telecom_customer_churn.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# To support both python 2 and python 3\n",
    "from __future__ import division, print_function, unicode_literals\n",
    "\n",
    "# Common imports\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "os.environ['KMP_DUPLICATE_LIB_OK']='True'\n",
    "\n",
    "# to make this notebook's output stable across runs\n",
    "np.random.seed(42)\n",
    "\n",
    "\n",
    "\n",
    "# Where to save the figures\n",
    "PROJECT_ROOT_DIR = \".\"\n",
    "images = \"images\"\n",
    "\n",
    "def save_fig(fig_id, tight_layout=True):\n",
    "    path = os.path.join(PROJECT_ROOT_DIR, images, fig_id + \".png\")\n",
    "    print(\"Saving figure\", fig_id)\n",
    "    if tight_layout:\n",
    "        plt.tight_layout()\n",
    "    plt.savefig(path, format='png', dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "DATA_PATH = os.path.join(\"datasets\", \"churn\")\n",
    "#fn that reads csv from the defined location, so if csv updates it can take care of it\n",
    "def load_data(data_path=DATA_PATH):\n",
    "    csv_path = os.path.join(data_path, \"customer_churn.csv\")\n",
    "    return pd.read_csv(csv_path,encoding=\"UTF-8\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "churn_data=load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "churn_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7043, 21)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#7043 users\n",
    "churn_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No     5174\n",
       "Yes    1869\n",
       "Name: Churn, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "churn_data['Churn'].value_counts()\n",
    "#imbalanced data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#if null is present\n",
    "churn_data.isnull().sum().values.sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#customer_id should not affect the prediction\n",
    "churn_data.drop(['customerID'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>tenure</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "    </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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "churn_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "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 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(17)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "churn_data.info()\n",
    "#TotalCharges column should be numbertype.. this raises little concern"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#no.of spaces in TotalCharges\n",
    "len(churn_data[churn_data['TotalCharges']==' '])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Engineering\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#Dropping the whitespaces data\n",
    "churn_data=churn_data[churn_data['TotalCharges']!=' ']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "churn_data['TotalCharges']=pd.to_numeric(churn_data['TotalCharges'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#seperating numerical and object dtype for proper understanding\n",
    "churn_data_num=churn_data.select_dtypes(include=[np.number])\n",
    "churn_data_object=churn_data.select_dtypes(include=[np.object])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>tenure</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>29.85</td>\n",
       "      <td>29.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>56.95</td>\n",
       "      <td>1889.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>53.85</td>\n",
       "      <td>108.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>42.30</td>\n",
       "      <td>1840.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>70.70</td>\n",
       "      <td>151.65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SeniorCitizen  tenure  MonthlyCharges  TotalCharges\n",
       "0              0       1           29.85         29.85\n",
       "1              0      34           56.95       1889.50\n",
       "2              0       2           53.85        108.15\n",
       "3              0      45           42.30       1840.75\n",
       "4              0       2           70.70        151.65"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "churn_data_num.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>Partner</th>\n",
       "      <th>Dependents</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>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</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>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>No</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>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>No</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>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>No</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>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>No</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>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Credit card (automatic)</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>No</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>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</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>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</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>Mailed check</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>Two year</td>\n",
       "      <td>No</td>\n",
       "      <td>Credit card (automatic)</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Credit card (automatic)</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Two year</td>\n",
       "      <td>No</td>\n",
       "      <td>Credit card (automatic)</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Two year</td>\n",
       "      <td>No</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>No</td>\n",
       "      <td>Credit card (automatic)</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>DSL</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Two year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Credit card (automatic)</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</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>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>No</td>\n",
       "      <td>Credit card (automatic)</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>No</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>Bank transfer (automatic)</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</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>No</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Two year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Credit card (automatic)</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7013</th>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7014</th>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7015</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Credit card (automatic)</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7016</th>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</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>No</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7017</th>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>Two year</td>\n",
