Classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Reading the csv file into dataframe 'df'\n",
    "df=pd.read_csv('C:\\\\Users\\\\user\\\\Downloads\\\\telecom_churn_data.csv',encoding='utf-8',low_memory=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mobile_number</th>\n",
       "      <th>circle_id</th>\n",
       "      <th>loc_og_t2o_mou</th>\n",
       "      <th>std_og_t2o_mou</th>\n",
       "      <th>loc_ic_t2o_mou</th>\n",
       "      <th>last_date_of_month_6</th>\n",
       "      <th>last_date_of_month_7</th>\n",
       "      <th>last_date_of_month_8</th>\n",
       "      <th>last_date_of_month_9</th>\n",
       "      <th>arpu_6</th>\n",
       "      <th>...</th>\n",
       "      <th>sachet_3g_9</th>\n",
       "      <th>fb_user_6</th>\n",
       "      <th>fb_user_7</th>\n",
       "      <th>fb_user_8</th>\n",
       "      <th>fb_user_9</th>\n",
       "      <th>aon</th>\n",
       "      <th>aug_vbc_3g</th>\n",
       "      <th>jul_vbc_3g</th>\n",
       "      <th>jun_vbc_3g</th>\n",
       "      <th>sep_vbc_3g</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7000842753</td>\n",
       "      <td>109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6/30/2014</td>\n",
       "      <td>7/31/2014</td>\n",
       "      <td>8/31/2014</td>\n",
       "      <td>9/30/2014</td>\n",
       "      <td>197.385</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>968</td>\n",
       "      <td>30.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>101.20</td>\n",
       "      <td>3.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7001865778</td>\n",
       "      <td>109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6/30/2014</td>\n",
       "      <td>7/31/2014</td>\n",
       "      <td>8/31/2014</td>\n",
       "      <td>9/30/2014</td>\n",
       "      <td>34.047</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1006</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7001625959</td>\n",
       "      <td>109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6/30/2014</td>\n",
       "      <td>7/31/2014</td>\n",
       "      <td>8/31/2014</td>\n",
       "      <td>9/30/2014</td>\n",
       "      <td>167.690</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1103</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.17</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7001204172</td>\n",
       "      <td>109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6/30/2014</td>\n",
       "      <td>7/31/2014</td>\n",
       "      <td>8/31/2014</td>\n",
       "      <td>9/30/2014</td>\n",
       "      <td>221.338</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2491</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7000142493</td>\n",
       "      <td>109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6/30/2014</td>\n",
       "      <td>7/31/2014</td>\n",
       "      <td>8/31/2014</td>\n",
       "      <td>9/30/2014</td>\n",
       "      <td>261.636</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1526</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 226 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   mobile_number  circle_id  loc_og_t2o_mou  std_og_t2o_mou  loc_ic_t2o_mou  \\\n",
       "0     7000842753        109             0.0             0.0             0.0   \n",
       "1     7001865778        109             0.0             0.0             0.0   \n",
       "2     7001625959        109             0.0             0.0             0.0   \n",
       "3     7001204172        109             0.0             0.0             0.0   \n",
       "4     7000142493        109             0.0             0.0             0.0   \n",
       "\n",
       "  last_date_of_month_6 last_date_of_month_7 last_date_of_month_8  \\\n",
       "0            6/30/2014            7/31/2014            8/31/2014   \n",
       "1            6/30/2014            7/31/2014            8/31/2014   \n",
       "2            6/30/2014            7/31/2014            8/31/2014   \n",
       "3            6/30/2014            7/31/2014            8/31/2014   \n",
       "4            6/30/2014            7/31/2014            8/31/2014   \n",
       "\n",
       "  last_date_of_month_9   arpu_6     ...      sachet_3g_9  fb_user_6  \\\n",
       "0            9/30/2014  197.385     ...                0        1.0   \n",
       "1            9/30/2014   34.047     ...                0        NaN   \n",
       "2            9/30/2014  167.690     ...                0        NaN   \n",
       "3            9/30/2014  221.338     ...                0        NaN   \n",
       "4            9/30/2014  261.636     ...                0        0.0   \n",
       "\n",
       "   fb_user_7  fb_user_8  fb_user_9   aon  aug_vbc_3g  jul_vbc_3g  jun_vbc_3g  \\\n",
       "0        1.0        1.0        NaN   968        30.4         0.0      101.20   \n",
       "1        1.0        1.0        NaN  1006         0.0         0.0        0.00   \n",
       "2        NaN        NaN        1.0  1103         0.0         0.0        4.17   \n",
       "3        NaN        NaN        NaN  2491         0.0         0.0        0.00   \n",
       "4        NaN        NaN        NaN  1526         0.0         0.0        0.00   \n",
       "\n",
       "   sep_vbc_3g  \n",
       "0        3.58  \n",
       "1        0.00  \n",
       "2        0.00  \n",
       "3        0.00  \n",
       "4        0.00  \n",
       "\n",
       "[5 rows x 226 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['mobile_number', 'circle_id', 'loc_og_t2o_mou', 'std_og_t2o_mou',\n",
       "       'loc_ic_t2o_mou', 'last_date_of_month_6', 'last_date_of_month_7',\n",
       "       'last_date_of_month_8', 'last_date_of_month_9', 'arpu_6',\n",
       "       ...\n",
       "       'sachet_3g_9', 'fb_user_6', 'fb_user_7', 'fb_user_8', 'fb_user_9',\n",
       "       'aon', 'aug_vbc_3g', 'jul_vbc_3g', 'jun_vbc_3g', 'sep_vbc_3g'],\n",
       "      dtype='object', length=226)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 99999 entries, 0 to 99998\n",
      "Columns: 226 entries, mobile_number to sep_vbc_3g\n",
      "dtypes: float64(179), int64(35), object(12)\n",
      "memory usage: 167.8+ MB\n"
     ]
    }
   ],
   "source": [
    "#Information about the dataframe is shown.\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(99999, 226)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Removing rows if there are any duplicates.\n",
    "df.drop_duplicates(keep=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mobile_number           False\n",
       "circle_id               False\n",
       "loc_og_t2o_mou          False\n",
       "std_og_t2o_mou          False\n",
       "loc_ic_t2o_mou          False\n",
       "last_date_of_month_6    False\n",
       "last_date_of_month_7    False\n",
       "last_date_of_month_8    False\n",
       "last_date_of_month_9    False\n",
       "arpu_6                  False\n",
       "arpu_7                  False\n",
       "arpu_8                  False\n",
       "arpu_9                  False\n",
       "onnet_mou_6             False\n",
       "onnet_mou_7             False\n",
       "onnet_mou_8             False\n",
       "onnet_mou_9             False\n",
       "offnet_mou_6            False\n",
       "offnet_mou_7            False\n",
       "offnet_mou_8            False\n",
       "offnet_mou_9            False\n",
       "roam_ic_mou_6           False\n",
       "roam_ic_mou_7           False\n",
       "roam_ic_mou_8           False\n",
       "roam_ic_mou_9           False\n",
       "roam_og_mou_6           False\n",
       "roam_og_mou_7           False\n",
       "roam_og_mou_8           False\n",
       "roam_og_mou_9           False\n",
       "loc_og_t2t_mou_6        False\n",
       "                        ...  \n",
       "arpu_2g_9               False\n",
       "night_pck_user_6        False\n",
       "night_pck_user_7        False\n",
       "night_pck_user_8        False\n",
       "night_pck_user_9        False\n",
