Churn_prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f78f15bf",
   "metadata": {},
   "source": [
    "#### Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d083dfd3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import seaborn as sns\n",
    "from scipy.stats import chi2_contingency\n",
    "from scipy.stats import chi2\n",
    "import scipy.stats as stats\n",
    "\n",
    "# Model Library\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "from sklearn.metrics import classification_report\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "498ee42a",
   "metadata": {},
   "source": [
    "### Importing Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ee68dde",
   "metadata": {},
   "source": [
    "Dataset used in this project is Telco Customer Churn Dataset. \n",
    "\n",
    "The target variable has two states (left company: yes/no or 1/0)\n",
    "* Total Records : 7043 \n",
    "* Independent Variables/ Features: 20\n",
    "* Dependent (Target) Variables / Features : 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6977ca9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"Telco-Customer-Churn.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41874a13",
   "metadata": {},
   "source": [
    "**Prediction column:**\n",
    "Churn: Whether the customer churned or not (Yes or No)\n",
    "\n",
    "**Two numerical columns:**\n",
    "1. MonthlyCharges: The amount charged to the customer monthly\n",
    "2. TotalCharges: The total amount charged to the customer\n",
    "\n",
    "**Eighteen categorical columns:**\n",
    "1. CustomerID: Customer ID unique for each customer\n",
    "2. gender: Whether the customer is a male or a female\n",
    "3. SeniorCitizen: Whether the customer is a senior citizen or not (1, 0)\n",
    "4. Partner: Whether the customer has a partner or not (Yes, No)\n",
    "5. Dependents: Whether the customer has dependents or not (Yes, No)\n",
    "6. Tenure: Number of months the customer has stayed with the company\n",
    "7. PhoneService: Whether the customer has a phone service or not (Yes, No)\n",
    "8. MultipleLines: Whether the customer has multiple lines or not (Yes, No, No phone service)\n",
    "9. InternetService: Customer’s internet service provider (DSL, Fiber optic, No)\n",
    "10. OnlineSecurity: Whether the customer has online security or not (Yes, No, No internet service)\n",
    "11. OnlineBackup: Whether the customer has an online backup or not (Yes, No, No internet service)\n",
    "12. DeviceProtection: Whether the customer has device protection or not (Yes, No, No internet service)\n",
    "13. TechSupport: Whether the customer has tech support or not (Yes, No, No internet service)\n",
    "14. StreamingTV: Whether the customer has streaming TV or not (Yes, No, No internet service)\n",
    "15. StreamingMovies: Whether the customer has streaming movies or not (Yes, No, No internet service)\n",
    "16. Contract: The contract term of the customer (Month-to-month, One year, Two years)\n",
    "17. PaperlessBilling: Whether the customer has paperless billing or not (Yes, No)\n",
    "18. PaymentMethod: The customer’s payment method (Electronic check, Mailed check, Bank transfer (automatic), Credit card (automatic))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "970cd26c",
   "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": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c47d570f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>tenure</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7043.000000</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.368612</td>\n",
       "      <td>24.559481</td>\n",
       "      <td>30.090047</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>35.500000</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>89.850000</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       SeniorCitizen       tenure  MonthlyCharges\n",
       "count    7043.000000  7043.000000     7043.000000\n",
       "mean        0.162147    32.371149       64.761692\n",
       "std         0.368612    24.559481       30.090047\n",
       "min         0.000000     0.000000       18.250000\n",
       "25%         0.000000     9.000000       35.500000\n",
       "50%         0.000000    29.000000       70.350000\n",
       "75%         0.000000    55.000000       89.850000\n",
       "max         1.000000    72.000000      118.750000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a4d6399f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59168478",
   "metadata": {},
   "source": [
    "### Scope of Work / Project Structure\n",
    "1. Data Cleaning\n",
    "2. Exploratory Data Analysis\n",
    "3. Data Preprocessing\n",
    "4. Model Creation\n",
    "5. Improving the model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "974db99f",
   "metadata": {},
   "source": [
    "### Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1598e5a1",
   "metadata": {},
   "source": [
    "Dropping the customerId column, since it has no value for the analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "dd6812db",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop(columns = [\"customerID\"], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ddced985",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7043 entries, 0 to 7042\n",
      "Data columns (total 20 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   gender            7043 non-null   object \n",
      " 1   SeniorCitizen     7043 non-null   int64  \n",
      " 2   Partner           7043 non-null   object \n",
      " 3   Dependents        7043 non-null   object \n",
      " 4   tenure            7043 non-null   int64  \n",
      " 5   PhoneService      7043 non-null   object \n",
      " 6   MultipleLines     7043 non-null   object \n",
      " 7   InternetService   7043 non-null   object \n",
      " 8   OnlineSecurity    7043 non-null   object \n",
      " 9   OnlineBackup      7043 non-null   object \n",
      " 10  DeviceProtection  7043 non-null   object \n",
      " 11  TechSupport       7043 non-null   object \n",
      " 12  StreamingTV       7043 non-null   object \n",