       "      <td>No</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7018</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</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>Mailed check</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7019</th>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>Two year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7020</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7021</th>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7022</th>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>One year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7023</th>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7024</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</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>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Credit card (automatic)</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7025</th>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7026</th>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</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>Bank transfer (automatic)</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7027</th>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7028</th>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Two year</td>\n",
       "      <td>No</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7029</th>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7030</th>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7031</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Credit card (automatic)</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7032</th>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</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>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7033</th>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>No</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>Credit card (automatic)</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7034</th>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Credit card (automatic)</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7035</th>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>No</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>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7036</th>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>No</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>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7037</th>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>Two year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7038</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>One year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7039</th>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>One year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Credit card (automatic)</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7040</th>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</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>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>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7041</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</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>Mailed check</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7042</th>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Two year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7032 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      gender Partner Dependents PhoneService     MultipleLines  \\\n",
       "0     Female     Yes         No           No  No phone service   \n",
       "1       Male      No         No          Yes                No   \n",
       "2       Male      No         No          Yes                No   \n",
       "3       Male      No         No           No  No phone service   \n",
       "4     Female      No         No          Yes                No   \n",
       "5     Female      No         No          Yes               Yes   \n",
       "6       Male      No        Yes          Yes               Yes   \n",
       "7     Female      No         No           No  No phone service   \n",
       "8     Female     Yes         No          Yes               Yes   \n",
       "9       Male      No        Yes          Yes                No   \n",
       "10      Male     Yes        Yes          Yes                No   \n",
       "11      Male      No         No          Yes                No   \n",
       "12      Male     Yes         No          Yes               Yes   \n",
       "13      Male      No         No          Yes               Yes   \n",
       "14      Male      No         No          Yes                No   \n",
       "15    Female     Yes        Yes          Yes               Yes   \n",
       "16    Female      No         No          Yes                No   \n",
       "17      Male      No        Yes          Yes               Yes   \n",
       "18    Female     Yes        Yes          Yes                No   \n",
       "19    Female      No         No          Yes                No   \n",
       "20      Male      No         No           No  No phone service   \n",
       "21      Male     Yes         No          Yes                No   \n",
       "22      Male      No         No          Yes                No   \n",
       "23    Female     Yes         No          Yes               Yes   \n",
       "24      Male     Yes        Yes          Yes                No   \n",
       "25    Female      No         No          Yes                No   \n",
       "26      Male     Yes        Yes          Yes               Yes   \n",
       "27      Male     Yes        Yes           No  No phone service   \n",
       "28      Male     Yes         No          Yes               Yes   \n",
       "29    Female      No        Yes          Yes                No   \n",
       "...      ...     ...        ...          ...               ...   \n",
       "7013  Female      No         No          Yes               Yes   \n",
       "7014    Male      No         No          Yes               Yes   \n",
       "7015    Male     Yes         No          Yes                No   \n",
       "7016  Female      No         No          Yes                No   \n",
       "7017  Female      No         No          Yes                No   \n",
       "7018    Male     Yes        Yes          Yes                No   \n",
       "7019  Female      No         No          Yes                No   \n",
       "7020    Male     Yes        Yes          Yes                No   \n",
       "7021    Male      No         No          Yes                No   \n",
       "7022    Male      No         No          Yes               Yes   \n",
       "7023  Female     Yes         No          Yes               Yes   \n",
       "7024    Male     Yes         No          Yes               Yes   \n",
       "7025  Female      No         No          Yes               Yes   \n",
       "7026  Female      No         No          Yes                No   \n",
       "7027    Male      No         No          Yes                No   \n",
       "7028  Female     Yes         No          Yes                No   \n",
       "7029  Female      No         No           No  No phone service   \n",
       "7030  Female      No         No          Yes                No   \n",
       "7031    Male     Yes         No          Yes               Yes   \n",
       "7032    Male      No         No          Yes               Yes   \n",
       "7033    Male      No         No          Yes                No   \n",
       "7034  Female      No         No          Yes               Yes   \n",
       "7035    Male      No         No          Yes                No   \n",
       "7036  Female      No         No           No  No phone service   \n",
       "7037  Female      No         No          Yes                No   \n",
       "7038    Male     Yes        Yes          Yes               Yes   \n",
       "7039  Female     Yes        Yes          Yes               Yes   \n",
       "7040  Female     Yes        Yes           No  No phone service   \n",
       "7041    Male     Yes         No          Yes               Yes   \n",
