Sample_Skeleton.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# INSAID Hiring Exercise"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Important: Kindly go through the instructions mentioned below.\n",
    "\n",
    "- The Sheet is structured in **4 steps**:\n",
    "    1. Understanding data and manipulation\n",
    "    2. Data visualization\n",
    "    3. Implementing Machine Learning models(Note: It should be more than 1 algorithm)\n",
    "    4. Model Evaluation and concluding with the best of the model.\n",
    "    \n",
    "    \n",
    "    \n",
    "\n",
    "- Try to break the codes in the **simplest form** and use number of code block with **proper comments** to them\n",
    "- We are providing **h** different dataset to choose from(Note: You need to select any one of the dataset from this sample sheet only)\n",
    "- The **interview calls** will be made solely based on how good you apply the **concepts**.\n",
    "- Good Luck! Happy Coding!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# use these links to do so:\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "Churn_data = pd.read_csv('Churn.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customerID</th>\n",
       "      <th>gender</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>tenure</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>InternetService</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>...</th>\n",
       "      <th>DeviceProtection</th>\n",
       "      <th>TechSupport</th>\n",
       "      <th>StreamingTV</th>\n",
       "      <th>StreamingMovies</th>\n",
       "      <th>Contract</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>PaymentMethod</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7590-VHVEG</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>29.85</td>\n",
       "      <td>29.85</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5575-GNVDE</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>34</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>56.95</td>\n",
       "      <td>1889.5</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3668-QPYBK</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>53.85</td>\n",
       "      <td>108.15</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7795-CFOCW</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>45</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>42.30</td>\n",
       "      <td>1840.75</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9237-HQITU</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>70.70</td>\n",
       "      <td>151.65</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   customerID  gender  SeniorCitizen Partner Dependents  tenure PhoneService  \\\n",
       "0  7590-VHVEG  Female              0     Yes         No       1           No   \n",
       "1  5575-GNVDE    Male              0      No         No      34          Yes   \n",
       "2  3668-QPYBK    Male              0      No         No       2          Yes   \n",
       "3  7795-CFOCW    Male              0      No         No      45           No   \n",
       "4  9237-HQITU  Female              0      No         No       2          Yes   \n",
       "\n",
       "      MultipleLines InternetService OnlineSecurity  ... DeviceProtection  \\\n",
       "0  No phone service             DSL             No  ...               No   \n",
       "1                No             DSL            Yes  ...              Yes   \n",
       "2                No             DSL            Yes  ...               No   \n",
       "3  No phone service             DSL            Yes  ...              Yes   \n",
       "4                No     Fiber optic             No  ...               No   \n",
       "\n",
       "  TechSupport StreamingTV StreamingMovies        Contract PaperlessBilling  \\\n",
       "0          No          No              No  Month-to-month              Yes   \n",
       "1          No          No              No        One year               No   \n",
       "2          No          No              No  Month-to-month              Yes   \n",
       "3         Yes          No              No        One year               No   \n",
       "4          No          No              No  Month-to-month              Yes   \n",
       "\n",
       "               PaymentMethod MonthlyCharges  TotalCharges Churn  \n",
       "0           Electronic check          29.85         29.85    No  \n",
       "1               Mailed check          56.95        1889.5    No  \n",
       "2               Mailed check          53.85        108.15   Yes  \n",
       "3  Bank transfer (automatic)          42.30       1840.75    No  \n",
       "4           Electronic check          70.70        151.65   Yes  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Churn_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Understanding the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7043, 21)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Churn_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7043 entries, 0 to 7042\n",
      "Data columns (total 21 columns):\n",
      "customerID          7043 non-null object\n",
      "gender              7043 non-null object\n",
      "SeniorCitizen       7043 non-null int64\n",
      "Partner             7043 non-null object\n",
      "Dependents          7043 non-null object\n",
      "tenure              7043 non-null int64\n",
      "PhoneService        7043 non-null object\n",
      "MultipleLines       7043 non-null object\n",
      "InternetService     7043 non-null object\n",
      "OnlineSecurity      7043 non-null object\n",
      "OnlineBackup        7043 non-null object\n",
      "DeviceProtection    7043 non-null object\n",
      "TechSupport         7043 non-null object\n",
      "StreamingTV         7043 non-null object\n",
      "StreamingMovies     7043 non-null object\n",
      "Contract            7043 non-null object\n",
      "PaperlessBilling    7043 non-null object\n",
      "PaymentMethod       7043 non-null object\n",
      "MonthlyCharges      7043 non-null float64\n",
      "TotalCharges        7043 non-null object\n",
      "Churn               7043 non-null object\n",
      "dtypes: float64(1), int64(2), object(18)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "Churn_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customerID          0\n",
       "gender              0\n",