      " 13  StreamingMovies   7043 non-null   object \n",
      " 14  Contract          7043 non-null   object \n",
      " 15  PaperlessBilling  7043 non-null   object \n",
      " 16  PaymentMethod     7043 non-null   object \n",
      " 17  MonthlyCharges    7043 non-null   float64\n",
      " 18  TotalCharges      7043 non-null   object \n",
      " 19  Churn             7043 non-null   object \n",
      "dtypes: float64(1), int64(2), object(17)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4395c7c7",
   "metadata": {},
   "source": [
    "Changing total Charges column type to numerical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "00e0a338",
   "metadata": {},
   "outputs": [],
   "source": [
    "data[\"TotalCharges\"] = pd.to_numeric(data['TotalCharges'], errors = 'coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "542a2299",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender               0\n",
       "SeniorCitizen        0\n",
       "Partner              0\n",
       "Dependents           0\n",
       "tenure               0\n",
       "PhoneService         0\n",
       "MultipleLines        0\n",
       "InternetService      0\n",
       "OnlineSecurity       0\n",
       "OnlineBackup         0\n",
       "DeviceProtection     0\n",
       "TechSupport          0\n",
       "StreamingTV          0\n",
       "StreamingMovies      0\n",
       "Contract             0\n",
       "PaperlessBilling     0\n",
       "PaymentMethod        0\n",
       "MonthlyCharges       0\n",
       "TotalCharges        11\n",
       "Churn                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9850cf54",
   "metadata": {},
   "source": [
    "11 missing values is present in “TotalCharges” column, after getting looking into the records having missing values it was found that all the records with tenure less than 1 month is having NaN values for “TotalCharges”, since for tenure less than 1 month total charges will be same as monthly charges hence imputing the missing “TotalCharges” field with corresponding “MonthlyCharges” will do the job."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d11c358a",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['TotalCharges'] = np.where(data['TotalCharges'].isnull(), data['MonthlyCharges'], data['TotalCharges'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "395749d2",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cedcce5f",
   "metadata": {},
   "source": [
    "### Univariate Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad7e1f29",
   "metadata": {},
   "source": [
    "Target Variable Class Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "abcb6a62",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Churn distribution\n",
      "\n",
      "No     73.46\n",
      "Yes    26.54\n",
      "Name: Churn, dtype: float64\n"
     ]
    },
    {
     "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": [
    "ax = sns.countplot(x = \"Churn\", data=data)\n",
    "print(\"Churn distribution\\n\")\n",
    "print(round(data['Churn'].value_counts()/data.shape[0]*100,2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7592f871",
   "metadata": {},
   "source": [
    "### Independent Features Distribution\n",
    "#### Categorical Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d55a7499",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class distribution for gender is\n",
      "\n",
      "Male      50.0\n",
      "Female    50.0\n",
      "Name: gender, dtype: float64 \n",
      "\n",
      "Class distribution for SeniorCitizen is\n",
      "\n",
      "0    84.0\n",
      "1    16.0\n",
      "Name: SeniorCitizen, dtype: float64 \n",
      "\n",
      "Class distribution for Partner is\n",
      "\n",
      "No     52.0\n",
      "Yes    48.0\n",
      "Name: Partner, dtype: float64 \n",
      "\n",
      "Class distribution for Dependents is\n",
      "\n",
      "No     70.0\n",
      "Yes    30.0\n",
      "Name: Dependents, dtype: float64 \n",
      "\n",
      "Class distribution for PhoneService is\n",
      "\n",
      "Yes    90.0\n",
      "No     10.0\n",
      "Name: PhoneService, dtype: float64 \n",
      "\n",
      "Class distribution for MultipleLines is\n",
      "\n",
      "No                  48.0\n",
      "Yes                 42.0\n",
      "No phone service    10.0\n",
      "Name: MultipleLines, dtype: float64 \n",
      "\n",
      "Class distribution for InternetService is\n",
      "\n",
      "Fiber optic    44.0\n",
      "DSL            34.0\n",
      "No             22.0\n",
      "Name: InternetService, dtype: float64 \n",
      "\n",
      "Class distribution for OnlineSecurity is\n",
      "\n",
      "No                     50.0\n",
      "Yes                    29.0\n",
      "No internet service    22.0\n",
      "Name: OnlineSecurity, dtype: float64 \n",
      "\n",
      "Class distribution for OnlineBackup is\n",
      "\n",
      "No                     44.0\n",
      "Yes                    34.0\n",
      "No internet service    22.0\n",
      "Name: OnlineBackup, dtype: float64 \n",
      "\n",
      "Class distribution for DeviceProtection is\n",
      "\n",
      "No                     44.0\n",
      "Yes                    34.0\n",
      "No internet service    22.0\n",
      "Name: DeviceProtection, dtype: float64 \n",
      "\n",
      "Class distribution for TechSupport is\n",
      "\n",
      "No                     49.0\n",
      "Yes                    29.0\n",
      "No internet service    22.0\n",
      "Name: TechSupport, dtype: float64 \n",
      "\n",
      "Class distribution for StreamingTV is\n",
      "\n",
      "No                     40.0\n",
      "Yes                    38.0\n",
      "No internet service    22.0\n",
      "Name: StreamingTV, dtype: float64 \n",
      "\n",
      "Class distribution for StreamingMovies is\n",
      "\n",
      "No                     40.0\n",
      "Yes                    39.0\n",
      "No internet service    22.0\n",
      "Name: StreamingMovies, dtype: float64 \n",
      "\n",
      "Class distribution for Contract is\n",
      "\n",
      "Month-to-month    55.0\n",
      "Two year          24.0\n",