       "7042    Male      No         No          Yes                No   \n",
       "\n",
       "     InternetService       OnlineSecurity         OnlineBackup  \\\n",
       "0                DSL                   No                  Yes   \n",
       "1                DSL                  Yes                   No   \n",
       "2                DSL                  Yes                  Yes   \n",
       "3                DSL                  Yes                   No   \n",
       "4        Fiber optic                   No                   No   \n",
       "5        Fiber optic                   No                   No   \n",
       "6        Fiber optic                   No                  Yes   \n",
       "7                DSL                  Yes                   No   \n",
       "8        Fiber optic                   No                   No   \n",
       "9                DSL                  Yes                  Yes   \n",
       "10               DSL                  Yes                   No   \n",
       "11                No  No internet service  No internet service   \n",
       "12       Fiber optic                   No                   No   \n",
       "13       Fiber optic                   No                  Yes   \n",
       "14       Fiber optic                  Yes                   No   \n",
       "15       Fiber optic                  Yes                  Yes   \n",
       "16                No  No internet service  No internet service   \n",
       "17       Fiber optic                  Yes                   No   \n",
       "18               DSL                   No                   No   \n",
       "19       Fiber optic                   No                  Yes   \n",
       "20               DSL                   No                   No   \n",
       "21                No  No internet service  No internet service   \n",
       "22                No  No internet service  No internet service   \n",
       "23               DSL                   No                  Yes   \n",
       "24               DSL                  Yes                  Yes   \n",
       "25               DSL                  Yes                  Yes   \n",
       "26       Fiber optic                   No                  Yes   \n",
       "27               DSL                   No                  Yes   \n",
       "28               DSL                  Yes                  Yes   \n",
       "29               DSL                   No                   No   \n",
       "...              ...                  ...                  ...   \n",
       "7013     Fiber optic                   No                  Yes   \n",
       "7014     Fiber optic                   No                  Yes   \n",
       "7015     Fiber optic                   No                   No   \n",
       "7016             DSL                   No                  Yes   \n",
       "7017              No  No internet service  No internet service   \n",
       "7018     Fiber optic                   No                   No   \n",
       "7019              No  No internet service  No internet service   \n",
       "7020              No  No internet service  No internet service   \n",
       "7021             DSL                   No                   No   \n",
       "7022     Fiber optic                   No                  Yes   \n",
       "7023     Fiber optic                   No                  Yes   \n",
       "7024     Fiber optic                  Yes                   No   \n",
       "7025     Fiber optic                   No                   No   \n",
       "7026             DSL                   No                   No   \n",
       "7027             DSL                   No                  Yes   \n",
       "7028             DSL                   No                  Yes   \n",
       "7029             DSL                   No                   No   \n",
       "7030              No  No internet service  No internet service   \n",
       "7031             DSL                  Yes                  Yes   \n",
       "7032     Fiber optic                   No                   No   \n",
       "7033     Fiber optic                   No                   No   \n",
       "7034     Fiber optic                  Yes                  Yes   \n",
       "7035     Fiber optic                   No                   No   \n",
       "7036             DSL                   No                  Yes   \n",
       "7037              No  No internet service  No internet service   \n",
       "7038             DSL                  Yes                   No   \n",
       "7039     Fiber optic                   No                  Yes   \n",
       "7040             DSL                  Yes                   No   \n",
       "7041     Fiber optic                   No                   No   \n",
       "7042     Fiber optic                  Yes                   No   \n",
       "\n",
       "         DeviceProtection          TechSupport          StreamingTV  \\\n",
       "0                      No                   No                   No   \n",
       "1                     Yes                   No                   No   \n",
       "2                      No                   No                   No   \n",
       "3                     Yes                  Yes                   No   \n",
       "4                      No                   No                   No   \n",
       "5                     Yes                   No                  Yes   \n",
       "6                      No                   No                  Yes   \n",
       "7                      No                   No                   No   \n",
       "8                     Yes                  Yes                  Yes   \n",
       "9                      No                   No                   No   \n",
       "10                     No                   No                   No   \n",
       "11    No internet service  No internet service  No internet service   \n",
       "12                    Yes                   No                  Yes   \n",
       "13                    Yes                   No                  Yes   \n",
       "14                    Yes                  Yes                  Yes   \n",
       "15                    Yes                  Yes                  Yes   \n",
       "16    No internet service  No internet service  No internet service   \n",
       "17                    Yes                   No                  Yes   \n",
       "18                    Yes                  Yes                   No   \n",
       "19                    Yes                   No                   No   \n",
       "20                    Yes                   No                   No   \n",
       "21    No internet service  No internet service  No internet service   \n",
       "22    No internet service  No internet service  No internet service   \n",
       "23                     No                  Yes                   No   \n",
       "24                     No                  Yes                   No   \n",
       "25                     No                   No                   No   \n",
       "26                     No                   No                  Yes   \n",
       "27                     No                   No                   No   \n",
       "28                    Yes                  Yes                  Yes   \n",
       "29                     No                   No                  Yes   \n",
       "...                   ...                  ...                  ...   \n",
       "7013                  Yes                   No                  Yes   \n",
       "7014                   No                   No                  Yes   \n",
       "7015                  Yes                   No                  Yes   \n",