       "SeniorCitizen       0\n",
       "Partner             0\n",
       "Dependents          0\n",
       "tenure              0\n",
       "PhoneService        0\n",
       "MultipleLines       0\n",
       "InternetService     0\n",
       "OnlineSecurity      0\n",
       "OnlineBackup        0\n",
       "DeviceProtection    0\n",
       "TechSupport         0\n",
       "StreamingTV         0\n",
       "StreamingMovies     0\n",
       "Contract            0\n",
       "PaperlessBilling    0\n",
       "PaymentMethod       0\n",
       "MonthlyCharges      0\n",
       "TotalCharges        0\n",
       "Churn               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking if any values in the variables are null \n",
    "Churn_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['customerID', 'gender', 'SeniorCitizen', 'Partner', 'Dependents',\n",
       "       'tenure', 'PhoneService', 'MultipleLines', 'InternetService',\n",
       "       'OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport',\n",
       "       'StreamingTV', 'StreamingMovies', 'Contract', 'PaperlessBilling',\n",
       "       'PaymentMethod', 'MonthlyCharges', 'TotalCharges', 'Churn'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Churn_data.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Manipulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Removing missing values \n",
    "Churn_data.dropna(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Remove customer IDs from the data set\n",
    "Churn_data1 = Churn_data.iloc[:,1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Converting the target variable in a numeric variable\n",
    "Churn_data1['Churn'].replace(to_replace='Yes', value=1, inplace=True)\n",
    "Churn_data1['Churn'].replace(to_replace='No',  value=0, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>tenure</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>InternetService</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>OnlineBackup</th>\n",
       "      <th>DeviceProtection</th>\n",
       "      <th>TechSupport</th>\n",
       "      <th>StreamingTV</th>\n",
       "      <th>StreamingMovies</th>\n",
       "      <th>Contract</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>PaymentMethod</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>29.85</td>\n",
       "      <td>29.85</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>34</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>56.95</td>\n",
       "      <td>1889.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>53.85</td>\n",
       "      <td>108.15</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>45</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>42.30</td>\n",
       "      <td>1840.75</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>70.70</td>\n",
       "      <td>151.65</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender  SeniorCitizen Partner Dependents  tenure PhoneService  \\\n",
       "0  Female              0     Yes         No       1           No   \n",
       "1    Male              0      No         No      34          Yes   \n",
       "2    Male              0      No         No       2          Yes   \n",
       "3    Male              0      No         No      45           No   \n",
       "4  Female              0      No         No       2          Yes   \n",
       "\n",
       "      MultipleLines InternetService OnlineSecurity OnlineBackup  \\\n",
       "0  No phone service             DSL             No          Yes   \n",
       "1                No             DSL            Yes           No   \n",
       "2                No             DSL            Yes          Yes   \n",
       "3  No phone service             DSL            Yes           No   \n",
       "4                No     Fiber optic             No           No   \n",
       "\n",
       "  DeviceProtection TechSupport StreamingTV StreamingMovies        Contract  \\\n",
       "0               No          No          No              No  Month-to-month   \n",
       "1              Yes          No          No              No        One year   \n",
       "2               No          No          No              No  Month-to-month   \n",
       "3              Yes         Yes          No              No        One year   \n",
       "4               No          No          No              No  Month-to-month   \n",
       "\n",
       "  PaperlessBilling              PaymentMethod  MonthlyCharges TotalCharges  \\\n",
       "0              Yes           Electronic check           29.85        29.85   \n",
       "1               No               Mailed check           56.95       1889.5   \n",
       "2              Yes               Mailed check           53.85       108.15   \n",
       "3               No  Bank transfer (automatic)           42.30      1840.75   \n",
       "4              Yes           Electronic check           70.70       151.65   \n",
       "\n",
       "   Churn  \n",
       "0      0  \n",
       "1      0  \n",
       "2      1  \n",
       "3      0  \n",
       "4      1  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Churn_data1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Replacing spaces with null values in total charges column\n",
    "Churn_data1['TotalCharges'] = Churn_data1[\"TotalCharges\"].replace(\" \",np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Dropping null values from total charges column which contain .15% missing data \n",
    "Churn_data1 =Churn_data1[Churn_data[\"TotalCharges\"].notnull()]\n",
    "Churn_data1 = Churn_data1.reset_index()[Churn_data1.columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#convert to float type\n",
    "Churn_data1[\"TotalCharges\"] = Churn_data1[\"TotalCharges\"].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "#replace values\n",
    "Churn_data1[\"TotalCharges\"] = Churn_data1[\"TotalCharges\"].replace(\"nan\",0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>SeniorCitizen</th>\n",
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       "      <th>Dependents</th>\n",
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       "      <th>PaymentMethod</th>\n",
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       "      <th>TotalCharges</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
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       "      <td>56.95</td>\n",