       "monthly_2g_6            False\n",
       "monthly_2g_7            False\n",
       "monthly_2g_8            False\n",
       "monthly_2g_9            False\n",
       "sachet_2g_6             False\n",
       "sachet_2g_7             False\n",
       "sachet_2g_8             False\n",
       "sachet_2g_9             False\n",
       "monthly_3g_6            False\n",
       "monthly_3g_7            False\n",
       "monthly_3g_8            False\n",
       "monthly_3g_9            False\n",
       "sachet_3g_6             False\n",
       "sachet_3g_7             False\n",
       "sachet_3g_8             False\n",
       "sachet_3g_9             False\n",
       "fb_user_6               False\n",
       "fb_user_7               False\n",
       "fb_user_8               False\n",
       "fb_user_9               False\n",
       "aon                     False\n",
       "aug_vbc_3g              False\n",
       "jul_vbc_3g              False\n",
       "jun_vbc_3g              False\n",
       "sep_vbc_3g              False\n",
       "Length: 226, dtype: bool"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking if the columns have all null values.\n",
    "df.isnull().all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Removing columns if they consist of all null values.\n",
    "df=df.dropna(axis=1,how='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(99999, 226)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mobile_number               0\n",
       "circle_id                   0\n",
       "loc_og_t2o_mou           1018\n",
       "std_og_t2o_mou           1018\n",
       "loc_ic_t2o_mou           1018\n",
       "last_date_of_month_6        0\n",
       "last_date_of_month_7      601\n",
       "last_date_of_month_8     1100\n",
       "last_date_of_month_9     1659\n",
       "arpu_6                      0\n",
       "arpu_7                      0\n",
       "arpu_8                      0\n",
       "arpu_9                      0\n",
       "onnet_mou_6              3937\n",
       "onnet_mou_7              3859\n",
       "onnet_mou_8              5378\n",
       "onnet_mou_9              7745\n",
       "offnet_mou_6             3937\n",
       "offnet_mou_7             3859\n",
       "offnet_mou_8             5378\n",
       "offnet_mou_9             7745\n",
       "roam_ic_mou_6            3937\n",
       "roam_ic_mou_7            3859\n",
       "roam_ic_mou_8            5378\n",
       "roam_ic_mou_9            7745\n",
       "roam_og_mou_6            3937\n",
       "roam_og_mou_7            3859\n",
       "roam_og_mou_8            5378\n",
       "roam_og_mou_9            7745\n",
       "loc_og_t2t_mou_6         3937\n",
       "                        ...  \n",
       "arpu_2g_9               74077\n",
       "night_pck_user_6        74846\n",
       "night_pck_user_7        74428\n",
       "night_pck_user_8        73660\n",
       "night_pck_user_9        74077\n",
       "monthly_2g_6                0\n",
       "monthly_2g_7                0\n",
       "monthly_2g_8                0\n",
       "monthly_2g_9                0\n",
       "sachet_2g_6                 0\n",
       "sachet_2g_7                 0\n",
       "sachet_2g_8                 0\n",
       "sachet_2g_9                 0\n",
       "monthly_3g_6                0\n",
       "monthly_3g_7                0\n",
       "monthly_3g_8                0\n",
       "monthly_3g_9                0\n",
       "sachet_3g_6                 0\n",
       "sachet_3g_7                 0\n",
       "sachet_3g_8                 0\n",
       "sachet_3g_9                 0\n",
       "fb_user_6               74846\n",
       "fb_user_7               74428\n",
       "fb_user_8               73660\n",
       "fb_user_9               74077\n",
       "aon                         0\n",
       "aug_vbc_3g                  0\n",
       "jul_vbc_3g                  0\n",
       "jun_vbc_3g                  0\n",
       "sep_vbc_3g                  0\n",
       "Length: 226, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mobile_number            0.00\n",
       "circle_id                0.00\n",
       "loc_og_t2o_mou           1.02\n",
       "std_og_t2o_mou           1.02\n",
       "loc_ic_t2o_mou           1.02\n",
       "last_date_of_month_6     0.00\n",
       "last_date_of_month_7     0.60\n",
       "last_date_of_month_8     1.10\n",
       "last_date_of_month_9     1.66\n",
       "arpu_6                   0.00\n",
       "arpu_7                   0.00\n",
       "arpu_8                   0.00\n",
       "arpu_9                   0.00\n",
       "onnet_mou_6              3.94\n",
       "onnet_mou_7              3.86\n",
       "onnet_mou_8              5.38\n",
       "onnet_mou_9              7.75\n",
       "offnet_mou_6             3.94\n",
       "offnet_mou_7             3.86\n",
       "offnet_mou_8             5.38\n",
       "offnet_mou_9             7.75\n",
       "roam_ic_mou_6            3.94\n",
       "roam_ic_mou_7            3.86\n",
       "roam_ic_mou_8            5.38\n",
       "roam_ic_mou_9            7.75\n",
       "roam_og_mou_6            3.94\n",
       "roam_og_mou_7            3.86\n",
       "roam_og_mou_8            5.38\n",
       "roam_og_mou_9            7.75\n",
       "loc_og_t2t_mou_6         3.94\n",
       "                        ...  \n",
       "arpu_2g_9               74.08\n",
       "night_pck_user_6        74.85\n",
       "night_pck_user_7        74.43\n",
       "night_pck_user_8        73.66\n",
       "night_pck_user_9        74.08\n",
       "monthly_2g_6             0.00\n",
       "monthly_2g_7             0.00\n",
       "monthly_2g_8             0.00\n",
       "monthly_2g_9             0.00\n",
       "sachet_2g_6              0.00\n",
       "sachet_2g_7              0.00\n",
       "sachet_2g_8              0.00\n",
       "sachet_2g_9              0.00\n",
       "monthly_3g_6             0.00\n",
       "monthly_3g_7             0.00\n",
       "monthly_3g_8             0.00\n",
       "monthly_3g_9             0.00\n",
       "sachet_3g_6              0.00\n",
       "sachet_3g_7              0.00\n",
       "sachet_3g_8              0.00\n",
       "sachet_3g_9              0.00\n",
       "fb_user_6               74.85\n",
       "fb_user_7               74.43\n",
       "fb_user_8               73.66\n",
       "fb_user_9               74.08\n",
       "aon                      0.00\n",
       "aug_vbc_3g               0.00\n",
       "jul_vbc_3g               0.00\n",
       "jun_vbc_3g               0.00\n",
       "sep_vbc_3g               0.00\n",
       "Length: 226, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the percentage of missing values\n",
    "round(100*(df.isnull().sum()/len(df)), 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         97\n",
       "1         20\n",
       "2         30\n",
       "3         40\n",
       "4         30\n",
       "5         40\n",
       "6         40\n",
       "7         69\n",
       "8         20\n",
       "9         30\n",
       "10        49\n",
       "11        31\n",
       "12        40\n",
       "13        30\n",
       "14        40\n",
       "15        69\n",
       "16        40\n",
       "17        40\n",
       "18        40\n",
       "19        10\n",
       "20        40\n",
       "21        40\n",
       "22        40\n",
       "23        10\n",
       "24        40\n",
       "25        40\n",
       "26        40\n",
       "27        40\n",
       "28        30\n",
       "29        69\n",
       "        ... \n",
       "99969     20\n",
       "99970     10\n",
       "99971     40\n",
       "99972     30\n",
       "99973     41\n",
       "99974     40\n",
       "99975     40\n",
       "99976     40\n",
       "99977     40\n",
       "99978    162\n",
       "99979     40\n",
       "99980     10\n",
       "99981     11\n",
       "99982     10\n",
       "99983     99\n",
       "99984      0\n",
       "99985     41\n",
       "99986      0\n",
       "99987     30\n",
       "99988     10\n",
