Telco Customer Churn Project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0ea11f70",
   "metadata": {},
   "source": [
    "#### Done visualisation using power bi - https://drive.google.com/file/d/1vYz0M7yZmkYlnlLSsm2z0rPCidH1z6wb/view?usp=share_link\n",
    "#### And also created a ppt report about this project - https://docs.google.com/presentation/d/1kIac8qdFwGeqmdv1JnY8ORI924n_A81I/edit?usp=share_link&ouid=106590392887676566980&rtpof=true&sd=true\n",
    "### Kindly Check it out"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9c583d1",
   "metadata": {},
   "source": [
    "### Aim of the project: The aim of this project is to perform analysis on the dataset provided by a telecom company, which is experiencing customer churn, and identify the reasons for the loss of customers.\n",
    "\n",
    "### Problem Statement: The telecom company is facing a customer churn, which is adversely affecting its business. The company wants to identify the root causes of customer churn and take appropriate measures to retain its customers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cabde92a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9502964",
   "metadata": {},
   "source": [
    "### Content: The dataset provided by the telecom company includes information on various customer attributes such as demographics, services availed, contract details, payment details, and customer churn status. The dataset contains 21 columns and 7,043 rows."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99c1993d",
   "metadata": {},
   "source": [
    "### Loading the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0169f5cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"WA_Fn-UseC_-Telco-Customer-Churn.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4cff35be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>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",
       "    <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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7038</th>\n",
       "      <td>6840-RESVB</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>24</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>One year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>84.80</td>\n",
       "      <td>1990.5</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7039</th>\n",
       "      <td>2234-XADUH</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>72</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>One year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Credit card (automatic)</td>\n",
       "      <td>103.20</td>\n",
       "      <td>7362.9</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7040</th>\n",
       "      <td>4801-JZAZL</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>11</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</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>29.60</td>\n",
       "      <td>346.45</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7041</th>\n",
       "      <td>8361-LTMKD</td>\n",
       "      <td>Male</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>74.40</td>\n",
       "      <td>306.6</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7042</th>\n",
       "      <td>3186-AJIEK</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>66</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Two year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>105.65</td>\n",
       "      <td>6844.5</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7043 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      customerID  gender  SeniorCitizen Partner Dependents  tenure  \\\n",
       "0     7590-VHVEG  Female              0     Yes         No       1   \n",
       "1     5575-GNVDE    Male              0      No         No      34   \n",
       "2     3668-QPYBK    Male              0      No         No       2   \n",
       "3     7795-CFOCW    Male              0      No         No      45   \n",
       "4     9237-HQITU  Female              0      No         No       2   \n",
       "...          ...     ...            ...     ...        ...     ...   \n",
       "7038  6840-RESVB    Male              0     Yes        Yes      24   \n",
       "7039  2234-XADUH  Female              0     Yes        Yes      72   \n",
       "7040  4801-JZAZL  Female              0     Yes        Yes      11   \n",
       "7041  8361-LTMKD    Male              1     Yes         No       4   \n",
       "7042  3186-AJIEK    Male              0      No         No      66   \n",
       "\n",
       "     PhoneService     MultipleLines InternetService OnlineSecurity  ...  \\\n",
       "0              No  No phone service             DSL             No  ...   \n",
       "1             Yes                No             DSL            Yes  ...   \n",
       "2             Yes                No             DSL            Yes  ...   \n",
       "3              No  No phone service             DSL            Yes  ...   \n",
       "4             Yes                No     Fiber optic             No  ...   \n",
       "...           ...               ...             ...            ...  ...   \n",
       "7038          Yes               Yes             DSL            Yes  ...   \n",
       "7039          Yes               Yes     Fiber optic             No  ...   \n",
       "7040           No  No phone service             DSL            Yes  ...   \n",