       "7016                   No                   No                   No   \n",
       "7017  No internet service  No internet service  No internet service   \n",
       "7018                   No                   No                   No   \n",
       "7019  No internet service  No internet service  No internet service   \n",
       "7020  No internet service  No internet service  No internet service   \n",
       "7021                   No                  Yes                  Yes   \n",
       "7022                  Yes                   No                  Yes   \n",
       "7023                  Yes                   No                  Yes   \n",
       "7024                  Yes                   No                   No   \n",
       "7025                  Yes                  Yes                   No   \n",
       "7026                   No                   No                   No   \n",
       "7027                   No                  Yes                  Yes   \n",
       "7028                   No                  Yes                  Yes   \n",
       "7029                   No                   No                  Yes   \n",
       "7030  No internet service  No internet service  No internet service   \n",
       "7031                   No                   No                   No   \n",
       "7032                   No                   No                   No   \n",
       "7033                   No                   No                   No   \n",
       "7034                  Yes                   No                  Yes   \n",
       "7035                   No                   No                  Yes   \n",
       "7036                  Yes                  Yes                  Yes   \n",
       "7037  No internet service  No internet service  No internet service   \n",
       "7038                  Yes                  Yes                  Yes   \n",
       "7039                  Yes                   No                  Yes   \n",
       "7040                   No                   No                   No   \n",
       "7041                   No                   No                   No   \n",
       "7042                  Yes                  Yes                  Yes   \n",
       "\n",
       "          StreamingMovies        Contract PaperlessBilling  \\\n",
       "0                      No  Month-to-month              Yes   \n",
       "1                      No        One year               No   \n",
       "2                      No  Month-to-month              Yes   \n",
       "3                      No        One year               No   \n",
       "4                      No  Month-to-month              Yes   \n",
       "5                     Yes  Month-to-month              Yes   \n",
       "6                      No  Month-to-month              Yes   \n",
       "7                      No  Month-to-month               No   \n",
       "8                     Yes  Month-to-month              Yes   \n",
       "9                      No        One year               No   \n",
       "10                     No  Month-to-month              Yes   \n",
       "11    No internet service        Two year               No   \n",
       "12                    Yes        One year               No   \n",
       "13                    Yes  Month-to-month              Yes   \n",
       "14                    Yes  Month-to-month              Yes   \n",
       "15                    Yes        Two year               No   \n",
       "16    No internet service        One year               No   \n",
       "17                    Yes        Two year               No   \n",
       "18                     No  Month-to-month               No   \n",
       "19                    Yes  Month-to-month              Yes   \n",
       "20                    Yes  Month-to-month              Yes   \n",
       "21    No internet service        One year               No   \n",
       "22    No internet service  Month-to-month               No   \n",
       "23                     No        Two year              Yes   \n",
       "24                     No  Month-to-month               No   \n",
       "25                     No  Month-to-month              Yes   \n",
       "26                    Yes  Month-to-month              Yes   \n",
       "27                     No  Month-to-month               No   \n",
       "28                    Yes        Two year              Yes   \n",
       "29                    Yes  Month-to-month              Yes   \n",
       "...                   ...             ...              ...   \n",
       "7013                   No  Month-to-month              Yes   \n",
       "7014                   No  Month-to-month              Yes   \n",
       "7015                   No  Month-to-month              Yes   \n",
       "7016                   No  Month-to-month               No   \n",
       "7017  No internet service        Two year               No   \n",
       "7018                   No  Month-to-month              Yes   \n",
       "7019  No internet service        Two year               No   \n",
       "7020  No internet service  Month-to-month              Yes   \n",
       "7021                   No        One year              Yes   \n",
       "7022                  Yes        One year              Yes   \n",
       "7023                  Yes  Month-to-month              Yes   \n",
       "7024                   No  Month-to-month              Yes   \n",
       "7025                  Yes  Month-to-month              Yes   \n",
       "7026                   No  Month-to-month              Yes   \n",
       "7027                  Yes  Month-to-month               No   \n",
       "7028                   No        Two year               No   \n",
       "7029                  Yes  Month-to-month              Yes   \n",
       "7030  No internet service  Month-to-month              Yes   \n",
       "7031                   No        One year               No   \n",
       "7032                   No  Month-to-month              Yes   \n",
       "7033                   No  Month-to-month              Yes   \n",
       "7034                   No  Month-to-month              Yes   \n",
       "7035                   No  Month-to-month              Yes   \n",
       "7036                  Yes        One year               No   \n",
       "7037  No internet service        Two year              Yes   \n",
       "7038                  Yes        One year              Yes   \n",
       "7039                  Yes        One year              Yes   \n",
       "7040                   No  Month-to-month              Yes   \n",
       "7041                   No  Month-to-month              Yes   \n",
       "7042                  Yes        Two year              Yes   \n",
       "\n",
       "                  PaymentMethod Churn  \n",
       "0              Electronic check    No  \n",
       "1                  Mailed check    No  \n",
       "2                  Mailed check   Yes  \n",
       "3     Bank transfer (automatic)    No  \n",
       "4              Electronic check   Yes  \n",
       "5              Electronic check   Yes  \n",
       "6       Credit card (automatic)    No  \n",
       "7                  Mailed check    No  \n",
       "8              Electronic check   Yes  \n",
       "9     Bank transfer (automatic)    No  \n",
       "10                 Mailed check    No  \n",
       "11      Credit card (automatic)    No  \n",
       "12      Credit card (automatic)    No  \n",
       "13    Bank transfer (automatic)   Yes  \n",
       "14             Electronic check    No  \n",
       "15      Credit card (automatic)    No  \n",
       "16                 Mailed check    No  \n",
       "17    Bank transfer (automatic)    No  \n",