      "One year          21.0\n",
      "Name: Contract, dtype: float64 \n",
      "\n",
      "Class distribution for PaperlessBilling is\n",
      "\n",
      "Yes    59.0\n",
      "No     41.0\n",
      "Name: PaperlessBilling, dtype: float64 \n",
      "\n",
      "Class distribution for PaymentMethod is\n",
      "\n",
      "Electronic check             34.0\n",
      "Mailed check                 23.0\n",
      "Bank transfer (automatic)    22.0\n",
      "Credit card (automatic)      22.0\n",
      "Name: PaymentMethod, dtype: float64 \n",
      "\n",
      "Class distribution for Churn is\n",
      "\n",
      "No     73.0\n",
      "Yes    27.0\n",
      "Name: Churn, dtype: float64 \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "# removing numerical features out of analysis.\n",
    "features = data.columns.values.tolist()\n",
    "num_feature = ['tenure','MonthlyCharges','TotalCharges']\n",
    "for feature in num_feature:\n",
    "    features.remove(feature)\n",
    "for i, feature in enumerate(features):\n",
    "    plt.figure(i)\n",
    "    ax = sns.countplot(x=feature, data=data)\n",
    "    print(\"Class distribution for %s is\\n\"%feature)\n",
    "    print(round(data[feature].value_counts()/data.shape[0]*100,0), \"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee537be8",
   "metadata": {},
   "source": [
    "#### Numerical Features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "effa264e",
   "metadata": {},
   "source": [
    "Tenure: Dividing the customer based on the tenure into equal size bin of 12 months"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0aa349dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0 - 12     29.0\n",
       "60 - 72    16.0\n",
       "12 - 24    15.0\n",
       "24 - 36    12.0\n",
       "48 - 60    12.0\n",
       "36 - 48    11.0\n",
       "Name: tenure_class, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels = []\n",
    "b = data['tenure'].max()\n",
    "a = data['tenure'].min()\n",
    "for i in range(a,b,12):\n",
    "        labels.append(\"{} - {}\".format(i, i+12))\n",
    "data[\"tenure_class\"] = pd.cut(data['tenure'], range(a,b+12,12), labels = labels, right = False)\n",
    "round(data['tenure_class'].value_counts()/data.shape[0]*100,0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57e404db",
   "metadata": {},
   "source": [
    "### Bi-Variate Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04db67ec",
   "metadata": {},
   "source": [
    "### Correlation between Numerical features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d9073147",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tenure</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>tenure</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.247900</td>\n",
       "      <td>0.826164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <td>0.247900</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.651182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TotalCharges</th>\n",
       "      <td>0.826164</td>\n",
       "      <td>0.651182</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  tenure  MonthlyCharges  TotalCharges\n",
       "tenure          1.000000        0.247900      0.826164\n",
       "MonthlyCharges  0.247900        1.000000      0.651182\n",
       "TotalCharges    0.826164        0.651182      1.000000"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[['tenure','MonthlyCharges','TotalCharges']].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "168fb9c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the heatmap\n",
    "corr = data[['tenure','MonthlyCharges','TotalCharges']].corr()\n",
    "sns.heatmap(corr, \n",
    "            cmap = sns.diverging_palette(250, 10, as_cmap=True),\n",
    "        xticklabels=corr.columns,\n",
    "        yticklabels=corr.columns, annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5abeaed7",
   "metadata": {},
   "source": [
    "Tenure and Total Charges has strong correlation which can be observed from the scatterplot;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "dac0a4b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='tenure', ylabel='TotalCharges'>"
      ]
     },
     "execution_count": 16,
     "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.scatterplot(data=data, x=\"tenure\", y=\"TotalCharges\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59d5f006",
   "metadata": {},
   "source": [
    "##### Distribution Plot between Categorical features & Numerical Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "18cac6a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2252ca1ca58>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 412x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 476.75x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 412x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(data=data, x=\"tenure\", hue='Partner', kind=\"kde\")\n",
    "sns.displot(data=data, x=\"tenure\", hue='Contract', kind=\"kde\")\n",
    "sns.displot(data=data, x=\"tenure\", hue='Churn', kind=\"kde\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f81262d6",
   "metadata": {},
   "source": [
    "1. People having partner have long history of service experience than without which may further translate that people with partner prefer to stay more long tenure than without.\n",
    "2. Customer who are enrolled in monthly contract have very short tenure in comparison to 1 year and 2 years contract, reason for this might be people with monthly contract are less bound to service provider hence more likely to leave the service if given better option while yearly contract (1 yr & 2 yrs) customer enjoy less freedom in term of switching to another service provider.\n",
    "3. People with short tenure high churn rate than long term customer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1a43d98b",
   "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>Churn</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>76.0</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>58.0</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Churn            No   Yes\n",
       "SeniorCitizen            \n",
