Telco_customer_churn.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TELOC customer churn Prediction\n",
    "\n",
    "#### Using Kaggle Telcom customer churn dataset\n",
    "\n",
    "####  I will be research the dataset with CRIP-DM process\n",
    "\n",
    "#### 1. Bussiness & Data Understanding\n",
    "\n",
    "##### **Why Customer move away to competitor is key question of any telecom operator** The purpose to identify the petential customer who could leave and do a proactive marketing offer to try to retain the customer moving to competitor.\n",
    "\n",
    "##### The dataset has 7043 customer observations with 21 features. The Churn column has a YES no value which indicate if the customer have left the services of the telecom company in with in last 1 month\n",
    "\n",
    "##### With the customer data for Kaggle.com I would like to answer following questions\n",
    "\n",
    "> **1. What factor most influence the customer churn,  Here churn mean probaility of the \n",
    "customer leaving with in 1 month**\n",
    "\n",
    "> **2. Classify the customer of who to poetntially churn or not**\n",
    "\n",
    "> **3. What is the proability of customer to leave the telcom services of the company** \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customerID</th>\n",
       "      <th>gender</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>tenure</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>InternetService</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>...</th>\n",
       "      <th>DeviceProtection</th>\n",
       "      <th>TechSupport</th>\n",
       "      <th>StreamingTV</th>\n",
       "      <th>StreamingMovies</th>\n",
       "      <th>Contract</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>PaymentMethod</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7590-VHVEG</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>29.85</td>\n",
       "      <td>29.85</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5575-GNVDE</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>34</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>56.95</td>\n",
       "      <td>1889.5</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3668-QPYBK</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>53.85</td>\n",
       "      <td>108.15</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7795-CFOCW</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>45</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>42.30</td>\n",
       "      <td>1840.75</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9237-HQITU</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>70.70</td>\n",
       "      <td>151.65</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   customerID  gender  SeniorCitizen Partner Dependents  tenure PhoneService  \\\n",
       "0  7590-VHVEG  Female              0     Yes         No       1           No   \n",
       "1  5575-GNVDE    Male              0      No         No      34          Yes   \n",
       "2  3668-QPYBK    Male              0      No         No       2          Yes   \n",
       "3  7795-CFOCW    Male              0      No         No      45           No   \n",
       "4  9237-HQITU  Female              0      No         No       2          Yes   \n",
       "\n",
       "      MultipleLines InternetService OnlineSecurity  ...  DeviceProtection  \\\n",
       "0  No phone service             DSL             No  ...                No   \n",
       "1                No             DSL            Yes  ...               Yes   \n",
       "2                No             DSL            Yes  ...                No   \n",
       "3  No phone service             DSL            Yes  ...               Yes   \n",
       "4                No     Fiber optic             No  ...                No   \n",
       "\n",
       "  TechSupport StreamingTV StreamingMovies        Contract PaperlessBilling  \\\n",
       "0          No          No              No  Month-to-month              Yes   \n",
       "1          No          No              No        One year               No   \n",
       "2          No          No              No  Month-to-month              Yes   \n",
       "3         Yes          No              No        One year               No   \n",
       "4          No          No              No  Month-to-month              Yes   \n",
       "\n",
       "               PaymentMethod MonthlyCharges  TotalCharges Churn  \n",
       "0           Electronic check          29.85         29.85    No  \n",
       "1               Mailed check          56.95        1889.5    No  \n",
       "2               Mailed check          53.85        108.15   Yes  \n",
       "3  Bank transfer (automatic)          42.30       1840.75    No  \n",
       "4           Electronic check          70.70        151.65   Yes  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import r2_score, mean_squared_error\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "df = pd.read_csv('./Telco-Customer-Churn.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customerID           object\n",
       "gender               object\n",
       "SeniorCitizen         int64\n",
       "Partner              object\n",
       "Dependents           object\n",
       "tenure                int64\n",
       "PhoneService         object\n",
       "MultipleLines        object\n",
       "InternetService      object\n",
       "OnlineSecurity       object\n",
       "OnlineBackup         object\n",
       "DeviceProtection     object\n",
       "TechSupport          object\n",
       "StreamingTV          object\n",
       "StreamingMovies      object\n",
       "Contract             object\n",
       "PaperlessBilling     object\n",
       "PaymentMethod        object\n",
       "MonthlyCharges      float64\n",
       "TotalCharges         object\n",
       "Churn                object\n",
       "dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>tenure</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7043.000000</td>\n",
       "      <td>7043.000000</td>\n",
       "      <td>7043.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.162147</td>\n",
       "      <td>32.371149</td>\n",
       "      <td>64.761692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.368612</td>\n",
       "      <td>24.559481</td>\n",
       "      <td>30.090047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>18.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>35.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>70.350000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>89.850000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>118.750000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       SeniorCitizen       tenure  MonthlyCharges\n",
       "count    7043.000000  7043.000000     7043.000000\n",
       "mean        0.162147    32.371149       64.761692\n",
       "std         0.368612    24.559481       30.090047\n",
       "min         0.000000     0.000000       18.250000\n",
       "25%         0.000000     9.000000       35.500000\n",
       "50%         0.000000    29.000000       70.350000\n",
       "75%         0.000000    55.000000       89.850000\n",
       "max         1.000000    72.000000      118.750000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "######  This seams to be perfect dataset with no missing values .  But ther are number of category object columns which will have to substitued for dummy variables\n",
    "\n",
    "###### Let us under stand the each object column has distinct and meaning ful catergories. Otherwise the data in these features have to made as unique categories\n",
    "\n",
    "###### 2. Data Wraningling "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def unique_val(df):\n",