       "99989     40\n",
       "99990     30\n",
       "99991     40\n",
       "99992     40\n",
       "99993    164\n",
       "99994     40\n",
       "99995     40\n",
       "99996    118\n",
       "99997      0\n",
       "99998    120\n",
       "Length: 99999, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking missing values along rows.\n",
    "df.isnull().sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        42.92\n",
       "1         8.85\n",
       "2        13.27\n",
       "3        17.70\n",
       "4        13.27\n",
       "5        17.70\n",
       "6        17.70\n",
       "7        30.53\n",
       "8         8.85\n",
       "9        13.27\n",
       "10       21.68\n",
       "11       13.72\n",
       "12       17.70\n",
       "13       13.27\n",
       "14       17.70\n",
       "15       30.53\n",
       "16       17.70\n",
       "17       17.70\n",
       "18       17.70\n",
       "19        4.42\n",
       "20       17.70\n",
       "21       17.70\n",
       "22       17.70\n",
       "23        4.42\n",
       "24       17.70\n",
       "25       17.70\n",
       "26       17.70\n",
       "27       17.70\n",
       "28       13.27\n",
       "29       30.53\n",
       "         ...  \n",
       "99969     8.85\n",
       "99970     4.42\n",
       "99971    17.70\n",
       "99972    13.27\n",
       "99973    18.14\n",
       "99974    17.70\n",
       "99975    17.70\n",
       "99976    17.70\n",
       "99977    17.70\n",
       "99978    71.68\n",
       "99979    17.70\n",
       "99980     4.42\n",
       "99981     4.87\n",
       "99982     4.42\n",
       "99983    43.81\n",
       "99984     0.00\n",
       "99985    18.14\n",
       "99986     0.00\n",
       "99987    13.27\n",
       "99988     4.42\n",
       "99989    17.70\n",
       "99990    13.27\n",
       "99991    17.70\n",
       "99992    17.70\n",
       "99993    72.57\n",
       "99994    17.70\n",
       "99995    17.70\n",
       "99996    52.21\n",
       "99997     0.00\n",
       "99998    53.10\n",
       "Length: 99999, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the percentage of missing values\n",
    "round(100*(df.isnull().sum(axis=1)/len(df.columns)), 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(99999, 226)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Removing rows if they consist of all null values.\n",
    "df=df.dropna(how='all')\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mobile_number</th>\n",
       "      <th>circle_id</th>\n",
       "      <th>loc_og_t2o_mou</th>\n",
       "      <th>std_og_t2o_mou</th>\n",
       "      <th>loc_ic_t2o_mou</th>\n",
       "      <th>last_date_of_month_6</th>\n",
       "      <th>last_date_of_month_7</th>\n",
       "      <th>last_date_of_month_8</th>\n",
       "      <th>last_date_of_month_9</th>\n",
       "      <th>arpu_6</th>\n",
       "      <th>...</th>\n",
       "      <th>sachet_3g_9</th>\n",
       "      <th>fb_user_6</th>\n",
       "      <th>fb_user_7</th>\n",
       "      <th>fb_user_8</th>\n",
       "      <th>fb_user_9</th>\n",
       "      <th>aon</th>\n",
       "      <th>aug_vbc_3g</th>\n",
       "      <th>jul_vbc_3g</th>\n",
       "      <th>jun_vbc_3g</th>\n",
       "      <th>sep_vbc_3g</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>7000842753</td>\n",
       "      <td>109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6/30/2014</td>\n",
       "      <td>7/31/2014</td>\n",
       "      <td>8/31/2014</td>\n",
       "      <td>9/30/2014</td>\n",
       "      <td>197.385</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>968</td>\n",
       "      <td>30.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>101.20</td>\n",
       "      <td>3.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7001865778</td>\n",
       "      <td>109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6/30/2014</td>\n",
       "      <td>7/31/2014</td>\n",
       "      <td>8/31/2014</td>\n",
       "      <td>9/30/2014</td>\n",
       "      <td>34.047</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1006</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7001625959</td>\n",
       "      <td>109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6/30/2014</td>\n",
       "      <td>7/31/2014</td>\n",
       "      <td>8/31/2014</td>\n",
       "      <td>9/30/2014</td>\n",
       "      <td>167.690</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1103</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.17</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7001204172</td>\n",
       "      <td>109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6/30/2014</td>\n",
       "      <td>7/31/2014</td>\n",
       "      <td>8/31/2014</td>\n",
       "      <td>9/30/2014</td>\n",
       "      <td>221.338</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2491</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7000142493</td>\n",
       "      <td>109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6/30/2014</td>\n",
       "      <td>7/31/2014</td>\n",
       "      <td>8/31/2014</td>\n",
       "      <td>9/30/2014</td>\n",
       "      <td>261.636</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1526</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 226 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   mobile_number  circle_id  loc_og_t2o_mou  std_og_t2o_mou  loc_ic_t2o_mou  \\\n",
       "0     7000842753        109             0.0             0.0             0.0   \n",
       "1     7001865778        109             0.0             0.0             0.0   \n",
       "2     7001625959        109             0.0             0.0             0.0   \n",
       "3     7001204172        109             0.0             0.0             0.0   \n",
       "4     7000142493        109             0.0             0.0             0.0   \n",
       "\n",
       "  last_date_of_month_6 last_date_of_month_7 last_date_of_month_8  \\\n",
       "0            6/30/2014            7/31/2014            8/31/2014   \n",
       "1            6/30/2014            7/31/2014            8/31/2014   \n",
       "2            6/30/2014            7/31/2014            8/31/2014   \n",
       "3            6/30/2014            7/31/2014            8/31/2014   \n",
       "4            6/30/2014            7/31/2014            8/31/2014   \n",
       "\n",
       "  last_date_of_month_9   arpu_6     ...      sachet_3g_9  fb_user_6  \\\n",
       "0            9/30/2014  197.385     ...                0        1.0   \n",
       "1            9/30/2014   34.047     ...                0        NaN   \n",
       "2            9/30/2014  167.690     ...                0        NaN   \n",
       "3            9/30/2014  221.338     ...                0        NaN   \n",
       "4            9/30/2014  261.636     ...                0        0.0   \n",
       "\n",
       "   fb_user_7  fb_user_8  fb_user_9   aon  aug_vbc_3g  jul_vbc_3g  jun_vbc_3g  \\\n",
       "0        1.0        1.0        NaN   968        30.4         0.0      101.20   \n",
       "1        1.0        1.0        NaN  1006         0.0         0.0        0.00   \n",
       "2        NaN        NaN        1.0  1103         0.0         0.0        4.17   \n",
       "3        NaN        NaN        NaN  2491         0.0         0.0        0.00   \n",
       "4        NaN        NaN        NaN  1526         0.0         0.0        0.00   \n",
       "\n",
       "   sep_vbc_3g  \n",
       "0        3.58  \n",
       "1        0.00  \n",
       "2        0.00  \n",
       "3        0.00  \n",
       "4        0.00  \n",
       "\n",
       "[5 rows x 226 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>loc_og_t2o_mou</th>\n",
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       "      <th>last_date_of_month_8</th>\n",
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       "      <th>arpu_7</th>\n",
       "      <th>...</th>\n",
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       "      <th>fb_user_8</th>\n",