       "0              76.0  24.0\n",
       "1              58.0  42.0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(pd.crosstab(data['SeniorCitizen'], data['Churn'],  normalize='index')*100,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b6a52da2",
   "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>Churn</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Partner</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>No</th>\n",
       "      <td>67.0</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>80.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Churn      No   Yes\n",
       "Partner            \n",
       "No       67.0  33.0\n",
       "Yes      80.0  20.0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(pd.crosstab(data['Partner'], data['Churn'],  normalize='index')*100,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8235836b",
   "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>Churn</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Contract</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Month-to-month</th>\n",
       "      <td>57.0</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>One year</th>\n",
       "      <td>89.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Two year</th>\n",
       "      <td>97.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Churn             No   Yes\n",
       "Contract                  \n",
       "Month-to-month  57.0  43.0\n",
       "One year        89.0  11.0\n",
       "Two year        97.0   3.0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(pd.crosstab(data['Contract'], data['Churn'],  normalize='index')*100,0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7370620d",
   "metadata": {},
   "source": [
    "### Chi Square Test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5663cd9d",
   "metadata": {},
   "source": [
    "To check which categorical variable has much influence over target variable ‘Churn’, we can perform ‘Chi Square test of Independence’. Features which are dependent of ‘Churn’ variable will define churn rate more significantly than those which are independent of the ‘Churn’ variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c1710cde",
   "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>CHI SQUARE Feature</th>\n",
       "      <th>CHI SQUARE P Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Contract</td>\n",
       "      <td>2.279132e-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>OnlineSecurity</td>\n",
       "      <td>1.978259e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>TechSupport</td>\n",
       "      <td>1.978259e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>InternetService</td>\n",
       "      <td>1.453946e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>OnlineBackup</td>\n",
       "      <td>2.019956e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>PaymentMethod</td>\n",
       "      <td>3.770492e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>DeviceProtection</td>\n",
       "      <td>5.275036e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>StreamingTV</td>\n",
       "      <td>7.861836e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>StreamingMovies</td>\n",
       "      <td>7.861836e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>PaperlessBilling</td>\n",
       "      <td>5.501725e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SeniorCitizen</td>\n",
       "      <td>1.057422e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Dependents</td>\n",
       "      <td>1.172264e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Partner</td>\n",
       "      <td>5.452435e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>MultipleLines</td>\n",
       "      <td>7.596254e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PhoneService</td>\n",
       "      <td>8.719308e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>gender</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CHI SQUARE Feature  CHI SQUARE P Value\n",
       "13           Contract        2.279132e-13\n",
       "7      OnlineSecurity        1.978259e-09\n",
       "10        TechSupport        1.978259e-09\n",
       "6     InternetService        1.453946e-08\n",
       "8        OnlineBackup        2.019956e-07\n",
       "15      PaymentMethod        3.770492e-07\n",
       "9    DeviceProtection        5.275036e-07\n",
       "11        StreamingTV        7.861836e-06\n",
       "12    StreamingMovies        7.861836e-06\n",
       "14   PaperlessBilling        5.501725e-03\n",
       "1       SeniorCitizen        1.057422e-02\n",
       "3          Dependents        1.172264e-02\n",
       "2             Partner        5.452435e-02\n",
       "5       MultipleLines        7.596254e-01\n",
       "4        PhoneService        8.719308e-01\n",
       "0              gender        1.000000e+00"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Chi_test_feature = data.columns.values.tolist()\n",
    "Chi_num_feature = ['tenure','MonthlyCharges','TotalCharges','tenure_class','Churn']\n",
    "for feature in Chi_num_feature:\n",
    "    Chi_test_feature.remove(feature)\n",
    "    \n",
    "Features = []\n",
    "P_value = []\n",
    "for Chi_feature in Chi_test_feature:\n",
    "    table = round(pd.crosstab(data[Chi_feature], data['Churn'],  normalize='index')*100,0)\n",
    "    stat, p, dof, expected = chi2_contingency(table)\n",
    "    Features.append(Chi_feature)\n",
    "    P_value.append(p)\n",
    "\n",
    "Chi_df = pd.DataFrame(list(zip(Features, P_value)), columns = ['CHI SQUARE Feature', 'CHI SQUARE P Value'])\n",
    "Chi_df.sort_values('CHI SQUARE P Value')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa61b767",
   "metadata": {},
   "source": [
    "### Anova\n",
    "With the help of ANOVA test we can filter those categorical features which explains most of the variance of target variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1b575fc8",
   "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>ANOVA Feature</th>\n",
       "      <th>ANOVA P Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Contract</td>\n",