       "7041          Yes               Yes     Fiber optic             No  ...   \n",
       "7042          Yes                No     Fiber optic            Yes  ...   \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",
       "7038              Yes         Yes         Yes             Yes        One year   \n",
       "7039              Yes          No         Yes             Yes        One year   \n",
       "7040               No          No          No              No  Month-to-month   \n",
       "7041               No          No          No              No  Month-to-month   \n",
       "7042              Yes         Yes         Yes             Yes        Two year   \n",
       "\n",
       "     PaperlessBilling              PaymentMethod MonthlyCharges  TotalCharges  \\\n",
       "0                 Yes           Electronic check          29.85         29.85   \n",
       "1                  No               Mailed check          56.95        1889.5   \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",
       "7038              Yes               Mailed check          84.80        1990.5   \n",
       "7039              Yes    Credit card (automatic)         103.20        7362.9   \n",
       "7040              Yes           Electronic check          29.60        346.45   \n",
       "7041              Yes               Mailed check          74.40         306.6   \n",
       "7042              Yes  Bank transfer (automatic)         105.65        6844.5   \n",
       "\n",
       "     Churn  \n",
       "0       No  \n",
       "1       No  \n",
       "2      Yes  \n",
       "3       No  \n",
       "4      Yes  \n",
       "...    ...  \n",
       "7038    No  \n",
       "7039    No  \n",
       "7040    No  \n",
       "7041   Yes  \n",
       "7042    No  \n",
       "\n",
       "[7043 rows x 21 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efbb1450",
   "metadata": {},
   "source": [
    "# Data Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18f1707e",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d1b4dd3",
   "metadata": {},
   "source": [
    "##### Checking the data types and non-null values of each column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1812dad5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7043 entries, 0 to 7042\n",
      "Data columns (total 21 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   customerID        7043 non-null   object \n",
      " 1   gender            7043 non-null   object \n",
      " 2   SeniorCitizen     7043 non-null   int64  \n",
      " 3   Partner           7043 non-null   object \n",
      " 4   Dependents        7043 non-null   object \n",
      " 5   tenure            7043 non-null   int64  \n",
      " 6   PhoneService      7043 non-null   object \n",
      " 7   MultipleLines     7043 non-null   object \n",
      " 8   InternetService   7043 non-null   object \n",
      " 9   OnlineSecurity    7043 non-null   object \n",
      " 10  OnlineBackup      7043 non-null   object \n",
      " 11  DeviceProtection  7043 non-null   object \n",
      " 12  TechSupport       7043 non-null   object \n",
      " 13  StreamingTV       7043 non-null   object \n",
      " 14  StreamingMovies   7043 non-null   object \n",
      " 15  Contract          7043 non-null   object \n",
      " 16  PaperlessBilling  7043 non-null   object \n",
      " 17  PaymentMethod     7043 non-null   object \n",
      " 18  MonthlyCharges    7043 non-null   float64\n",
      " 19  TotalCharges      7043 non-null   object \n",
      " 20  Churn             7043 non-null   object \n",
      "dtypes: float64(1), int64(2), object(18)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a40dd267",
   "metadata": {},
   "source": [
    "#### total charges must be float type, but here it is howing object type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cd281b18",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df[\"TotalCharges\"] = df[\"TotalCharges\"].astype(float)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a013c738",
   "metadata": {},
   "source": [
    "#### as i tried to convert the total charges from object type to float type i got a error, so lets drop those empty spaces in the total charges and fill mean values to it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b94cac2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.mask(df==\" \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5d74780d",
   "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        11\n",
       "Churn                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a442c621",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df['TotalCharges']!=' ']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0409dd50",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['TotalCharges'] = pd.to_numeric(df['TotalCharges'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e2d44aba",
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_value = df['TotalCharges'].mean()\n",
    "df['TotalCharges'].fillna(mean_value, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "707399b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 7043 entries, 0 to 7042\n",
      "Data columns (total 21 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   customerID        7043 non-null   object \n",
      " 1   gender            7043 non-null   object \n",