       "      <th>fb_user_9</th>\n",
       "      <th>aon</th>\n",
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       "      <th>jun_vbc_3g</th>\n",
       "      <th>sep_vbc_3g</th>\n",
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       "    <tr>\n",
       "      <th>last_date_of_month_6</th>\n",
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       "  <tbody>\n",
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       "      <th>6/30/2014</th>\n",
       "      <td>99999</td>\n",
       "      <td>99999</td>\n",
       "      <td>98981</td>\n",
       "      <td>98981</td>\n",
       "      <td>98981</td>\n",
       "      <td>99398</td>\n",
       "      <td>98899</td>\n",
       "      <td>98340</td>\n",
       "      <td>99999</td>\n",
       "      <td>99999</td>\n",
       "      <td>...</td>\n",
       "      <td>99999</td>\n",
       "      <td>25153</td>\n",
       "      <td>25571</td>\n",
       "      <td>26339</td>\n",
       "      <td>25922</td>\n",
       "      <td>99999</td>\n",
       "      <td>99999</td>\n",
       "      <td>99999</td>\n",
       "      <td>99999</td>\n",
       "      <td>99999</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 225 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      mobile_number  circle_id  loc_og_t2o_mou  \\\n",
       "last_date_of_month_6                                             \n",
       "6/30/2014                     99999      99999           98981   \n",
       "\n",
       "                      std_og_t2o_mou  loc_ic_t2o_mou  last_date_of_month_7  \\\n",
       "last_date_of_month_6                                                         \n",
       "6/30/2014                      98981           98981                 99398   \n",
       "\n",
       "                      last_date_of_month_8  last_date_of_month_9  arpu_6  \\\n",
       "last_date_of_month_6                                                       \n",
       "6/30/2014                            98899                 98340   99999   \n",
       "\n",
       "                      arpu_7     ...      sachet_3g_9  fb_user_6  fb_user_7  \\\n",
       "last_date_of_month_6             ...                                          \n",
       "6/30/2014              99999     ...            99999      25153      25571   \n",
       "\n",
       "                      fb_user_8  fb_user_9    aon  aug_vbc_3g  jul_vbc_3g  \\\n",
       "last_date_of_month_6                                                        \n",
       "6/30/2014                 26339      25922  99999       99999       99999   \n",
       "\n",
       "                      jun_vbc_3g  sep_vbc_3g  \n",
       "last_date_of_month_6                          \n",
       "6/30/2014                  99999       99999  \n",
       "\n",
       "[1 rows x 225 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('last_date_of_month_6').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>last_date_of_month_6</th>\n",
       "      <th>last_date_of_month_8</th>\n",
       "      <th>last_date_of_month_9</th>\n",
       "      <th>arpu_6</th>\n",
       "      <th>arpu_7</th>\n",
       "      <th>...</th>\n",
       "      <th>sachet_3g_9</th>\n",
       "      <th>fb_user_6</th>\n",
       "      <th>fb_user_7</th>\n",
       "      <th>fb_user_8</th>\n",
       "      <th>fb_user_9</th>\n",
       "      <th>aon</th>\n",
       "      <th>aug_vbc_3g</th>\n",
       "      <th>jul_vbc_3g</th>\n",
       "      <th>jun_vbc_3g</th>\n",
       "      <th>sep_vbc_3g</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>last_date_of_month_7</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7/31/2014</th>\n",
       "      <td>99398</td>\n",
       "      <td>99398</td>\n",
       "      <td>98703</td>\n",
       "      <td>98703</td>\n",
       "      <td>98703</td>\n",
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       "      <td>98834</td>\n",
       "      <td>98340</td>\n",
       "      <td>99398</td>\n",
       "      <td>99398</td>\n",
       "      <td>...</td>\n",
       "      <td>99398</td>\n",
       "      <td>25085</td>\n",
       "      <td>25571</td>\n",
       "      <td>26339</td>\n",
       "      <td>25922</td>\n",
       "      <td>99398</td>\n",
       "      <td>99398</td>\n",
       "      <td>99398</td>\n",
       "      <td>99398</td>\n",
       "      <td>99398</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 225 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      mobile_number  circle_id  loc_og_t2o_mou  \\\n",
       "last_date_of_month_7                                             \n",
       "7/31/2014                     99398      99398           98703   \n",
       "\n",
       "                      std_og_t2o_mou  loc_ic_t2o_mou  last_date_of_month_6  \\\n",
       "last_date_of_month_7                                                         \n",
       "7/31/2014                      98703           98703                 99398   \n",
       "\n",
       "                      last_date_of_month_8  last_date_of_month_9  arpu_6  \\\n",
       "last_date_of_month_7                                                       \n",
       "7/31/2014                            98834                 98340   99398   \n",
       "\n",
       "                      arpu_7     ...      sachet_3g_9  fb_user_6  fb_user_7  \\\n",
       "last_date_of_month_7             ...                                          \n",
       "7/31/2014              99398     ...            99398      25085      25571   \n",
       "\n",
       "                      fb_user_8  fb_user_9    aon  aug_vbc_3g  jul_vbc_3g  \\\n",
       "last_date_of_month_7                                                        \n",
       "7/31/2014                 26339      25922  99398       99398       99398   \n",
       "\n",
       "                      jun_vbc_3g  sep_vbc_3g  \n",
       "last_date_of_month_7                          \n",
       "7/31/2014                  99398       99398  \n",
       "\n",
       "[1 rows x 225 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('last_date_of_month_7').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>8/31/2014</th>\n",
       "      <td>98899</td>\n",
       "      <td>98899</td>\n",
       "      <td>98230</td>\n",
       "      <td>98230</td>\n",
       "      <td>98230</td>\n",
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       "      <td>98340</td>\n",
       "      <td>98899</td>\n",
       "      <td>98899</td>\n",
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       "      <td>25922</td>\n",
       "      <td>98899</td>\n",
       "      <td>98899</td>\n",
       "      <td>98899</td>\n",
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       "      <td>98899</td>\n",
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       "<p>1 rows × 225 columns</p>\n",
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      ],
      "text/plain": [
       "                      mobile_number  circle_id  loc_og_t2o_mou  \\\n",
       "last_date_of_month_8                                             \n",
       "8/31/2014                     98899      98899           98230   \n",
       "\n",
       "                      std_og_t2o_mou  loc_ic_t2o_mou  last_date_of_month_6  \\\n",
       "last_date_of_month_8                                                         \n",
       "8/31/2014                      98230           98230                 98899   \n",
       "\n",
       "                      last_date_of_month_7  last_date_of_month_9  arpu_6  \\\n",
       "last_date_of_month_8                                                       \n",
       "8/31/2014                            98834                 98340   98899   \n",
       "\n",
       "                      arpu_7     ...      sachet_3g_9  fb_user_6  fb_user_7  \\\n",
       "last_date_of_month_8             ...                                          \n",
       "8/31/2014              98899     ...            98899      24911      25510   \n",
       "\n",