    "    '''\n",
    "    INPUT: \n",
    "    df - pandas dataframe with categorical variables you want to find unique value.\n",
    "        \n",
    "    OUTPUT:\n",
    "    print of Unique value of object columns\n",
    "    \n",
    "    \n",
    "    '''\n",
    "    \n",
    "    for col in df.select_dtypes(include=['object']).columns.tolist():\n",
    "        print('column {} has {} values which are {}'.\n",
    "              format(col,df[col].nunique(),\n",
    "                     df[col].unique())\n",
    "             )\n",
    "    \n",
    "    return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "column gender has 2 values which are ['Female' 'Male']\n",
      "column Partner has 2 values which are ['Yes' 'No']\n",
      "column Dependents has 2 values which are ['No' 'Yes']\n",
      "column PhoneService has 2 values which are ['No' 'Yes']\n",
      "column MultipleLines has 3 values which are ['No phone service' 'No' 'Yes']\n",
      "column InternetService has 3 values which are ['DSL' 'Fiber optic' 'No']\n",
      "column OnlineSecurity has 3 values which are ['No' 'Yes' 'No internet service']\n",
      "column OnlineBackup has 3 values which are ['Yes' 'No' 'No internet service']\n",
      "column DeviceProtection has 3 values which are ['No' 'Yes' 'No internet service']\n",
      "column TechSupport has 3 values which are ['No' 'Yes' 'No internet service']\n",
      "column StreamingTV has 3 values which are ['No' 'Yes' 'No internet service']\n",
      "column StreamingMovies has 3 values which are ['No' 'Yes' 'No internet service']\n",
      "column Contract has 3 values which are ['Month-to-month' 'One year' 'Two year']\n",
      "column PaperlessBilling has 2 values which are ['Yes' 'No']\n",
      "column PaymentMethod has 4 values which are ['Electronic check' 'Mailed check' 'Bank transfer (automatic)'\n",
      " 'Credit card (automatic)']\n",
      "column TotalCharges has 6531 values which are ['29.85' '1889.5' '108.15' ..., '346.45' '306.6' '6844.5']\n",
      "column Churn has 2 values which are ['No' 'Yes']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calling the function for except the customer ID\n",
    "unique_val(df.iloc[:,1:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### The unqiue values are all fine but the total charges which is supposed to be float is represented as object.  This should be converted as float\n",
    "There seems to be few instances of blanks which I will fill up with 0.0 and then convert float"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Issue converting >> <<\n",
      "Issue converting >> <<\n",
      "Issue converting >> <<\n",
      "Issue converting >> <<\n",
      "Issue converting >> <<\n",
      "Issue converting >> <<\n",
      "Issue converting >> <<\n",
      "Issue converting >> <<\n",
      "Issue converting >> <<\n",
      "Issue converting >> <<\n",
      "Issue converting >> <<\n"
     ]
    }
   ],
   "source": [
    "for x in df['TotalCharges']:\n",
    "    try: \n",
    "        float(x)\n",
    "    except:\n",
    "        print('Issue converting >>{}<<'.format(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Replace the spaces with float 0.0 values\n",
    "df['TotalCharges'].replace(' ','0.0',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "for x in df['TotalCharges']:\n",
    "    try: \n",
    "        float(x)\n",
    "    except:\n",
    "        print('Issue converting >>{}<<'.format(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['TotalCharges'] = df['TotalCharges'].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customerID           object\n",
       "gender               object\n",
       "SeniorCitizen         int64\n",
       "Partner              object\n",
       "Dependents           object\n",
       "tenure                int64\n",
       "PhoneService         object\n",
       "MultipleLines        object\n",
       "InternetService      object\n",
       "OnlineSecurity       object\n",
       "OnlineBackup         object\n",
       "DeviceProtection     object\n",
       "TechSupport          object\n",
       "StreamingTV          object\n",
       "StreamingMovies      object\n",
       "Contract             object\n",
       "PaperlessBilling     object\n",
       "PaymentMethod        object\n",
       "MonthlyCharges      float64\n",
       "TotalCharges        float64\n",
       "Churn                object\n",
       "dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### 3. Data Modelling \n",
    "\n",
    "######  The data is in good shape, Let me explore to answering the questions. Distribution of  features with respect to churn will give us some idea of which features could contribute to churn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def feature_count(df,cols,col_lst):\n",
    "    '''\n",
    "    function to plot induvidual count plots into tiles\n",
    "    INPUT:\n",
    "    df -  Pandas dataframe to create plot\n",
    "    cols - number of columns to arrage the plots\n",
    "    col_lst -  the list of column from dataframe to arrage into plots\n",
    "    \n",
    "    OUTPUT:\n",
    "    seaborn count plot of each column from the column list grouped by churn \n",
    "    '''\n",
    "    \n",
    "    num_rows = round((len(col_lst)/cols))\n",
    "    f,axes = plt.subplots(num_rows, cols, figsize=(num_rows * 5, cols * 4) )\n",
    "    print('number of rows {}'.format(num_rows))\n",
    "    i,j = 0,0\n",
    "    \n",
    "    for col in col_lst:\n",
    "        g = sns.countplot(data=df, \n",
    "                          x=col,\n",
    "                          hue='Churn',\n",
    "                          ax=axes[i,j]);\n",
    "        for p in g.patches:\n",
    "            g.annotate(format(p.get_height()),\n",
    "                             (p.get_x() + p.get_width() / 2.,\n",
    "                              p.get_height()\n",
    "                             ), \n",
    "                             ha = 'center',\n",
    "                             va = 'center',\n",
    "                             xytext = (0, 10),\n",
    "                             textcoords = 'offset points'\n",
    "                      )\n",
    "        j+=1 \n",
    "        \n",
    "        if j == num_rows: \n",
    "            i += 1;\n",
    "            j = 0;\n",
    "                \n",
    "    return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of rows 4\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KxZuju7Nnzx4SExMj3RUROUXKRcVXxFzgI81su5llBbcuYfs8FMwFvt7MOofF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jjz6aBx54gC5dujBmzBg2bdrEnXfemapDKldFj3fz5s24O02aNGHatGncdNNNvPXWWwAceuih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\nAIApCS8BAAAAgCn9O5eMVE4/rsfPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xcb40d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "col_lst = df.select_dtypes(include=['object']).columns[1:].tolist()\n",
    "\n",
    "feature_count(df, 4, col_lst)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### I could see that compartively high level of Churn with \n",
    "> * Customers using fiberoptics\n",
    "> * Customers with no online Security enrolled\n",
    "> * Customers with no Online backup service enrolled\n",
    "> * customers with no device protuction enrolled\n",
    "> * customers with no Tech support enrolled\n",
    "> * customer who pay month on month\n",
    "> * Cusotmer who have electronic billing enrolled\n",
    "##### This could indicate more services the customers use more likely they are going to stay.  Let us see how the combination of all this factors contribute to the over all Churn percentage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Churn by major cause 1851\n",