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       "      <td>0</td>\n",
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       "      <td>3</td>\n",
       "      <td>53.85</td>\n",
       "      <td>108.15</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>42.30</td>\n",
       "      <td>1840.75</td>\n",
       "      <td>0</td>\n",
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       "      <th>4</th>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>70.70</td>\n",
       "      <td>151.65</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender  SeniorCitizen  Partner  Dependents  tenure  PhoneService  \\\n",
       "0       0              0        1           0       1             0   \n",
       "1       1              0        0           0      34             1   \n",
       "2       1              0        0           0       2             1   \n",
       "3       1              0        0           0      45             0   \n",
       "4       0              0        0           0       2             1   \n",
       "\n",
       "   MultipleLines  InternetService  OnlineSecurity  OnlineBackup  \\\n",
       "0              1                0               0             2   \n",
       "1              0                0               2             0   \n",
       "2              0                0               2             2   \n",
       "3              1                0               2             0   \n",
       "4              0                1               0             0   \n",
       "\n",
       "   DeviceProtection  TechSupport  StreamingTV  StreamingMovies  Contract  \\\n",
       "0                 0            0            0                0         0   \n",
       "1                 2            0            0                0         1   \n",
       "2                 0            0            0                0         0   \n",
       "3                 2            2            0                0         1   \n",
       "4                 0            0            0                0         0   \n",
       "\n",
       "   PaperlessBilling  PaymentMethod  MonthlyCharges  TotalCharges  Churn  \n",
       "0                 1              2           29.85         29.85      0  \n",
       "1                 0              3           56.95       1889.50      0  \n",
       "2                 1              3           53.85        108.15      1  \n",
       "3                 0              0           42.30       1840.75      0  \n",
       "4                 1              2           70.70        151.65      1  "
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Churn_data1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Let's convert all the categorical variables into numerical variables\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "list=['gender','Partner','Dependents','PhoneService','MultipleLines','InternetService','OnlineSecurity','OnlineBackup','DeviceProtection',\n",
    "       'TechSupport','StreamingTV','StreamingMovies','Contract','PaperlessBilling','PaymentMethod','TotalCharges']\n",
    "encoder = LabelEncoder()\n",
    "for i in list:\n",
    "    Churn_data1[i]=encoder.fit_transform(Churn_data1[i]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Churn</th>\n",
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       "      <td>3</td>\n",
       "      <td>53.85</td>\n",
       "      <td>536</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>42.30</td>\n",
       "      <td>3570</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>70.70</td>\n",
       "      <td>674</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender  SeniorCitizen  Partner  Dependents  tenure  PhoneService  \\\n",
       "0       0              0        1           0       1             0   \n",
       "1       1              0        0           0      34             1   \n",
       "2       1              0        0           0       2             1   \n",
       "3       1              0        0           0      45             0   \n",
       "4       0              0        0           0       2             1   \n",
       "\n",
       "   MultipleLines  InternetService  OnlineSecurity  OnlineBackup  \\\n",
       "0              1                0               0             2   \n",
       "1              0                0               2             0   \n",
       "2              0                0               2             2   \n",
       "3              1                0               2             0   \n",
       "4              0                1               0             0   \n",
       "\n",
       "   DeviceProtection  TechSupport  StreamingTV  StreamingMovies  Contract  \\\n",
       "0                 0            0            0                0         0   \n",
       "1                 2            0            0                0         1   \n",
       "2                 0            0            0                0         0   \n",
       "3                 2            2            0                0         1   \n",
       "4                 0            0            0                0         0   \n",
       "\n",
       "   PaperlessBilling  PaymentMethod  MonthlyCharges  TotalCharges  Churn  \n",
       "0                 1              2           29.85            74      0  \n",
       "1                 0              3           56.95          3624      0  \n",
       "2                 1              3           53.85           536      1  \n",
       "3                 0              0           42.30          3570      0  \n",
       "4                 1              2           70.70           674      1  "
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Churn_data1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "#analysing data like\n",
    "#GOAL : 2 classes Churners and non-Churners\n",
    "#which features are the driving factors for churn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "#checking the distribution of variables-normal distrubition(-1.96 to +1.96)\n",
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "skewness and Kurtosis for gender: -0.019026844201506316 and -1.9996379791997316\n",
      "skewness and Kurtosis for Partner: 0.06790791663616631 and -1.9953885148581358\n",
      "skewness and Kurtosis for Dependents: 0.8750121687324416 and -1.2343537045701498\n",
      "skewness and Kurtosis for PhoneService: -2.726572082066742 and 5.43419531870577\n",