       "      <td>[2.9921663019905003e-282]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>InternetService</td>\n",
       "      <td>[1.457260455919398e-168]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>PaymentMethod</td>\n",
       "      <td>[5.583595720582596e-147]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>OnlineSecurity</td>\n",
       "      <td>[1.6314797439339531e-100]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>TechSupport</td>\n",
       "      <td>[6.755162959270331e-96]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>PaperlessBilling</td>\n",
       "      <td>[2.3565543205872124e-59]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>OnlineBackup</td>\n",
       "      <td>[6.225925993543879e-49]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Dependents</td>\n",
       "      <td>[9.140432989108222e-44]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>DeviceProtection</td>\n",
       "      <td>[4.044837139187934e-40]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SeniorCitizen</td>\n",
       "      <td>[3.839860055793829e-37]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Partner</td>\n",
       "      <td>[6.220731129635033e-37]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MultipleLines</td>\n",
       "      <td>[0.0013391771071827286]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>StreamingMovies</td>\n",
       "      <td>[0.002869218881092956]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>StreamingTV</td>\n",
       "      <td>[0.00590875174315587]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PhoneService</td>\n",
       "      <td>[0.31631478134436675]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>gender</td>\n",
       "      <td>[0.4699045390966534]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ANOVA Feature              ANOVA P Value\n",
       "14          Contract  [2.9921663019905003e-282]\n",
       "13   InternetService   [1.457260455919398e-168]\n",
       "15     PaymentMethod   [5.583595720582596e-147]\n",
       "5     OnlineSecurity  [1.6314797439339531e-100]\n",
       "8        TechSupport    [6.755162959270331e-96]\n",
       "11  PaperlessBilling   [2.3565543205872124e-59]\n",
       "6       OnlineBackup    [6.225925993543879e-49]\n",
       "2         Dependents    [9.140432989108222e-44]\n",
       "7   DeviceProtection    [4.044837139187934e-40]\n",
       "0      SeniorCitizen    [3.839860055793829e-37]\n",
       "1            Partner    [6.220731129635033e-37]\n",
       "4      MultipleLines    [0.0013391771071827286]\n",
       "10   StreamingMovies     [0.002869218881092956]\n",
       "9        StreamingTV      [0.00590875174315587]\n",
       "3       PhoneService      [0.31631478134436675]\n",
       "12            gender       [0.4699045390966534]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Converting Senior Citizen into Categorical Variable before applying ANOVA Test\n",
    "data['SeniorCitizen'].replace(1, \"Yes\", inplace = True)\n",
    "data['SeniorCitizen'].replace(0, \"No\", inplace = True)\n",
    "\n",
    "ANOVA_feature = data.columns.values.tolist()\n",
    "ANOVA_num_feature = ['tenure','MonthlyCharges','TotalCharges','tenure_class','Churn', 'gender',\n",
    "                     'InternetService','PaymentMethod','Contract']\n",
    "for feature in ANOVA_num_feature:\n",
    "    ANOVA_feature.remove(feature)\n",
    "\n",
    "P_value = []\n",
    "Feature_name = []\n",
    "for ANOVA in ANOVA_feature:\n",
    "    ANOVA_test = data[data[ANOVA] == \"Yes\"]\n",
    "    ANOVA_test = ANOVA_test.loc[:, ['Churn']]\n",
    "    A = np.where(ANOVA_test == \"Yes\", 1, 0)\n",
    "    ANOVA_test = data[data[ANOVA] == \"No\"]\n",
    "    ANOVA_test = ANOVA_test.loc[:, ['Churn']]\n",
    "    B = np.where(ANOVA_test == \"Yes\", 1, 0)\n",
    "    fvalue, pvalue = stats.f_oneway(A, B)\n",
    "    P_value.append(pvalue)\n",
    "    Feature_name.append(ANOVA)\n",
    "    \n",
    "gender = data[data['gender'] == \"Male\"]\n",
    "gender = gender.loc[:, ['Churn']]\n",
    "A = np.where(gender == \"Yes\", 1, 0)\n",
    "gender = data[data[\"gender\"] == \"Female\"]\n",
    "gender = gender.loc[:, ['Churn']]\n",
    "B = np.where(gender == \"Yes\", 1, 0)\n",
    "fvalue, pvalue = stats.f_oneway(A, B)\n",
    "P_value.append(pvalue)\n",
    "Feature_name.append('gender')\n",
    "\n",
    "InternetService = data[data['InternetService'] == \"Fiber optic\"]\n",
    "InternetService = InternetService.loc[:, ['Churn']]\n",
    "A = np.where(InternetService == \"Yes\", 1, 0)\n",
    "InternetService = data[data[\"InternetService\"] == \"DSL\"]\n",
    "InternetService = InternetService.loc[:, ['Churn']]\n",
    "B = np.where(InternetService == \"Yes\", 1, 0)\n",
    "InternetService = data[data[\"InternetService\"] == \"No\"]\n",
    "InternetService = InternetService.loc[:, ['Churn']]\n",
    "C = np.where(InternetService == \"Yes\", 1, 0)\n",
    "fvalue, pvalue = stats.f_oneway(A, B, C)\n",
    "P_value.append(pvalue)\n",
    "Feature_name.append('InternetService')\n",
    "\n",
    "Contract = data[data['Contract'] == \"Month-to-month\"]\n",
    "Contract = Contract.loc[:, ['Churn']]\n",
    "A = np.where(Contract == \"Yes\", 1, 0)\n",
    "Contract = data[data[\"Contract\"] == \"Two year\"]\n",
    "Contract = Contract.loc[:, ['Churn']]\n",
    "B = np.where(Contract == \"Yes\", 1, 0)\n",
    "Contract = data[data[\"Contract\"] == \"One year\"]\n",
    "Contract = Contract.loc[:, ['Churn']]\n",
    "C = np.where(Contract == \"Yes\", 1, 0)\n",
    "fvalue, pvalue = stats.f_oneway(A, B, C)\n",
    "P_value.append(pvalue)\n",
    "Feature_name.append('Contract')\n",
    "\n",
    "PaymentMethod = data[data['PaymentMethod'] == \"Electronic check\"]\n",
    "PaymentMethod = PaymentMethod.loc[:, ['Churn']]\n",
    "A = np.where(PaymentMethod == \"Yes\", 1, 0)\n",
    "PaymentMethod = data[data['PaymentMethod'] == \"Mailed check\"]\n",
    "PaymentMethod = PaymentMethod.loc[:, ['Churn']]\n",
    "B = np.where(PaymentMethod == \"Yes\", 1, 0)\n",
    "PaymentMethod = data[data['PaymentMethod'] == \"Bank transfer (automatic)\"]\n",
