Telco Churn Analysis and Modeling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "147ac725",
   "metadata": {},
   "source": [
    "# Telco Churn Analysis and Modeling\n",
    "\n",
    "#### Problem Statement\n",
    "\n",
    "There are promotional charges called as Acquisition Cost and Retention Cost in a telco company. The cost of acquiring new consumers is referred to as acquisition cost. Meanwhile, the expense of retaining existing clients is known as Retention Cost.\n",
    "\n",
    "We are frequently inaccurate in our predictions of which customers would churn and which ones will stay due to human limitations. As a result, the allocation of money may be incorrect, resulting in a higher amount of funds being issued.\n",
    "\n",
    "Moreover, according to some sources, the acquisition cost is 5x greater than the retantion cost. If we are wrong in predicting a customer who will actually churn, but it turns out that we predict as a customer who will retain, then we need to spend more than it should be.\n",
    "\n",
    "#### What to do\n",
    "\n",
    "I will try to create a Machine Learning model to predict customer churn and retantion.\n",
    "\n",
    "#### Goal\n",
    "\n",
    "Machine Learning has a goal so that cost allocation can be done as precisely as possible.\n",
    "\n",
    "#### Value\n",
    "\n",
    "There is no wasted cost allocation."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "340cf015",
   "metadata": {},
   "source": [
    "Make sure you have installed xgbost by-\n",
    "``` pip install xgboost ```  in command line promt or in anconda prompt "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "002431de",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the libraries\n",
    "import pandas as pd # load ans manipulate data and one hot encoding\n",
    "import numpy as np # Calculate the mean and std\n",
    "import xgboost as xgb # xgboost stuff\n",
    "from sklearn.model_selection import train_test_split #Splitting data into training and test set\n",
    "from sklearn.metrics import roc_auc_score, balanced_accuracy_score, make_scorer # for evaluating performace of model (Imbalance data)\n",
    "from sklearn.model_selection import GridSearchCV # for cross validation \n",
    "from sklearn.metrics import confusion_matrix, plot_confusion_matrix # Creating confusion metrics, and plotting it\n",
    "import matplotlib.pyplot as plt #visualizing the graph\n",
    "import seaborn as sns # visualizing the graph\n",
    "pd.set_option('display.max_columns', 500) # for displaying all the columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3040a426",
   "metadata": {},
   "source": [
    "### Import the dataset\n",
    "Now we will import the data set from https://community.ibm.com/community/user/businessanalytics/blogs/steven-macko/2019/07/11/telco-customer-churn-1113"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4d4bd00b",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Count</th>\n",
       "      <th>Country</th>\n",
       "      <th>State</th>\n",
       "      <th>City</th>\n",
       "      <th>Zip Code</th>\n",
       "      <th>Lat Long</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Senior Citizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Tenure Months</th>\n",
       "      <th>Phone Service</th>\n",
       "      <th>Multiple Lines</th>\n",
       "      <th>Internet Service</th>\n",
       "      <th>Online Security</th>\n",
       "      <th>Online Backup</th>\n",
       "      <th>Device Protection</th>\n",
       "      <th>Tech Support</th>\n",
       "      <th>Streaming TV</th>\n",
       "      <th>Streaming Movies</th>\n",
       "      <th>Contract</th>\n",
       "      <th>Paperless Billing</th>\n",
       "      <th>Payment Method</th>\n",
       "      <th>Monthly Charges</th>\n",
       "      <th>Total Charges</th>\n",
       "      <th>Churn Label</th>\n",
       "      <th>Churn Value</th>\n",
       "      <th>Churn Score</th>\n",
       "      <th>CLTV</th>\n",
       "      <th>Churn Reason</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3668-QPYBK</td>\n",
       "      <td>1</td>\n",
       "      <td>United States</td>\n",
       "      <td>California</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>90003</td>\n",
       "      <td>33.964131, -118.272783</td>\n",
       "      <td>33.964131</td>\n",
       "      <td>-118.272783</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>53.85</td>\n",
       "      <td>108.15</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>86</td>\n",
       "      <td>3239</td>\n",
       "      <td>Competitor made better offer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9237-HQITU</td>\n",
       "      <td>1</td>\n",
       "      <td>United States</td>\n",
       "      <td>California</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>90005</td>\n",
       "      <td>34.059281, -118.30742</td>\n",
       "      <td>34.059281</td>\n",
       "      <td>-118.307420</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>70.70</td>\n",
       "      <td>151.65</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>67</td>\n",
       "      <td>2701</td>\n",
       "      <td>Moved</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9305-CDSKC</td>\n",
       "      <td>1</td>\n",
       "      <td>United States</td>\n",
       "      <td>California</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>90006</td>\n",
       "      <td>34.048013, -118.293953</td>\n",
       "      <td>34.048013</td>\n",
       "      <td>-118.293953</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>8</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>99.65</td>\n",
       "      <td>820.5</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>86</td>\n",
       "      <td>5372</td>\n",
       "      <td>Moved</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7892-POOKP</td>\n",
       "      <td>1</td>\n",
       "      <td>United States</td>\n",
       "      <td>California</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>90010</td>\n",
       "      <td>34.062125, -118.315709</td>\n",
       "      <td>34.062125</td>\n",
       "      <td>-118.315709</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>28</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>104.80</td>\n",
       "      <td>3046.05</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>84</td>\n",
       "      <td>5003</td>\n",
       "      <td>Moved</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0280-XJGEX</td>\n",
       "      <td>1</td>\n",
       "      <td>United States</td>\n",
       "      <td>California</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>90015</td>\n",
       "      <td>34.039224, -118.266293</td>\n",
       "      <td>34.039224</td>\n",
       "      <td>-118.266293</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>49</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>103.70</td>\n",
       "      <td>5036.3</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>5340</td>\n",
       "      <td>Competitor had better devices</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerID  Count        Country       State         City  Zip Code  \\\n",
       "0  3668-QPYBK      1  United States  California  Los Angeles     90003   \n",
       "1  9237-HQITU      1  United States  California  Los Angeles     90005   \n",
       "2  9305-CDSKC      1  United States  California  Los Angeles     90006   \n",
       "3  7892-POOKP      1  United States  California  Los Angeles     90010   \n",
       "4  0280-XJGEX      1  United States  California  Los Angeles     90015   \n",
       "\n",
       "                 Lat Long   Latitude   Longitude  Gender Senior Citizen  \\\n",
       "0  33.964131, -118.272783  33.964131 -118.272783    Male             No   \n",
       "1   34.059281, -118.30742  34.059281 -118.307420  Female             No   \n",
       "2  34.048013, -118.293953  34.048013 -118.293953  Female             No   \n",
       "3  34.062125, -118.315709  34.062125 -118.315709  Female             No   \n",
       "4  34.039224, -118.266293  34.039224 -118.266293    Male             No   \n",
       "\n",
       "  Partner Dependents  Tenure Months Phone Service Multiple Lines  \\\n",
       "0      No         No              2           Yes             No   \n",
       "1      No        Yes              2           Yes             No   \n",
       "2      No        Yes              8           Yes            Yes   \n",
       "3     Yes        Yes             28           Yes            Yes   \n",
       "4      No        Yes             49           Yes            Yes   \n",
       "\n",
       "  Internet Service Online Security Online Backup Device Protection  \\\n",
       "0              DSL             Yes           Yes                No   \n",
       "1      Fiber optic              No            No                No   \n",
       "2      Fiber optic              No            No               Yes   \n",
       "3      Fiber optic              No            No               Yes   \n",
       "4      Fiber optic              No           Yes               Yes   \n",
       "\n",
       "  Tech Support Streaming TV Streaming Movies        Contract  \\\n",
       "0           No           No               No  Month-to-month   \n",
       "1           No           No               No  Month-to-month   \n",
       "2           No          Yes              Yes  Month-to-month   \n",
       "3          Yes          Yes              Yes  Month-to-month   \n",
       "4           No          Yes              Yes  Month-to-month   \n",
       "\n",
       "  Paperless Billing             Payment Method  Monthly Charges Total Charges  \\\n",
       "0               Yes               Mailed check            53.85        108.15   \n",
       "1               Yes           Electronic check            70.70        151.65   \n",
       "2               Yes           Electronic check            99.65         820.5   \n",
       "3               Yes           Electronic check           104.80       3046.05   \n",
       "4               Yes  Bank transfer (automatic)           103.70        5036.3   \n",
       "\n",
       "  Churn Label  Churn Value  Churn Score  CLTV                   Churn Reason  \n",
       "0         Yes            1           86  3239   Competitor made better offer  \n",
       "1         Yes            1           67  2701                          Moved  \n",
       "2         Yes            1           86  5372                          Moved  \n",
       "3         Yes            1           84  5003                          Moved  \n",
       "4         Yes            1           89  5340  Competitor had better devices  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load the dataset in df and lest look at the first 5 rows of df\n",
    "df= pd.read_excel('Telco_customer_churn.xlsx')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57df356b",
   "metadata": {},
   "source": [
    "### Executive Summary\n",
    "Telco Co. provided select, historical data on 7,043 customers including an indicator as to whether each customer churned. After analyzing and transforming the data, we optimized several classification models. Each model was trained on 75% of the historical data and then asked to predict churn scores on the remaining 25% test data. Each model was compared with the previous best model with model XG Boost performing the best:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9c3975a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7043, 33)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lets look at the shape of dataset\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2f822057",
   "metadata": {},
   "outputs": [
    {
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       "      <th>Zip Code</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Tenure Months</th>\n",
       "      <th>Monthly Charges</th>\n",
       "      <th>Churn Value</th>\n",
       "      <th>Churn Score</th>\n",
       "      <th>CLTV</th>\n",
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       "      <th>count</th>\n",
       "      <td>7043.0</td>\n",
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       "      <td>7043.000000</td>\n",
       "      <td>7043.000000</td>\n",
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       "      <th>mean</th>\n",
       "      <td>1.0</td>\n",
       "      <td>93521.964646</td>\n",
       "      <td>36.282441</td>\n",
       "      <td>-119.798880</td>\n",
       "      <td>32.371149</td>\n",
       "      <td>64.761692</td>\n",
       "      <td>0.265370</td>\n",
       "      <td>58.699418</td>\n",
       "      <td>4400.295755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1865.794555</td>\n",
       "      <td>2.455723</td>\n",
       "      <td>2.157889</td>\n",
       "      <td>24.559481</td>\n",
       "      <td>30.090047</td>\n",
       "      <td>0.441561</td>\n",
       "      <td>21.525131</td>\n",
       "      <td>1183.057152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.0</td>\n",
       "      <td>90001.000000</td>\n",
       "      <td>32.555828</td>\n",
       "      <td>-124.301372</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>18.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2003.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.0</td>\n",
       "      <td>92102.000000</td>\n",
       "      <td>34.030915</td>\n",
       "      <td>-121.815412</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>35.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>3469.000000</td>\n",
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       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.0</td>\n",
       "      <td>93552.000000</td>\n",
       "      <td>36.391777</td>\n",
       "      <td>-119.730885</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>70.350000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>4527.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.0</td>\n",