      "skewness and Kurtosis for MultipleLines: 0.11869417122754078 and -1.877896163136919\n",
      "skewness and Kurtosis for InternetService: 0.2053797021050899 and -1.1455439228510642\n",
      "skewness and Kurtosis for OnlineSecurity: 0.41689625409816844 and -1.52073777435781\n",
      "skewness and Kurtosis for OnlineBackup: 0.18289142401475716 and -1.6845474279139756\n",
      "skewness and Kurtosis for DeviceProtection: 0.1868074171892961 and -1.6828642131633627\n",
      "skewness and Kurtosis for TechSupport: 0.40227880430729396 and -1.534763421989415\n",
      "skewness and Kurtosis for StreamingTV: 0.028479564059039238 and -1.7224591162360174\n",
      "skewness and Kurtosis for StreamingMovies: 0.014653534183632504 and -1.7231512084922107\n",
      "skewness and Kurtosis for Contract: 0.6308247103807392 and -1.27215841203629\n",
      "skewness and Kurtosis for PaperlessBilling: -0.37531579195390014 and -1.8591380563100173\n",
      "skewness and Kurtosis for PaymentMethod: -0.17009317978593452 and -1.211757804132143\n",
      "skewness and Kurtosis for MonthlyCharges: -0.2204774644391769 and -1.2572191009381857\n",
      "skewness and Kurtosis for TotalCharges: 0.05855287095371767 and -1.2299495066377608\n"
     ]
    }
   ],
   "source": [
    "list1=['gender','Partner','Dependents','PhoneService','MultipleLines','InternetService','OnlineSecurity','OnlineBackup','DeviceProtection','TechSupport',\n",
    "      'StreamingTV', 'StreamingMovies','Contract','PaperlessBilling','PaymentMethod','MonthlyCharges','TotalCharges']\n",
    "for i in list1:\n",
    "    print('skewness and Kurtosis for '+ str(i)+': '+str(stats.skew(Churn_data1[i]))+' and '+ str(stats.kurtosis(Churn_data1[i])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                    gender  SeniorCitizen   Partner  Dependents    tenure  \\\n",
      "gender            1.000000      -0.001874 -0.001808    0.010517  0.005106   \n",
      "SeniorCitizen    -0.001874       1.000000  0.016479   -0.211185  0.016567   \n",
      "Partner          -0.001808       0.016479  1.000000    0.452676  0.379697   \n",
      "Dependents        0.010517      -0.211185  0.452676    1.000000  0.159712   \n",
      "tenure            0.005106       0.016567  0.379697    0.159712  1.000000   \n",
      "PhoneService     -0.006488       0.008576  0.017706   -0.001762  0.008448   \n",
      "MultipleLines    -0.006739       0.146185  0.142410   -0.024991  0.343032   \n",
      "InternetService  -0.000863      -0.032310  0.000891    0.044590 -0.030359   \n",
      "OnlineSecurity   -0.015017      -0.128221  0.150828    0.152166  0.325468   \n",
      "OnlineBackup     -0.012057      -0.013632  0.153130    0.091015  0.370876   \n",
      "DeviceProtection  0.000549      -0.021398  0.166330    0.080537  0.371105   \n",
      "TechSupport      -0.006825      -0.151268  0.126733    0.133524  0.322942   \n",
      "StreamingTV      -0.006421       0.030776  0.137341    0.046885  0.289373   \n",
      "StreamingMovies  -0.008743       0.047266  0.129574    0.021321  0.296866   \n",
      "Contract          0.000126      -0.142554  0.294806    0.243187  0.671607   \n",
      "PaperlessBilling -0.011754       0.156530 -0.014877   -0.111377  0.006152   \n",
      "PaymentMethod     0.017352      -0.038551 -0.154798   -0.040292 -0.370436   \n",
      "MonthlyCharges   -0.014569       0.220173  0.096848   -0.113890  0.247900   \n",
      "TotalCharges     -0.000351       0.105713  0.343055    0.085493  0.864745   \n",
      "Churn            -0.008612       0.150889 -0.150448   -0.164221 -0.352229   \n",
      "\n",
      "                  PhoneService  MultipleLines  InternetService  \\\n",
      "gender               -0.006488      -0.006739        -0.000863   \n",
      "SeniorCitizen         0.008576       0.146185        -0.032310   \n",
      "Partner               0.017706       0.142410         0.000891   \n",
      "Dependents           -0.001762      -0.024991         0.044590   \n",
      "tenure                0.008448       0.343032        -0.030359   \n",
      "PhoneService          1.000000      -0.020538         0.387436   \n",
      "MultipleLines        -0.020538       1.000000        -0.109216   \n",
      "InternetService       0.387436      -0.109216         1.000000   \n",
      "OnlineSecurity       -0.015198       0.007141        -0.028416   \n",
      "OnlineBackup          0.024105       0.117327         0.036138   \n",
      "DeviceProtection      0.003727       0.122318         0.044944   \n",
      "TechSupport          -0.019158       0.011466        -0.026047   \n",
      "StreamingTV           0.055353       0.175059         0.107417   \n",
      "StreamingMovies       0.043870       0.180957         0.098350   \n",
      "Contract              0.002247       0.110842         0.099721   \n",
      "PaperlessBilling      0.016505       0.165146        -0.138625   \n",
      "PaymentMethod        -0.004184      -0.176793         0.086140   \n",
      "MonthlyCharges        0.247398       0.433576        -0.323260   \n",
      "TotalCharges          0.084815       0.461989        -0.208125   \n",
      "Churn                 0.011942       0.038037        -0.047291   \n",
      "\n",
      "                  OnlineSecurity  OnlineBackup  DeviceProtection  TechSupport  \\\n",
      "gender                 -0.015017     -0.012057          0.000549    -0.006825   \n",
      "SeniorCitizen          -0.128221     -0.013632         -0.021398    -0.151268   \n",
      "Partner                 0.150828      0.153130          0.166330     0.126733   \n",
      "Dependents              0.152166      0.091015          0.080537     0.133524   \n",
      "tenure                  0.325468      0.370876          0.371105     0.322942   \n",
      "PhoneService           -0.015198      0.024105          0.003727    -0.019158   \n",
      "MultipleLines           0.007141      0.117327          0.122318     0.011466   \n",
      "InternetService        -0.028416      0.036138          0.044944    -0.026047   \n",
      "OnlineSecurity          1.000000      0.185126          0.175985     0.285028   \n",
      "OnlineBackup            0.185126      1.000000          0.187757     0.195748   \n",
      "DeviceProtection        0.175985      0.187757          1.000000     0.240593   \n",
      "TechSupport             0.285028      0.195748          0.240593     1.000000   \n",