      "Total Churn 1869\n",
      "Pecentage of Churn by major cause out of total churn 99.04%\n"
     ]
    }
   ],
   "source": [
    "major_churn = df.query('(InternetService == \"Fiber optic\"   \\\n",
    "                         | OnlineSecurity == \"No\"           \\\n",
    "                         | OnlineBackup == \"No\"             \\\n",
    "                         | DeviceProtection == \"No\"         \\\n",
    "                         | TechSupport == \"No\"              \\\n",
    "                         | Contract == \"Month-to-month\"     \\\n",
    "                         | PaymentMethod == \"Electronic check\"  \\\n",
    "                        ) & Churn == \"Yes\" ')['Churn'].count()\n",
    "\n",
    "total_churn = df.query('Churn == \"Yes\"')['Churn'].count()\n",
    "\n",
    "print(\"Churn by major cause {}\".format(major_churn))\n",
    "print(\"Total Churn {}\".format(total_churn))\n",
    "\n",
    "print(\"Pecentage of Churn by major cause out of total churn {:.2f}%\".\n",
    "      format(major_churn/total_churn*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Let me create some plot to understand the other numerical variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0xd3bcfd0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IezTBl5/dz8uvazZXRAqDElyRc4i+EsUdcVOxoGJczxteFQY3RF+Kjut5RUQu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\nERERkRHh/wH6pZF1b7MGmwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xdc20320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(16,7))\n",
    "\n",
    "sns.distplot(df.query('Churn == \"Yes\"')['MonthlyCharges'], label ='Churn')\n",
    "sns.distplot(df.query('Churn == \"No\"')['MonthlyCharges'],color='m', label = 'Stay')\n",
    "plt.title('Churn by Monthly charges ')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### There seems to be more churn in the range of $70 to $110 per month. Let us check for TotalCharges. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0xd5a4fd0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+1332jGJuGnwgjSSuWr6ALc1NHVCpJEmSpN1hwFW3M5utzGMbZ9OPg6Kiw/od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\nggJcERERERERWRD+f6wkhhGrAW4bAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd56cb38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(16,7))\n",
    "sns.distplot(df.query('Churn == \"Yes\"')['TotalCharges'],\n",
    "             label ='Churn'\n",
    "            )\n",
    "sns.distplot(df.query('Churn == \"No\"')['TotalCharges'],\n",
    "             color='m',\n",
    "             label = 'Stay'\n",
    "            )\n",
    "\n",
    "plt.title('Churn by Total Charges ')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Interestingly the is more Churn in lower total Charge, which could indicate more customers are leaving early before spending much with the Telco operator.  We could get better Idea when we check the tenure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0xdc75ba8>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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BAABgc1qTa2wBAABgWRS2AAAAjJrCFgAAgFFT2AIAADBqClsAAABGTWELAADAqClsAQAA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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xdc756a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(16,7))\n",
    "sns.countplot(df['tenure'], \n",
    "              hue=df['Churn'])\n",
    "\n",
    "plt.title('Churn by Tenure in months ')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### The plot shows the there are high chances of customer leaving before month 15. the long the cusotmer stays less likely he/she will leave the services.\n",
    "\n",
    "###### Let me move on to Classification of customer who could have high probality to churn.\n",
    "\n",
    "###### Base Model is smiple logistic regression with numeric encoding of feature, another models will be logistic regression after dummy variable substitution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Function to numeric encoding of features for Logistic regression\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "df_enc = df.copy()\n",
    "## filter categorical columns using mask and turn it into a list\n",
    "\n",
    "categorical_feature_mask = df_enc.dtypes==object \n",
    "categorical_cols = df_enc.columns[categorical_feature_mask].tolist()[1:]\n",
    "\n",
    "# categorical_cols\n",
    "le = LabelEncoder()\n",
    "\n",
    "df_enc[categorical_cols] = df_enc[categorical_cols].                     \\\n",
    "                           apply(lambda col: le.fit_transform(col))\n",
    "\n",
    "df_enc[categorical_cols].head(10)\n",
    "\n",
    "df_enc['customerID'] = df['customerID']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>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>...</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>29.85</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5575-GNVDE</td>\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>...</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>1889.50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3668-QPYBK</td>\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>...</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>108.15</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7795-CFOCW</td>\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>...</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>1840.75</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9237-HQITU</td>\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>...</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>151.65</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>9305-CDSKC</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>99.65</td>\n",
       "      <td>820.50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1452-KIOVK</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>89.10</td>\n",
       "      <td>1949.40</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6713-OKOMC</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</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>3</td>\n",
       "      <td>29.75</td>\n",
       "      <td>301.90</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7892-POOKP</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>104.80</td>\n",
       "      <td>3046.05</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>6388-TABGU</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>62</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>56.15</td>\n",
       "      <td>3487.95</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   customerID  gender  SeniorCitizen  Partner  Dependents  tenure  \\\n",
       "0  7590-VHVEG       0              0        1           0       1   \n",
       "1  5575-GNVDE       1              0        0           0      34   \n",
       "2  3668-QPYBK       1              0        0           0       2   \n",
       "3  7795-CFOCW       1              0        0           0      45   \n",
       "4  9237-HQITU       0              0        0           0       2   \n",
       "5  9305-CDSKC       0              0        0           0       8   \n",
       "6  1452-KIOVK       1              0        0           1      22   \n",
       "7  6713-OKOMC       0              0        0           0      10   \n",
       "8  7892-POOKP       0              0        1           0      28   \n",
       "9  6388-TABGU       1              0        0           1      62   \n",
       "\n",
       "   PhoneService  MultipleLines  InternetService  OnlineSecurity  ...    \\\n",
       "0             0              1                0               0  ...     \n",