       "      <td>95351.000000</td>\n",
       "      <td>38.224869</td>\n",
       "      <td>-118.043237</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>89.850000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>5380.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.0</td>\n",
       "      <td>96161.000000</td>\n",
       "      <td>41.962127</td>\n",
       "      <td>-114.192901</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>118.750000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>6500.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "        Count      Zip Code     Latitude    Longitude  Tenure Months  \\\n",
       "count  7043.0   7043.000000  7043.000000  7043.000000    7043.000000   \n",
       "mean      1.0  93521.964646    36.282441  -119.798880      32.371149   \n",
       "std       0.0   1865.794555     2.455723     2.157889      24.559481   \n",
       "min       1.0  90001.000000    32.555828  -124.301372       0.000000   \n",
       "25%       1.0  92102.000000    34.030915  -121.815412       9.000000   \n",
       "50%       1.0  93552.000000    36.391777  -119.730885      29.000000   \n",
       "75%       1.0  95351.000000    38.224869  -118.043237      55.000000   \n",
       "max       1.0  96161.000000    41.962127  -114.192901      72.000000   \n",
       "\n",
       "       Monthly Charges  Churn Value  Churn Score         CLTV  \n",
       "count      7043.000000  7043.000000  7043.000000  7043.000000  \n",
       "mean         64.761692     0.265370    58.699418  4400.295755  \n",
       "std          30.090047     0.441561    21.525131  1183.057152  \n",
       "min          18.250000     0.000000     5.000000  2003.000000  \n",
       "25%          35.500000     0.000000    40.000000  3469.000000  \n",
       "50%          70.350000     0.000000    61.000000  4527.000000  \n",
       "75%          89.850000     1.000000    75.000000  5380.500000  \n",
       "max         118.750000     1.000000   100.000000  6500.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lets describe the dataset\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "af677b54",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7043 entries, 0 to 7042\n",
      "Data columns (total 33 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   CustomerID         7043 non-null   object \n",
      " 1   Count              7043 non-null   int64  \n",
      " 2   Country            7043 non-null   object \n",
      " 3   State              7043 non-null   object \n",
      " 4   City               7043 non-null   object \n",
      " 5   Zip Code           7043 non-null   int64  \n",
      " 6   Lat Long           7043 non-null   object \n",
      " 7   Latitude           7043 non-null   float64\n",
      " 8   Longitude          7043 non-null   float64\n",
      " 9   Gender             7043 non-null   object \n",
      " 10  Senior Citizen     7043 non-null   object \n",
      " 11  Partner            7043 non-null   object \n",
      " 12  Dependents         7043 non-null   object \n",
      " 13  Tenure Months      7043 non-null   int64  \n",
      " 14  Phone Service      7043 non-null   object \n",
      " 15  Multiple Lines     7043 non-null   object \n",
      " 16  Internet Service   7043 non-null   object \n",
      " 17  Online Security    7043 non-null   object \n",
      " 18  Online Backup      7043 non-null   object \n",
      " 19  Device Protection  7043 non-null   object \n",
      " 20  Tech Support       7043 non-null   object \n",
      " 21  Streaming TV       7043 non-null   object \n",
      " 22  Streaming Movies   7043 non-null   object \n",
      " 23  Contract           7043 non-null   object \n",
      " 24  Paperless Billing  7043 non-null   object \n",
      " 25  Payment Method     7043 non-null   object \n",
      " 26  Monthly Charges    7043 non-null   float64\n",
      " 27  Total Charges      7043 non-null   object \n",
      " 28  Churn Label        7043 non-null   object \n",
      " 29  Churn Value        7043 non-null   int64  \n",
      " 30  Churn Score        7043 non-null   int64  \n",
      " 31  CLTV               7043 non-null   int64  \n",
      " 32  Churn Reason       1869 non-null   object \n",
      "dtypes: float64(3), int64(6), object(24)\n",
      "memory usage: 1.8+ MB\n"
     ]
    }
   ],
   "source": [
    "# Lets look at the data type of each columns in dataset\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7ae7ab8",
   "metadata": {},
   "source": [
    "So, Df dataset contains 7043 rows , 33 columns "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd18f6da",
   "metadata": {},
   "source": [
    "## Data Pre-processing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d48b8d10",
   "metadata": {},
   "source": [
    "##### People already left the elco compant so, we will remove these columns from data set. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "60d63a5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Count</th>\n",
       "      <th>Country</th>\n",
       "      <th>State</th>\n",
       "      <th>City</th>\n",
       "      <th>Zip Code</th>\n",
       "      <th>Lat Long</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Senior Citizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Tenure Months</th>\n",
       "      <th>Phone Service</th>\n",
       "      <th>Multiple Lines</th>\n",
       "      <th>Internet Service</th>\n",
       "      <th>Online Security</th>\n",
       "      <th>Online Backup</th>\n",
       "      <th>Device Protection</th>\n",
       "      <th>Tech Support</th>\n",
       "      <th>Streaming TV</th>\n",
       "      <th>Streaming Movies</th>\n",
       "      <th>Contract</th>\n",
       "      <th>Paperless Billing</th>\n",
       "      <th>Payment Method</th>\n",
       "      <th>Monthly Charges</th>\n",
       "      <th>Total Charges</th>\n",
       "      <th>Churn Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3668-QPYBK</td>\n",
       "      <td>1</td>\n",
       "      <td>United States</td>\n",
       "      <td>California</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>90003</td>\n",
       "      <td>33.964131, -118.272783</td>\n",
       "      <td>33.964131</td>\n",
       "      <td>-118.272783</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>53.85</td>\n",
       "      <td>108.15</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9237-HQITU</td>\n",
       "      <td>1</td>\n",
       "      <td>United States</td>\n",
       "      <td>California</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>90005</td>\n",
       "      <td>34.059281, -118.30742</td>\n",
       "      <td>34.059281</td>\n",
       "      <td>-118.307420</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>70.70</td>\n",
       "      <td>151.65</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9305-CDSKC</td>\n",
       "      <td>1</td>\n",
       "      <td>United States</td>\n",
       "      <td>California</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>90006</td>\n",
       "      <td>34.048013, -118.293953</td>\n",
       "      <td>34.048013</td>\n",
       "      <td>-118.293953</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>8</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>99.65</td>\n",
       "      <td>820.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7892-POOKP</td>\n",
       "      <td>1</td>\n",
       "      <td>United States</td>\n",
       "      <td>California</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>90010</td>\n",
       "      <td>34.062125, -118.315709</td>\n",
       "      <td>34.062125</td>\n",
       "      <td>-118.315709</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>28</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>104.80</td>\n",
       "      <td>3046.05</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0280-XJGEX</td>\n",
       "      <td>1</td>\n",
       "      <td>United States</td>\n",
       "      <td>California</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>90015</td>\n",
       "      <td>34.039224, -118.266293</td>\n",
       "      <td>34.039224</td>\n",
       "      <td>-118.266293</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>49</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>103.70</td>\n",
       "      <td>5036.3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerID  Count        Country       State         City  Zip Code  \\\n",
       "0  3668-QPYBK      1  United States  California  Los Angeles     90003   \n",
       "1  9237-HQITU      1  United States  California  Los Angeles     90005   \n",
       "2  9305-CDSKC      1  United States  California  Los Angeles     90006   \n",
       "3  7892-POOKP      1  United States  California  Los Angeles     90010   \n",
       "4  0280-XJGEX      1  United States  California  Los Angeles     90015   \n",
       "\n",
       "                 Lat Long   Latitude   Longitude  Gender Senior Citizen  \\\n",
       "0  33.964131, -118.272783  33.964131 -118.272783    Male             No   \n",
       "1   34.059281, -118.30742  34.059281 -118.307420  Female             No   \n",
       "2  34.048013, -118.293953  34.048013 -118.293953  Female             No   \n",
       "3  34.062125, -118.315709  34.062125 -118.315709  Female             No   \n",
       "4  34.039224, -118.266293  34.039224 -118.266293    Male             No   \n",
       "\n",
       "  Partner Dependents  Tenure Months Phone Service Multiple Lines  \\\n",
       "0      No         No              2           Yes             No   \n",
       "1      No        Yes              2           Yes             No   \n",
       "2      No        Yes              8           Yes            Yes   \n",
       "3     Yes        Yes             28           Yes            Yes   \n",
       "4      No        Yes             49           Yes            Yes   \n",
       "\n",
       "  Internet Service Online Security Online Backup Device Protection  \\\n",
       "0              DSL             Yes           Yes                No   \n",
       "1      Fiber optic              No            No                No   \n",
       "2      Fiber optic              No            No               Yes   \n",
       "3      Fiber optic              No            No               Yes   \n",
       "4      Fiber optic              No           Yes               Yes   \n",
       "\n",
       "  Tech Support Streaming TV Streaming Movies        Contract  \\\n",
       "0           No           No               No  Month-to-month   \n",
       "1           No           No               No  Month-to-month   \n",
       "2           No          Yes              Yes  Month-to-month   \n",
       "3          Yes          Yes              Yes  Month-to-month   \n",
       "4           No          Yes              Yes  Month-to-month   \n",
       "\n",
       "  Paperless Billing             Payment Method  Monthly Charges Total Charges  \\\n",
       "0               Yes               Mailed check            53.85        108.15   \n",
       "1               Yes           Electronic check            70.70        151.65   \n",
       "2               Yes           Electronic check            99.65         820.5   \n",
       "3               Yes           Electronic check           104.80       3046.05   \n",
       "4               Yes  Bank transfer (automatic)           103.70        5036.3   \n",
       "\n",
       "   Churn Value  \n",
       "0            1  \n",
       "1            1  \n",
       "2            1  \n",
       "3            1  \n",
       "4            1  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Removing these columns so, we did axis=1, and inplace= True, means not making the copy of dataset\n",
    "df.drop(['Churn Reason','CLTV','Churn Score','Churn Label'], axis=1, inplace= True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "556e09c6",
   "metadata": {},
   "source": [
    "Some of columns contains only single values which From the dataframe above, there are several columns that have one unique value, namely the column [Count, Country, State]. In addition, I will not use the CustomerID column because the customerID does not determine the probability that someone will churn or not.\n",
    "\n",
    "Zip code, Lat Long, Latitude, Longitude will also be deleted. I won't use it to build Machine Learning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a144685a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1], dtype=int64)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Count'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d14b10b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['United States'], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Country'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5c4ad551",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['California'], dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['State'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "aee0879a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Senior Citizen</th>\n",
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       "      <th>Churn Value</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
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       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>70.70</td>\n",
       "      <td>151.65</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>8</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>99.65</td>\n",
       "      <td>820.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>28</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>104.80</td>\n",
       "      <td>3046.05</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>49</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>103.70</td>\n",
       "      <td>5036.3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender Senior Citizen Partner Dependents  Tenure Months Phone Service  \\\n",