       "18      Credit card (automatic)   Yes  \n",
       "19             Electronic check    No  \n",
       "20             Electronic check   Yes  \n",
       "21    Bank transfer (automatic)    No  \n",
       "22                 Mailed check   Yes  \n",
       "23      Credit card (automatic)    No  \n",
       "24      Credit card (automatic)    No  \n",
       "25    Bank transfer (automatic)    No  \n",
       "26             Electronic check   Yes  \n",
       "27             Electronic check   Yes  \n",
       "28      Credit card (automatic)    No  \n",
       "29                 Mailed check   Yes  \n",
       "...                         ...   ...  \n",
       "7013  Bank transfer (automatic)    No  \n",
       "7014           Electronic check    No  \n",
       "7015    Credit card (automatic)    No  \n",
       "7016           Electronic check    No  \n",
       "7017  Bank transfer (automatic)    No  \n",
       "7018               Mailed check   Yes  \n",
       "7019               Mailed check    No  \n",
       "7020           Electronic check    No  \n",
       "7021           Electronic check   Yes  \n",
       "7022           Electronic check    No  \n",
       "7023           Electronic check    No  \n",
       "7024    Credit card (automatic)    No  \n",
       "7025  Bank transfer (automatic)    No  \n",
       "7026  Bank transfer (automatic)   Yes  \n",
       "7027               Mailed check    No  \n",
       "7028  Bank transfer (automatic)    No  \n",
       "7029           Electronic check    No  \n",
       "7030               Mailed check    No  \n",
       "7031    Credit card (automatic)    No  \n",
       "7032           Electronic check   Yes  \n",
       "7033    Credit card (automatic)    No  \n",
       "7034    Credit card (automatic)   Yes  \n",
       "7035  Bank transfer (automatic)    No  \n",
       "7036           Electronic check    No  \n",
       "7037  Bank transfer (automatic)    No  \n",
       "7038               Mailed check    No  \n",
       "7039    Credit card (automatic)    No  \n",
       "7040           Electronic check    No  \n",
       "7041               Mailed check   Yes  \n",
       "7042  Bank transfer (automatic)    No  \n",
       "\n",
       "[7032 rows x 16 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "churn_data_object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/onion8/miniconda3/lib/python2.7/site-packages/pandas/core/generic.py:5890: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self._update_inplace(new_data)\n"
     ]
    }
   ],
   "source": [
    "churn_data_object['MultipleLines'].replace('No phone service','No', inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No     4065\n",
       "Yes    2967\n",
       "Name: MultipleLines, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "churn_data_object['MultipleLines'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "churn_data=pd.merge(churn_data_object, churn_data_num,left_index=True, right_index=True, how='outer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>Partner</th>\n",
       "      <th>Dependents</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>Churn</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>tenure</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</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>No</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>29.85</td>\n",
       "      <td>29.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>No</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>No</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>56.95</td>\n",
       "      <td>1889.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>No</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>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>53.85</td>\n",
       "      <td>108.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</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>No</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>42.30</td>\n",
       "      <td>1840.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>No</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>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>70.70</td>\n",
       "      <td>151.65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender Partner Dependents PhoneService MultipleLines InternetService  \\\n",
       "0  Female     Yes         No           No            No             DSL   \n",
       "1    Male      No         No          Yes            No             DSL   \n",
       "2    Male      No         No          Yes            No             DSL   \n",
       "3    Male      No         No           No            No             DSL   \n",
       "4  Female      No         No          Yes            No     Fiber optic   \n",
       "\n",
       "  OnlineSecurity OnlineBackup DeviceProtection TechSupport StreamingTV  \\\n",
       "0             No          Yes               No          No          No   \n",
       "1            Yes           No              Yes          No          No   \n",
       "2            Yes          Yes               No          No          No   \n",
       "3            Yes           No              Yes         Yes          No   \n",
       "4             No           No               No          No          No   \n",
       "\n",
       "  StreamingMovies        Contract PaperlessBilling              PaymentMethod  \\\n",
       "0              No  Month-to-month              Yes           Electronic check   \n",
       "1              No        One year               No               Mailed check   \n",
       "2              No  Month-to-month              Yes               Mailed check   \n",
       "3              No        One year               No  Bank transfer (automatic)   \n",
       "4              No  Month-to-month              Yes           Electronic check   \n",
       "\n",
       "  Churn  SeniorCitizen  tenure  MonthlyCharges  TotalCharges  \n",
       "0    No              0       1           29.85         29.85  \n",
       "1    No              0      34           56.95       1889.50  \n",
       "2   Yes              0       2           53.85        108.15  \n",
       "3    No              0      45           42.30       1840.75  \n",
       "4   Yes              0       2           70.70        151.65  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "churn_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dividing among feature and label\n",
    "churn_data_feat=churn_data.drop('Churn',axis=1)\n",
    "churn_data_labels=churn_data['Churn']\n",
    "churn_data_num= churn_data_feat.select_dtypes(include=[np.number])\n",
    "churn_data_cat=churn_data_feat.select_dtypes(include=[np.object])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn_pandas import CategoricalImputer\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "#numerical pipeline\n",
    "num_pipeline = Pipeline([\n",
    "        \n",
    "        ('std_scaler', StandardScaler()),\n",
    "    ])\n",
    "#categorical pipeline\n",
    "cat_pipeline= Pipeline([\n",
    "        \n",
    "        \n",
    "        ('1-hot', OneHotEncoder()),\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/onion8/miniconda3/lib/python2.7/site-packages/sklearn/preprocessing/data.py:625: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
      "  return self.partial_fit(X, y)\n",
      "/Users/onion8/miniconda3/lib/python2.7/site-packages/sklearn/base.py:462: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
      "  return self.fit(X, **fit_params).transform(X)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.compose import ColumnTransformer\n",
    "num_attribs = list(churn_data_num)\n",
    "cat_attribs = list(churn_data_cat)\n",