       "                      fb_user_8  fb_user_9    aon  aug_vbc_3g  jul_vbc_3g  \\\n",
       "last_date_of_month_8                                                        \n",
       "8/31/2014                 26339      25922  98899       98899       98899   \n",
       "\n",
       "                      jun_vbc_3g  sep_vbc_3g  \n",
       "last_date_of_month_8                          \n",
       "8/31/2014                  98899       98899  \n",
       "\n",
       "[1 rows x 225 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('last_date_of_month_8').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>98340</td>\n",
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       "</table>\n",
       "<p>1 rows × 225 columns</p>\n",
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      ],
      "text/plain": [
       "                      mobile_number  circle_id  loc_og_t2o_mou  \\\n",
       "last_date_of_month_9                                             \n",
       "9/30/2014                     98340      98340           97728   \n",
       "\n",
       "                      std_og_t2o_mou  loc_ic_t2o_mou  last_date_of_month_6  \\\n",
       "last_date_of_month_9                                                         \n",
       "9/30/2014                      97728           97728                 98340   \n",
       "\n",
       "                      last_date_of_month_7  last_date_of_month_8  arpu_6  \\\n",
       "last_date_of_month_9                                                       \n",
       "9/30/2014                            98340                 98340   98340   \n",
       "\n",
       "                      arpu_7     ...      sachet_3g_9  fb_user_6  fb_user_7  \\\n",
       "last_date_of_month_9             ...                                          \n",
       "9/30/2014              98340     ...            98340      24742      25341   \n",
       "\n",
       "                      fb_user_8  fb_user_9    aon  aug_vbc_3g  jul_vbc_3g  \\\n",
       "last_date_of_month_9                                                        \n",
       "9/30/2014                 26267      25922  98340       98340       98340   \n",
       "\n",
       "                      jun_vbc_3g  sep_vbc_3g  \n",
       "last_date_of_month_9                          \n",
       "9/30/2014                  98340       98340  \n",
       "\n",
       "[1 rows x 225 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('last_date_of_month_9').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Since these columns consist of only one value, these can be removed.\n",
    "del df['last_date_of_month_6']\n",
    "del df['last_date_of_month_7']\n",
    "del df['last_date_of_month_8']\n",
    "del df['last_date_of_month_9']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1526</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 222 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   mobile_number  circle_id  loc_og_t2o_mou  std_og_t2o_mou  loc_ic_t2o_mou  \\\n",
       "0     7000842753        109             0.0             0.0             0.0   \n",
       "1     7001865778        109             0.0             0.0             0.0   \n",
       "2     7001625959        109             0.0             0.0             0.0   \n",
       "3     7001204172        109             0.0             0.0             0.0   \n",
       "4     7000142493        109             0.0             0.0             0.0   \n",
       "\n",
       "    arpu_6   arpu_7   arpu_8   arpu_9  onnet_mou_6     ...      sachet_3g_9  \\\n",
       "0  197.385  214.816  213.803   21.100          NaN     ...                0   \n",
       "1   34.047  355.074  268.321   86.285        24.11     ...                0   \n",
       "2  167.690  189.058  210.226  290.714        11.54     ...                0   \n",
       "3  221.338  251.102  508.054  389.500        99.91     ...                0   \n",
       "4  261.636  309.876  238.174  163.426        50.31     ...                0   \n",
       "\n",
       "   fb_user_6  fb_user_7  fb_user_8  fb_user_9   aon  aug_vbc_3g  jul_vbc_3g  \\\n",
       "0        1.0        1.0        1.0        NaN   968        30.4         0.0   \n",
       "1        NaN        1.0        1.0        NaN  1006         0.0         0.0   \n",
       "2        NaN        NaN        NaN        1.0  1103         0.0         0.0   \n",
       "3        NaN        NaN        NaN        NaN  2491         0.0         0.0   \n",
       "4        0.0        NaN        NaN        NaN  1526         0.0         0.0   \n",
       "\n",
       "   jun_vbc_3g  sep_vbc_3g  \n",
       "0      101.20        3.58  \n",
       "1        0.00        0.00  \n",
       "2        4.17        0.00  \n",
       "3        0.00        0.00  \n",
       "4        0.00        0.00  \n",
       "\n",
       "[5 rows x 222 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>mobile_number</th>\n",
       "      <th>circle_id</th>\n",
       "      <th>loc_og_t2o_mou</th>\n",
       "      <th>std_og_t2o_mou</th>\n",
       "      <th>loc_ic_t2o_mou</th>\n",
       "      <th>arpu_6</th>\n",
       "      <th>arpu_7</th>\n",
       "      <th>arpu_8</th>\n",
       "      <th>arpu_9</th>\n",
       "      <th>onnet_mou_6</th>\n",
       "      <th>...</th>\n",
       "      <th>sachet_3g_9</th>\n",
       "      <th>fb_user_6</th>\n",
       "      <th>fb_user_7</th>\n",
       "      <th>fb_user_8</th>\n",
       "      <th>fb_user_9</th>\n",
       "      <th>aon</th>\n",
       "      <th>aug_vbc_3g</th>\n",
       "      <th>jul_vbc_3g</th>\n",
       "      <th>jun_vbc_3g</th>\n",
       "      <th>sep_vbc_3g</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9.999900e+04</td>\n",
       "      <td>99999.0</td>\n",
       "      <td>98981.0</td>\n",
       "      <td>98981.0</td>\n",
       "      <td>98981.0</td>\n",
       "      <td>99999.000000</td>\n",
       "      <td>99999.000000</td>\n",
       "      <td>99999.000000</td>\n",
       "      <td>99999.000000</td>\n",
       "      <td>96062.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>99999.000000</td>\n",
       "      <td>25153.000000</td>\n",
       "      <td>25571.000000</td>\n",
       "      <td>26339.000000</td>\n",
       "      <td>25922.000000</td>\n",
       "      <td>99999.000000</td>\n",
       "      <td>99999.000000</td>\n",
       "      <td>99999.000000</td>\n",
       "      <td>99999.000000</td>\n",
       "      <td>99999.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.001207e+09</td>\n",
       "      <td>109.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>282.987358</td>\n",
       "      <td>278.536648</td>\n",
       "      <td>279.154731</td>\n",
       "      <td>261.645069</td>\n",
       "      <td>132.395875</td>\n",
       "      <td>...</td>\n",
       "      <td>0.084581</td>\n",
       "      <td>0.914404</td>\n",
       "      <td>0.908764</td>\n",
       "      <td>0.890808</td>\n",
       "      <td>0.860968</td>\n",
       "      <td>1219.854749</td>\n",
       "      <td>68.170248</td>\n",
       "      <td>66.839062</td>\n",
       "      <td>60.021204</td>\n",
       "      <td>3.299373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.956694e+05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>328.439770</td>\n",
       "      <td>338.156291</td>\n",
       "      <td>344.474791</td>\n",
       "      <td>341.998630</td>\n",
       "      <td>297.207406</td>\n",
       "      <td>...</td>\n",
       "      <td>0.650457</td>\n",
       "      <td>0.279772</td>\n",
       "      <td>0.287950</td>\n",
       "      <td>0.311885</td>\n",
       "      <td>0.345987</td>\n",
       "      <td>954.733842</td>\n",
       "      <td>267.580450</td>\n",
       "      <td>271.201856</td>\n",
       "      <td>253.938223</td>\n",
       "      <td>32.408353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>7.000000e+09</td>\n",
       "      <td>109.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2258.709000</td>\n",