       "1             1              0                0               2  ...     \n",
       "2             1              0                0               2  ...     \n",
       "3             0              1                0               2  ...     \n",
       "4             1              0                1               0  ...     \n",
       "5             1              2                1               0  ...     \n",
       "6             1              2                1               0  ...     \n",
       "7             0              1                0               2  ...     \n",
       "8             1              2                1               0  ...     \n",
       "9             1              0                0               2  ...     \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",
       "5                 2            0            2                2         0   \n",
       "6                 0            0            2                0         0   \n",
       "7                 0            0            0                0         0   \n",
       "8                 2            2            2                2         0   \n",
       "9                 0            0            0                0         1   \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  \n",
       "5                 1              2           99.65        820.50      1  \n",
       "6                 1              1           89.10       1949.40      0  \n",
       "7                 0              3           29.75        301.90      0  \n",
       "8                 1              2          104.80       3046.05      1  \n",
       "9                 0              0           56.15       3487.95      0  \n",
       "\n",
       "[10 rows x 21 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_enc.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Now the data is ready for Logistic Regression \n",
    "\n",
    "###### 4. Predicting "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\App-V\\95FD7873-726E-40E9-B477-CC4010411846\\9727BF3A-F7F8-45A4-AD38-2F35B6E0FB2A\\Root\\lib\\site-packages\\statsmodels\\compat\\pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
      "  from pandas.core import datetools\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score \n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.metrics import scorer\n",
    "from sklearn.metrics import f1_score\n",
    "import statsmodels.api as sm\n",
    "from sklearn.metrics import precision_score,recall_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4930, 19)\n",
      "(4930,)\n",
      "(2113, 19)\n",
      "(2113,)\n"
     ]
    }
   ],
   "source": [
    "# Split data into train and test data\n",
    "\n",
    "(x_train, x_test, y_train, y_test) = train_test_split(df_enc.iloc[:,1:-1],\n",
    "                                                      df_enc.iloc[:,-1],\n",
    "                                                      test_size = .30,\n",
    "                                                      random_state = 42)\n",
    "\n",
    "print(x_train.shape)\n",
    "print(y_train.shape)\n",
    "print(x_test.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Logistic Regression fit\n",
    "\n",
    "logit  = LogisticRegression(C=1.0,\n",
    "                            class_weight=None,\n",
    "                            dual=False, \n",
    "                            fit_intercept=True,\n",
    "                            intercept_scaling=1,\n",
    "                            max_iter=100,\n",
    "                            multi_class='ovr',\n",
    "                            n_jobs=1,\n",
    "                            penalty='l2', \n",
    "                            random_state=42,\n",
    "                            solver='liblinear', \n",
    "                            tol=0.0001,\n",
    "                            verbose=0,\n",
    "                            warm_start=False)\n",
    "\n",
    "# train model\n",
    "logit.fit(x_train,y_train)\n",
    "\n",
    "predictions   = logit.predict(x_test)\n",
    "probabilities = logit.predict_proba(x_test)\n",
    "\n",
    "#coeffs\n",
    "coefficients  = pd.DataFrame(logit.coef_.ravel())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.3957838 ,  0.6042162 ],\n",
       "       [ 0.93791876,  0.06208124],\n",
       "       [ 0.99541311,  0.00458689],\n",
       "       ..., \n",
       "       [ 0.99149561,  0.00850439],\n",
       "       [ 0.42587605,  0.57412395],\n",
       "       [ 0.79028687,  0.20971313]])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "probabilities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
      "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
      "          penalty='l2', random_state=42, solver='liblinear', tol=0.0001,\n",
      "          verbose=0, warm_start=False)\n",
      "\n",
      "Classification report : \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          0       0.85      0.90      0.87      1539\n",
      "          1       0.67      0.56      0.61       574\n",
      "\n",
      "avg / total       0.80      0.81      0.80      2113\n",
      "\n",
      "Accuracy   Score :  0.805016564127\n",
      "Area under curve :  0.727959804661 \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xcec3f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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NJ463GexZDf/9s+eEJ9HW47e09qaC0Po9RxkxOZuDxwr51w3dg+pxYmdKk14A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\neiIiEhkqeiIiEhn/D83xSBUPr91AAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xcec1438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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DVhERERHRslKsRkRERETLSrEaERERES0re1YjIiJ6scvkI5udAgBX7HlKs1OI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\nRNNKshoRERERTSvJakREREQ0rf8D8exvAO2rqVwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd210048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.805016564127 0.727959804661\n"
     ]
    }
   ],
   "source": [
    "column_df     = pd.DataFrame(df_enc.iloc[:,1:-1].columns.tolist())\n",
    "\n",
    "coef_sumry    = pd.merge(coefficients,\n",
    "                         column_df,\n",
    "                         left_index= True,\n",
    "                         right_index= True,\n",
    "                         how = \"left\"\n",
    "                        )\n",
    "coef_sumry.columns = [\"coefficients\",\"features\"]\n",
    "coef_sumry = coef_sumry.sort_values(by = \"coefficients\",\n",
    "                                    ascending = False\n",
    "                                   )\n",
    "    \n",
    "print (logit)\n",
    "print (\"\\nClassification report : \\n\",\n",
    "       classification_report(y_test,\n",
    "                             predictions)\n",
    "      )\n",
    "print (\"Accuracy   Score : \",accuracy_score(y_test,\n",
    "                                            predictions)\n",
    "      )\n",
    "\n",
    "logit_score = accuracy_score(y_test, predictions)\n",
    "\n",
    "#confusion matrix\n",
    "conf_matrix = confusion_matrix(y_test,\n",
    "                               predictions\n",
    "                              )\n",
    "#roc_auc_score\n",
    "model_roc_auc = roc_auc_score(y_test,predictions)\n",
    "print (\"Area under curve : \",\n",
    "       model_roc_auc,\n",
    "       \"\\n\"\n",
    "      )\n",
    "\n",
    "logit_roc = roc_auc_score(y_test,predictions)\n",
    "\n",
    "fpr,tpr,thresholds = roc_curve(y_test,\n",
    "                               probabilities[:,1])\n",
    "\n",
    "\n",
    "#plot confusion matrix\n",
    "fig, ax = plt.subplots(figsize=(7,6))\n",
