test)\n",
    "accuracy_knn = knn_model.score(x_test,y_test)\n",
    "print(\"KNN accuracy according to K=11 is :\",accuracy_knn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression accuracy is : 0.798862828713575\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "# %%Logistic regression classification\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "lr_model = LogisticRegression()\n",
    "lr_model.fit(x_train,y_train)\n",
    "accuracy_lr = lr_model.score(x_test,y_test)\n",
    "print(\"Logistic Regression accuracy is :\",accuracy_lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVM accuracy is : 0.7953091684434968\n"
     ]
    }
   ],
   "source": [
    "# %%SVM Classification\n",
    "from sklearn.svm import SVC\n",
    "svc_model = SVC(random_state = 1)\n",
    "svc_model.fit(x_train,y_train)\n",
    "accuracy_svc = svc_model.score(x_test,y_test)\n",
    "print(\"SVM accuracy is :\",accuracy_svc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive Bayes accuracy is : 0.7213930348258707\n"
     ]
    }
   ],
   "source": [
    "# %%Naive Bayes Classification\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "nb_model = GaussianNB()\n",
    "nb_model.fit(x_train,y_train)\n",
    "accuracy_nb = nb_model.score(x_test,y_test)\n",
    "print(\"Naive Bayes accuracy is :\",accuracy_nb)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree accuracy is : 0.7135749822316987\n"
     ]
    }
   ],
   "source": [
    "# %%Decision Tree Classification\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "dt_model = DecisionTreeClassifier()\n",
    "dt_model.fit(x_train,y_train)\n",
    "accuracy_dt = dt_model.score(x_test,y_test)\n",
    "print(\"Decision Tree accuracy is :\",accuracy_dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "score_array = []\n",
    "for each in range(1,50):\n",
    "    rf_loop = RandomForestClassifier(n_estimators = each, random_state = 1) #set K neighbor as 3\n",
    "    rf_loop.fit(x_train,y_train)\n",
    "    score_array.append(rf_loop.score(x_test,y_test))\n",
    "    \n",
    "plt.plot(range(1,50),score_array)\n",
    "plt.xlabel(\"Range\")\n",
    "plt.ylabel(\"Score\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest accuracy for 33 trees is : 0.7938877043354655\n"
     ]
    }
   ],
   "source": [
    "rf_model = RandomForestClassifier(n_estimators = 33, random_state = 1) #set tree number as 33\n",
    "rf_model.fit(x_train,y_train)\n",
    "accuracy_rf = rf_model.score(x_test,y_test)\n",
    "print(\"Random Forest accuracy for 33 trees is :\",accuracy_rf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# %%Confusion Matrix libraries\n",
    "from sklearn.metrics import recall_score, confusion_matrix, precision_score, f1_score, accuracy_score, classification_report\n",
    "\n",
    "#for Logistic Regression\n",
    "cm_lr = confusion_matrix(y_test,lr_model.predict(x_test))\n",
    "\n",
    "# %% confusion matrix visualization\n",
    "import seaborn as sns\n",
    "f, ax = plt.subplots(figsize = (5,5))\n",
    "sns.heatmap(cm_lr, annot = True, linewidths = 0.5, color = \"red\", fmt = \".0f\", ax=ax)\n",
    "plt.xlabel(\"y_predicted\")\n",
    "plt.ylabel(\"y_true\")\n",
    "plt.title(\"Confusion Matrix of Logistic Regression\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# the function that prints all scores\n",
    "def print_scores(headline, y_true, y_pred):\n",
    "    print(headline)\n",
    "    acc_score = accuracy_score(y_true, y_pred)\n",
    "    print(\"accuracy: \",acc_score)\n",
    "    pre_score = precision_score(y_true, y_pred)\n",
    "    print(\"precision: \",pre_score)\n",
    "    rec_score = recall_score(y_true, y_pred)                            \n",
    "    print(\"recall: \",rec_score)\n",
    "    f_score = f1_score(y_true, y_pred, average='weighted')\n",
    "    print(\"f1_score: \",f_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression;\n",
      "accuracy:  0.798862828713575\n",
      "precision:  0.6334405144694534\n",
      "recall:  0.5382513661202186\n",
      "f1_score:  0.7932808947173186\n",
      "SVC;\n",
      "accuracy:  0.7953091684434968\n",
      "precision:  0.6335616438356164\n",
      "recall:  0.505464480874317\n",
      "f1_score:  0.7873119809594623\n",
      "KNN;\n",
      "accuracy:  0.7874911158493249\n",
      "precision:  0.6006006006006006\n",
      "recall:  0.546448087431694\n",
      "f1_score:  0.7841326824444294\n",
      "Naive Bayes;\n",
      "accuracy:  0.7213930348258707\n",
      "precision:  0.4796875\n",
      "recall:  0.8387978142076503\n",
      "f1_score:  0.738223282153598\n",
      "Decision Tree;\n",
      "accuracy:  0.7135749822316987\n",
      "precision:  0.45363408521303256\n",
      "recall:  0.49453551912568305\n",
      "f1_score:  0.7174462795177583\n",
      "Random Forest;\n",
      "accuracy:  0.7938877043354655\n",
      "precision:  0.6233766233766234\n",
      "recall:  0.5245901639344263\n",
      "f1_score:  0.7878124816879418\n"
     ]
    }
   ],
   "source": [
    "print_scores(\"Logistic Regression;\",y_test, lr_model.predict(x_test))\n",
    "print_scores(\"SVC;\",y_test, svc_model.predict(x_test))\n",
    "print_scores(\"KNN;\",y_test, knn_model.predict(x_test))\n",
    "print_scores(\"Naive Bayes;\",y_test, nb_model.predict(x_test))\n",
    "print_scores(\"Decision Tree;\",y_test, dt_model.predict(x_test))\n",
    "print_scores(\"Random Forest;\",y_test, rf_model.predict(x_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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