    "\n",
    "#Merging both pipeline\n",
    "full_pipeline = ColumnTransformer([\n",
    "        (\"num\", num_pipeline, num_attribs),\n",
    "        (\"cat\", cat_pipeline, cat_attribs)\n",
    "    ])\n",
    "\n",
    "#preparing features  for training\n",
    "churn_data_prepared = full_pipeline.fit_transform(churn_data_feat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "#Splitting training and testing features and labels(using same random_state allows split at the same point)\n",
    "train_set, test_set = train_test_split(churn_data_prepared, test_size=0.2, random_state=42)\n",
    "train_labels,test_labels=train_test_split(churn_data_labels, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.over_sampling import SMOTE\n",
    "# smote for imbalanced data\n",
    "smote = SMOTE(ratio='minority',random_state=42,kind='svm')\n",
    "X_sm, y_sm = smote.fit_sample(train_set, train_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating various models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SGDClassifier(alpha=0.0001, average=False, class_weight=None,\n",
       "       early_stopping=False, epsilon=0.1, eta0=0.0, fit_intercept=True,\n",
       "       l1_ratio=0.15, learning_rate='optimal', loss='hinge', max_iter=5,\n",
       "       n_iter=None, n_iter_no_change=5, n_jobs=None, penalty='l2',\n",
       "       power_t=0.5, random_state=42, shuffle=True, tol=-inf,\n",
       "       validation_fraction=0.1, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import SGDClassifier\n",
    "\n",
    "sgd_clf = SGDClassifier(max_iter=5, tol=-np.infty, random_state=42)\n",
    "sgd_clf.fit(X_sm, y_sm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SGDClassifier with accuracy of around 70 is not bad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.71822803, 0.66230937, 0.71039244])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "cross_val_score(sgd_clf, X_sm, y_sm, cv=3, scoring=\"accuracy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "forest_clf = RandomForestClassifier(n_estimators=40, n_jobs=-1, random_state=42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, 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=40, n_jobs=-1,\n",
       "            oob_score=False, random_state=42, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest_clf.fit(X_sm, y_sm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction=forest_clf.predict((test_set))\n",
    "actual_labels=list((test_labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "u'Yes'"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "actual_labels[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "#changing categorical var to binary var\n",
    "Y_train=np.where(y_sm =='Yes', 1, 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random forest of 40 decision trees has F1 score of around 77%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7718550106609808"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "\n",
    "f1_score(test_labels, prediction, average=\"micro\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "for i in range(len(test_set)):\n",
    "    if prediction[i]=='Yes':\n",
    "        prediction[i]=1\n",
    "    else:\n",
    "        prediction[i]=0\n",
    "    if actual_labels[i]=='Yes':\n",
    "        actual_labels[i]=1\n",
    "    else:\n",
    "        actual_labels[i]=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "actual_labels[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "       estimator=RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, 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='warn', n_jobs=None,\n",
       "            oob_score=False, random_state=42, verbose=0, warm_start=False),\n",
       "       fit_params=None, iid='warn', n_jobs=None,\n",
       "       param_grid=[{u'n_estimators': [25, 35, 42], u'max_features': [2, 4, 6, 8]}, {u'n_estimators': [35, 140], u'max_features': [2, 3, 4], u'bootstrap': [False]}],\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
       "       scoring=u'neg_mean_squared_error', verbose=0)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#trying to find proper hyperparameters\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "param_grid = [\n",
    "    # try 12 (3×4) combinations of hyperparameters\n",
    "    {'n_estimators': [25, 35, 42], 'max_features': [2, 4, 6, 8]},\n",
    "    # then try 6 (2×3) combinations with bootstrap set as False\n",
    "    {'bootstrap': [False], 'n_estimators': [35, 140], 'max_features': [2, 3, 4]},\n",
    "  ]\n",
    "\n",
    "forest_clf = RandomForestClassifier(random_state=42)\n",
    "# train across 5 folds, that's a total of (12+6)*5=90 rounds of training \n",
    "grid_search = GridSearchCV(forest_clf, param_grid, cv=5,\n",
    "                           scoring='neg_mean_squared_error', return_train_score=True)\n",
    "grid_search.fit(X_sm, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{u'bootstrap': False, u'max_features': 2, u'n_estimators': 140}"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#best parameters\n",
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=False, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features=2, 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=140, n_jobs=None,\n",
       "            oob_score=False, random_state=42, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.3795626385857405 {u'max_features': 2, u'n_estimators': 25}\n",
      "0.375231970222418 {u'max_features': 2, u'n_estimators': 35}\n",
      "0.37587669916903366 {u'max_features': 2, u'n_estimators': 42}\n",
      "0.3779644730092272 {u'max_features': 4, u'n_estimators': 25}\n",
      "0.3778042846261013 {u'max_features': 4, u'n_estimators': 35}\n",
      "0.37410102091531233 {u'max_features': 4, u'n_estimators': 42}\n",
      "0.3860459519248695 {u'max_features': 6, u'n_estimators': 25}\n",
      "0.3813128843069675 {u'max_features': 6, u'n_estimators': 35}\n",
      "0.3803592032364707 {u'max_features': 6, u'n_estimators': 42}\n",
      "0.3882349704133001 {u'max_features': 8, u'n_estimators': 25}\n",
      "0.3817888315082132 {u'max_features': 8, u'n_estimators': 35}\n",
      "0.3786045487819098 {u'max_features': 8, u'n_estimators': 42}\n",
      "0.37442449786370047 {u'max_features': 2, u'n_estimators': 35, u'bootstrap': False}\n",
      "0.3700337004480975 {u'max_features': 2, u'n_estimators': 140, u'bootstrap': False}\n",
      "0.375231970222418 {u'max_features': 3, u'n_estimators': 35, u'bootstrap': False}\n",
      "0.3705241363166782 {u'max_features': 3, u'n_estimators': 140, u'bootstrap': False}\n",
      "0.376681058529257 {u'max_features': 4, u'n_estimators': 35, u'bootstrap': False}\n",
      "0.37296664223863274 {u'max_features': 4, u'n_estimators': 140, u'bootstrap': False}\n"
     ]
    }
   ],
   "source": [
    "cvres = grid_search.cv_results_\n",
    "for mean_score, params in zip(cvres[\"mean_test_score\"], cvres[\"params\"]):\n",
    "    print(np.sqrt(-mean_score), params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.1184477754204422, u'PhoneService'),\n",
       " (0.10887709510583436, u'Partner'),\n",
       " (0.10531119890061903, u'Dependents'),\n",
       " (0.0340067478543729, u'tenure'),\n",
       " (0.02381506824746138, u'TotalCharges'),\n",
       " (0.019394013232273667, u'Churn'),\n",
       " (0.017155369608848742, u'OnlineSecurity'),\n",
       " (0.016988470149351034, u'InternetService'),\n",
       " (0.016706644676984628, u'MultipleLines'),\n",
       " (0.015797115974356457, u'OnlineBackup'),\n",
       " (0.014699077425883788, u'gender'),\n",
       " (0.014488571585528758, u'DeviceProtection'),\n",
       " (0.01407346427039836, u'TechSupport'),\n",
       " (0.012868090170418412, u'PaymentMethod'),\n",
       " (0.012591957633504243, u'Contract'),\n",