    "trace1 = sns.heatmap(data = conf_matrix ,\n",
    "                        xticklabels = [\"Stay\",\"Churn\"],\n",
    "                        yticklabels = [\"Stay\",\"Churn\"],\n",
    "                        cmap = \"Blues\"\n",
    "                    )\n",
    "plt.title('Confusion Matrix');\n",
    "plt.show()\n",
    "\n",
    "# plot the ROC curve for the model\n",
    "fig, ax = plt.subplots(figsize=(7,6))\n",
    "plt.plot([0,1], [0,1], linestyle='--', label='No Skill')\n",
    "plt.plot(fpr, tpr, marker='.', label='Logistic')\n",
    "\n",
    "# axis labels\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.grid(b=True)\n",
    "plt.title('Receiver Operating Charateristic Curve  - AUC {:.5f}'.  \\\n",
    "          format(model_roc_auc)\n",
    "         )\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# Feature Impartance\n",
    "feature_impartant = coef_sumry.sort_values(by='coefficients')\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10,7))\n",
    "sns.barplot(x=feature_impartant['coefficients'],\n",
    "            y=feature_impartant['features'], \n",
    "            orient=\"h\"\n",
    "           )\n",
    "plt.title('Feature Impartance')\n",
    "plt.show()\n",
    "\n",
    "print(logit_score, logit_roc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Logistic regression with dummy variables of feature. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Change the seniorcitizens columns to string type \n",
    "# column to include in dummy variable conversion\n",
    "df_prep = df.copy()\n",
    "\n",
    "df_prep['SeniorCitizen'] = df_prep['SeniorCitizen'].astype('object')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['gender',\n",
       " 'SeniorCitizen',\n",
       " 'Partner',\n",
       " 'Dependents',\n",
       " 'PhoneService',\n",
       " 'MultipleLines',\n",
       " 'InternetService',\n",
       " 'OnlineSecurity',\n",
       " 'OnlineBackup',\n",
       " 'DeviceProtection',\n",
       " 'TechSupport',\n",
       " 'StreamingTV',\n",
       " 'StreamingMovies',\n",
       " 'Contract',\n",
       " 'PaperlessBilling',\n",
       " 'PaymentMethod']"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "catergory_columns = df_prep.select_dtypes(include=['object']).columns.tolist()\n",
    "\n",
    "# remove Unnessary column for dummy variable\n",
    "catergory_columns.remove('customerID')\n",
    "\n",
    "# remove target columns from dummy variable replacement\n",
    "catergory_columns.remove('Churn')      \n",
    "catergory_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_dummy_features(df, column_list):\n",
    "    \n",
    "    '''\n",
    "    Input :  \n",
    "    df - Pandas Dataframe whose columns will be replaced with dummy variables columns\n",
    "    column_list - List of columns which have to replaced with dummy variables\n",
    "    \n",
    "    Output :\n",
    "    df_dummy - pandas dataframe. catergory columns replaced with dummy variable columns\n",
    "    '''\n",
    "    df_dummy = df.copy()\n",
    "    \n",
    "    for column in column_list:\n",
    "        df_dummy = pd.merge(df_dummy.drop([column],axis=1),\n",
    "                            pd.get_dummies(df_dummy, columns=[column])\n",
    "                           )\n",
    "        \n",
    "    return df_dummy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customerID</th>\n",
       "      <th>tenure</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>Churn</th>\n",
       "      <th>gender_Female</th>\n",
       "      <th>gender_Male</th>\n",
       "      <th>SeniorCitizen_0</th>\n",
       "      <th>SeniorCitizen_1</th>\n",
       "      <th>Partner_No</th>\n",
       "      <th>...</th>\n",
       "      <th>StreamingMovies_Yes</th>\n",
       "      <th>Contract_Month-to-month</th>\n",
       "      <th>Contract_One year</th>\n",
       "      <th>Contract_Two year</th>\n",
       "      <th>PaperlessBilling_No</th>\n",
       "      <th>PaperlessBilling_Yes</th>\n",
       "      <th>PaymentMethod_Bank transfer (automatic)</th>\n",
       "      <th>PaymentMethod_Credit card (automatic)</th>\n",
       "      <th>PaymentMethod_Electronic check</th>\n",
       "      <th>PaymentMethod_Mailed check</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7590-VHVEG</td>\n",
       "      <td>1</td>\n",
       "      <td>29.85</td>\n",
       "      <td>29.85</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5575-GNVDE</td>\n",
       "      <td>34</td>\n",
       "      <td>56.95</td>\n",
       "      <td>1889.50</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3668-QPYBK</td>\n",
       "      <td>2</td>\n",
       "      <td>53.85</td>\n",
       "      <td>108.15</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7795-CFOCW</td>\n",
       "      <td>45</td>\n",
       "      <td>42.30</td>\n",
       "      <td>1840.75</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9237-HQITU</td>\n",
       "      <td>2</td>\n",
       "      <td>70.70</td>\n",
       "      <td>151.65</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 48 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   customerID  tenure  MonthlyCharges  TotalCharges Churn  gender_Female  \\\n",
       "0  7590-VHVEG       1           29.85         29.85    No              1   \n",
       "1  5575-GNVDE      34           56.95       1889.50    No              0   \n",
       "2  3668-QPYBK       2           53.85        108.15   Yes              0   \n",
       "3  7795-CFOCW      45           42.30       1840.75    No              0   \n",
       "4  9237-HQITU       2           70.70        151.65   Yes              1   \n",
       "\n",
       "   gender_Male  SeniorCitizen_0  SeniorCitizen_1  Partner_No  \\\n",
       "0            0                1                0           0   \n",
       "1            1                1                0           1   \n",
       "2            1                1                0           1   \n",
       "3            1                1                0           1   \n",
       "4            0                1                0           1   \n",
       "\n",
       "              ...              StreamingMovies_Yes  Contract_Month-to-month  \\\n",
       "0             ...                                0                        1   \n",
       "1             ...                                0                        0   \n",
       "2             ...                                0                        1   \n",
       "3             ...                                0                        0   \n",
       "4             ...                                0                        1   \n",
       "\n",
       "   Contract_One year  Contract_Two year  PaperlessBilling_No  \\\n",
       "0                  0                  0                    0   \n",
       "1                  1                  0                    1   \n",
       "2                  0                  0                    0   \n",
       "3                  1                  0                    1   \n",
       "4                  0                  0                    0   \n",
       "\n",
       "   PaperlessBilling_Yes  PaymentMethod_Bank transfer (automatic)  \\\n",
       "0                     1                                        0   \n",
       "1                     0                                        0   \n",
       "2                     1                                        0   \n",
       "3                     0                                        1   \n",
       "4                     1                                        0   \n",
       "\n",
       "   PaymentMethod_Credit card (automatic)  PaymentMethod_Electronic check  \\\n",