    "PaymentMethod = PaymentMethod.loc[:, ['Churn']]\n",
    "C = np.where(PaymentMethod == \"Yes\", 1, 0)\n",
    "PaymentMethod = data[data['PaymentMethod'] == \"Credit card (automatic)\"]\n",
    "PaymentMethod = PaymentMethod.loc[:, ['Churn']]\n",
    "D = np.where(PaymentMethod == \"Yes\", 1, 0)\n",
    "fvalue, pvalue = stats.f_oneway(A, B, C, D)\n",
    "P_value.append(pvalue)\n",
    "Feature_name.append('PaymentMethod')\n",
    "ANOVA_df = pd.DataFrame(list(zip(Feature_name, P_value)), columns = ['ANOVA Feature', 'ANOVA P Value'])\n",
    "ANOVA_df.sort_values('ANOVA P Value')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35ad2a9c",
   "metadata": {},
   "source": [
    "Result of the above Chi Square Test and ANOVA test illustrate that following features are insignificant since these statistical test doesn’t find strong evidence to reject Null Hypothesis:\n",
    "\n",
    "* SeniorCitizen\n",
    "* MultipleLines\n",
    "* gender\n",
    "* PhoneService\n",
    "* StreamingTV\n",
    "* StreamingMovie"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "307e10c8",
   "metadata": {},
   "source": [
    "### Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "cc6d54a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.get_dummies(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6c9d6096",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop(columns = ['gender_Female', 'SeniorCitizen_No', 'Partner_No', 'Dependents_No', 'PhoneService_No', 'MultipleLines_No', 'InternetService_No', 'OnlineSecurity_No', 'OnlineBackup_No', 'DeviceProtection_No', 'TechSupport_No', 'StreamingTV_No', 'StreamingMovies_No', 'Contract_Two year', 'PaperlessBilling_No','PaymentMethod_Mailed check', 'Churn_No'], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "cf1feae4",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = data.drop(columns = ['Churn_Yes'])\n",
    "y = data['Churn_Yes']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ddda659",
   "metadata": {},
   "source": [
    "### Class Balancing\n",
    " SMOTE technique to make dataset balance with respect to ‘Churn’ variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "1d22ed30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7043, 36)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7a8a717c",
   "metadata": {},
   "outputs": [],
   "source": [
    "sm = SMOTE()\n",
    "x_resampled, y_resampled = sm.fit_resample(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e8ad69de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10348,)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_resampled.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "204d0e67",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Churn distribution\n",
      "\n",
      "1    50.0\n",
      "0    50.0\n",
      "Name: Churn_Yes, dtype: float64\n"
     ]
    },
    {
     "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": [
    "y_resampled_data = pd.DataFrame(y_resampled, columns=['Churn_Yes'])\n",
    "ax = sns.countplot(x = \"Churn_Yes\", data=y_resampled_data)\n",
    "print(\"Churn distribution\\n\")\n",
    "print(round(y_resampled_data['Churn_Yes'].value_counts()/y_resampled_data.shape[0]*100,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "6b908712",
   "metadata": {},
   "outputs": [],
   "source": [
    "xr_train,xr_test,yr_train,yr_test=train_test_split(x_resampled, y_resampled,test_size=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e4f996c",
   "metadata": {},
   "source": [
    "### Model\n",
    "\n",
    "#### Depth of Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e4721f8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def run_cross_validation_on_trees(x_train, y_train, tree_depths, cv=5, scoring='roc_auc'):\n",
    "    cv_scores_list = []\n",
    "    cv_scores_std = []\n",
    "    cv_scores_mean = []\n",
    "    AUC_scores = []\n",
    "    for depth in tree_depths:\n",
    "        tree_model = DecisionTreeClassifier(max_depth=depth)\n",
    "        cv_scores = cross_val_score(tree_model, x_train, y_train, cv=5, scoring='roc_auc')\n",
    "        cv_scores_list.append(cv_scores)\n",
    "        cv_scores_mean.append(cv_scores.mean())\n",
    "        cv_scores_std.append(cv_scores.std())\n",
    "        tree_model.fit(x_train, y_train)    \n",
    "        train_pred = tree_model.predict(x_train)\n",
    "        false_positive_rate, true_positive_rate, thresholds = roc_curve(y_train, train_pred)\n",
    "        roc_auc = auc(false_positive_rate, true_positive_rate)\n",
    "        AUC_scores.append(roc_auc)\n",
    "    cv_scores_mean = np.array(cv_scores_mean)\n",
    "    cv_scores_std = np.array(cv_scores_std)\n",
    "    AUC_scores = np.array(AUC_scores)\n",
    "    return cv_scores_mean, cv_scores_std, AUC_scores\n",
    "\n",
    "def plot_cross_validation_on_trees(depths, cv_scores_mean, cv_scores_std, AUC_scores, title):\n",
    "    fig, ax = plt.subplots(1,1, figsize=(15,5))\n",
    "    ax.plot(depths, cv_scores_mean, '-o', label='mean cross-validation AUC score', alpha=0.9)\n",
    "    ax.fill_between(depths, cv_scores_mean-2*cv_scores_std, cv_scores_mean+2*cv_scores_std, alpha=0.2)\n",
    "    ylim = plt.ylim()\n",
    "    ax.plot(depths, AUC_scores, '-*', label='train AUC Score', alpha=0.9)\n",
    "    ax.set_title(title, fontsize=16)\n",
    "    ax.set_xlabel('Tree depth', fontsize=14)\n",
    "    ax.set_ylabel('AUC', fontsize=14)\n",
    "    ax.set_ylim(ylim)\n",
    "    ax.set_xticks(depths)\n",
    "    ax.legend()\n",
    "\n",
    "sm_tree_depths = range(1,25)\n",
    "sm_cv_scores_mean, sm_cv_scores_std, sm_AUC_scores = run_cross_validation_on_trees(xr_train, yr_train, sm_tree_depths)\n",
    "plot_cross_validation_on_trees(sm_tree_depths, sm_cv_scores_mean, sm_cv_scores_std, sm_AUC_scores, \n",
    "                               'AUC per decision tree depth on training data')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ea0b1e3",
   "metadata": {},
   "source": [
    "#### Minimum No of Sample at Leaf Node"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "fdf52fee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def run_cross_validation_on_trees(x_train, y_train, sample_leaf, cv=5, scoring='roc_auc'):\n",