      " 2   SeniorCitizen     7043 non-null   int64  \n",
      " 3   Partner           7043 non-null   object \n",
      " 4   Dependents        7043 non-null   object \n",
      " 5   tenure            7043 non-null   int64  \n",
      " 6   PhoneService      7043 non-null   object \n",
      " 7   MultipleLines     7043 non-null   object \n",
      " 8   InternetService   7043 non-null   object \n",
      " 9   OnlineSecurity    7043 non-null   object \n",
      " 10  OnlineBackup      7043 non-null   object \n",
      " 11  DeviceProtection  7043 non-null   object \n",
      " 12  TechSupport       7043 non-null   object \n",
      " 13  StreamingTV       7043 non-null   object \n",
      " 14  StreamingMovies   7043 non-null   object \n",
      " 15  Contract          7043 non-null   object \n",
      " 16  PaperlessBilling  7043 non-null   object \n",
      " 17  PaymentMethod     7043 non-null   object \n",
      " 18  MonthlyCharges    7043 non-null   float64\n",
      " 19  TotalCharges      7043 non-null   float64\n",
      " 20  Churn             7043 non-null   object \n",
      "dtypes: float64(2), int64(2), object(17)\n",
      "memory usage: 1.2+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7d2d263c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "545290d9",
   "metadata": {},
   "source": [
    "##### Checking the summary statistics of numerical columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "57d8971b",
   "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>SeniorCitizen</th>\n",
       "      <th>tenure</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7043.000000</td>\n",
       "      <td>7043.000000</td>\n",
       "      <td>7043.000000</td>\n",
       "      <td>7043.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.162147</td>\n",
       "      <td>32.371149</td>\n",
       "      <td>64.761692</td>\n",
       "      <td>2283.300441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.368612</td>\n",
       "      <td>24.559481</td>\n",
       "      <td>30.090047</td>\n",
       "      <td>2265.000258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>18.250000</td>\n",
       "      <td>18.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>35.500000</td>\n",
       "      <td>402.225000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>70.350000</td>\n",
       "      <td>1400.550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>89.850000</td>\n",
       "      <td>3786.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>118.750000</td>\n",
       "      <td>8684.800000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       SeniorCitizen       tenure  MonthlyCharges  TotalCharges\n",
       "count    7043.000000  7043.000000     7043.000000   7043.000000\n",
       "mean        0.162147    32.371149       64.761692   2283.300441\n",
       "std         0.368612    24.559481       30.090047   2265.000258\n",
       "min         0.000000     0.000000       18.250000     18.800000\n",
       "25%         0.000000     9.000000       35.500000    402.225000\n",
       "50%         0.000000    29.000000       70.350000   1400.550000\n",
       "75%         0.000000    55.000000       89.850000   3786.600000\n",
       "max         1.000000    72.000000      118.750000   8684.800000"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b26f62c",
   "metadata": {},
   "source": [
    "# Data Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3ea423e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(18,9))\n",
    "plt.pie(df['Churn'].value_counts(),labels=['No','Yes'],autopct='%0.2f',colors=['black','yellow'])\n",
    "plt.title('Distribution of Churned and Non-Churned Customers')\n",
    "centre_circle = plt.Circle((0,0),0.80,fc='white')\n",
    "fig = plt.gcf()\n",
    "fig.gca().add_artist(centre_circle)\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5520936",
   "metadata": {},
   "source": [
    "#### From this we can say that non churned is more than churn rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "db87b67c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors = ['k', 'purple', 'b']\n",
    "\n",
    "churned = df[df['Churn'] == 'Yes']\n",
    "churned_by_contract = churned.groupby('Contract')['Churn'].count()\n",
    "\n",
    "plt.figure(figsize=(7, 7))\n",
    "plt.pie(churned_by_contract, labels=churned_by_contract.index, autopct='%1.1f%%', colors=colors)\n",
    "plt.title('Percentage of Churned Customers by Contract Type', fontsize=18)\n",
    "plt.axis('equal')\n",
    "centre_circle = plt.Circle((0,0),0.80,fc='white')\n",
    "fig = plt.gcf()\n",
    "fig.gca().add_artist(centre_circle)\n",
    "plt.show\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5498f83c",
   "metadata": {},
   "source": [
    "#### From this we can say that the churn rate is more when the contarct type is Month-to-Month, so within a year people are leaving this telco service because of some issues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "45487008",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_palette('Paired')\n",
    "\n",
    "sns.set_style('darkgrid')\n",
    "\n",
    "plt.figure(figsize=(12,6))\n",
    "sns.countplot(x='Contract', hue='Churn', data=df)\n",
    "plt.title('Customer Churn Rate by Contract', fontsize=20, color='k')\n",
    "plt.xlabel('Contract', fontsize=16, color='k')\n",
    "plt.ylabel('Count', fontsize=16, color='k')\n",