       " (0.012081920688939683, u'PaperlessBilling'),\n",
       " (0.007311353246423249, u'MonthlyCharges'),\n",
       " (0.004212477293503952, u'StreamingTV'),\n",
       " (0.004071143272323674, u'StreamingMovies'),\n",
       " (0.00383141051422615, u'SeniorCitizen')]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calculating feature importance\n",
    "feature_importances = grid_search.best_estimator_.feature_importances_\n",
    "sorted(zip(feature_importances, churn_data.columns), reverse=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Finding params for rand Forest by GridSearchCV gives F1 score around 77%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7690120824449184"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "f1_score(actual_labels, final_predictions, average=\"micro\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "#ensemble method -1\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import VotingClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "log_clf = LogisticRegression(solver=\"liblinear\", random_state=42)\n",
    "rnd_clf = RandomForestClassifier(n_estimators=10, random_state=42)\n",
    "svm_clf = SVC(gamma=\"auto\", random_state=42)\n",
    "\n",
    "voting_clf = VotingClassifier(\n",
    "    estimators=[('lr', log_clf), ('rf', rnd_clf), ('svc', svm_clf)],\n",
    "    voting='hard')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VotingClassifier(estimators=[(u'lr', LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='warn',\n",
       "          n_jobs=None, penalty='l2', random_state=42, solver=u'liblinear',\n",
       "          tol=0.0001, verbose=0, warm_start=False)), (u'rf', Ra...f',\n",
       "  max_iter=-1, probability=False, random_state=42, shrinking=True,\n",
       "  tol=0.001, verbose=False))],\n",
       "         flatten_transform=None, n_jobs=None, voting=u'hard', weights=None)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "voting_clf.fit(X_sm, y_sm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ensemble method always gives better result then the individual learners"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression 0.7412935323383084\n",
      "RandomForestClassifier 0.751954513148543\n",
      "SVC 0.720682302771855\n",
      "VotingClassifier 0.738450604122246\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "for clf in (log_clf, rnd_clf, svm_clf, voting_clf):\n",
    "    clf.fit(X_sm, y_sm)\n",
    "    y_pred = clf.predict(test_set)\n",
    "    print(clf.__class__.__name__, accuracy_score(test_labels, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VotingClassifier(estimators=[(u'lr', LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='warn',\n",
       "          n_jobs=None, penalty='l2', random_state=42, solver=u'liblinear',\n",
       "          tol=0.0001, verbose=0, warm_start=False)), (u'rf', Ra...bf',\n",
       "  max_iter=-1, probability=True, random_state=42, shrinking=True,\n",
       "  tol=0.001, verbose=False))],\n",
       "         flatten_transform=None, n_jobs=None, voting=u'soft', weights=None)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#ensemble method -2\n",
    "log_clf = LogisticRegression(solver=\"liblinear\", random_state=42)\n",
    "rnd_clf = RandomForestClassifier(n_estimators=10, random_state=42)\n",
    "svm_clf = SVC(gamma=\"auto\", probability=True, random_state=42)\n",
    "\n",
    "voting_clf = VotingClassifier(\n",
    "    estimators=[('lr', log_clf), ('rf', rnd_clf), ('svc', svm_clf)],\n",
    "    voting='soft')\n",
    "voting_clf.fit(X_sm, y_sm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression 0.7412935323383084\n",
      "RandomForestClassifier 0.751954513148543\n",
      "SVC 0.720682302771855\n",
      "VotingClassifier 0.7448471926083866\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "for clf in (log_clf, rnd_clf, svm_clf, voting_clf):\n",
    "    clf.fit(X_sm, y_sm)\n",
    "    y_pred = clf.predict(test_set)\n",
    "    print(clf.__class__.__name__, accuracy_score(test_labels, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating BaggingClassifier with bootstrap=False of 10000 decision trees\n",
    "from sklearn.ensemble import BaggingClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "bag_clf = BaggingClassifier(\n",
    "    DecisionTreeClassifier(random_state=42), n_estimators=10000,\n",
    "    max_samples=5000, bootstrap=False, n_jobs=-1, random_state=42)\n",
    "bag_clf.fit(X_sm, y_sm)\n",
    "y_pred = bag_clf.predict(test_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bagging Classifier with bootstrap=False(Pasting) and 10000 decision trees gives best F1 score till now 77.6%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7759327230585151"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(test_labels, y_pred, average=\"weighted\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7759327230585151"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "#using 'micro' it gives global\n",
    "f1_score(test_labels, y_pred, average=\"weighted\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    import xgboost\n",
    "except ImportError as ex:\n",
    "    print(\"Error: the xgboost library is not installed.\")\n",
    "    xgboost = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "\n",
    "if xgboost is not None:  \n",
    "    xgb_clf = xgboost.XGBClassifier(random_state=42)\n",
    "    xgb_clf.fit(X_sm, y_sm)\n",
    "    y_pred = xgb_clf.predict(test_set)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation MSE: 0.3845060412224591\n"
     ]
    }
   ],
   "source": [
    "    y_pred=np.where(y_pred =='Yes', 1, 0)\n",
    "    val_error = mean_squared_error(actual_labels, y_pred)\n",
    "    print(\"Validation MSE:\", val_error)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## xgboost does not perform very well with the given params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7636836713662205"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(test_labels, y_pred, average=\"weighted\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\tvalidation_0-error:0.276475\n",
      "Will train until validation_0-error hasn't improved in 1000 rounds.\n",
      "[1]\tvalidation_0-error:0.276475\n",
      "[2]\tvalidation_0-error:0.297086\n",
      "[3]\tvalidation_0-error:0.290689\n",
      "[4]\tvalidation_0-error:0.287846\n",
      "[5]\tvalidation_0-error:0.292111\n",
      "[6]\tvalidation_0-error:0.297797\n",
      "[7]\tvalidation_0-error:0.285714\n",
      "[8]\tvalidation_0-error:0.289979\n",
      "[9]\tvalidation_0-error:0.289979\n",
      "[10]\tvalidation_0-error:0.285714\n",
      "[11]\tvalidation_0-error:0.289979\n",
      "[12]\tvalidation_0-error:0.282161\n",
      "[13]\tvalidation_0-error:0.282161\n",
      "[14]\tvalidation_0-error:0.282161\n",
      "[15]\tvalidation_0-error:0.279318\n",
      "[16]\tvalidation_0-error:0.280028\n",
      "[17]\tvalidation_0-error:0.280028\n",
      "[18]\tvalidation_0-error:0.274343\n",
      "[19]\tvalidation_0-error:0.273632\n",
      "[20]\tvalidation_0-error:0.275764\n",
      "[21]\tvalidation_0-error:0.272921\n",
      "[22]\tvalidation_0-error:0.265103\n",
      "[23]\tvalidation_0-error:0.267235\n",
      "[24]\tvalidation_0-error:0.264392\n",
      "[25]\tvalidation_0-error:0.261549\n",
      "[26]\tvalidation_0-error:0.261549\n",
      "[27]\tvalidation_0-error:0.26226\n",
      "[28]\tvalidation_0-error:0.262971\n",
      "[29]\tvalidation_0-error:0.263682\n",
      "[30]\tvalidation_0-error:0.262971\n",
      "[31]\tvalidation_0-error:0.261549\n",
      "[32]\tvalidation_0-error:0.260128\n",
      "[33]\tvalidation_0-error:0.259417\n",
      "[34]\tvalidation_0-error:0.26226\n",
      "[35]\tvalidation_0-error:0.260839\n",
      "[36]\tvalidation_0-error:0.26226\n",