       "      <td>-2014.045000</td>\n",
       "      <td>-945.808000</td>\n",
       "      <td>-1899.505000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7.000606e+09</td>\n",
       "      <td>109.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>93.411500</td>\n",
       "      <td>86.980500</td>\n",
       "      <td>84.126000</td>\n",
       "      <td>62.685000</td>\n",
       "      <td>7.380000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>467.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.001205e+09</td>\n",
       "      <td>109.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>197.704000</td>\n",
       "      <td>191.640000</td>\n",
       "      <td>192.080000</td>\n",
       "      <td>176.849000</td>\n",
       "      <td>34.310000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>863.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.001812e+09</td>\n",
       "      <td>109.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>371.060000</td>\n",
       "      <td>365.344500</td>\n",
       "      <td>369.370500</td>\n",
       "      <td>353.466500</td>\n",
       "      <td>118.740000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1807.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.002411e+09</td>\n",
       "      <td>109.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27731.088000</td>\n",
       "      <td>35145.834000</td>\n",
       "      <td>33543.624000</td>\n",
       "      <td>38805.617000</td>\n",
       "      <td>7376.710000</td>\n",
       "      <td>...</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4337.000000</td>\n",
       "      <td>12916.220000</td>\n",
       "      <td>9165.600000</td>\n",
       "      <td>11166.210000</td>\n",
       "      <td>2618.570000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 214 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       mobile_number  circle_id  loc_og_t2o_mou  std_og_t2o_mou  \\\n",
       "count   9.999900e+04    99999.0         98981.0         98981.0   \n",
       "mean    7.001207e+09      109.0             0.0             0.0   \n",
       "std     6.956694e+05        0.0             0.0             0.0   \n",
       "min     7.000000e+09      109.0             0.0             0.0   \n",
       "25%     7.000606e+09      109.0             0.0             0.0   \n",
       "50%     7.001205e+09      109.0             0.0             0.0   \n",
       "75%     7.001812e+09      109.0             0.0             0.0   \n",
       "max     7.002411e+09      109.0             0.0             0.0   \n",
       "\n",
       "       loc_ic_t2o_mou        arpu_6        arpu_7        arpu_8        arpu_9  \\\n",
       "count         98981.0  99999.000000  99999.000000  99999.000000  99999.000000   \n",
       "mean              0.0    282.987358    278.536648    279.154731    261.645069   \n",
       "std               0.0    328.439770    338.156291    344.474791    341.998630   \n",
       "min               0.0  -2258.709000  -2014.045000   -945.808000  -1899.505000   \n",
       "25%               0.0     93.411500     86.980500     84.126000     62.685000   \n",
       "50%               0.0    197.704000    191.640000    192.080000    176.849000   \n",
       "75%               0.0    371.060000    365.344500    369.370500    353.466500   \n",
       "max               0.0  27731.088000  35145.834000  33543.624000  38805.617000   \n",
       "\n",
       "        onnet_mou_6      ...        sachet_3g_9     fb_user_6     fb_user_7  \\\n",
       "count  96062.000000      ...       99999.000000  25153.000000  25571.000000   \n",
       "mean     132.395875      ...           0.084581      0.914404      0.908764   \n",
       "std      297.207406      ...           0.650457      0.279772      0.287950   \n",
       "min        0.000000      ...           0.000000      0.000000      0.000000   \n",
       "25%        7.380000      ...           0.000000      1.000000      1.000000   \n",
       "50%       34.310000      ...           0.000000      1.000000      1.000000   \n",
       "75%      118.740000      ...           0.000000      1.000000      1.000000   \n",
       "max     7376.710000      ...          49.000000      1.000000      1.000000   \n",
       "\n",
       "          fb_user_8     fb_user_9           aon    aug_vbc_3g    jul_vbc_3g  \\\n",
       "count  26339.000000  25922.000000  99999.000000  99999.000000  99999.000000   \n",
       "mean       0.890808      0.860968   1219.854749     68.170248     66.839062   \n",
       "std        0.311885      0.345987    954.733842    267.580450    271.201856   \n",
       "min        0.000000      0.000000    180.000000      0.000000      0.000000   \n",
       "25%        1.000000      1.000000    467.000000      0.000000      0.000000   \n",
       "50%        1.000000      1.000000    863.000000      0.000000      0.000000   \n",
       "75%        1.000000      1.000000   1807.500000      0.000000      0.000000   \n",
       "max        1.000000      1.000000   4337.000000  12916.220000   9165.600000   \n",
       "\n",
       "         jun_vbc_3g    sep_vbc_3g  \n",
       "count  99999.000000  99999.000000  \n",
       "mean      60.021204      3.299373  \n",
       "std      253.938223     32.408353  \n",
       "min        0.000000      0.000000  \n",
       "25%        0.000000      0.000000  \n",
       "50%        0.000000      0.000000  \n",
       "75%        0.000000      0.000000  \n",
       "max    11166.210000   2618.570000  \n",
       "\n",
       "[8 rows x 214 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Circle id is having only one value, hence it can be removed\n",
    "del df['circle_id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['mobile_number', 'loc_og_t2o_mou', 'std_og_t2o_mou', 'loc_ic_t2o_mou',\n",
       "       'arpu_6', 'arpu_7', 'arpu_8', 'arpu_9', 'onnet_mou_6', 'onnet_mou_7',\n",
       "       ...\n",
       "       'sachet_3g_9', 'fb_user_6', 'fb_user_7', 'fb_user_8', 'fb_user_9',\n",
       "       'aon', 'aug_vbc_3g', 'jul_vbc_3g', 'jun_vbc_3g', 'sep_vbc_3g'],\n",
       "      dtype='object', length=221)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Calculating average redcharge amount\n",
    "def avg(x):\n",
    "    return (x['total_rech_amt_6']+x['total_rech_amt_7'])/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Average_amount']=df.apply(avg,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "368.5\n"
     ]
    }
   ],
   "source": [
    "#Calculating min avg recharge amount that helps us fing the type of customers\n",
    "percentile=np.percentile(df['Average_amount'],70)\n",
    "print(percentile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Defining customers ino high valued and not high valued\n",
    "def func(x):\n",
    "    if(x['Average_amount']>=percentile):\n",
    "        return 'High Value'\n",
    "    else:\n",
    "        return 'Not High Value'    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Customers']=df.apply(func,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Not High Value\n",
       "1    Not High Value\n",
       "2    Not High Value\n",
       "3    Not High Value\n",
       "4    Not High Value\n",
       "Name: Customers, dtype: object"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Customers'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df[df['Customers']=='High Value']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30011, 223)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7       0.00\n",
       "8      34.91\n",
       "13    631.86\n",
       "16    334.56\n",
       "17     87.13\n",
       "Name: total_ic_mou_9, dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['total_ic_mou_9'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7       0.00\n",
       "8     142.18\n",
       "13    446.09\n",
       "16    255.74\n",
       "17    631.08\n",
       "Name: total_og_mou_9, dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['total_og_mou_9'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7     0.0\n",