    "plt.xticks(fontsize=14, color='k')\n",
    "plt.yticks(fontsize=14, color='k')\n",
    "plt.legend(fontsize=14, title='Churn', title_fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f97f384f",
   "metadata": {},
   "source": [
    "#### From this we can say that churn rate is high in month-to-month and the people who have taken the contract above 2 years are more compared to the rate of people who have left the telco service in the 2nd year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4756ea28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_palette('Paired')\n",
    "\n",
    "sns.set_style('darkgrid')\n",
    "\n",
    "plt.figure(figsize=(12,6))\n",
    "sns.countplot(x='InternetService', hue='Churn', data=df)\n",
    "plt.title('Customer Churn Rate by Internet Service', fontsize=20, color='k')\n",
    "plt.xlabel('Internet Service', fontsize=16, color='k')\n",
    "plt.ylabel('Count', fontsize=16, color='k')\n",
    "plt.xticks(fontsize=14, color='k', rotation=0)\n",
    "plt.yticks(fontsize=14, color='k')\n",
    "plt.legend(fontsize=14, title='Churn', title_fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4f1c76e",
   "metadata": {},
   "source": [
    "#### From this we can say that internet service may be good at some area but not everywhere, internet service in the fiber optic area is very poor so this may be the reason for the high rate of churn in the fiber optic area "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7f579594",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "colors = sns.color_palette(\"Paired\")\n",
    "sns.countplot(x='PaymentMethod', hue='Churn', data=df, palette=colors)\n",
    "plt.title('Customer Churn Rate by Payment Method', fontsize=20)\n",
    "plt.xlabel('Payment Method', fontsize=16)\n",
    "plt.ylabel('Number of Customers', fontsize=16)\n",
    "plt.grid(axis='y', alpha=0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f073503c",
   "metadata": {},
   "source": [
    "#### From this we can say that some people may be facing some difficulty in the electronic check, so telco service have to look after that area to reduce the churn rate in electronic check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0844b0b1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_palette(\"Paired\")\n",
    "\n",
    "plt.figure(figsize=(25,10))\n",
    "sns.countplot(x=\"tenure\",hue=\"Churn\",data=df)\n",
    "plt.title(\"Customer Churn by Tenure\",size=30)\n",
    "plt.xlabel(\"Tenure (months)\", color='k',size=20)\n",
    "plt.ylabel(\"Count\", color='k',size=20)\n",
    "plt.xticks(color='k',size=12)\n",
    "plt.yticks(color='k',size=12)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ba4485a",
   "metadata": {},
   "source": [
    "#### As we can see here the churn rate is very high at the beginning itself, so company can reduce the customer churn rate by offering longer contract periods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "55b715e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['tenure_group'] = pd.cut(df['tenure'], bins=[0,12,24,48,60], labels=['0-1 Year', '1-2 Years', '2-4 Years', '4-5 Years'])\n",
    "df = df.drop(['customerID', 'tenure'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8319aa1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_palette(\"Paired\")\n",
    "\n",
    "plt.figure(figsize=(12,6))\n",
    "sns.countplot(x=\"tenure_group\",hue='Churn',data=df)\n",
    "plt.xlabel(\"Tenure Group (months)\", color='k',size=14)\n",
    "plt.ylabel(\"Count\", color='k',size=14)\n",
    "plt.xticks(color='k',size=12)\n",
    "plt.yticks(color='k',size=12)\n",
    "plt.legend(title='Churn', labels=['No', 'Yes'], fontsize=14, title_fontsize=16)\n",
    "plt.title(\"Customer Churn by Tenure Group\",size=18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f0c6e14",
   "metadata": {},
   "source": [
    "#### As we can see here the churn rate is very high at the 0-1, so company can reduce the customer churn rate by offering longer contract periods"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a06c0991",
   "metadata": {},
   "source": [
    "## Encoding Categorical Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a1a07385",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "le = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "306e7dbb",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['gender'] = le.fit_transform(df['gender'])\n",
    "df['Partner'] = le.fit_transform(df['Partner'])\n",
    "df['Dependents'] = le.fit_transform(df['Dependents'])\n",
    "df['PhoneService'] = le.fit_transform(df['PhoneService'])\n",
    "df['MultipleLines'] = le.fit_transform(df['MultipleLines'])\n",
    "df['InternetService'] = le.fit_transform(df['InternetService'])\n",
    "df['OnlineSecurity'] = le.fit_transform(df['OnlineSecurity'])\n",
    "df['OnlineBackup'] = le.fit_transform(df['OnlineBackup'])\n",
    "df['DeviceProtection'] = le.fit_transform(df['DeviceProtection'])\n",
    "df['TechSupport'] = le.fit_transform(df['TechSupport'])\n",
    "df['StreamingTV'] = le.fit_transform(df['StreamingTV'])\n",
    "df['StreamingMovies'] = le.fit_transform(df['StreamingMovies'])\n",
    "df['Contract'] = le.fit_transform(df['Contract'])\n",
    "df['PaperlessBilling'] = le.fit_transform(df['PaperlessBilling'])\n",
    "df['PaymentMethod'] = le.fit_transform(df['PaymentMethod'])\n",
    "df['Churn'] = le.fit_transform(df['Churn'])\n",