      "[37]\tvalidation_0-error:0.261549\n",
      "[38]\tvalidation_0-error:0.259417\n",
      "[39]\tvalidation_0-error:0.258706\n",
      "[40]\tvalidation_0-error:0.258706\n",
      "[41]\tvalidation_0-error:0.257285\n",
      "[42]\tvalidation_0-error:0.256574\n",
      "[43]\tvalidation_0-error:0.259417\n",
      "[44]\tvalidation_0-error:0.257996\n",
      "[45]\tvalidation_0-error:0.258706\n",
      "[46]\tvalidation_0-error:0.258706\n",
      "[47]\tvalidation_0-error:0.258706\n",
      "[48]\tvalidation_0-error:0.257996\n",
      "[49]\tvalidation_0-error:0.258706\n",
      "[50]\tvalidation_0-error:0.257996\n",
      "[51]\tvalidation_0-error:0.258706\n",
      "[52]\tvalidation_0-error:0.256574\n",
      "[53]\tvalidation_0-error:0.256574\n",
      "[54]\tvalidation_0-error:0.255864\n",
      "[55]\tvalidation_0-error:0.255864\n",
      "[56]\tvalidation_0-error:0.255864\n",
      "[57]\tvalidation_0-error:0.255864\n",
      "[58]\tvalidation_0-error:0.255864\n",
      "[59]\tvalidation_0-error:0.256574\n",
      "[60]\tvalidation_0-error:0.255864\n",
      "[61]\tvalidation_0-error:0.255864\n",
      "[62]\tvalidation_0-error:0.255864\n",
      "[63]\tvalidation_0-error:0.255864\n",
      "[64]\tvalidation_0-error:0.256574\n",
      "[65]\tvalidation_0-error:0.257285\n",
      "[66]\tvalidation_0-error:0.255864\n",
      "[67]\tvalidation_0-error:0.256574\n",
      "[68]\tvalidation_0-error:0.256574\n",
      "[69]\tvalidation_0-error:0.256574\n",
      "[70]\tvalidation_0-error:0.255153\n",
      "[71]\tvalidation_0-error:0.254442\n",
      "[72]\tvalidation_0-error:0.254442\n",
      "[73]\tvalidation_0-error:0.254442\n",
      "[74]\tvalidation_0-error:0.253731\n",
      "[75]\tvalidation_0-error:0.253731\n",
      "[76]\tvalidation_0-error:0.253731\n",
      "[77]\tvalidation_0-error:0.253731\n",
      "[78]\tvalidation_0-error:0.253021\n",
      "[79]\tvalidation_0-error:0.25231\n",
      "[80]\tvalidation_0-error:0.25231\n",
      "[81]\tvalidation_0-error:0.253731\n",
      "[82]\tvalidation_0-error:0.251599\n",
      "[83]\tvalidation_0-error:0.250888\n",
      "[84]\tvalidation_0-error:0.250888\n",
      "[85]\tvalidation_0-error:0.250888\n",
      "[86]\tvalidation_0-error:0.250888\n",
      "[87]\tvalidation_0-error:0.250178\n",
      "[88]\tvalidation_0-error:0.249467\n",
      "[89]\tvalidation_0-error:0.249467\n",
      "[90]\tvalidation_0-error:0.249467\n",
      "[91]\tvalidation_0-error:0.249467\n",
      "[92]\tvalidation_0-error:0.248756\n",
      "[93]\tvalidation_0-error:0.250178\n",
      "[94]\tvalidation_0-error:0.250178\n",
      "[95]\tvalidation_0-error:0.250178\n",
      "[96]\tvalidation_0-error:0.248756\n",
      "[97]\tvalidation_0-error:0.248045\n",
      "[98]\tvalidation_0-error:0.248045\n",
      "[99]\tvalidation_0-error:0.248045\n"
     ]
    }
   ],
   "source": [
    "if xgboost is not None:  \n",
    "    xgb_clf.fit(X_sm, y_sm,\n",
    "                    eval_set=[(test_set, test_labels)], early_stopping_rounds=1000)\n",
    "    y_pred = xgb_clf.predict(test_set)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/onion8/miniconda3/lib/python2.7/site-packages/ipykernel_launcher.py:1: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n",
      "/Users/onion8/miniconda3/lib/python2.7/site-packages/ipykernel_launcher.py:1: UnicodeWarning: Unicode equal comparison failed to convert both arguments to Unicode - interpreting them as being unequal\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "y_pred=np.where(y_pred =='Yes', 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation MSE: 0.3845060412224591\n"
     ]
    }
   ],
   "source": [
    "    val_error = mean_squared_error(actual_labels, y_pred)\n",
    "    print(\"Validation MSE:\", val_error)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VotingClassifier(estimators=[(u'lr', LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='warn',\n",
       "          n_jobs=None, penalty='l2', random_state=42, solver=u'liblinear',\n",
       "          tol=0.0001, verbose=0, warm_start=False)), (u'rf', Ra...timators=10000, n_jobs=-1, oob_score=False,\n",
       "         random_state=42, verbose=0, warm_start=False))],\n",
       "         flatten_transform=None, n_jobs=None, voting=u'soft', weights=None)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ensemble -3\n",
    "log_clf = LogisticRegression(solver=\"liblinear\", random_state=42)\n",
    "rnd_clf = RandomForestClassifier(n_estimators=40, random_state=42)\n",
    "bag_clf = bag_clf\n",
    "voting_clf = VotingClassifier(\n",
    "    estimators=[('lr', log_clf), ('rf', rnd_clf), ('bag', bag_clf)],\n",
    "    voting='soft')\n",
    "voting_clf.fit(X_sm, y_sm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression 0.7412935323383084\n",
      "RandomForestClassifier 0.7718550106609808\n",
      "BaggingClassifier 0.7725657427149965\n",
      "VotingClassifier 0.7611940298507462\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "for clf in (log_clf, rnd_clf, bag_clf, voting_clf):\n",
    "    clf.fit(X_sm, y_sm)\n",
    "    y_pred = clf.predict(test_set)\n",
    "    print(clf.__class__.__name__, accuracy_score(test_labels, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7683203776192072"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(test_labels, y_pred, average=\"weighted\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [],
   "source": [
    "from catboost import CatBoostClassifier\n",
    "cat_clf=CatBoostClassifier(iterations=85,learning_rate=0.4,depth=6,logging_level='Silent')\n",
    "cat_clf.fit(X_sm, Y_train)\n",
    "y_pred = cat_clf.predict(test_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_tr_pred=cat_clf.predict(train_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation MSE: 0.22174840085287847\n"
     ]
    }
   ],
   "source": [
    "    val_error = mean_squared_error(actual_labels, y_pred)\n",
    "    print(\"Validation MSE:\", val_error)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cat boosting performs much better than others with f1 score as 81.443(train data) and 78.21501(test data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7821501834596946"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(actual_labels, y_pred, average=\"weighted\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_tr=np.where(train_labels =='Yes', 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8144353109999827"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_tr, y_tr_pred, average=\"weighted\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VotingClassifier(estimators=[(u'lr', LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='warn',\n",
       "          n_jobs=None, penalty='l2', random_state=42, solver=u'liblinear',\n",
       "          tol=0.0001, verbose=0, warm_start=False)), (u'rf', Ra...timators=10000, n_jobs=-1, oob_score=False,\n",
       "         random_state=42, verbose=0, warm_start=False))],\n",
       "         flatten_transform=None, n_jobs=None, voting=u'soft', weights=None)"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#ensemble 4\n",
    "log_clf = LogisticRegression(solver=\"liblinear\", random_state=42)\n",
    "rnd_clf = RandomForestClassifier(n_estimators=40, random_state=42)\n",
    "cat_clf = cat_clf\n",
    "bag_clf=bag_clf\n",
    "voting_clf = VotingClassifier(\n",
    "    estimators=[('lr', log_clf), ('rf', rnd_clf), ('cat', cat_clf), ('bag',bag_clf) ],\n",
    "    voting='soft')\n",
    "voting_clf.fit(X_sm, y_sm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = voting_clf.predict(test_set)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7768347279301747"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(test_labels, y_pred, average=\"weighted\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}