      "StreamingTV             0.044669      0.147186          0.276652     0.161305   \n",
      "StreamingMovies         0.055954      0.136722          0.288799     0.161316   \n",
      "Contract                0.374416      0.280980          0.350277     0.425367   \n",
      "PaperlessBilling       -0.157641     -0.013370         -0.038234    -0.113600   \n",
      "PaymentMethod          -0.096726     -0.124847         -0.135750    -0.104670   \n",
      "MonthlyCharges         -0.053878      0.119777          0.163652    -0.008682   \n",
      "TotalCharges            0.247784      0.352561          0.370704     0.263741   \n",
      "Churn                  -0.289309     -0.195525         -0.178134    -0.282492   \n",
      "\n",
      "                  StreamingTV  StreamingMovies  Contract  PaperlessBilling  \\\n",
      "gender              -0.006421        -0.008743  0.000126         -0.011754   \n",
      "SeniorCitizen        0.030776         0.047266 -0.142554          0.156530   \n",
      "Partner              0.137341         0.129574  0.294806         -0.014877   \n",
      "Dependents           0.046885         0.021321  0.243187         -0.111377   \n",
      "tenure               0.289373         0.296866  0.671607          0.006152   \n",
      "PhoneService         0.055353         0.043870  0.002247          0.016505   \n",
      "MultipleLines        0.175059         0.180957  0.110842          0.165146   \n",
      "InternetService      0.107417         0.098350  0.099721         -0.138625   \n",
      "OnlineSecurity       0.044669         0.055954  0.374416         -0.157641   \n",
      "OnlineBackup         0.147186         0.136722  0.280980         -0.013370   \n",
      "DeviceProtection     0.276652         0.288799  0.350277         -0.038234   \n",
      "TechSupport          0.161305         0.161316  0.425367         -0.113600   \n",
      "StreamingTV          1.000000         0.434772  0.227116          0.096642   \n",
      "StreamingMovies      0.434772         1.000000  0.231226          0.083700   \n",
      "Contract             0.227116         0.231226  1.000000         -0.176733   \n",
      "PaperlessBilling     0.096642         0.083700 -0.176733          1.000000   \n",
      "PaymentMethod       -0.104234        -0.111241 -0.227543         -0.062904   \n",
      "MonthlyCharges       0.336706         0.335459 -0.074195          0.352150   \n",
      "TotalCharges         0.367744         0.371821  0.458165          0.151699   \n",
      "Churn               -0.036581        -0.038492 -0.396713          0.191825   \n",
      "\n",
      "                  PaymentMethod  MonthlyCharges  TotalCharges     Churn  \n",
      "gender                 0.017352       -0.014569     -0.000351 -0.008612  \n",
      "SeniorCitizen         -0.038551        0.220173      0.105713  0.150889  \n",
      "Partner               -0.154798        0.096848      0.343055 -0.150448  \n",
      "Dependents            -0.040292       -0.113890      0.085493 -0.164221  \n",
      "tenure                -0.370436        0.247900      0.864745 -0.352229  \n",
      "PhoneService          -0.004184        0.247398      0.084815  0.011942  \n",
      "MultipleLines         -0.176793        0.433576      0.461989  0.038037  \n",
      "InternetService        0.086140       -0.323260     -0.208125 -0.047291  \n",
      "OnlineSecurity        -0.096726       -0.053878      0.247784 -0.289309  \n",
      "OnlineBackup          -0.124847        0.119777      0.352561 -0.195525  \n",
      "DeviceProtection      -0.135750        0.163652      0.370704 -0.178134  \n",
      "TechSupport           -0.104670       -0.008682      0.263741 -0.282492  \n",
      "StreamingTV           -0.104234        0.336706      0.367744 -0.036581  \n",
      "StreamingMovies       -0.111241        0.335459      0.371821 -0.038492  \n",
      "Contract              -0.227543       -0.074195      0.458165 -0.396713  \n",
      "PaperlessBilling      -0.062904        0.352150      0.151699  0.191825  \n",
      "PaymentMethod          1.000000       -0.193407     -0.365031  0.107062  \n",
      "MonthlyCharges        -0.193407        1.000000      0.623193  0.193356  \n",
      "TotalCharges          -0.365031        0.623193      1.000000 -0.231873  \n",
      "Churn                  0.107062        0.193356     -0.231873  1.000000  \n"
     ]
    }
   ],
   "source": [
    "print(Churn_data1.corr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "#conclusion: we can see the Normality for PhoneService calls is not statisfied\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#checking the Gender distribution- it is normal\n",
    "print(sns.distplot([Churn_data1.gender]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "#both male and female is equally distrubuted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method Axis.set_major_formatter of <matplotlib.axis.YAxis object at 0x000001C0D35D13C8>>"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_d = (Churn_data1['SeniorCitizen'].value_counts()*100.0 /len(Churn_data1))\\\n",
    ".plot.pie(autopct='%.4f%%', labels = ['No', 'Yes'],figsize =(5,5), fontsize = 12 )                                                                           \n",
    "plot_d.yaxis.set_major_formatter\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "#83% of people are younger and 16% people are seniorcitizens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c0d7cab748>"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='Churn',y='TotalCharges',data=Churn_data1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c0d7d47550>"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='Churn',y='PhoneService',data=Churn_data1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#too much of outlayers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c0d7dd3f60>"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='Churn',y='tenure',data=Churn_data1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#churn when compared with tenure has an outlier\n",
    " It seems that there is higer churn when the total charges are lower."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '# of Customers by Contract Type')"
      ]
     },
     "execution_count": 122,
     "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": [
    "CCn = Churn_data1['Contract'].value_counts().plot(kind = 'bar',rot = 0, width = 0.3)\n",
    "CCn.set_ylabel('# of Customers')\n",