       "0    Male             No      No         No              2           Yes   \n",
       "1  Female             No      No        Yes              2           Yes   \n",
       "2  Female             No      No        Yes              8           Yes   \n",
       "3  Female             No     Yes        Yes             28           Yes   \n",
       "4    Male             No      No        Yes             49           Yes   \n",
       "\n",
       "  Multiple Lines Internet Service Online Security Online Backup  \\\n",
       "0             No              DSL             Yes           Yes   \n",
       "1             No      Fiber optic              No            No   \n",
       "2            Yes      Fiber optic              No            No   \n",
       "3            Yes      Fiber optic              No            No   \n",
       "4            Yes      Fiber optic              No           Yes   \n",
       "\n",
       "  Device Protection Tech Support Streaming TV Streaming Movies  \\\n",
       "0                No           No           No               No   \n",
       "1                No           No           No               No   \n",
       "2               Yes           No          Yes              Yes   \n",
       "3               Yes          Yes          Yes              Yes   \n",
       "4               Yes           No          Yes              Yes   \n",
       "\n",
       "         Contract Paperless Billing             Payment Method  \\\n",
       "0  Month-to-month               Yes               Mailed check   \n",
       "1  Month-to-month               Yes           Electronic check   \n",
       "2  Month-to-month               Yes           Electronic check   \n",
       "3  Month-to-month               Yes           Electronic check   \n",
       "4  Month-to-month               Yes  Bank transfer (automatic)   \n",
       "\n",
       "   Monthly Charges Total Charges  Churn Value  \n",
       "0            53.85        108.15            1  \n",
       "1            70.70        151.65            1  \n",
       "2            99.65         820.5            1  \n",
       "3           104.80       3046.05            1  \n",
       "4           103.70        5036.3            1  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(['Count','City','Country','State','CustomerID','Zip Code', 'Lat Long', 'Latitude', 'Longitude'],\n",
    "       axis=1, inplace= True)\n",
    "# Lets see first 5 rows \n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "716b2211",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Gender', 'Senior_Citizen', 'Partner', 'Dependents', 'Tenure_Months',\n",
       "       'Phone_Service', 'Multiple_Lines', 'Internet_Service',\n",
       "       'Online_Security', 'Online_Backup', 'Device_Protection', 'Tech_Support',\n",
       "       'Streaming_TV', 'Streaming_Movies', 'Contract', 'Paperless_Billing',\n",
       "       'Payment_Method', 'Monthly_Charges', 'Total_Charges', 'Churn_Value'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Replacing the space from column name \n",
    "df.columns= df.columns.str.replace(' ','_') #replacing space with undescore\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "69adb325",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Senior_Citizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Tenure_Months</th>\n",
       "      <th>Phone_Service</th>\n",
       "      <th>Multiple_Lines</th>\n",
       "      <th>Internet_Service</th>\n",
       "      <th>Online_Security</th>\n",
       "      <th>Online_Backup</th>\n",
       "      <th>Device_Protection</th>\n",
       "      <th>Tech_Support</th>\n",
       "      <th>Streaming_TV</th>\n",
       "      <th>Streaming_Movies</th>\n",
       "      <th>Contract</th>\n",
       "      <th>Paperless_Billing</th>\n",
       "      <th>Payment_Method</th>\n",
       "      <th>Monthly_Charges</th>\n",
       "      <th>Total_Charges</th>\n",
       "      <th>Churn_Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed_check</td>\n",
       "      <td>53.85</td>\n",
       "      <td>108.15</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber_optic</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic_check</td>\n",
       "      <td>70.70</td>\n",
       "      <td>151.65</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>8</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber_optic</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic_check</td>\n",
       "      <td>99.65</td>\n",
       "      <td>820.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>28</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber_optic</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic_check</td>\n",
       "      <td>104.80</td>\n",
       "      <td>3046.05</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>49</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber_optic</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Bank_transfer_(automatic)</td>\n",
       "      <td>103.70</td>\n",
       "      <td>5036.3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender Senior_Citizen Partner Dependents  Tenure_Months Phone_Service  \\\n",
       "0    Male             No      No         No              2           Yes   \n",
       "1  Female             No      No        Yes              2           Yes   \n",
       "2  Female             No      No        Yes              8           Yes   \n",
       "3  Female             No     Yes        Yes             28           Yes   \n",
       "4    Male             No      No        Yes             49           Yes   \n",
       "\n",
       "  Multiple_Lines Internet_Service Online_Security Online_Backup  \\\n",
       "0             No              DSL             Yes           Yes   \n",
       "1             No      Fiber_optic              No            No   \n",
       "2            Yes      Fiber_optic              No            No   \n",
       "3            Yes      Fiber_optic              No            No   \n",
       "4            Yes      Fiber_optic              No           Yes   \n",
       "\n",
       "  Device_Protection Tech_Support Streaming_TV Streaming_Movies  \\\n",
       "0                No           No           No               No   \n",
       "1                No           No           No               No   \n",
       "2               Yes           No          Yes              Yes   \n",
       "3               Yes          Yes          Yes              Yes   \n",
       "4               Yes           No          Yes              Yes   \n",
       "\n",
       "         Contract Paperless_Billing             Payment_Method  \\\n",
       "0  Month-to-month               Yes               Mailed_check   \n",
       "1  Month-to-month               Yes           Electronic_check   \n",
       "2  Month-to-month               Yes           Electronic_check   \n",
       "3  Month-to-month               Yes           Electronic_check   \n",
       "4  Month-to-month               Yes  Bank_transfer_(automatic)   \n",
       "\n",
       "   Monthly_Charges Total_Charges  Churn_Value  \n",
       "0            53.85        108.15            1  \n",
       "1            70.70        151.65            1  \n",
       "2            99.65         820.5            1  \n",
       "3           104.80       3046.05            1  \n",
       "4           103.70        5036.3            1  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now replacing white spaces with underscore\n",
    "df.replace(' ', '_', regex=True, inplace =True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5e12ac8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender               0\n",
       "Senior_Citizen       0\n",
       "Partner              0\n",
       "Dependents           0\n",
       "Tenure_Months        0\n",
       "Phone_Service        0\n",
       "Multiple_Lines       0\n",
       "Internet_Service     0\n",
       "Online_Security      0\n",
       "Online_Backup        0\n",
       "Device_Protection    0\n",
       "Tech_Support         0\n",
       "Streaming_TV         0\n",
       "Streaming_Movies     0\n",
       "Contract             0\n",
       "Paperless_Billing    0\n",
       "Payment_Method       0\n",
       "Monthly_Charges      0\n",
       "Total_Charges        0\n",
       "Churn_Value          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking missing values \n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40e6f994",
   "metadata": {},
   "source": [
    " ##### We donot find any missing values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9c735864",
   "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   Senior_Citizen     7043 non-null   object \n",
      " 2   Partner            7043 non-null   object \n",
      " 3   Dependents         7043 non-null   object \n",
      " 4   Tenure_Months      7043 non-null   int64  \n",
      " 5   Phone_Service      7043 non-null   object \n",
      " 6   Multiple_Lines     7043 non-null   object \n",
      " 7   Internet_Service   7043 non-null   object \n",
      " 8   Online_Security    7043 non-null   object \n",
      " 9   Online_Backup      7043 non-null   object \n",
      " 10  Device_Protection  7043 non-null   object \n",
      " 11  Tech_Support       7043 non-null   object \n",
      " 12  Streaming_TV       7043 non-null   object \n",
      " 13  Streaming_Movies   7043 non-null   object \n",
      " 14  Contract           7043 non-null   object \n",
      " 15  Paperless_Billing  7043 non-null   object \n",
      " 16  Payment_Method     7043 non-null   object \n",
      " 17  Monthly_Charges    7043 non-null   float64\n",
      " 18  Total_Charges      7043 non-null   object \n",
      " 19  Churn_Value        7043 non-null   int64  \n",
      "dtypes: float64(1), int64(2), object(17)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "# Lets see the dataype of each columns\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c06f2b9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Senior_Citizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Tenure_Months</th>\n",
       "      <th>Phone_Service</th>\n",
       "      <th>Multiple_Lines</th>\n",
       "      <th>Internet_Service</th>\n",
       "      <th>Online_Security</th>\n",
       "      <th>Online_Backup</th>\n",
       "      <th>Device_Protection</th>\n",
       "      <th>Tech_Support</th>\n",
       "      <th>Streaming_TV</th>\n",
       "      <th>Streaming_Movies</th>\n",
       "      <th>Contract</th>\n",
       "      <th>Paperless_Billing</th>\n",
       "      <th>Payment_Method</th>\n",
       "      <th>Monthly_Charges</th>\n",
       "      <th>Total_Charges</th>\n",
       "      <th>Churn_Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2234</th>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No_phone_service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Two_year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Bank_transfer_(automatic)</td>\n",
       "      <td>52.55</td>\n",
       "      <td>_</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2438</th>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No_internet_service</td>\n",
       "      <td>No_internet_service</td>\n",
       "      <td>No_internet_service</td>\n",
       "      <td>No_internet_service</td>\n",
       "      <td>No_internet_service</td>\n",
       "      <td>No_internet_service</td>\n",
       "      <td>Two_year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed_check</td>\n",
       "      <td>20.25</td>\n",
       "      <td>_</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2568</th>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Two_year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed_check</td>\n",
       "      <td>80.85</td>\n",
       "      <td>_</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2667</th>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No_internet_service</td>\n",
       "      <td>No_internet_service</td>\n",
       "      <td>No_internet_service</td>\n",
       "      <td>No_internet_service</td>\n",
       "      <td>No_internet_service</td>\n",
       "      <td>No_internet_service</td>\n",
       "      <td>Two_year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed_check</td>\n",
       "      <td>25.75</td>\n",
       "      <td>_</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2856</th>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No_phone_service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Two_year</td>\n",
       "      <td>No</td>\n",
       "      <td>Credit_card_(automatic)</td>\n",
       "      <td>56.05</td>\n",
       "      <td>_</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4331</th>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No_internet_service</td>\n",
       "      <td>No_internet_service</td>\n",
       "      <td>No_internet_service</td>\n",
       "      <td>No_internet_service</td>\n",
       "      <td>No_internet_service</td>\n",
       "      <td>No_internet_service</td>\n",
       "      <td>Two_year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed_check</td>\n",
       "      <td>19.85</td>\n",
       "      <td>_</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4687</th>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No_internet_service</td>\n",
       "      <td>No_internet_service</td>\n",
       "      <td>No_internet_service</td>\n",
       "      <td>No_internet_service</td>\n",
       "      <td>No_internet_service</td>\n",
       "      <td>No_internet_service</td>\n",
       "      <td>Two_year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed_check</td>\n",
       "      <td>25.35</td>\n",
       "      <td>_</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5104</th>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No_internet_service</td>\n",
       "      <td>No_internet_service</td>\n",
       "      <td>No_internet_service</td>\n",
       "      <td>No_internet_service</td>\n",