    "df['tenure_group'] = le.fit_transform(df['tenure_group'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f399d54",
   "metadata": {},
   "source": [
    "### Splitting Data into Training and Testing Sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6ac5cfe3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X = df.drop(['Churn'], axis=1)\n",
    "y = df['Churn']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "627fc347",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc7aef8c",
   "metadata": {},
   "source": [
    "### Feature Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "4890ecec",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "X_train = sc.fit_transform(X_train)\n",
    "X_test = sc.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51bb32cd",
   "metadata": {},
   "source": [
    "### Model Building and Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f380fdf2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "cb325329",
   "metadata": {},
   "outputs": [],
   "source": [
    "lr=LogisticRegression()\n",
    "dt=DecisionTreeClassifier()\n",
    "rf=RandomForestClassifier(n_estimators=100)\n",
    "svc=SVC()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "dd7616ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "acc = []\n",
    "pre = []\n",
    "re = []\n",
    "models = [lr,dt,rf,svc]\n",
    "models_name = ['LogisticRegression','DecisionTreeClassifier','RandomForestClassifier','SVC']\n",
    "\n",
    "for i in models:\n",
    "  i.fit(X_train,y_train)\n",
    "  y_pred= i.predict(X_test)\n",
    "  accuracy=accuracy_score(y_test,y_pred)\n",
    "  precision=precision_score(y_test,y_pred)\n",
    "  recall= recall_score(y_test,y_pred)\n",
    "\n",
    "  acc.append(accuracy)\n",
    "  pre.append(precision)\n",
    "  re.append(recall)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "62844fd6",
   "metadata": {},
   "outputs": [],
   "source": [
    "mods = pd.DataFrame()\n",
    "mods['Models']=models_name\n",
    "mods['Accuracy']= acc\n",
    "mods['Precision']= pre\n",
    "mods['Recall']= re"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db912af7",
   "metadata": {},
   "source": [
    "### Printing the Accuracy, precision and Recall scores of all the models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d91da98b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Models</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.817601</td>\n",
       "      <td>0.687097</td>\n",
       "      <td>0.571046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DecisionTreeClassifier</td>\n",
       "      <td>0.719659</td>\n",
       "      <td>0.471204</td>\n",
       "      <td>0.482574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>0.785664</td>\n",
       "      <td>0.634981</td>\n",
       "      <td>0.447721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SVC</td>\n",
       "      <td>0.806246</td>\n",
       "      <td>0.682482</td>\n",
       "      <td>0.501340</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Models  Accuracy  Precision    Recall\n",
       "0      LogisticRegression  0.817601   0.687097  0.571046\n",
       "1  DecisionTreeClassifier  0.719659   0.471204  0.482574\n",
       "2  RandomForestClassifier  0.785664   0.634981  0.447721\n",
       "3                     SVC  0.806246   0.682482  0.501340"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mods"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "689a9f98",
   "metadata": {},
   "source": [
    "##### After running the code, we obtain the Accuracy, precision and Recall scores of all the models. We can then choose the best model to make predictions on new data.\n",
    "##### Some of the potential factors that could be contributing to customer churn in a telecom company include:\n",
    "##### 1.Poor customer service or support\n",
    "##### 2.High prices or billing issues\n",
    "##### 3.Network quality or coverage issues\n",
    "##### 4.Limited service or product offerings\n",
    "##### 5.Contract or commitment terms\n",
    "##### From the data visualization, we can see that the customers who have opted for a shorter contract period have a higher churn rate. Similarly, customers who have opted for fiber optic internet service and electronic check payment method also have a higher churn rate. On the other hand, customers who have opted for a longer contract period and automatic payment method have a lower churn rate\n",
    "##### Based on the results of the machine learning models, the Logistic regression performs the best with an accuracy score of 81.7%\n",
    "##### In conclusion, the telecom company can reduce the customer churn rate by offering longer contract periods, improving the quality of the fiber optic internet service, and providing more convenient payment methods in electronic check. The Logistic regression model can be used to predict which customers are likely to churn and offer them incentives or discounts to retain them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "b195e44d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install autoviz\n",
    "# from autoviz.AutoViz_Class import AutoViz_Class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "51580a24",
   "metadata": {},
   "outputs": [],
   "source": [
    "# AV = AutoViz_Class()\n",
    "# df = AV.AutoViz('WA_Fn-UseC_-Telco-Customer-Churn.csv')"
   ]
  }
 ],
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