       "0                                      0                               1   \n",
       "1                                      0                               0   \n",
       "2                                      0                               0   \n",
       "3                                      0                               0   \n",
       "4                                      0                               1   \n",
       "\n",
       "   PaymentMethod_Mailed check  \n",
       "0                           0  \n",
       "1                           1  \n",
       "2                           1  \n",
       "3                           0  \n",
       "4                           0  \n",
       "\n",
       "[5 rows x 48 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Replace category columns of churn datasets with dummy dataset\n",
    "df_dummy = get_dummy_features(df_prep,\n",
    "                              catergory_columns\n",
    "                             )                             \n",
    "df_dummy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customerID</th>\n",
       "      <th>tenure</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>Churn</th>\n",
       "      <th>gender_Female</th>\n",
       "      <th>gender_Male</th>\n",
       "      <th>SeniorCitizen_0</th>\n",
       "      <th>SeniorCitizen_1</th>\n",
       "      <th>Partner_No</th>\n",
       "      <th>...</th>\n",
       "      <th>StreamingMovies_Yes</th>\n",
       "      <th>Contract_Month-to-month</th>\n",
       "      <th>Contract_One year</th>\n",
       "      <th>Contract_Two year</th>\n",
       "      <th>PaperlessBilling_No</th>\n",
       "      <th>PaperlessBilling_Yes</th>\n",
       "      <th>PaymentMethod_Bank transfer (automatic)</th>\n",
       "      <th>PaymentMethod_Credit card (automatic)</th>\n",
       "      <th>PaymentMethod_Electronic check</th>\n",
       "      <th>PaymentMethod_Mailed check</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7590-VHVEG</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5575-GNVDE</td>\n",
       "      <td>34</td>\n",
       "      <td>56.95</td>\n",
       "      <td>1889.50</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3668-QPYBK</td>\n",
       "      <td>2</td>\n",
       "      <td>53.85</td>\n",
       "      <td>108.15</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7795-CFOCW</td>\n",
       "      <td>45</td>\n",
       "      <td>42.30</td>\n",
       "      <td>1840.75</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9237-HQITU</td>\n",
       "      <td>2</td>\n",
       "      <td>70.70</td>\n",
       "      <td>151.65</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 48 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   customerID  tenure  MonthlyCharges  TotalCharges  Churn  gender_Female  \\\n",
       "0  7590-VHVEG       1           29.85         29.85      0              1   \n",
       "1  5575-GNVDE      34           56.95       1889.50      0              0   \n",
       "2  3668-QPYBK       2           53.85        108.15      1              0   \n",
       "3  7795-CFOCW      45           42.30       1840.75      0              0   \n",
       "4  9237-HQITU       2           70.70        151.65      1              1   \n",
       "\n",
       "   gender_Male  SeniorCitizen_0  SeniorCitizen_1  Partner_No  \\\n",
       "0            0                1                0           0   \n",
       "1            1                1                0           1   \n",
       "2            1                1                0           1   \n",
       "3            1                1                0           1   \n",
       "4            0                1                0           1   \n",
       "\n",
       "              ...              StreamingMovies_Yes  Contract_Month-to-month  \\\n",
       "0             ...                                0                        1   \n",
       "1             ...                                0                        0   \n",
       "2             ...                                0                        1   \n",
       "3             ...                                0                        0   \n",
       "4             ...                                0                        1   \n",
       "\n",
       "   Contract_One year  Contract_Two year  PaperlessBilling_No  \\\n",
       "0                  0                  0                    0   \n",
       "1                  1                  0                    1   \n",
       "2                  0                  0                    0   \n",
       "3                  1                  0                    1   \n",
       "4                  0                  0                    0   \n",
       "\n",
       "   PaperlessBilling_Yes  PaymentMethod_Bank transfer (automatic)  \\\n",
       "0                     1                                        0   \n",
       "1                     0                                        0   \n",
       "2                     1                                        0   \n",
       "3                     0                                        1   \n",
       "4                     1                                        0   \n",
       "\n",
       "   PaymentMethod_Credit card (automatic)  PaymentMethod_Electronic check  \\\n",
       "0                                      0                               1   \n",
       "1                                      0                               0   \n",
       "2                                      0                               0   \n",
       "3                                      0                               0   \n",
       "4                                      0                               1   \n",
       "\n",
       "   PaymentMethod_Mailed check  \n",
       "0                           0  \n",
       "1                           1  \n",
       "2                           1  \n",
       "3                           0  \n",
       "4                           0  \n",
       "\n",
       "[5 rows x 48 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#change Churn column to numeric endoding before log regression\n",
    "df_dummy['Churn'] = df_enc['Churn']\n",
    "df_dummy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['tenure',\n",
       " 'MonthlyCharges',\n",
       " 'TotalCharges',\n",
       " 'gender_Female',\n",
       " 'gender_Male',\n",
       " 'SeniorCitizen_0',\n",
       " 'SeniorCitizen_1',\n",
       " 'Partner_No',\n",
       " 'Partner_Yes',\n",
       " 'Dependents_No',\n",
       " 'Dependents_Yes',\n",
       " 'PhoneService_No',\n",
       " 'PhoneService_Yes',\n",
       " 'MultipleLines_No',\n",
       " 'MultipleLines_No phone service',\n",
       " 'MultipleLines_Yes',\n",
       " 'InternetService_DSL',\n",
       " 'InternetService_Fiber optic',\n",
       " 'InternetService_No',\n",
       " 'OnlineSecurity_No',\n",
       " 'OnlineSecurity_No internet service',\n",
       " 'OnlineSecurity_Yes',\n",
       " 'OnlineBackup_No',\n",
       " 'OnlineBackup_No internet service',\n",
       " 'OnlineBackup_Yes',\n",
       " 'DeviceProtection_No',\n",
       " 'DeviceProtection_No internet service',\n",
       " 'DeviceProtection_Yes',\n",
       " 'TechSupport_No',\n",
       " 'TechSupport_No internet service',\n",
       " 'TechSupport_Yes',\n",
       " 'StreamingTV_No',\n",
       " 'StreamingTV_No internet service',\n",
       " 'StreamingTV_Yes',\n",
       " 'StreamingMovies_No',\n",
       " 'StreamingMovies_No internet service',\n",
       " 'StreamingMovies_Yes',\n",
       " 'Contract_Month-to-month',\n",
       " 'Contract_One year',\n",
       " 'Contract_Two year',\n",