    "CCn.set_title('# of Customers by Contract Type')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#As we can see from this graph most of the customers are in the month to month contract. While there are equal number of customers in the 1 year and 2 year contracts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x864 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "services_data = ['PhoneService','MultipleLines','InternetService','OnlineSecurity',\n",
    "           'OnlineBackup','DeviceProtection','TechSupport','StreamingTV','StreamingMovies']\n",
    "\n",
    "fig, axes = plt.subplots(nrows = 3,ncols = 3,figsize = (15,12))\n",
    "for i, item in enumerate(services_data):\n",
    "    if i < 3:\n",
    "        ax = Churn_data1[item].value_counts().plot(kind = 'bar',ax=axes[i,0],rot = 0)\n",
    "        \n",
    "    elif i >=3 and i < 6:\n",
    "        ax = Churn_data1[item].value_counts().plot(kind = 'bar',ax=axes[i-3,1],rot = 0)\n",
    "        \n",
    "    elif i < 9:\n",
    "        ax = Churn_data1[item].value_counts().plot(kind = 'bar',ax=axes[i-6,2],rot = 0)\n",
    "    ax.set_title(item)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "most of the monthly contracts last for 1-2 months, while the 2 year contracts tend to last for about 70 months. This shows that the customers taking a longer contract are more loyal to the company and tend to stay with it for a longer period of time.\n",
    "This is also what we saw in the earlier chart on correlation with the churn rate."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "some of the analysis is done in exel using pivot tables and a copy will be sent"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implement Machine Learning Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>tenure</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>InternetService</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>OnlineBackup</th>\n",
       "      <th>DeviceProtection</th>\n",
       "      <th>TechSupport</th>\n",
       "      <th>StreamingTV</th>\n",
       "      <th>StreamingMovies</th>\n",
       "      <th>Contract</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>PaymentMethod</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7043.000000</td>\n",
       "      <td>7043.000000</td>\n",
       "      <td>7043.000000</td>\n",
       "      <td>7043.000000</td>\n",
       "      <td>7043.000000</td>\n",
       "      <td>7043.000000</td>\n",
       "      <td>7043.000000</td>\n",
       "      <td>7043.000000</td>\n",
       "      <td>7043.000000</td>\n",
       "      <td>7043.000000</td>\n",
       "      <td>7043.000000</td>\n",
       "      <td>7043.000000</td>\n",
       "      <td>7043.000000</td>\n",
       "      <td>7043.000000</td>\n",
       "      <td>7043.000000</td>\n",
       "      <td>7043.000000</td>\n",
       "      <td>7043.000000</td>\n",
       "      <td>7043.000000</td>\n",
       "      <td>7043.000000</td>\n",
       "      <td>7043.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.504756</td>\n",
       "      <td>0.162147</td>\n",
       "      <td>0.483033</td>\n",
       "      <td>0.299588</td>\n",
       "      <td>32.371149</td>\n",
       "      <td>0.903166</td>\n",
       "      <td>0.940508</td>\n",
       "      <td>0.872923</td>\n",
       "      <td>0.790004</td>\n",
       "      <td>0.906432</td>\n",
       "      <td>0.904444</td>\n",
       "      <td>0.797104</td>\n",
       "      <td>0.985376</td>\n",
       "      <td>0.992475</td>\n",
       "      <td>0.690473</td>\n",
       "      <td>0.592219</td>\n",
       "      <td>1.574329</td>\n",
       "      <td>64.761692</td>\n",
       "      <td>3106.343888</td>\n",
       "      <td>0.265370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.500013</td>\n",
       "      <td>0.368612</td>\n",
       "      <td>0.499748</td>\n",
       "      <td>0.458110</td>\n",
       "      <td>24.559481</td>\n",
       "      <td>0.295752</td>\n",
       "      <td>0.948554</td>\n",
       "      <td>0.737796</td>\n",
       "      <td>0.859848</td>\n",
       "      <td>0.880162</td>\n",
       "      <td>0.879949</td>\n",
       "      <td>0.861551</td>\n",
       "      <td>0.885002</td>\n",
       "      <td>0.885091</td>\n",
       "      <td>0.833755</td>\n",
       "      <td>0.491457</td>\n",
       "      <td>1.068104</td>\n",
       "      <td>30.090047</td>\n",
       "      <td>1945.973288</td>\n",
       "      <td>0.441561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>18.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>35.500000</td>\n",
       "      <td>1382.500000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>70.350000</td>\n",
       "      <td>3070.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>89.850000</td>\n",
       "      <td>4790.500000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>118.750000</td>\n",
       "      <td>6540.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            gender  SeniorCitizen      Partner   Dependents       tenure  \\\n",
       "count  7043.000000    7043.000000  7043.000000  7043.000000  7043.000000   \n",
       "mean      0.504756       0.162147     0.483033     0.299588    32.371149   \n",
       "std       0.500013       0.368612     0.499748     0.458110    24.559481   \n",
       "min       0.000000       0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.000000       0.000000     0.000000     0.000000     9.000000   \n",
       "50%       1.000000       0.000000     0.000000     0.000000    29.000000   \n",
       "75%       1.000000       0.000000     1.000000     1.000000    55.000000   \n",
       "max       1.000000       1.000000     1.000000     1.000000    72.000000   \n",
       "\n",
       "       PhoneService  MultipleLines  InternetService  OnlineSecurity  \\\n",
       "count   7043.000000    7043.000000      7043.000000     7043.000000   \n",
       "mean       0.903166       0.940508         0.872923        0.790004   \n",
       "std        0.295752       0.948554         0.737796        0.859848   \n",
       "min        0.000000       0.000000         0.000000        0.000000   \n",
       "25%        1.000000       0.000000         0.000000        0.000000   \n",
       "50%        1.000000       1.000000         1.000000        1.000000   \n",
       "75%        1.000000       2.000000         1.000000        2.000000   \n",