       "      <td>No_internet_service</td>\n",
       "      <td>No_internet_service</td>\n",
       "      <td>Two_year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed_check</td>\n",
       "      <td>20.00</td>\n",
       "      <td>_</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5719</th>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No_internet_service</td>\n",
       "      <td>No_internet_service</td>\n",
       "      <td>No_internet_service</td>\n",
       "      <td>No_internet_service</td>\n",
       "      <td>No_internet_service</td>\n",
       "      <td>No_internet_service</td>\n",
       "      <td>One_year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed_check</td>\n",
       "      <td>19.70</td>\n",
       "      <td>_</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6772</th>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>DSL</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Two_year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed_check</td>\n",
       "      <td>73.35</td>\n",
       "      <td>_</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6840</th>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Two_year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Bank_transfer_(automatic)</td>\n",
       "      <td>61.90</td>\n",
       "      <td>_</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Gender Senior_Citizen Partner Dependents  Tenure_Months Phone_Service  \\\n",
       "2234  Female             No     Yes         No              0            No   \n",
       "2438    Male             No      No         No              0           Yes   \n",
       "2568  Female             No     Yes         No              0           Yes   \n",
       "2667    Male             No     Yes        Yes              0           Yes   \n",
       "2856  Female             No     Yes         No              0            No   \n",
       "4331    Male             No     Yes        Yes              0           Yes   \n",
       "4687    Male             No     Yes        Yes              0           Yes   \n",
       "5104  Female             No     Yes        Yes              0           Yes   \n",
       "5719    Male             No     Yes        Yes              0           Yes   \n",
       "6772  Female             No     Yes        Yes              0           Yes   \n",
       "6840    Male             No      No        Yes              0           Yes   \n",
       "\n",
       "        Multiple_Lines Internet_Service      Online_Security  \\\n",
       "2234  No_phone_service              DSL                  Yes   \n",
       "2438                No               No  No_internet_service   \n",
       "2568                No              DSL                  Yes   \n",
       "2667               Yes               No  No_internet_service   \n",
       "2856  No_phone_service              DSL                  Yes   \n",
       "4331                No               No  No_internet_service   \n",
       "4687               Yes               No  No_internet_service   \n",
       "5104                No               No  No_internet_service   \n",
       "5719                No               No  No_internet_service   \n",
       "6772               Yes              DSL                   No   \n",
       "6840               Yes              DSL                  Yes   \n",
       "\n",
       "            Online_Backup    Device_Protection         Tech_Support  \\\n",
       "2234                   No                  Yes                  Yes   \n",
       "2438  No_internet_service  No_internet_service  No_internet_service   \n",
       "2568                  Yes                  Yes                   No   \n",
       "2667  No_internet_service  No_internet_service  No_internet_service   \n",
       "2856                  Yes                  Yes                  Yes   \n",
       "4331  No_internet_service  No_internet_service  No_internet_service   \n",
       "4687  No_internet_service  No_internet_service  No_internet_service   \n",
       "5104  No_internet_service  No_internet_service  No_internet_service   \n",
       "5719  No_internet_service  No_internet_service  No_internet_service   \n",
       "6772                  Yes                  Yes                  Yes   \n",
       "6840                  Yes                   No                  Yes   \n",
       "\n",
       "             Streaming_TV     Streaming_Movies  Contract Paperless_Billing  \\\n",
       "2234                  Yes                   No  Two_year               Yes   \n",
       "2438  No_internet_service  No_internet_service  Two_year                No   \n",
       "2568                  Yes                  Yes  Two_year                No   \n",
       "2667  No_internet_service  No_internet_service  Two_year                No   \n",
       "2856                  Yes                   No  Two_year                No   \n",
       "4331  No_internet_service  No_internet_service  Two_year                No   \n",
       "4687  No_internet_service  No_internet_service  Two_year                No   \n",
       "5104  No_internet_service  No_internet_service  Two_year                No   \n",
       "5719  No_internet_service  No_internet_service  One_year               Yes   \n",
       "6772                  Yes                   No  Two_year                No   \n",
       "6840                   No                   No  Two_year               Yes   \n",
       "\n",
       "                 Payment_Method  Monthly_Charges Total_Charges  Churn_Value  \n",
       "2234  Bank_transfer_(automatic)            52.55             _            0  \n",
       "2438               Mailed_check            20.25             _            0  \n",
       "2568               Mailed_check            80.85             _            0  \n",
       "2667               Mailed_check            25.75             _            0  \n",
       "2856    Credit_card_(automatic)            56.05             _            0  \n",
       "4331               Mailed_check            19.85             _            0  \n",
       "4687               Mailed_check            25.35             _            0  \n",
       "5104               Mailed_check            20.00             _            0  \n",
       "5719               Mailed_check            19.70             _            0  \n",
       "6772               Mailed_check            73.35             _            0  \n",
       "6840  Bank_transfer_(automatic)            61.90             _            0  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lets look at the column of df['Total_Charges']\n",
    "df[df['Total_Charges']==\"_\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "549ce452",
   "metadata": {},
   "outputs": [],
   "source": [
    "# replacing the colum Total_Charges with zero\n",
    "df['Total_Charges']= np.where(df['Total_Charges']==\"_\",0,df['Total_Charges'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ec00b1a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lets change the datatype of column Total_Charges to numeric\n",
    "df['Total_Charges']= pd.to_numeric(df['Total_Charges'])\n",
    "df['Total_Charges'].dtype\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f61e346",
   "metadata": {},
   "source": [
    "## EDA \n",
    "The preliminary analysis of data to discover relationships between measures in the data and to gain an insight on the trends, patterns, and relationships among various entities present in the data set with the help of statistics and visualization tools is called Exploratory Data Analysis (EDA). \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3956e1c9",
   "metadata": {},
   "source": [
    "### Univariate analysis:\n",
    "Uni means one and variate means variable, so in univariate analysis, there is only one dependable variable. The objective of univariate analysis is to derive the data, define and summarize it, and analyze the pattern present in it. In a dataset, it explores each variable separately. It is possible for two kinds of variables- Categorical and Numerical."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "fe275746",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0], dtype=int64)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Churn_Value'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "bdfa7d2a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Rohit Kumar (Prince)\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Churn_Value', ylabel='count'>"
      ]
     },
     "execution_count": 19,
     "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": [
    "# Lets visualize the Churn_Value column\n",
    "sns.countplot(df['Churn_Value'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "593affce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    5174\n",
       "1    1869\n",
       "Name: Churn_Value, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Churn_Value'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f20927f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "plt.pie(df['Churn_Value'].value_counts(), labels=df['Churn_Value'].unique(), autopct='%.2f%%')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cd1bb07",
   "metadata": {},
   "source": [
    "By looking at columns df['Churn_Value'] we conclude thata data are imbalance, we have to handle the imbalnce data before building the machine learning model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a037b027",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Rohit Kumar (Prince)\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Male counts Vs female count ')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5,5))\n",
    "sns.countplot(df.Gender)\n",
    "plt.title('Male counts Vs female count ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "97126a7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Histogram of Monthly_Charges')"
      ]
     },
     "execution_count": 23,
     "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": [
    "\n",
    "sns.histplot(df.Monthly_Charges)\n",
    "plt.title('Histogram of Monthly_Charges')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1165a2ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Rohit Kumar (Prince)\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Bar plot of Senior_Citizen ')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5,5))\n",
    "sns.countplot(df.Senior_Citizen)\n",
    "plt.title('Bar plot of Senior_Citizen ')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48554e7d",
   "metadata": {},
   "source": [
    "### Bivariate Analysis\n",
    "Bi means two and variate means variable, so here there are two variables. The analysis is related to cause and the relationship between the two variables. There are three types of bivariate analysis\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "34d81b3a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No     5901\n",
       "Yes    1142\n",
       "Name: Senior_Citizen, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# column Senior_Citizen\n",
    "df['Senior_Citizen'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ff516d13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2283bd9c280>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 576x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 790.625x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "sns.catplot(x='Gender', hue='Churn_Value', col='Senior_Citizen', kind='count', data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f5cdb33",
   "metadata": {},
   "source": [
    "From the visualization above, we can see that the churn and retain ratio for senior citizens, both male and female, is almost the same. Likewise, the churn and retain ratio for non-senior citizens. But if we look, the churn ratio has increased in senior citizens when compared to non-senior citizens."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "1ba860c7",
   "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>Senior_Citizen</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Churn_Value</th>\n",
       "      <th>Ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>No</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0.760616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>No</td>\n",
       "      <td>Female</td>\n",
       "      <td>1</td>\n",
       "      <td>0.239384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>No</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0.767192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>No</td>\n",
       "      <td>Male</td>\n",
       "      <td>1</td>\n",
       "      <td>0.232808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0.577465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Female</td>\n",
       "      <td>1</td>\n",
       "      <td>0.422535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0.588850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Male</td>\n",
       "      <td>1</td>\n",
       "      <td>0.411150</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Senior_Citizen  Gender  Churn_Value     Ratio\n",
       "0             No  Female            0  0.760616\n",
       "1             No  Female            1  0.239384\n",
       "2             No    Male            0  0.767192\n",
       "3             No    Male            1  0.232808\n",
       "4            Yes  Female            0  0.577465\n",
       "5            Yes  Female            1  0.422535\n",
       "6            Yes    Male            0  0.588850\n",
       "7            Yes    Male            1  0.411150"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "by_gender_senior = df.groupby(['Senior_Citizen', 'Gender'])['Churn_Value'].value_counts(normalize=True).to_frame().rename(columns={'Churn_Value': 'Ratio'}).reset_index().sort_values('Senior_Citizen')\n",