       "8     0.0\n",
       "13    0.0\n",
       "16    0.0\n",
       "17    0.0\n",
       "Name: vol_2g_mb_9, dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['vol_2g_mb_9'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7     0.0\n",
       "8     0.0\n",
       "13    0.0\n",
       "16    0.0\n",
       "17    0.0\n",
       "Name: vol_3g_mb_9, dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['vol_3g_mb_9'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Calculating churn values of every customer.\n",
    "def churnfunc(x):\n",
    "    if(x['total_ic_mou_9']==0.0 and x['total_og_mou_9']==0.0 and x['vol_2g_mb_9']==0.0 and x['vol_3g_mb_9']==0.0):\n",
    "        return 1\n",
    "    else:\n",
    "        return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Churn']=df.apply(churnfunc,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7     1\n",
       "8     0\n",
       "13    0\n",
       "16    0\n",
       "17    0\n",
       "Name: Churn, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Churn'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Removing columns as they're no longer required.\n",
    "del df['total_ic_mou_9']\n",
    "del df['total_og_mou_9']\n",
    "del df['vol_3g_mb_9']\n",
    "del df['vol_2g_mb_9']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>mobile_number</th>\n",
       "      <th>loc_og_t2o_mou</th>\n",
       "      <th>std_og_t2o_mou</th>\n",
       "      <th>loc_ic_t2o_mou</th>\n",
       "      <th>arpu_6</th>\n",
       "      <th>arpu_7</th>\n",
       "      <th>arpu_8</th>\n",
       "      <th>arpu_9</th>\n",
       "      <th>onnet_mou_6</th>\n",
       "      <th>onnet_mou_7</th>\n",
       "      <th>...</th>\n",
       "      <th>fb_user_7</th>\n",
       "      <th>fb_user_8</th>\n",
       "      <th>fb_user_9</th>\n",
       "      <th>aon</th>\n",
       "      <th>aug_vbc_3g</th>\n",
       "      <th>jul_vbc_3g</th>\n",
       "      <th>jun_vbc_3g</th>\n",
       "      <th>sep_vbc_3g</th>\n",
       "      <th>Average_amount</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3.001100e+04</td>\n",
       "      <td>29897.0</td>\n",
       "      <td>29897.0</td>\n",
       "      <td>29897.0</td>\n",
       "      <td>30011.000000</td>\n",
       "      <td>30011.000000</td>\n",
       "      <td>30011.000000</td>\n",
       "      <td>30011.000000</td>\n",
       "      <td>29695.000000</td>\n",
       "      <td>29708.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>11662.000000</td>\n",
       "      <td>11754.000000</td>\n",
       "      <td>11461.000000</td>\n",
       "      <td>30011.000000</td>\n",
       "      <td>30011.000000</td>\n",
       "      <td>30011.000000</td>\n",
       "      <td>30011.000000</td>\n",
       "      <td>30011.000000</td>\n",
       "      <td>30011.000000</td>\n",
       "      <td>30011.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.001223e+09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>587.284404</td>\n",
       "      <td>589.135427</td>\n",
       "      <td>534.857433</td>\n",
       "      <td>488.656197</td>\n",
       "      <td>299.184718</td>\n",
       "      <td>307.447285</td>\n",
       "      <td>...</td>\n",
       "      <td>0.889813</td>\n",
       "      <td>0.837332</td>\n",
       "      <td>0.832126</td>\n",
       "      <td>1264.064776</td>\n",
       "      <td>129.439626</td>\n",
       "      <td>135.127102</td>\n",
       "      <td>121.360548</td>\n",
       "      <td>6.562685</td>\n",
       "      <td>696.664356</td>\n",
       "      <td>0.086402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.846405e+05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>442.722413</td>\n",
       "      <td>462.897814</td>\n",
       "      <td>492.259586</td>\n",
       "      <td>496.486834</td>\n",
       "      <td>462.202367</td>\n",
       "      <td>483.244786</td>\n",
       "      <td>...</td>\n",
       "      <td>0.313136</td>\n",
       "      <td>0.369078</td>\n",
       "      <td>0.373770</td>\n",
       "      <td>975.263117</td>\n",
       "      <td>390.478591</td>\n",
       "      <td>408.024394</td>\n",
       "      <td>389.726031</td>\n",
       "      <td>48.638658</td>\n",
       "      <td>488.782088</td>\n",
       "      <td>0.280961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>7.000000e+09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2258.709000</td>\n",
       "      <td>-2014.045000</td>\n",
       "      <td>-945.808000</td>\n",
       "      <td>-1899.505000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>368.500000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7.000641e+09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>364.161000</td>\n",
       "      <td>365.004500</td>\n",
       "      <td>289.609500</td>\n",
       "      <td>239.184000</td>\n",
       "      <td>42.960000</td>\n",
       "      <td>42.660000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>480.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>450.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.001232e+09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>495.682000</td>\n",
       "      <td>493.561000</td>\n",
       "      <td>452.091000</td>\n",
       "      <td>413.152000</td>\n",
       "      <td>128.330000</td>\n",
       "      <td>127.950000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>914.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>568.500000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.001814e+09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>703.922000</td>\n",
       "      <td>700.788000</td>\n",
       "      <td>671.150000</td>\n",
       "      <td>635.553000</td>\n",
       "      <td>357.120000</td>\n",
       "      <td>364.567500</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1924.000000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>1.990000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>795.500000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.002411e+09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27731.088000</td>\n",
       "      <td>35145.834000</td>\n",
       "      <td>33543.624000</td>\n",
       "      <td>38805.617000</td>\n",
       "      <td>7376.710000</td>\n",
       "      <td>8157.780000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4321.000000</td>\n",
       "      <td>12916.220000</td>\n",
       "      <td>9165.600000</td>\n",
       "      <td>11166.210000</td>\n",
       "      <td>2618.570000</td>\n",
       "      <td>37762.500000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 211 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       mobile_number  loc_og_t2o_mou  std_og_t2o_mou  loc_ic_t2o_mou  \\\n",
       "count   3.001100e+04         29897.0         29897.0         29897.0   \n",
       "mean    7.001223e+09             0.0             0.0             0.0   \n",
       "std     6.846405e+05             0.0             0.0             0.0   \n",
       "min     7.000000e+09             0.0             0.0             0.0   \n",
       "25%     7.000641e+09             0.0             0.0             0.0   \n",
       "50%     7.001232e+09             0.0             0.0             0.0   \n",
       "75%     7.001814e+09             0.0             0.0             0.0   \n",
       "max     7.002411e+09             0.0             0.0             0.0   \n",
       "\n",
       "             arpu_6        arpu_7        arpu_8        arpu_9   onnet_mou_6  \\\n",
       "count  30011.000000  30011.000000  30011.000000  30011.000000  29695.000000   \n",
       "mean     587.284404    589.135427    534.857433    488.656197    299.184718   \n",
       "std      442.722413    462.897814    492.259586    496.486834    462.202367   \n",
       "min    -2258.709000  -2014.045000   -945.808000  -1899.505000      0.000000   \n",
       "25%      364.161000    365.004500    289.609500    239.184000     42.960000   \n",
       "50%      495.682000    493.561000    452.091000    413.152000    128.330000   \n",