       "max        1.000000       2.000000         2.000000        2.000000   \n",
       "\n",
       "       OnlineBackup  DeviceProtection  TechSupport  StreamingTV  \\\n",
       "count   7043.000000       7043.000000  7043.000000  7043.000000   \n",
       "mean       0.906432          0.904444     0.797104     0.985376   \n",
       "std        0.880162          0.879949     0.861551     0.885002   \n",
       "min        0.000000          0.000000     0.000000     0.000000   \n",
       "25%        0.000000          0.000000     0.000000     0.000000   \n",
       "50%        1.000000          1.000000     1.000000     1.000000   \n",
       "75%        2.000000          2.000000     2.000000     2.000000   \n",
       "max        2.000000          2.000000     2.000000     2.000000   \n",
       "\n",
       "       StreamingMovies     Contract  PaperlessBilling  PaymentMethod  \\\n",
       "count      7043.000000  7043.000000       7043.000000    7043.000000   \n",
       "mean          0.992475     0.690473          0.592219       1.574329   \n",
       "std           0.885091     0.833755          0.491457       1.068104   \n",
       "min           0.000000     0.000000          0.000000       0.000000   \n",
       "25%           0.000000     0.000000          0.000000       1.000000   \n",
       "50%           1.000000     0.000000          1.000000       2.000000   \n",
       "75%           2.000000     1.000000          1.000000       2.000000   \n",
       "max           2.000000     2.000000          1.000000       3.000000   \n",
       "\n",
       "       MonthlyCharges  TotalCharges        Churn  \n",
       "count     7043.000000   7043.000000  7043.000000  \n",
       "mean        64.761692   3106.343888     0.265370  \n",
       "std         30.090047   1945.973288     0.441561  \n",
       "min         18.250000      0.000000     0.000000  \n",
       "25%         35.500000   1382.500000     0.000000  \n",
       "50%         70.350000   3070.000000     0.000000  \n",
       "75%         89.850000   4790.500000     1.000000  \n",
       "max        118.750000   6540.000000     1.000000  "
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Scaling of the features\n",
    "#Lets check the variance in magnitude of the features\n",
    "Churn_data1.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "#to scale import standardizer\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>tenure</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>InternetService</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>OnlineBackup</th>\n",
       "      <th>DeviceProtection</th>\n",
       "      <th>TechSupport</th>\n",
       "      <th>StreamingTV</th>\n",
       "      <th>StreamingMovies</th>\n",
       "      <th>Contract</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>PaymentMethod</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</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",
       "      <td>2</td>\n",
       "      <td>29.85</td>\n",
       "      <td>74</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>56.95</td>\n",
       "      <td>3624</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</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",
       "      <td>3</td>\n",
       "      <td>53.85</td>\n",
       "      <td>536</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>42.30</td>\n",
       "      <td>3570</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "      <td>2</td>\n",
       "      <td>70.70</td>\n",
       "      <td>674</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender  SeniorCitizen  Partner  Dependents  tenure  PhoneService  \\\n",
       "0       0              0        1           0       1             0   \n",
       "1       1              0        0           0      34             1   \n",
       "2       1              0        0           0       2             1   \n",
       "3       1              0        0           0      45             0   \n",
       "4       0              0        0           0       2             1   \n",
       "\n",
       "   MultipleLines  InternetService  OnlineSecurity  OnlineBackup  \\\n",
       "0              1                0               0             2   \n",
       "1              0                0               2             0   \n",
       "2              0                0               2             2   \n",
       "3              1                0               2             0   \n",
       "4              0                1               0             0   \n",
       "\n",
       "   DeviceProtection  TechSupport  StreamingTV  StreamingMovies  Contract  \\\n",
       "0                 0            0            0                0         0   \n",
       "1                 2            0            0                0         1   \n",
       "2                 0            0            0                0         0   \n",
       "3                 2            2            0                0         1   \n",
       "4                 0            0            0                0         0   \n",
       "\n",
       "   PaperlessBilling  PaymentMethod  MonthlyCharges  TotalCharges  Churn  \n",
       "0                 1              2           29.85            74      0  \n",
       "1                 0              3           56.95          3624      0  \n",
       "2                 1              3           53.85           536      1  \n",
       "3                 0              0           42.30          3570      0  \n",
       "4                 1              2           70.70           674      1  "
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Churn_data1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "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",
      "gender              7043 non-null int64\n",
      "SeniorCitizen       7043 non-null int64\n",
      "Partner             7043 non-null int64\n",
      "Dependents          7043 non-null int64\n",
      "tenure              7043 non-null int64\n",
      "PhoneService        7043 non-null int64\n",
      "MultipleLines       7043 non-null int64\n",
      "InternetService     7043 non-null int64\n",
      "OnlineSecurity      7043 non-null int64\n",
      "OnlineBackup        7043 non-null int64\n",
      "DeviceProtection    7043 non-null int64\n",
      "TechSupport         7043 non-null int64\n",
      "StreamingTV         7043 non-null int64\n",