    "by_gender_senior\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "062789a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2283bd9df70>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 790.625x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x= 'Gender', hue= 'Churn_Value',col='Partner', kind= 'count', data= df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4073df68",
   "metadata": {},
   "source": [
    "From the visualization above, both female and male who do not have a partner are more likely to churn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "48be46b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 430.625x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x='Payment_Method', hue='Churn_Value', kind='count', data=df)\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "91ebc04e",
   "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_Value</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Payment_Method</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bank_transfer_(automatic)</th>\n",
       "      <td>0.832902</td>\n",
       "      <td>0.167098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Credit_card_(automatic)</th>\n",
       "      <td>0.847569</td>\n",
       "      <td>0.152431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Electronic_check</th>\n",
       "      <td>0.547146</td>\n",
       "      <td>0.452854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mailed_check</th>\n",
       "      <td>0.808933</td>\n",
       "      <td>0.191067</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Churn_Value                       0         1\n",
       "Payment_Method                               \n",
       "Bank_transfer_(automatic)  0.832902  0.167098\n",
       "Credit_card_(automatic)    0.847569  0.152431\n",
       "Electronic_check           0.547146  0.452854\n",
       "Mailed_check               0.808933  0.191067"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df['Payment_Method'], df['Churn_Value'], normalize=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "996f938e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Rohit Kumar (Prince)\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\Rohit Kumar (Prince)\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\Rohit Kumar (Prince)\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,5))\n",
    "plt.subplot(1,2,1)\n",
    "plt.title('Monthly Charge distribution')\n",
    "sns.distplot(df[df['Churn_Value'] == 1]['Monthly_Charges'], label='Churn')\n",
    "plt.subplot(1,2,2)\n",
    "plt.title('Monthly Charge distribution Split by Gender')\n",
    "sns.distplot(df[(df['Churn_Value'] == 1) & (df['Gender'] == 'Male')]['Monthly_Charges'], label='Male')\n",
    "sns.distplot(df[(df['Churn_Value'] == 1) & (df['Gender'] == 'Female')]['Monthly_Charges'], label='Female')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00177d71",
   "metadata": {},
   "source": [
    "From the visualization above, the odds between men and women to churn is almost the same in every level of monthly charges."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "08e316ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Senior_Citizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Tenure_Months</th>\n",
       "      <th>Phone_Service</th>\n",
       "      <th>Multiple_Lines</th>\n",
       "      <th>Internet_Service</th>\n",
       "      <th>Online_Security</th>\n",
       "      <th>Online_Backup</th>\n",
       "      <th>Device_Protection</th>\n",
       "      <th>Tech_Support</th>\n",
       "      <th>Streaming_TV</th>\n",
       "      <th>Streaming_Movies</th>\n",
       "      <th>Contract</th>\n",
       "      <th>Paperless_Billing</th>\n",
       "      <th>Payment_Method</th>\n",
       "      <th>Monthly_Charges</th>\n",
       "      <th>Total_Charges</th>\n",
       "      <th>Churn_Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed_check</td>\n",
       "      <td>53.85</td>\n",
       "      <td>108.15</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber_optic</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic_check</td>\n",
       "      <td>70.70</td>\n",
       "      <td>151.65</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>8</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber_optic</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic_check</td>\n",
       "      <td>99.65</td>\n",
       "      <td>820.50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>28</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber_optic</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic_check</td>\n",
       "      <td>104.80</td>\n",
       "      <td>3046.05</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>49</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber_optic</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Bank_transfer_(automatic)</td>\n",
       "      <td>103.70</td>\n",
       "      <td>5036.30</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender Senior_Citizen Partner Dependents  Tenure_Months Phone_Service  \\\n",
       "0    Male             No      No         No              2           Yes   \n",
       "1  Female             No      No        Yes              2           Yes   \n",
       "2  Female             No      No        Yes              8           Yes   \n",
       "3  Female             No     Yes        Yes             28           Yes   \n",
       "4    Male             No      No        Yes             49           Yes   \n",
       "\n",
       "  Multiple_Lines Internet_Service Online_Security Online_Backup  \\\n",
       "0             No              DSL             Yes           Yes   \n",
       "1             No      Fiber_optic              No            No   \n",
       "2            Yes      Fiber_optic              No            No   \n",
       "3            Yes      Fiber_optic              No            No   \n",
       "4            Yes      Fiber_optic              No           Yes   \n",
       "\n",
       "  Device_Protection Tech_Support Streaming_TV Streaming_Movies  \\\n",
       "0                No           No           No               No   \n",
       "1                No           No           No               No   \n",
       "2               Yes           No          Yes              Yes   \n",
       "3               Yes          Yes          Yes              Yes   \n",
       "4               Yes           No          Yes              Yes   \n",
       "\n",
       "         Contract Paperless_Billing             Payment_Method  \\\n",
       "0  Month-to-month               Yes               Mailed_check   \n",
       "1  Month-to-month               Yes           Electronic_check   \n",
       "2  Month-to-month               Yes           Electronic_check   \n",
       "3  Month-to-month               Yes           Electronic_check   \n",
       "4  Month-to-month               Yes  Bank_transfer_(automatic)   \n",
       "\n",
       "   Monthly_Charges  Total_Charges  Churn_Value  \n",
       "0            53.85         108.15            1  \n",
       "1            70.70         151.65            1  \n",
       "2            99.65         820.50            1  \n",
       "3           104.80        3046.05            1  \n",
       "4           103.70        5036.30            1  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "e4f98a8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Senior_Citizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Tenure_Months</th>\n",
       "      <th>Phone_Service</th>\n",
       "      <th>Multiple_Lines</th>\n",
       "      <th>Internet_Service</th>\n",
       "      <th>Online_Security</th>\n",
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       "      <th>Tech_Support</th>\n",
       "      <th>Streaming_TV</th>\n",
       "      <th>Streaming_Movies</th>\n",
       "      <th>Contract</th>\n",
       "      <th>Paperless_Billing</th>\n",
       "      <th>Payment_Method</th>\n",
       "      <th>Monthly_Charges</th>\n",
       "      <th>Total_Charges</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed_check</td>\n",
       "      <td>53.85</td>\n",
       "      <td>108.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber_optic</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic_check</td>\n",
       "      <td>70.70</td>\n",
       "      <td>151.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Female</td>\n",
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       "      <td>Yes</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic_check</td>\n",
       "      <td>99.65</td>\n",
       "      <td>820.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>28</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber_optic</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic_check</td>\n",
       "      <td>104.80</td>\n",
       "      <td>3046.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>49</td>\n",
       "      <td>Yes</td>\n",
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       "      <td>Fiber_optic</td>\n",
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       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Bank_transfer_(automatic)</td>\n",
       "      <td>103.70</td>\n",
       "      <td>5036.30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender Senior_Citizen Partner Dependents  Tenure_Months Phone_Service  \\\n",
       "0    Male             No      No         No              2           Yes   \n",
       "1  Female             No      No        Yes              2           Yes   \n",
       "2  Female             No      No        Yes              8           Yes   \n",
       "3  Female             No     Yes        Yes             28           Yes   \n",
       "4    Male             No      No        Yes             49           Yes   \n",
       "\n",
       "  Multiple_Lines Internet_Service Online_Security Online_Backup  \\\n",
       "0             No              DSL             Yes           Yes   \n",
       "1             No      Fiber_optic              No            No   \n",
       "2            Yes      Fiber_optic              No            No   \n",
       "3            Yes      Fiber_optic              No            No   \n",
       "4            Yes      Fiber_optic              No           Yes   \n",
       "\n",
       "  Device_Protection Tech_Support Streaming_TV Streaming_Movies  \\\n",
       "0                No           No           No               No   \n",
       "1                No           No           No               No   \n",
       "2               Yes           No          Yes              Yes   \n",
       "3               Yes          Yes          Yes              Yes   \n",
       "4               Yes           No          Yes              Yes   \n",
       "\n",
       "         Contract Paperless_Billing             Payment_Method  \\\n",
       "0  Month-to-month               Yes               Mailed_check   \n",
       "1  Month-to-month               Yes           Electronic_check   \n",
       "2  Month-to-month               Yes           Electronic_check   \n",
       "3  Month-to-month               Yes           Electronic_check   \n",
       "4  Month-to-month               Yes  Bank_transfer_(automatic)   \n",
       "\n",
       "   Monthly_Charges  Total_Charges  \n",
       "0            53.85         108.15  \n",
       "1            70.70         151.65  \n",
       "2            99.65         820.50  \n",
       "3           104.80        3046.05  \n",
       "4           103.70        5036.30  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating independent and depedent variable \n",
    "X= df.iloc[:,:-1] #Independent variable \n",
    "y= df['Churn_Value'] ## Dependent variable\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a58a1898",
   "metadata": {},
   "source": [
    "### Feature Engineering \n",
    "- ```One_hot_encoding``` will do for the nominal data:We cannot Rank the data, Example like zip-code\n",
    "- ```Lavel_encoding``` we do for ordinal data : we can give a rank , example like Grade, education status\n",
    "\n",
    "We doing here **One_hot_encoding** Because machine learning algorithm understand only numerical data, and we have some of predictor are categorical type data we will use ```get_dummies()```|"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "4f9caf67",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_encoded= pd.get_dummies(X,columns=['Gender','Senior_Citizen','Partner','Dependents','Phone_Service',\n",
    "                                    'Multiple_Lines','Internet_Service','Online_Security','Online_Backup',\n",
    "                                    'Device_Protection','Tech_Support','Streaming_TV','Contract','Paperless_Billing',\n",
    "                                    'Payment_Method','Streaming_Movies'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "9684c629",
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Tenure_Months  Monthly_Charges  Total_Charges  Gender_Female  Gender_Male  \\\n",
       "0              2            53.85         108.15              0            1   \n",
       "1              2            70.70         151.65              1            0   \n",
       "2              8            99.65         820.50              1            0   \n",
       "3             28           104.80        3046.05              1            0   \n",
       "4             49           103.70        5036.30              0            1   \n",
       "\n",
       "   Senior_Citizen_No  Senior_Citizen_Yes  Partner_No  Partner_Yes  \\\n",
       "0                  1                   0           1            0   \n",
       "1                  1                   0           1            0   \n",
       "2                  1                   0           1            0   \n",
       "3                  1                   0           0            1   \n",
       "4                  1                   0           1            0   \n",
       "\n",
       "   Dependents_No  Dependents_Yes  Phone_Service_No  Phone_Service_Yes  \\\n",
       "0              1               0                 0                  1   \n",