    "    cv_scores_list = []\n",
    "    cv_scores_std = []\n",
    "    cv_scores_mean = []\n",
    "    AUC_scores = []\n",
    "    for sample_leaf in sample_leaf:\n",
    "        tree_model = DecisionTreeClassifier(min_samples_leaf=sample_leaf)\n",
    "        cv_scores = cross_val_score(tree_model, x_train, y_train, cv=5, scoring='roc_auc')\n",
    "        cv_scores_list.append(cv_scores)\n",
    "        cv_scores_mean.append(cv_scores.mean())\n",
    "        cv_scores_std.append(cv_scores.std())\n",
    "        tree_model.fit(x_train, y_train)    \n",
    "        train_pred = tree_model.predict(x_train)\n",
    "        false_positive_rate, true_positive_rate, thresholds = roc_curve(y_train, train_pred)\n",
    "        roc_auc = auc(false_positive_rate, true_positive_rate)\n",
    "        AUC_scores.append(roc_auc)\n",
    "    cv_scores_mean = np.array(cv_scores_mean)\n",
    "    cv_scores_std = np.array(cv_scores_std)\n",
    "    AUC_scores = np.array(AUC_scores)\n",
    "    return cv_scores_mean, cv_scores_std, AUC_scores\n",
    "\n",
    "def plot_cross_validation_on_trees(sample_leaf, cv_scores_mean, cv_scores_std, AUC_scores, title):\n",
    "    fig, ax = plt.subplots(1,1, figsize=(30,10))\n",
    "    ax.plot(sample_leaf, cv_scores_mean, '-o', label='mean cross-validation AUC Score', alpha=0.9)\n",
    "    ax.fill_between(sample_leaf, cv_scores_mean-2*cv_scores_std, cv_scores_mean+2*cv_scores_std, alpha=0.2)\n",
    "    ylim = plt.ylim()\n",
    "    ax.plot(sample_leaf, AUC_scores, '-*', label='train AUC score', alpha=0.9)\n",
    "    ax.set_title(title, fontsize=16)\n",
    "    ax.set_xlabel('No of sample at leaf', fontsize=14)\n",
    "    ax.set_ylabel('AUC Score', fontsize=14)\n",
    "    ax.set_ylim(ylim)\n",
    "    ax.set_xticks(sample_leaf)\n",
    "    ax.legend()\n",
    "sm_sample_leaf = range(1,100)\n",
    "sm_cv_scores_mean, sm_cv_scores_std, sm_AUC_scores = run_cross_validation_on_trees(xr_train, yr_train, sm_sample_leaf)\n",
    "plot_cross_validation_on_trees(sm_sample_leaf, sm_cv_scores_mean, sm_cv_scores_std, sm_AUC_scores, \n",
    "                               'AUC score per decision tree depth on training data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "3700b1e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight='balanced', max_depth=4,\n",
       "                       min_samples_leaf=30, random_state=100)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_dt=DecisionTreeClassifier(criterion = \"gini\",class_weight = \"balanced\", random_state = 100,max_depth=4,min_samples_leaf=30)\n",
    "model_dt.fit(xr_train,yr_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "4ae566ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC value for Decision Tree Model after SMOTE Sampling is 75.0%\n",
      "\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.77      0.73      0.75      1034\n",
      "           1       0.74      0.78      0.76      1036\n",
      "\n",
      "    accuracy                           0.75      2070\n",
      "   macro avg       0.75      0.75      0.75      2070\n",
      "weighted avg       0.75      0.75      0.75      2070\n",
      "\n"
     ]
    }
   ],
   "source": [
    "test_pred = model_dt.predict(xr_test)\n",
    "false_positive_rate, true_positive_rate, thresholds = roc_curve(yr_test, test_pred)\n",
    "roc_auc = auc(false_positive_rate, true_positive_rate)\n",
    "print(f\"AUC value for Decision Tree Model after SMOTE Sampling is {round(roc_auc*100,0)}%\\n\\n\")\n",
    "print(classification_report(yr_test,test_pred, labels=[0,1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5bbd1f14",
   "metadata": {},
   "source": [
    "#### Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "1427354f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC value for Random Forest Model after SMOTE Sampling is 80.0%\n",
      "\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.76      0.79      1034\n",
      "           1       0.78      0.83      0.80      1036\n",
      "\n",
      "    accuracy                           0.80      2070\n",
      "   macro avg       0.80      0.80      0.80      2070\n",
      "weighted avg       0.80      0.80      0.80      2070\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model_rf=RandomForestClassifier(n_estimators=100, criterion='gini', random_state = 100,max_depth= 4, min_samples_leaf= 30)\n",
    "model_rf.fit(xr_train,yr_train)\n",
    "test_pred = model_rf.predict(xr_test)\n",
    "false_positive_rate, true_positive_rate, thresholds = roc_curve(yr_test, test_pred)\n",
    "roc_auc = auc(false_positive_rate, true_positive_rate)\n",
    "print(f\"AUC value for Random Forest Model after SMOTE Sampling is {round(roc_auc*100,0)}%\\n\\n\")\n",
    "print(classification_report(yr_test,test_pred, labels=[0,1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc7e97cf",
   "metadata": {},
   "source": [
    "#### Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e0c2bdc4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Feature</th>\n",
       "      <th>Importance Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Contract_Month-to-month</td>\n",
       "      <td>0.155330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tenure</td>\n",
       "      <td>0.104068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>OnlineSecurity_Yes</td>\n",
       "      <td>0.096458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>TechSupport_Yes</td>\n",
       "      <td>0.073463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>InternetService_Fiber optic</td>\n",
       "      <td>0.068015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>tenure_class_0 - 12</td>\n",
       "      <td>0.065139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MonthlyCharges</td>\n",
       "      <td>0.043825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>OnlineBackup_No internet service</td>\n",
       "      <td>0.042232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Partner_Yes</td>\n",
       "      <td>0.039265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>DeviceProtection_No internet service</td>\n",