      "StreamingMovies     7043 non-null int64\n",
      "Contract            7043 non-null int64\n",
      "PaperlessBilling    7043 non-null int64\n",
      "PaymentMethod       7043 non-null int64\n",
      "MonthlyCharges      7043 non-null float64\n",
      "TotalCharges        7043 non-null int64\n",
      "Churn               7043 non-null int64\n",
      "dtypes: float64(1), int64(19)\n",
      "memory usage: 1.1 MB\n"
     ]
    }
   ],
   "source": [
    "Churn_data1.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "#scaling on the quantitative data\n",
    "# We will use the data frame where we had created dummy variables\n",
    "y = Churn_data1['Churn'].values\n",
    "X = Churn_data1.drop(columns = ['Churn'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scaling all the variables to a range of 0 to 1\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "features = X.columns.values\n",
    "scaler = MinMaxScaler(feature_range = (0,1))\n",
    "scaler.fit(X)\n",
    "X = pd.DataFrame(scaler.transform(X))\n",
    "X.columns = features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Training the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create Train & Test Data\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of x_train: (4930, 19)\n",
      "shape of y_train: (2113, 19)\n"
     ]
    }
   ],
   "source": [
    "print('shape of x_train:',X_train.shape)\n",
    "print('shape of y_train:',X_test.shape)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Applying Logestic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "# Running logistic regression model\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "model = LogisticRegression()\n",
    "result = model.fit(X_train, y_train)\n",
    "from sklearn import metrics\n",
    "prediction_test = model.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7950780880265026\n"
     ]
    }
   ],
   "source": [
    "# Print the prediction accuracy\n",
    "print (metrics.accuracy_score(y_test, prediction_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#To get the weights of all the variables\n",
    "weights = pd.Series(model.coef_[0],\n",
    "                 index=X.columns.values)\n",
    "print (weights.sort_values(ascending = False)[:10].plot(kind='bar'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Applying Random Forest\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "                       max_depth=None, max_features='auto', max_leaf_nodes=30,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, n_estimators=1000,\n",
       "                       n_jobs=-1, oob_score=True, random_state=50, verbose=0,\n",
       "                       warm_start=False)"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=101)\n",
    "model_rf = RandomForestClassifier(n_estimators=1000 , oob_score = True, n_jobs = -1,\n",
    "                                  random_state =50, max_features = \"auto\",\n",
    "                                  max_leaf_nodes = 30)\n",
    "model_rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.794180269694819\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "## Make predictions\n",
    "prediction_test = model_rf.predict(X_test)\n",
    "print (metrics.accuracy_score(y_test, prediction_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.794180269694819\n"
     ]
    }
   ],
   "source": [
    "# Print the prediction accuracy\n",
    "print (metrics.accuracy_score(y_test, prediction_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c0e4596b38>"
      ]
     },
     "execution_count": 146,
     "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": [
    "importances = model_rf.feature_importances_\n",
    "weights = pd.Series(importances,\n",
    "                 index=X.columns.values)\n",
    "weights.sort_values()[-10:].plot(kind = 'barh')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Applying SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7977288857345636"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Applying SVM\n",
    "from sklearn.svm import SVC\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=101)\n",
    "model = SVC()\n",
    "model.svm = SVC(kernel='linear') \n",
    "model.svm.fit(X_train,y_train)\n",
    "preds = model.svm.predict(X_test)\n",
    "metrics.accuracy_score(y_test, preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Model Evolution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7977288857345636\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "## Make predictions\n",
    "prediction_test = model_rf.predict(X_test)\n",
    "print (metrics.accuracy_score(y_test,preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[971  55]\n",
      " [230 153]]\n"
     ]
    }
   ],
   "source": [
    "# Create the Confusion matrix\n",
    "from sklearn.metrics import classification_report, confusion_matrix  \n",
    "print(confusion_matrix(y_test,preds))  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Final Conclusions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Observations(Logestic Regression)\n",
    "\n",
    "We can see that some variables have a negative relation to our predicted variable (Churn), while some have positive relation. Negative relation means that likeliness of churn decreases with that variable. Let us summarize some of the interesting features below:\n",
    "\n",
    "As we saw in our EDA, having a 2 month contract reduces chances of churn. 2 month contract along with tenure have the most negative relation with Churn as predicted by logistic regressions\n",
    "Having DSL internet service also reduces the proability of Churn\n",
    "Lastly, total charges, monthly contracts, fibre optic internet services and seniority can lead to higher churn rates. This is interesting because although fibre optic services are faster, customers are likely to churn because of it. I think we need to explore more to better understad why this is happening"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observations:(Random Forest)\n",
    "\n",
    "1.From random forest algorithm, monthly contract, tenure and total charges are the most important predictor variables to predict churn.\n",
    "2.The results from random forest are very similar to that of the logistic regression and in line to what we had expected from our EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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