       " 'PaperlessBilling_No',\n",
       " 'PaperlessBilling_Yes',\n",
       " 'PaymentMethod_Bank transfer (automatic)',\n",
       " 'PaymentMethod_Credit card (automatic)',\n",
       " 'PaymentMethod_Electronic check',\n",
       " 'PaymentMethod_Mailed check']"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Feature_columns = [col for col in df_dummy.columns.tolist() if col not in ['Churn','customerID']  ]\n",
    "# Feature_columns\n",
    "Feature_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    0\n",
       "2    1\n",
       "3    0\n",
       "4    1\n",
       "Name: Churn, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#target column\n",
    "df_dummy['Churn'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4930, 46)\n",
      "(4930,)\n",
      "(2113, 46)\n",
      "(2113,)\n"
     ]
    }
   ],
   "source": [
    "# Split data into train and test data of dummy variable dataset\n",
    "\n",
    "(x_train, x_test, y_train, y_test) = train_test_split(df_dummy[Feature_columns],\n",
    "                                                      df_dummy['Churn'],\n",
    "                                                      test_size = .30,\n",
    "                                                      random_state = 42)\n",
    "\n",
    "print(x_train.shape)\n",
    "print(y_train.shape)\n",
    "print(x_test.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Logistic Regression fit with Dummy column dataset\n",
    "\n",
    "logit_dummy  = LogisticRegression(C=1.0,\n",
    "                            class_weight=None,\n",
    "                            dual=False, \n",
    "                            fit_intercept=True,\n",
    "                            intercept_scaling=1,\n",
    "                            max_iter=100,\n",
    "                            multi_class='ovr',\n",
    "                            n_jobs=1,\n",
    "                            penalty='l2', \n",
    "                            random_state=42,\n",
    "                            solver='liblinear', \n",
    "                            tol=0.0001,\n",
    "                            verbose=0,\n",
    "                            warm_start=False)\n",
    "\n",
    "# train model\n",
    "logit_dummy.fit(x_train,y_train)\n",
    "\n",
    "predictions   = logit_dummy.predict(x_test)\n",
    "probabilities = logit_dummy.predict_proba(x_test)\n",
    "\n",
    "#coeffs\n",
    "coefficients  = pd.DataFrame(logit_dummy.coef_.ravel())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "column_df  = pd.DataFrame(Feature_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
      "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
      "          penalty='l2', random_state=42, solver='liblinear', tol=0.0001,\n",
      "          verbose=0, warm_start=False)\n",
      "\n",
      "Classification report for dataset with Dummy variable replacement: \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          0       0.85      0.90      0.87      1539\n",
      "          1       0.68      0.57      0.62       574\n",
      "\n",
      "avg / total       0.80      0.81      0.81      2113\n",
      "\n",
      "Accuracy   Score :  0.811168954094\n",
      "Area under curve :  0.737099071074 \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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SloEVjiQ1YuAFzsoHzo+//o6VPoT0W+uvuWFzT0FT5n5323bZ9mXTgCSpF0M/xzH09ydJ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\nJKkXBo4kqRcGjiSpFwaOJKkX/x+Tt+MMGP4+ZAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd22eda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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3CSvHK2KSnnbZ980as1edEKeRCFIBvvyj9ePMVMDqOVBR7rb6UZWJ7tAuvRlc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\nhMcnxByfbmY5tdxuKdA7fO+ocK21teEaZE3D4z+342sb3h8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      "text/plain": [
       "<matplotlib.figure.Figure at 0xdfc41d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Bgwf3ZBwREREREX0iixBFdy1W9uuEagb01+X4EEm7lOOPAasD/wBmApeW8v8D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\nT7m0xqgcK7e1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe039c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.811168954094 0.737099071074\n"
     ]
    }
   ],
   "source": [
    "# Printing and ploting the metrics\n",
    "\n",
    "coef_sumry    = pd.merge(coefficients,\n",
    "                         column_df,\n",
    "                         left_index= True,\n",
    "                         right_index= True,\n",
    "                         how = \"left\"\n",
    "                        )\n",
    "coef_sumry.columns = [\"coefficients\",\"features\"]\n",
    "coef_sumry         = coef_sumry.sort_values(by = \"coefficients\",ascending = False)\n",
    "    \n",
    "print (logit_dummy)\n",
    "print (\"\\nClassification report for dataset with Dummy variable replacement: \\n\",\n",
    "       classification_report(y_test,\n",
    "                             predictions)\n",
    "      )\n",
    "print (\"Accuracy   Score : \",accuracy_score(y_test,\n",
    "                                            predictions)\n",
    "      )\n",
    "\n",
    "logit_dummy_score = accuracy_score(y_test,predictions)\n",
    "\n",
    "#confusion matrix\n",
    "conf_matrix = confusion_matrix(y_test,\n",
    "                               predictions\n",
    "                              )\n",
    "#roc_auc_score\n",
    "model_roc_auc = roc_auc_score(y_test,predictions)\n",
    "print (\"Area under curve : \",\n",
    "       model_roc_auc,\n",
    "       \"\\n\"\n",
    "      )\n",
    "\n",
    "logit_dummy_roc = roc_auc_score(y_test,predictions)\n",
    "\n",
    "fpr,tpr,thresholds = roc_curve(y_test,\n",
    "                               probabilities[:,1])\n",
    "\n",
    "\n",
    "#plot confusion matrix\n",
    "fig, ax = plt.subplots(figsize=(7,6))\n",
    "trace1 = sns.heatmap(data = conf_matrix ,\n",
    "                     xticklabels = [\"Stay\",\"Churn\"],\n",
    "                     yticklabels = [\"Stay\",\"Churn\"],\n",
    "                     cmap = \"Blues\"\n",
    "                    )\n",
    "plt.title('Confusion Matrix');\n",
    "plt.show()\n",
    "\n",
    "# plot the ROC curve for the model\n",
    "fig, ax = plt.subplots(figsize=(7,6))\n",
    "plt.plot([0,1], [0,1], linestyle='--', label='No Skill')\n",
    "plt.plot(fpr, tpr, marker='.', label='Logistic')\n",
    "\n",
    "# axis labels\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.grid(b=True)\n",
    "plt.title('Receiver Operating Charateristic Curve  - AUC {:.5f}'. \\\n",
    "          format(model_roc_auc))\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# Feature Impartance\n",
    "feature_impartant = coef_sumry.sort_values(by='coefficients')\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(12,9))\n",
    "sns.barplot(x=feature_impartant['coefficients'],\n",
    "            y=feature_impartant['features'], \n",
    "            orient=\"V\"\n",
    "           )\n",
    "plt.title('Feature Impartance')\n",
    "plt.show()\n",
    "\n",
    "print(logit_dummy_score, logit_dummy_roc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### There is very slight improvement of 0.1 value between dummy variable dataset and numberic encoded dataset user in regression, We could say both models are almost the same at this stage\n",
    "###### I am trying Random forrest Ensamble for churn predicition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4930, 46)\n",
      "(4930,)\n",
      "(2113, 46)\n",
      "(2113,)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "#  Same data split for train Test data split already can be used.  \n",