       "      <td>0.038199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>OnlineSecurity_No internet service</td>\n",
       "      <td>0.033631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>InternetService_DSL</td>\n",
       "      <td>0.031622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>StreamingMovies_No internet service</td>\n",
       "      <td>0.028473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>OnlineBackup_Yes</td>\n",
       "      <td>0.027111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>StreamingTV_No internet service</td>\n",
       "      <td>0.024932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Contract_One year</td>\n",
       "      <td>0.023348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>TechSupport_No internet service</td>\n",
       "      <td>0.022001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TotalCharges</td>\n",
       "      <td>0.021192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Dependents_Yes</td>\n",
       "      <td>0.015307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>PaymentMethod_Bank transfer (automatic)</td>\n",
       "      <td>0.012688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>DeviceProtection_Yes</td>\n",
       "      <td>0.010106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>PaymentMethod_Credit card (automatic)</td>\n",
       "      <td>0.006690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>tenure_class_60 - 72</td>\n",
       "      <td>0.005952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>gender_Male</td>\n",
       "      <td>0.003049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>tenure_class_48 - 60</td>\n",
       "      <td>0.001845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>PhoneService_Yes</td>\n",
       "      <td>0.001683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>MultipleLines_No phone service</td>\n",
       "      <td>0.000992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>StreamingMovies_Yes</td>\n",
       "      <td>0.000922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>tenure_class_12 - 24</td>\n",
       "      <td>0.000638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>PaymentMethod_Electronic check</td>\n",
       "      <td>0.000547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>StreamingTV_Yes</td>\n",
       "      <td>0.000465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>tenure_class_24 - 36</td>\n",
       "      <td>0.000280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>MultipleLines_Yes</td>\n",
       "      <td>0.000209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>tenure_class_36 - 48</td>\n",
       "      <td>0.000148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SeniorCitizen_Yes</td>\n",
       "      <td>0.000093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>PaperlessBilling_Yes</td>\n",
       "      <td>0.000082</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    Feature  Importance Value\n",
       "24                  Contract_Month-to-month          0.155330\n",
       "0                                    tenure          0.104068\n",
       "13                       OnlineSecurity_Yes          0.096458\n",
       "19                          TechSupport_Yes          0.073463\n",
       "11              InternetService_Fiber optic          0.068015\n",
       "30                      tenure_class_0 - 12          0.065139\n",
       "1                            MonthlyCharges          0.043825\n",
       "14         OnlineBackup_No internet service          0.042232\n",
       "5                               Partner_Yes          0.039265\n",
       "16     DeviceProtection_No internet service          0.038199\n",
       "12       OnlineSecurity_No internet service          0.033631\n",
       "10                      InternetService_DSL          0.031622\n",
       "22      StreamingMovies_No internet service          0.028473\n",
       "15                         OnlineBackup_Yes          0.027111\n",
       "20          StreamingTV_No internet service          0.024932\n",
       "25                        Contract_One year          0.023348\n",
       "18          TechSupport_No internet service          0.022001\n",
       "2                              TotalCharges          0.021192\n",
       "6                            Dependents_Yes          0.015307\n",
       "27  PaymentMethod_Bank transfer (automatic)          0.012688\n",
       "17                     DeviceProtection_Yes          0.010106\n",
       "28    PaymentMethod_Credit card (automatic)          0.006690\n",
       "35                     tenure_class_60 - 72          0.005952\n",
       "3                               gender_Male          0.003049\n",
       "34                     tenure_class_48 - 60          0.001845\n",
       "7                          PhoneService_Yes          0.001683\n",
       "8            MultipleLines_No phone service          0.000992\n",
       "23                      StreamingMovies_Yes          0.000922\n",
       "31                     tenure_class_12 - 24          0.000638\n",
       "29           PaymentMethod_Electronic check          0.000547\n",
       "21                          StreamingTV_Yes          0.000465\n",
       "32                     tenure_class_24 - 36          0.000280\n",
       "9                         MultipleLines_Yes          0.000209\n",
       "33                     tenure_class_36 - 48          0.000148\n",
       "4                         SeniorCitizen_Yes          0.000093\n",
       "26                     PaperlessBilling_Yes          0.000082"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = x.columns.values.tolist()\n",
    "importance = model_rf.feature_importances_\n",
    "plt.bar([x for x in range(len(importance))], importance)\n",
    "plt.show()\n",
    "df = pd.DataFrame(list(zip(features, importance)), columns = ['Feature', 'Importance Value'])\n",
    "df.sort_values('Importance Value', ascending= False)"
   ]
  }
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