       "1              0               1                 0                  1   \n",
       "2              0               1                 0                  1   \n",
       "3              0               1                 0                  1   \n",
       "4              0               1                 0                  1   \n",
       "\n",
       "   Multiple_Lines_No  Multiple_Lines_No_phone_service  Multiple_Lines_Yes  \\\n",
       "0                  1                                0                   0   \n",
       "1                  1                                0                   0   \n",
       "2                  0                                0                   1   \n",
       "3                  0                                0                   1   \n",
       "4                  0                                0                   1   \n",
       "\n",
       "   Internet_Service_DSL  Internet_Service_Fiber_optic  Internet_Service_No  \\\n",
       "0                     1                             0                    0   \n",
       "1                     0                             1                    0   \n",
       "2                     0                             1                    0   \n",
       "3                     0                             1                    0   \n",
       "4                     0                             1                    0   \n",
       "\n",
       "   Online_Security_No  Online_Security_No_internet_service  \\\n",
       "0                   0                                    0   \n",
       "1                   1                                    0   \n",
       "2                   1                                    0   \n",
       "3                   1                                    0   \n",
       "4                   1                                    0   \n",
       "\n",
       "   Online_Security_Yes  Online_Backup_No  Online_Backup_No_internet_service  \\\n",
       "0                    1                 0                                  0   \n",
       "1                    0                 1                                  0   \n",
       "2                    0                 1                                  0   \n",
       "3                    0                 1                                  0   \n",
       "4                    0                 0                                  0   \n",
       "\n",
       "   Online_Backup_Yes  Device_Protection_No  \\\n",
       "0                  1                     1   \n",
       "1                  0                     1   \n",
       "2                  0                     0   \n",
       "3                  0                     0   \n",
       "4                  1                     0   \n",
       "\n",
       "   Device_Protection_No_internet_service  Device_Protection_Yes  \\\n",
       "0                                      0                      0   \n",
       "1                                      0                      0   \n",
       "2                                      0                      1   \n",
       "3                                      0                      1   \n",
       "4                                      0                      1   \n",
       "\n",
       "   Tech_Support_No  Tech_Support_No_internet_service  Tech_Support_Yes  \\\n",
       "0                1                                 0                 0   \n",
       "1                1                                 0                 0   \n",
       "2                1                                 0                 0   \n",
       "3                0                                 0                 1   \n",
       "4                1                                 0                 0   \n",
       "\n",
       "   Streaming_TV_No  Streaming_TV_No_internet_service  Streaming_TV_Yes  \\\n",
       "0                1                                 0                 0   \n",
       "1                1                                 0                 0   \n",
       "2                0                                 0                 1   \n",
       "3                0                                 0                 1   \n",
       "4                0                                 0                 1   \n",
       "\n",
       "   Contract_Month-to-month  Contract_One_year  Contract_Two_year  \\\n",
       "0                        1                  0                  0   \n",
       "1                        1                  0                  0   \n",
       "2                        1                  0                  0   \n",
       "3                        1                  0                  0   \n",
       "4                        1                  0                  0   \n",
       "\n",
       "   Paperless_Billing_No  Paperless_Billing_Yes  \\\n",
       "0                     0                      1   \n",
       "1                     0                      1   \n",
       "2                     0                      1   \n",
       "3                     0                      1   \n",
       "4                     0                      1   \n",
       "\n",
       "   Payment_Method_Bank_transfer_(automatic)  \\\n",
       "0                                         0   \n",
       "1                                         0   \n",
       "2                                         0   \n",
       "3                                         0   \n",
       "4                                         1   \n",
       "\n",
       "   Payment_Method_Credit_card_(automatic)  Payment_Method_Electronic_check  \\\n",
       "0                                       0                                0   \n",
       "1                                       0                                1   \n",
       "2                                       0                                1   \n",
       "3                                       0                                1   \n",
       "4                                       0                                0   \n",
       "\n",
       "   Payment_Method_Mailed_check  Streaming_Movies_No  \\\n",
       "0                            1                    1   \n",
       "1                            0                    1   \n",
       "2                            0                    0   \n",
       "3                            0                    0   \n",
       "4                            0                    0   \n",
       "\n",
       "   Streaming_Movies_No_internet_service  Streaming_Movies_Yes  \n",
       "0                                     0                     0  \n",
       "1                                     0                     0  \n",
       "2                                     0                     1  \n",
       "3                                     0                     1  \n",
       "4                                     0                     1  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_encoded.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "7cc1cf48",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7043, 46)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X=X_encoded.copy()\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f616b1d6",
   "metadata": {},
   "source": [
    "## Building a XGBoost model\n",
    "\n",
    "#### The XGBoost Advantage\n",
    "I’ve always admired the boosting capabilities that this algorithm infuses in a predictive model. When I explored more about its performance and science behind its high accuracy, I discovered many advantages:\n",
    "\n",
    "#### Regularization:\n",
    "- Standard GBM implementation has no regularization like XGBoost, therefore it also helps to reduce overfitting.\n",
    "- In fact, XGBoost is also known as a ‘regularized boosting‘ technique.\n",
    "\n",
    "\n",
    "We willsplit the data into traing and tesing set, but before going to split lets see how much percentage of people leaving the company, We will do simply by adding all the **y=1** values divided by total lenght of dtat "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "e0719a16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2653698707936959"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(y)/len(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0518489f",
   "metadata": {},
   "source": [
    "**So, see that only 27% of poeple are left the company, so it conclude the dataset are preety much imbalanced**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "0e2d8ef1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Spliting the data\n",
    "X_train,X_test,y_train,y_test= train_test_split(X,y,random_state=42,stratify=y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99697187",
   "metadata": {},
   "source": [
    "Now lets verify that ```stratify``` worked as expected."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a7df6ceb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2654297614539947"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(y_train)/len(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "d255d55e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.26519023282226006"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(y_test)/len(y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6ee63cf",
   "metadata": {},
   "source": [
    "verify that ```stratify``` worked as expected, y_train and y_test has same percentage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "8c071967",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lets build the Xgboost model without cross validation\n",
    "clf_xgb= xgb.XGBClassifier(objective='binary:logistic',max_depth=5,learning_rate= 0.1,\n",
    "                           colsample_bytree = 0.5,\n",
    "                           subsample=0.90, reg_lambda= 10,use_label_encoder=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "92a2ff07",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\tvalidation_0-aucpr:0.59281\n",
      "[1]\tvalidation_0-aucpr:0.60752\n",
      "[2]\tvalidation_0-aucpr:0.61600\n",
      "[3]\tvalidation_0-aucpr:0.64896\n",
      "[4]\tvalidation_0-aucpr:0.65489\n",
      "[5]\tvalidation_0-aucpr:0.65807\n",
      "[6]\tvalidation_0-aucpr:0.65623\n",
      "[7]\tvalidation_0-aucpr:0.65691\n",
      "[8]\tvalidation_0-aucpr:0.65602\n",
      "[9]\tvalidation_0-aucpr:0.65662\n",
      "[10]\tvalidation_0-aucpr:0.65586\n",
      "[11]\tvalidation_0-aucpr:0.64999\n",
      "[12]\tvalidation_0-aucpr:0.64981\n",
      "[13]\tvalidation_0-aucpr:0.65280\n",
      "[14]\tvalidation_0-aucpr:0.65484\n",
      "[15]\tvalidation_0-aucpr:0.65365\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "              colsample_bynode=1, colsample_bytree=0.5,\n",
       "              enable_categorical=False, gamma=0, gpu_id=-1,\n",
       "              importance_type=None, interaction_constraints='',\n",
       "              learning_rate=0.1, max_delta_step=0, max_depth=5,\n",
       "              min_child_weight=1, missing=nan, monotone_constraints='()',\n",
       "              n_estimators=100, n_jobs=8, num_parallel_tree=1, predictor='auto',\n",
       "              random_state=0, reg_alpha=0, reg_lambda=10, scale_pos_weight=1,\n",
       "              subsample=0.9, tree_method='exact', use_label_encoder=False,\n",
       "              validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#fitting the model\n",
    "# Here early_stopping_rounds=10: means if te prediction is not improving after building 10 more tree\n",
    "#if none of 10 build tree not improving the prediction then,it will stop\n",
    "clf_xgb.fit(X_train,y_train,early_stopping_rounds=10,eval_metric='aucpr',eval_set=[(X_test,y_test)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "09511043",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predicting the model\n",
    "y_pred= clf_xgb.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "1044c051",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x2283db0e250>"
      ]
     },
     "execution_count": 44,
     "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": [
    "# ploting the confusion matrics\n",
    "plot_confusion_matrix(clf_xgb,X_test,y_test,values_format='d',display_labels=['did not left','left'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b970e4e",
   "metadata": {},
   "source": [
    "Peopele who did not leave the company the model (91%) correctly classified. but not model not performing well with people who had left the company, only(48%) corrrectly classified. XGBoost has a parameter that ```scale_pos_weight``` that helps with imbalanced data. we try to improve the accuracy by using cross validation."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ead47b4",
   "metadata": {},
   "source": [
    "Now we will evaluate the model, since this dataset is completley imbalanced so we will get accuracy by  ```roc_auc_score```.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "4debdea1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of the model is: 0.7002066861051998\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.93      0.87      1294\n",
      "           1       0.70      0.48      0.56       467\n",
      "\n",
      "    accuracy                           0.81      1761\n",
      "   macro avg       0.76      0.70      0.72      1761\n",
      "weighted avg       0.79      0.81      0.79      1761\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('The accuracy of the model is:',roc_auc_score(y_test,y_pred))\n",
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2a70b63",
   "metadata": {},
   "source": [
    "## XGBoost Parameters\n",
    "\n",
    "##### max_depth [default=6]\n",
    "- The maximum depth of a tree, same as GBM.\n",
    "- Used to control over-fitting as higher depth will allow model to learn relations very specific to a particular sample.\n",
    "- Should be tuned using CV.\n",
    "- Typical values: 3-10\n",
    "\n",