    "\n",
    "print(x_train.shape)\n",
    "print(y_train.shape)\n",
    "print(x_test.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#random forest classifier\n",
    "rfc   = RandomForestClassifier(n_estimators = 100,\n",
    "                               max_depth = 3,\n",
    "                               criterion = 'entropy',\n",
    "                              )\n",
    "rfc.fit(x_train,y_train)\n",
    "\n",
    "estimated_tree = rfc.estimators_[99]\n",
    "\n",
    "predictions   = rfc.predict(x_test)\n",
    "probabilities = rfc.predict_proba(x_test)\n",
    "\n",
    "#coeffs\n",
    "coefficients  = pd.DataFrame(rfc.feature_importances_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='entropy',\n",
      "            max_depth=3, max_features='auto', max_leaf_nodes=None,\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=100, n_jobs=1,\n",
      "            oob_score=False, random_state=None, verbose=0,\n",
      "            warm_start=False)\n",
      "\n",
      "Classification report for Random Forest Ensamble: \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          0       0.78      0.96      0.86      1539\n",
      "          1       0.74      0.28      0.41       574\n",
      "\n",
      "avg / total       0.77      0.78      0.74      2113\n",
      "\n",
      "Accuracy   Score :  0.777567439659\n",
      "Area under curve :  0.621179190071 \n",
      "\n"
     ]
    },
    {
     "data": {
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RpJWSCUw9Nm4COQl4JnBRktclecBCCydZnWRtkrWbr7pw2UFKkrZ9YyWcqvr3qnoW8BBg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\n1s10AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe039e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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pRbr5U9zLkvRaJdWw7S4q4ba5yzhwpIzEuFimn9ldE57yiya9SDbz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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112b6da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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HxNJSAlfjGVci4AcRcflyhWnK76psUlv+DPqQkus3yv8ualhI+geCT1AY9TQz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471SaA30EfSp4Djgafi9tfv1cT2aeB529vb3ga4VdIKTcbyE8qs9O7R/jlglO13q67X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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe1c6fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.777567439659 0.621179190071\n"
     ]
    }
   ],
   "source": [
    "# Printing and ploting the metrics\n",
    "\n",
    "column_df  = pd.DataFrame(Feature_columns)\n",
    "\n",
    "coef_sumry    = pd.merge(coefficients,\n",
    "                         column_df,\n",
    "                         left_index= True,\n",
    "                         right_index= True,\n",
    "                         how = \"left\"\n",
    "                        )\n",
    "\n",
    "coef_sumry.columns = [\"coefficients\",\"features\"]\n",
    "coef_sumry         = coef_sumry.sort_values(by = \"coefficients\",\n",
    "                                            ascending = False\n",
    "                                           )\n",
    "    \n",
    "print (rfc)\n",
    "print (\"\\nClassification report for Random Forest Ensamble: \\n\",\n",
    "       classification_report(y_test,\n",
    "                             predictions)\n",
    "      )\n",
    "print (\"Accuracy   Score : \",accuracy_score(y_test,\n",
    "                                            predictions)\n",
    "      )\n",
    "\n",
    "rfc_score = accuracy_score(y_test, predictions)\n",
    "\n",
    "#confusion matrix\n",
    "conf_matrix = confusion_matrix(y_test,\n",
    "                               predictions\n",
    "                              )\n",
    "#roc_auc_score\n",
    "model_roc_auc = roc_auc_score(y_test,predictions)\n",
    "print (\"Area under curve : \",\n",
    "       model_roc_auc,\n",
    "       \"\\n\"\n",
    "      )\n",
    "\n",
    "rfc_roc = roc_auc_score(y_test,predictions)\n",
    "\n",
    "fpr,tpr,thresholds = roc_curve(y_test,\n",
    "                               probabilities[:,1])\n",
    "\n",
    "\n",
    "#plot confusion matrix\n",
    "fig, ax = plt.subplots(figsize=(7,6))\n",
    "trace1 = sns.heatmap(data = conf_matrix ,\n",
    "                     xticklabels = [\"Stay\",\"Churn\"],\n",
    "                     yticklabels = [\"Stay\",\"Churn\"],\n",
    "                     cmap = \"Blues\"\n",
    "                    )\n",
    "plt.title('Confusion Matrix for Random Forest prediction');\n",
    "plt.show()\n",
    "\n",
    "# plot the ROC curve for the model\n",
    "fig, ax = plt.subplots(figsize=(7,6))\n",
    "plt.plot([0,1], [0,1], linestyle='--', label='No Skill')\n",
    "plt.plot(fpr, tpr, marker='.', label='Logistic')\n",
    "\n",
    "# axis labels\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.grid(b=True)\n",
    "plt.title('Receiver Operating Charateristic Curve  - AUC {:.5f}'.\n",
    "           format(model_roc_auc)\n",
    "          )\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# Feature Impartance\n",
    "feature_impartant = coef_sumry.sort_values(by='coefficients')\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(12,9))\n",
    "sns.barplot(x=feature_impartant['coefficients'],\n",
    "            y=feature_impartant['features'], \n",
    "            orient=\"V\"\n",
    "           )\n",
    "plt.title('Feature Impartance')\n",
    "plt.show()\n",
    "\n",
    "print(rfc_score, rfc_roc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Let us compare the models metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Algorithm</th>\n",
       "      <th>Aqu_Score</th>\n",
       "      <th>ROC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LogReg</td>\n",
       "      <td>0.805017</td>\n",
       "      <td>0.727960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LogREg_DUmmy</td>\n",
       "      <td>0.811169</td>\n",
       "      <td>0.737099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.777567</td>\n",
       "      <td>0.621179</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Algorithm  Aqu_Score       ROC\n",
       "0         LogReg   0.805017  0.727960\n",
       "1   LogREg_DUmmy   0.811169  0.737099\n",
       "2  Random Forest   0.777567  0.621179"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metric_df = pd.DataFrame([['LogReg', logit_score ,logit_roc],\n",
    "                          ['LogREg_DUmmy', logit_dummy_score,logit_dummy_roc],\n",
    "                          ['Random Forest', rfc_score, rfc_roc]\n",
    "                         ],\n",
    "                         columns=['Algorithm', 'Aqu_Score', 'ROC'])\n",
    "\n",
    "metric_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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kSZIaMVhJkiQ1YrCSJElqxGAlSZLUiMFK0lhLsirJ9Um2JTlnium/nOQLSXYm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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe085358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fix, (ax1, ax2)=plt.subplots(1,2, figsize=(10,6))\n",
    "\n",
    "g = ax1.bar(x=metric_df['Algorithm'], height=metric_df['Aqu_Score'])\n",
    "ax1.set_title('Comparision of Accuracy scores')\n",
    "\n",
    "ax2.bar(x=metric_df['Algorithm'], height=metric_df['ROC'])\n",
    "ax2.set_title('Comparision of ROC')\n",
    "          \n",
    "plt.show()         "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### WIth the limited analysis I have done the best algorithm to predict Churn is Logsitic Regression with Dummy Variable substition\n",
    "###### The model also outputs the probabilty of a customer churn"
   ]
  }
 ],
 "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}