    "##### max_leaf_nodes\n",
    "- The maximum number of terminal nodes or leaves in a tree.\n",
    "- Can be defined in place of max_depth. Since binary trees are created, a depth of ‘n’ would produce a maximum of 2^n leaves.\n",
    "- If this is defined, GBM will ignore max_depth.\n",
    "\n",
    "#### gamma [default=0]\n",
    "- A node is split only when the resulting split gives a positive reduction in the loss function. Gamma specifies the minimum loss reduction required to make a split.\n",
    "- Makes the algorithm conservative. The values can vary depending on the loss function and should be tuned.\n",
    "\n",
    "#### max_delta_step [default=0]\n",
    "- In maximum delta step we allow each tree’s weight estimation to be. If the value is set to 0, it means there is no constraint. If it is set to a positive value, it can help making the update step more conservative.\n",
    "- Usually this parameter is not needed, but it might help in logistic regression when class is extremely imbalanced.\n",
    "- This is generally not used but you can explore further if you wish.\n",
    "\n",
    "#### subsample [default=1]\n",
    "- Same as the subsample of GBM. Denotes the fraction of observations to be randomly samples for each tree.\n",
    "- Lower values make the algorithm more conservative and prevents overfitting but too small values might lead to under-fitting.\n",
    "- Typical values: 0.5-1\n",
    "\n",
    "#### colsample_bytree [default=1]\n",
    "- Similar to max_features in GBM. Denotes the fraction of columns to be randomly samples for each tree.\n",
    "- Typical values: 0.5-1\n",
    "\n",
    "#### colsample_bylevel [default=1]\n",
    "- Denotes the subsample ratio of columns for each split, in each level.\n",
    "- I don’t use this often because subsample and colsample_bytree will do the job for you. but you can explore further if you feel so.\n",
    "\n",
    "#### lambda [default=1]\n",
    "- L2 regularization term on weights (analogous to Ridge regression)\n",
    "- This used to handle the regularization part of XGBoost. Though many data scientists don’t use it often, it should be explored to reduce overfitting.\n",
    "\n",
    "#### alpha [default=0]\n",
    "- L1 regularization term on weight (analogous to Lasso regression)\n",
    "- Can be used in case of very high dimensionality so that the algorithm runs faster when implemented\n",
    "\n",
    "#### scale_pos_weight [default=1]\n",
    "- A value greater than 0 should be used in case of high class imbalance as it helps in faster convergence.\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45f2113a",
   "metadata": {},
   "source": [
    "### Gridsearch Cv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "c5c9456e",
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid={'max_depth':[3,4,5],\n",
    "            'gamma':[0,0.25,1.0],\n",
    "            'learning_rate':[0.1,0.5,1.0],\n",
    "            'reg_lambda':[1,10,20],\n",
    "            'scale_pos_weight':[1,3,5],\n",
    "    \n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "df2e27b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "optimal_param= GridSearchCV(estimator=xgb.XGBClassifier(objective='binary:logistic',seed=42,\n",
    "                                                        subsample=0.9,colsample_bytree=0.5,use_label_encoder=False),\n",
    "                            param_grid=param_grid,\n",
    "                            scoring='roc_auc',\n",
    "                            verbose=2, \n",
    "                            cv=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "fa87d7d6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 243 candidates, totalling 729 fits\n",
      "[CV] END gamma=0, learning_rate=0.1, max_depth=3, reg_lambda=1, scale_pos_weight=1; total time=   0.2s\n",
      "[CV] END gamma=0, learning_rate=0.1, max_depth=3, reg_lambda=1, scale_pos_weight=1; total time=   0.1s\n",
      "[CV] END gamma=0, learning_rate=0.1, max_depth=3, reg_lambda=1, scale_pos_weight=1; total time=   0.2s\n",
      "[CV] END gamma=0, learning_rate=0.1, max_depth=3, reg_lambda=1, scale_pos_weight=3; total time=   0.2s\n",
      "[CV] END gamma=0, learning_rate=0.1, max_depth=3, reg_lambda=1, scale_pos_weight=3; total time=   0.1s\n",
      "[CV] END gamma=0, learning_rate=0.1, max_depth=3, reg_lambda=1, scale_pos_weight=3; total time=   0.2s\n",
      "[CV] END gamma=0, learning_rate=0.1, max_depth=3, reg_lambda=1, scale_pos_weight=5; total time=   0.0s\n",
      "[CV] END gamma=0, learning_rate=0.1, max_depth=3, reg_lambda=1, scale_pos_weight=5; total time=   0.1s\n",
      "[CV] END gamma=0, learning_rate=0.1, max_depth=3, reg_lambda=1, scale_pos_weight=5; total time=   0.1s\n",
      "[CV] END gamma=0, learning_rate=0.1, max_depth=3, reg_lambda=10, scale_pos_weight=1; total time=   0.2s\n",
      "[CV] END gamma=0, learning_rate=0.1, max_depth=3, reg_lambda=10, scale_pos_weight=1; total time=   0.2s\n",
      "[CV] END gamma=0, learning_rate=0.1, max_depth=3, reg_lambda=10, scale_pos_weight=1; total time=   0.3s\n",
      "[CV] END gamma=0, learning_rate=0.1, max_depth=3, reg_lambda=10, scale_pos_weight=3; total time=   0.2s\n",
      "[CV] END gamma=0, learning_rate=0.1, max_depth=3, reg_lambda=10, scale_pos_weight=3; total time=   0.1s\n",
      "[CV] END gamma=0, learning_rate=0.1, max_depth=3, reg_lambda=10, scale_pos_weight=3; total time=   0.2s\n",
      "[CV] END gamma=0, learning_rate=0.1, max_depth=3, reg_lambda=10, scale_pos_weight=5; total time=   0.0s\n",
      "[CV] END gamma=0, learning_rate=0.1, max_depth=3, reg_lambda=10, scale_pos_weight=5; total time=   0.2s\n",
      "[CV] END gamma=0, learning_rate=0.1, max_depth=3, reg_lambda=10, scale_pos_weight=5; total time=   0.2s\n",
      "[CV] END gamma=0, learning_rate=0.1, max_depth=3, reg_lambda=20, scale_pos_weight=1; total time=   0.2s\n",
      "[CV] END gamma=0, learning_rate=0.1, max_depth=3, reg_lambda=20, scale_pos_weight=1; total time=   0.2s\n",
      "[CV] END gamma=0, learning_rate=0.1, max_depth=3, reg_lambda=20, scale_pos_weight=1; total time=   0.2s\n",
      "[CV] END gamma=0, learning_rate=0.1, max_depth=3, reg_lambda=20, scale_pos_weight=3; total time=   0.2s\n",
      "[CV] END gamma=0, learning_rate=0.1, max_depth=3, reg_lambda=20, scale_pos_weight=3; total time=   0.3s\n",
      "[CV] END gamma=0, learning_rate=0.1, max_depth=3, reg_lambda=20, scale_pos_weight=3; total time=   0.4s\n",
      "[CV] END gamma=0, learning_rate=0.1, max_depth=3, reg_lambda=20, scale_pos_weight=5; total time=   0.3s\n",
      "[CV] END gamma=0, learning_rate=0.1, max_depth=3, reg_lambda=20, scale_pos_weight=5; total time=   0.2s\n",
      "[CV] END gamma=0, learning_rate=0.1, max_depth=3, reg_lambda=20, scale_pos_weight=5; total time=   0.4s\n",
      "[CV] END gamma=0, learning_rate=0.1, max_depth=4, reg_lambda=1, scale_pos_weight=1; total time=   0.2s\n",
      "[CV] END gamma=0, learning_rate=0.1, max_depth=4, reg_lambda=1, scale_pos_weight=1; total time=   0.1s\n",
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     ]
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END gamma=0, learning_rate=0.1, max_depth=5, reg_lambda=20, scale_pos_weight=5; total time=   0.2s\n",
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     ]
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END gamma=0, learning_rate=1.0, max_depth=5, reg_lambda=20, scale_pos_weight=5; total time=   0.0s\n",
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     ]
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      "[CV] END gamma=0.25, learning_rate=0.1, max_depth=5, reg_lambda=20, scale_pos_weight=1; total time=   0.3s\n",
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     ]
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END gamma=0.25, learning_rate=0.5, max_depth=5, reg_lambda=20, scale_pos_weight=1; total time=   0.1s\n",
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     ]
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      "[CV] END gamma=0.25, learning_rate=1.0, max_depth=5, reg_lambda=10, scale_pos_weight=3; total time=   0.0s\n",
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     ]
    },
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END gamma=1.0, learning_rate=0.1, max_depth=5, reg_lambda=10, scale_pos_weight=1; total time=   0.4s\n",
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     ]
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      "[CV] END gamma=1.0, learning_rate=0.5, max_depth=5, reg_lambda=1, scale_pos_weight=5; total time=   0.0s\n",
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      "[CV] END gamma=1.0, learning_rate=1.0, max_depth=3, reg_lambda=1, scale_pos_weight=3; total time=   0.0s\n",
      "[CV] END gamma=1.0, learning_rate=1.0, max_depth=3, reg_lambda=1, scale_pos_weight=3; total time=   0.0s\n",
      "[CV] END gamma=1.0, learning_rate=1.0, max_depth=3, reg_lambda=1, scale_pos_weight=3; total time=   0.0s\n",
      "[CV] END gamma=1.0, learning_rate=1.0, max_depth=3, reg_lambda=1, scale_pos_weight=5; total time=   0.0s\n",
      "[CV] END gamma=1.0, learning_rate=1.0, max_depth=3, reg_lambda=1, scale_pos_weight=5; total time=   0.0s\n",
      "[CV] END gamma=1.0, learning_rate=1.0, max_depth=3, reg_lambda=1, scale_pos_weight=5; total time=   0.0s\n",
      "[CV] END gamma=1.0, learning_rate=1.0, max_depth=3, reg_lambda=10, scale_pos_weight=1; total time=   0.0s\n",
      "[CV] END gamma=1.0, learning_rate=1.0, max_depth=3, reg_lambda=10, scale_pos_weight=1; total time=   0.0s\n",
      "[CV] END gamma=1.0, learning_rate=1.0, max_depth=3, reg_lambda=10, scale_pos_weight=1; total time=   0.0s\n",
      "[CV] END gamma=1.0, learning_rate=1.0, max_depth=3, reg_lambda=10, scale_pos_weight=3; total time=   0.0s\n",
      "[CV] END gamma=1.0, learning_rate=1.0, max_depth=3, reg_lambda=10, scale_pos_weight=3; total time=   0.0s\n",
      "[CV] END gamma=1.0, learning_rate=1.0, max_depth=3, reg_lambda=10, scale_pos_weight=3; total time=   0.0s\n",
      "[CV] END gamma=1.0, learning_rate=1.0, max_depth=3, reg_lambda=10, scale_pos_weight=5; total time=   0.0s\n",
      "[CV] END gamma=1.0, learning_rate=1.0, max_depth=3, reg_lambda=10, scale_pos_weight=5; total time=   0.0s\n",
      "[CV] END gamma=1.0, learning_rate=1.0, max_depth=3, reg_lambda=10, scale_pos_weight=5; total time=   0.0s\n",
      "[CV] END gamma=1.0, learning_rate=1.0, max_depth=3, reg_lambda=20, scale_pos_weight=1; total time=   0.0s\n",
      "[CV] END gamma=1.0, learning_rate=1.0, max_depth=3, reg_lambda=20, scale_pos_weight=1; total time=   0.0s\n",
      "[CV] END gamma=1.0, learning_rate=1.0, max_depth=3, reg_lambda=20, scale_pos_weight=1; total time=   0.0s\n",
      "[CV] END gamma=1.0, learning_rate=1.0, max_depth=3, reg_lambda=20, scale_pos_weight=3; total time=   0.0s\n",
      "[CV] END gamma=1.0, learning_rate=1.0, max_depth=3, reg_lambda=20, scale_pos_weight=3; total time=   0.0s\n",
      "[CV] END gamma=1.0, learning_rate=1.0, max_depth=3, reg_lambda=20, scale_pos_weight=3; total time=   0.0s\n",
      "[CV] END gamma=1.0, learning_rate=1.0, max_depth=3, reg_lambda=20, scale_pos_weight=5; total time=   0.0s\n",
      "[CV] END gamma=1.0, learning_rate=1.0, max_depth=3, reg_lambda=20, scale_pos_weight=5; total time=   0.0s\n",
      "[CV] END gamma=1.0, learning_rate=1.0, max_depth=3, reg_lambda=20, scale_pos_weight=5; total time=   0.0s\n",
      "[CV] END gamma=1.0, learning_rate=1.0, max_depth=4, reg_lambda=1, scale_pos_weight=1; total time=   0.0s\n",
      "[CV] END gamma=1.0, learning_rate=1.0, max_depth=4, reg_lambda=1, scale_pos_weight=1; total time=   0.0s\n",
      "[CV] END gamma=1.0, learning_rate=1.0, max_depth=4, reg_lambda=1, scale_pos_weight=1; total time=   0.0s\n",
      "[CV] END gamma=1.0, learning_rate=1.0, max_depth=4, reg_lambda=1, scale_pos_weight=3; total time=   0.0s\n",
      "[CV] END gamma=1.0, learning_rate=1.0, max_depth=4, reg_lambda=1, scale_pos_weight=3; total time=   0.0s\n",
      "[CV] END gamma=1.0, learning_rate=1.0, max_depth=4, reg_lambda=1, scale_pos_weight=3; total time=   0.0s\n",
      "[CV] END gamma=1.0, learning_rate=1.0, max_depth=4, reg_lambda=1, scale_pos_weight=5; total time=   0.0s\n",
      "[CV] END gamma=1.0, learning_rate=1.0, max_depth=4, reg_lambda=1, scale_pos_weight=5; total time=   0.0s\n",
      "[CV] END gamma=1.0, learning_rate=1.0, max_depth=4, reg_lambda=1, scale_pos_weight=5; total time=   0.0s\n",
      "[CV] END gamma=1.0, learning_rate=1.0, max_depth=4, reg_lambda=10, scale_pos_weight=1; total time=   0.0s\n",
      "[CV] END gamma=1.0, learning_rate=1.0, max_depth=4, reg_lambda=10, scale_pos_weight=1; total time=   0.0s\n",
      "[CV] END gamma=1.0, learning_rate=1.0, max_depth=4, reg_lambda=10, scale_pos_weight=1; total time=   0.0s\n",
      "[CV] END gamma=1.0, learning_rate=1.0, max_depth=4, reg_lambda=10, scale_pos_weight=3; total time=   0.0s\n",
      "[CV] END gamma=1.0, learning_rate=1.0, max_depth=4, reg_lambda=10, scale_pos_weight=3; total time=   0.0s\n",
      "[CV] END gamma=1.0, learning_rate=1.0, max_depth=4, reg_lambda=10, scale_pos_weight=3; total time=   0.0s\n",
      "[CV] END gamma=1.0, learning_rate=1.0, max_depth=4, reg_lambda=10, scale_pos_weight=5; total time=   0.0s\n",
      "[CV] END gamma=1.0, learning_rate=1.0, max_depth=4, reg_lambda=10, scale_pos_weight=5; total time=   0.0s\n",
      "[CV] END gamma=1.0, learning_rate=1.0, max_depth=4, reg_lambda=10, scale_pos_weight=5; total time=   0.0s\n",
      "[CV] END gamma=1.0, learning_rate=1.0, max_depth=4, reg_lambda=20, scale_pos_weight=1; total time=   0.0s\n",
      "[CV] END gamma=1.0, learning_rate=1.0, max_depth=4, reg_lambda=20, scale_pos_weight=1; total time=   0.0s\n",
      "[CV] END gamma=1.0, learning_rate=1.0, max_depth=4, reg_lambda=20, scale_pos_weight=1; total time=   0.0s\n",
      "[CV] END gamma=1.0, learning_rate=1.0, max_depth=4, reg_lambda=20, scale_pos_weight=3; total time=   0.0s\n",
      "[CV] END gamma=1.0, learning_rate=1.0, max_depth=4, reg_lambda=20, scale_pos_weight=3; total time=   0.0s\n",
      "[CV] END gamma=1.0, learning_rate=1.0, max_depth=4, reg_lambda=20, scale_pos_weight=3; total time=   0.0s\n",
      "[CV] END gamma=1.0, learning_rate=1.0, max_depth=4, reg_lambda=20, scale_pos_weight=5; total time=   0.0s\n",
      "[CV] END gamma=1.0, learning_rate=1.0, max_depth=4, reg_lambda=20, scale_pos_weight=5; total time=   0.0s\n",
      "[CV] END gamma=1.0, learning_rate=1.0, max_depth=4, reg_lambda=20, scale_pos_weight=5; total time=   0.0s\n",
      "[CV] END gamma=1.0, learning_rate=1.0, max_depth=5, reg_lambda=1, scale_pos_weight=1; total time=   0.0s\n",
      "[CV] END gamma=1.0, learning_rate=1.0, max_depth=5, reg_lambda=1, scale_pos_weight=1; total time=   0.0s\n",
      "[CV] END gamma=1.0, learning_rate=1.0, max_depth=5, reg_lambda=1, scale_pos_weight=1; total time=   0.0s\n",
      "[CV] END gamma=1.0, learning_rate=1.0, max_depth=5, reg_lambda=1, scale_pos_weight=3; total time=   0.0s\n",
      "[CV] END gamma=1.0, learning_rate=1.0, max_depth=5, reg_lambda=1, scale_pos_weight=3; total time=   0.0s\n",
      "[CV] END gamma=1.0, learning_rate=1.0, max_depth=5, reg_lambda=1, scale_pos_weight=3; total time=   0.0s\n",
      "[CV] END gamma=1.0, learning_rate=1.0, max_depth=5, reg_lambda=1, scale_pos_weight=5; total time=   0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END gamma=1.0, learning_rate=1.0, max_depth=5, reg_lambda=1, scale_pos_weight=5; total time=   0.0s\n",
      "[CV] END gamma=1.0, learning_rate=1.0, max_depth=5, reg_lambda=1, scale_pos_weight=5; total time=   0.0s\n",
      "[CV] END gamma=1.0, learning_rate=1.0, max_depth=5, reg_lambda=10, scale_pos_weight=1; total time=   0.0s\n",
      "[CV] END gamma=1.0, learning_rate=1.0, max_depth=5, reg_lambda=10, scale_pos_weight=1; total time=   0.0s\n",
      "[CV] END gamma=1.0, learning_rate=1.0, max_depth=5, reg_lambda=10, scale_pos_weight=1; total time=   0.0s\n",
      "[CV] END gamma=1.0, learning_rate=1.0, max_depth=5, reg_lambda=10, scale_pos_weight=3; total time=   0.0s\n",
      "[CV] END gamma=1.0, learning_rate=1.0, max_depth=5, reg_lambda=10, scale_pos_weight=3; total time=   0.0s\n",
      "[CV] END gamma=1.0, learning_rate=1.0, max_depth=5, reg_lambda=10, scale_pos_weight=3; total time=   0.0s\n",
      "[CV] END gamma=1.0, learning_rate=1.0, max_depth=5, reg_lambda=10, scale_pos_weight=5; total time=   0.0s\n",
      "[CV] END gamma=1.0, learning_rate=1.0, max_depth=5, reg_lambda=10, scale_pos_weight=5; total time=   0.0s\n",
      "[CV] END gamma=1.0, learning_rate=1.0, max_depth=5, reg_lambda=10, scale_pos_weight=5; total time=   0.0s\n",
      "[CV] END gamma=1.0, learning_rate=1.0, max_depth=5, reg_lambda=20, scale_pos_weight=1; total time=   0.0s\n",
      "[CV] END gamma=1.0, learning_rate=1.0, max_depth=5, reg_lambda=20, scale_pos_weight=1; total time=   0.0s\n",
      "[CV] END gamma=1.0, learning_rate=1.0, max_depth=5, reg_lambda=20, scale_pos_weight=1; total time=   0.0s\n",
      "[CV] END gamma=1.0, learning_rate=1.0, max_depth=5, reg_lambda=20, scale_pos_weight=3; total time=   0.0s\n",
      "[CV] END gamma=1.0, learning_rate=1.0, max_depth=5, reg_lambda=20, scale_pos_weight=3; total time=   0.0s\n",
      "[CV] END gamma=1.0, learning_rate=1.0, max_depth=5, reg_lambda=20, scale_pos_weight=3; total time=   0.0s\n",
      "[CV] END gamma=1.0, learning_rate=1.0, max_depth=5, reg_lambda=20, scale_pos_weight=5; total time=   0.0s\n",
      "[CV] END gamma=1.0, learning_rate=1.0, max_depth=5, reg_lambda=20, scale_pos_weight=5; total time=   0.0s\n",
      "[CV] END gamma=1.0, learning_rate=1.0, max_depth=5, reg_lambda=20, scale_pos_weight=5; total time=   0.0s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=3,\n",
       "             estimator=XGBClassifier(base_score=None, booster=None,\n",
       "                                     colsample_bylevel=None,\n",
       "                                     colsample_bynode=None,\n",
       "                                     colsample_bytree=0.5,\n",
       "                                     enable_categorical=False, gamma=None,\n",
       "                                     gpu_id=None, importance_type=None,\n",
       "                                     interaction_constraints=None,\n",
       "                                     learning_rate=None, max_delta_step=None,\n",
       "                                     max_depth=None, min_child_weight=None,\n",
       "                                     missing=nan, monotone_constraints=None,\n",
       "                                     n...\n",
       "                                     num_parallel_tree=None, predictor=None,\n",
       "                                     random_state=None, reg_alpha=None,\n",
       "                                     reg_lambda=None, scale_pos_weight=None,\n",
       "                                     seed=42, subsample=0.9, tree_method=None,\n",
       "                                     use_label_encoder=False,\n",
       "                                     validate_parameters=None, verbosity=None),\n",
       "             param_grid={'gamma': [0, 0.25, 1.0],\n",
       "                         'learning_rate': [0.1, 0.5, 1.0],\n",
       "                         'max_depth': [3, 4, 5], 'reg_lambda': [1, 10, 20],\n",
       "                         'scale_pos_weight': [1, 3, 5]},\n",
       "             scoring='roc_auc', verbose=2)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fitting the gridsearch\n",
    "optimal_param.fit(X_train,y_train,early_stopping_rounds=10,eval_metric='auc',eval_set=[(X_test,y_test)],verbose=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "6ed9d502",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'gamma': 1.0, 'learning_rate': 0.1, 'max_depth': 3, 'reg_lambda': 10, 'scale_pos_weight': 3}\n"
     ]
    }
   ],
   "source": [
    "# Printing the best parameter\n",
    "print(optimal_param.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea29576a",
   "metadata": {},
   "source": [
    "### Building model, evaluating the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "556e4ebf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# After getting the best parameter, now we will biuld the final classifier model\n",
    "clf_classifier= xgb.XGBClassifier(objective='binary:logistic',\n",
    "                                  gamma=1.0,\n",
    "                                  learning_rate=0.1,\n",
    "                                  max_depth=3,\n",
    "                                  reg_lambda=10,\n",
    "                                  scale_pos_weight=3,\n",
    "                                  subsample=0.9, \n",
    "                                  colsample_bytree=0.50,\n",
    "                                  use_label_encoder=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "c99b201b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\tvalidation_0-aucpr:0.52166\n",
      "[1]\tvalidation_0-aucpr:0.61012\n",
      "[2]\tvalidation_0-aucpr:0.60563\n",
      "[3]\tvalidation_0-aucpr:0.61296\n",
      "[4]\tvalidation_0-aucpr:0.61505\n",
      "[5]\tvalidation_0-aucpr:0.61431\n",
      "[6]\tvalidation_0-aucpr:0.61931\n",
      "[7]\tvalidation_0-aucpr:0.62261\n",
      "[8]\tvalidation_0-aucpr:0.62296\n",
      "[9]\tvalidation_0-aucpr:0.62339\n",
      "[10]\tvalidation_0-aucpr:0.62399\n",
      "[11]\tvalidation_0-aucpr:0.62399\n",
      "[12]\tvalidation_0-aucpr:0.63023\n",
      "[13]\tvalidation_0-aucpr:0.62841\n",
      "[14]\tvalidation_0-aucpr:0.62948\n",
      "[15]\tvalidation_0-aucpr:0.63128\n",
      "[16]\tvalidation_0-aucpr:0.63139\n",
      "[17]\tvalidation_0-aucpr:0.63402\n",
      "[18]\tvalidation_0-aucpr:0.63455\n",
      "[19]\tvalidation_0-aucpr:0.63596\n",
      "[20]\tvalidation_0-aucpr:0.63609\n",
      "[21]\tvalidation_0-aucpr:0.63613\n",
      "[22]\tvalidation_0-aucpr:0.64345\n",
      "[23]\tvalidation_0-aucpr:0.64387\n",
      "[24]\tvalidation_0-aucpr:0.64503\n",
      "[25]\tvalidation_0-aucpr:0.64214\n",
      "[26]\tvalidation_0-aucpr:0.64540\n",
      "[27]\tvalidation_0-aucpr:0.65382\n",
      "[28]\tvalidation_0-aucpr:0.65461\n",
      "[29]\tvalidation_0-aucpr:0.65068\n",
      "[30]\tvalidation_0-aucpr:0.65604\n",
      "[31]\tvalidation_0-aucpr:0.65076\n",
      "[32]\tvalidation_0-aucpr:0.65813\n",
      "[33]\tvalidation_0-aucpr:0.65938\n",
      "[34]\tvalidation_0-aucpr:0.65879\n",
      "[35]\tvalidation_0-aucpr:0.65744\n",
      "[36]\tvalidation_0-aucpr:0.65745\n",
      "[37]\tvalidation_0-aucpr:0.65770\n",
      "[38]\tvalidation_0-aucpr:0.65694\n",
      "[39]\tvalidation_0-aucpr:0.66012\n",
      "[40]\tvalidation_0-aucpr:0.66146\n",
      "[41]\tvalidation_0-aucpr:0.66132\n",
      "[42]\tvalidation_0-aucpr:0.66213\n",
      "[43]\tvalidation_0-aucpr:0.66452\n",
      "[44]\tvalidation_0-aucpr:0.66503\n",
      "[45]\tvalidation_0-aucpr:0.66315\n",
      "[46]\tvalidation_0-aucpr:0.66482\n",
      "[47]\tvalidation_0-aucpr:0.66296\n",
      "[48]\tvalidation_0-aucpr:0.66334\n",
      "[49]\tvalidation_0-aucpr:0.66544\n",
      "[50]\tvalidation_0-aucpr:0.66479\n",
      "[51]\tvalidation_0-aucpr:0.66608\n",
      "[52]\tvalidation_0-aucpr:0.66654\n",
      "[53]\tvalidation_0-aucpr:0.66670\n",
      "[54]\tvalidation_0-aucpr:0.66610\n",
      "[55]\tvalidation_0-aucpr:0.66605\n",
      "[56]\tvalidation_0-aucpr:0.66577\n",
      "[57]\tvalidation_0-aucpr:0.66573\n",
      "[58]\tvalidation_0-aucpr:0.66505\n",
      "[59]\tvalidation_0-aucpr:0.66532\n",
      "[60]\tvalidation_0-aucpr:0.66404\n",
      "[61]\tvalidation_0-aucpr:0.66354\n",
      "[62]\tvalidation_0-aucpr:0.66487\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "              colsample_bynode=1, colsample_bytree=0.5,\n",
       "              enable_categorical=False, gamma=1.0, gpu_id=-1,\n",
       "              importance_type=None, interaction_constraints='',\n",
       "              learning_rate=0.1, max_delta_step=0, max_depth=3,\n",
       "              min_child_weight=1, missing=nan, monotone_constraints='()',\n",
       "              n_estimators=100, n_jobs=8, num_parallel_tree=1, predictor='auto',\n",
       "              random_state=0, reg_alpha=0, reg_lambda=10, scale_pos_weight=3,\n",
       "              subsample=0.9, tree_method='exact', use_label_encoder=False,\n",
       "              validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fitting the model\n",
    "clf_classifier.fit(X_train,y_train,early_stopping_rounds=10,eval_metric='aucpr',eval_set=[(X_test,y_test)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "f2816d2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predicting the model\n",
    "y_pred= clf_classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "0e89ffbd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x2283f17da00>"
      ]
     },
     "execution_count": 54,
     "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": [
    "# Plotting the confusion matrics\n",
    "plot_confusion_matrix(clf_classifier,X_test,y_test,values_format='d',display_labels=['did not leave','left'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8b738af",
   "metadata": {},
   "source": [
    "We see the total people **1294** and people did not leave the company **937**(72%) correctly classified.out of **467** and left the company **385**(83%) correctly classified. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "e54c49d6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of the model: 0.7742612088737676\n"
     ]
    }
   ],
   "source": [
    "# Priting the accuracy and the model and lassification report\n",
    "print('The accuracy of the model:',roc_auc_score(y_test,y_pred))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "2b615114",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      0.72      0.81      1294\n",
      "           1       0.52      0.82      0.64       467\n",
      "\n",
      "    accuracy                           0.75      1761\n",
      "   macro avg       0.72      0.77      0.72      1761\n",
      "weighted avg       0.81      0.75      0.76      1761\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "4ff4fc15",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.precision_recall_curve.PrecisionRecallDisplay at 0x2283d826bb0>"
      ]
     },
     "execution_count": 61,
     "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": [
    "from sklearn.metrics import plot_precision_recall_curve\n",
    "plot_precision_recall_curve(clf_classifier,X_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07ab4fcf",
   "metadata": {},
   "source": [
    "**We improe the correctly classified, who left the company from 52 % to 82%**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4146729",
   "metadata": {},
   "source": [
    "### Model XG Boost Summary\n",
    "\n",
    "Target Variable: Churn_Value\n",
    "\n",
    "- (Strong) Contract length: Month-to-month contracts churn much more than 1- or 2-year contracts\n",
    "- (Moderate) Internet services: Internet Fiber or DSL services churned worse than other services\n",
    "- (Moderate) Dependents: Customers supporting dependents (kids/elders) churned less\n",
    "- (Low) Paperless billing: Customers signing up for paperless billing churned more\n",
    "- (Low) Streams: Indicates whether customer is using streaming of TV or Movies on Internet service\n",
    "- (Low) Automatic payment: Customers with automatic payment set up churned less\n",
    "- (Low) Partner: Customers with a partner (married) churned less\n",
    "- (Low) Charges Index: Customers monthly charges indexed against average customer\n",
    "\n",
    "#### XG Boost Key Metrics (Averages)\n",
    "\n",
    "- 78% AUC indicates model is correct on nearly 4 out of 5 customers cases\n",
    "- 83% Recall means model predicts 83% of customer churn cases correctly\n",
    "- 54% Precision means model predicts nearly 1 in 2 false positives"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a571e04c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}