       "75%      703.922000    700.788000    671.150000    635.553000    357.120000   \n",
       "max    27731.088000  35145.834000  33543.624000  38805.617000   7376.710000   \n",
       "\n",
       "        onnet_mou_7      ...          fb_user_7     fb_user_8     fb_user_9  \\\n",
       "count  29708.000000      ...       11662.000000  11754.000000  11461.000000   \n",
       "mean     307.447285      ...           0.889813      0.837332      0.832126   \n",
       "std      483.244786      ...           0.313136      0.369078      0.373770   \n",
       "min        0.000000      ...           0.000000      0.000000      0.000000   \n",
       "25%       42.660000      ...           1.000000      1.000000      1.000000   \n",
       "50%      127.950000      ...           1.000000      1.000000      1.000000   \n",
       "75%      364.567500      ...           1.000000      1.000000      1.000000   \n",
       "max     8157.780000      ...           1.000000      1.000000      1.000000   \n",
       "\n",
       "                aon    aug_vbc_3g    jul_vbc_3g    jun_vbc_3g    sep_vbc_3g  \\\n",
       "count  30011.000000  30011.000000  30011.000000  30011.000000  30011.000000   \n",
       "mean    1264.064776    129.439626    135.127102    121.360548      6.562685   \n",
       "std      975.263117    390.478591    408.024394    389.726031     48.638658   \n",
       "min      180.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%      480.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "50%      914.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "75%     1924.000000      1.600000      1.990000      0.000000      0.000000   \n",
       "max     4321.000000  12916.220000   9165.600000  11166.210000   2618.570000   \n",
       "\n",
       "       Average_amount         Churn  \n",
       "count    30011.000000  30011.000000  \n",
       "mean       696.664356      0.086402  \n",
       "std        488.782088      0.280961  \n",
       "min        368.500000      0.000000  \n",
       "25%        450.000000      0.000000  \n",
       "50%        568.500000      0.000000  \n",
       "75%        795.500000      0.000000  \n",
       "max      37762.500000      1.000000  \n",
       "\n",
       "[8 rows x 211 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "#REmoving columns ending with _9\n",
    "delete_col = [col for col in df if col.endswith('_9')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['arpu_9',\n",
       " 'onnet_mou_9',\n",
       " 'offnet_mou_9',\n",
       " 'roam_ic_mou_9',\n",
       " 'roam_og_mou_9',\n",
       " 'loc_og_t2t_mou_9',\n",
       " 'loc_og_t2m_mou_9',\n",
       " 'loc_og_t2f_mou_9',\n",
       " 'loc_og_t2c_mou_9',\n",
       " 'loc_og_mou_9',\n",
       " 'std_og_t2t_mou_9',\n",
       " 'std_og_t2m_mou_9',\n",
       " 'std_og_t2f_mou_9',\n",
       " 'std_og_t2c_mou_9',\n",
       " 'std_og_mou_9',\n",
       " 'isd_og_mou_9',\n",
       " 'spl_og_mou_9',\n",
       " 'og_others_9',\n",
       " 'loc_ic_t2t_mou_9',\n",
       " 'loc_ic_t2m_mou_9',\n",
       " 'loc_ic_t2f_mou_9',\n",
       " 'loc_ic_mou_9',\n",
       " 'std_ic_t2t_mou_9',\n",
       " 'std_ic_t2m_mou_9',\n",
       " 'std_ic_t2f_mou_9',\n",
       " 'std_ic_t2o_mou_9',\n",
       " 'std_ic_mou_9',\n",
       " 'spl_ic_mou_9',\n",
       " 'isd_ic_mou_9',\n",
       " 'ic_others_9',\n",
       " 'total_rech_num_9',\n",
       " 'total_rech_amt_9',\n",
       " 'max_rech_amt_9',\n",
       " 'date_of_last_rech_9',\n",
       " 'last_day_rch_amt_9',\n",
       " 'date_of_last_rech_data_9',\n",
       " 'total_rech_data_9',\n",
       " 'max_rech_data_9',\n",
       " 'count_rech_2g_9',\n",
       " 'count_rech_3g_9',\n",
       " 'av_rech_amt_data_9',\n",
       " 'arpu_3g_9',\n",
       " 'arpu_2g_9',\n",
       " 'night_pck_user_9',\n",
       " 'monthly_2g_9',\n",
       " 'sachet_2g_9',\n",
       " 'monthly_3g_9',\n",
       " 'sachet_3g_9',\n",
       " 'fb_user_9']"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delete_col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df=df.drop(df[delete_col],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30011, 171)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 30011 entries, 7 to 99997\n",
      "Columns: 171 entries, mobile_number to Churn\n",
      "dtypes: float64(137), int64(27), object(7)\n",
      "memory usage: 38.6+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['mobile_number', 'loc_og_t2o_mou', 'std_og_t2o_mou', 'loc_ic_t2o_mou',\n",
       "       'arpu_6', 'arpu_7', 'arpu_8', 'onnet_mou_6', 'onnet_mou_7',\n",
       "       'onnet_mou_8',\n",
       "       ...\n",
       "       'fb_user_7', 'fb_user_8', 'aon', 'aug_vbc_3g', 'jul_vbc_3g',\n",
       "       'jun_vbc_3g', 'sep_vbc_3g', 'Average_amount', 'Customers', 'Churn'],\n",
       "      dtype='object', length=171)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>mobile_number</th>\n",
       "      <th>loc_og_t2o_mou</th>\n",
       "      <th>std_og_t2o_mou</th>\n",
       "      <th>loc_ic_t2o_mou</th>\n",
       "      <th>arpu_6</th>\n",
       "      <th>arpu_7</th>\n",
       "      <th>arpu_8</th>\n",
       "      <th>onnet_mou_6</th>\n",
       "      <th>onnet_mou_7</th>\n",
       "      <th>onnet_mou_8</th>\n",
       "      <th>...</th>\n",
       "      <th>fb_user_6</th>\n",
       "      <th>fb_user_7</th>\n",
       "      <th>fb_user_8</th>\n",
       "      <th>aon</th>\n",
       "      <th>aug_vbc_3g</th>\n",
       "      <th>jul_vbc_3g</th>\n",
       "      <th>jun_vbc_3g</th>\n",
       "      <th>sep_vbc_3g</th>\n",
       "      <th>Average_amount</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3.001100e+04</td>\n",
       "      <td>29897.0</td>\n",
       "      <td>29897.0</td>\n",
       "      <td>29897.0</td>\n",
       "      <td>30011.000000</td>\n",
       "      <td>30011.000000</td>\n",
       "      <td>30011.000000</td>\n",
       "      <td>29695.000000</td>\n",
       "      <td>29708.000000</td>\n",
       "      <td>29073.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>11397.000000</td>\n",
       "      <td>11662.000000</td>\n",
       "      <td>11754.000000</td>\n",
       "      <td>30011.000000</td>\n",
       "      <td>30011.000000</td>\n",
       "      <td>30011.000000</td>\n",
       "      <td>30011.000000</td>\n",
       "      <td>30011.000000</td>\n",
       "      <td>30011.000000</td>\n",
       "      <td>30011.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.001223e+09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>587.284404</td>\n",
       "      <td>589.135427</td>\n",
       "      <td>534.857433</td>\n",
       "      <td>299.184718</td>\n",
       "      <td>307.447285</td>\n",
       "      <td>276.234168</td>\n",
       "      <td>...</td>\n",
       "      <td>0.897605</td>\n",
       "      <td>0.889813</td>\n",
       "      <td>0.837332</td>\n",
       "      <td>1264.064776</td>\n",
       "      <td>129.439626</td>\n",
       "      <td>135.127102</td>\n",
       "      <td>121.360548</td>\n",
       "      <td>6.562685</td>\n",
       "      <td>696.664356</td>\n",
       "      <td>0.086402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.846405e+05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>442.722413</td>\n",
       "      <td>462.897814</td>\n",
       "      <td>492.259586</td>\n",
       "      <td>462.202367</td>\n",
       "      <td>483.244786</td>\n",
       "      <td>471.505518</td>\n",
       "      <td>...</td>\n",
       "      <td>0.303181</td>\n",
       "      <td>0.313136</td>\n",
       "      <td>0.369078</td>\n",
       "      <td>975.263117</td>\n",
       "      <td>390.478591</td>\n",
       "      <td>408.024394</td>\n",
       "      <td>389.726031</td>\n",
       "      <td>48.638658</td>\n",
       "      <td>488.782088</td>\n",
       "      <td>0.280961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>7.000000e+09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2258.709000</td>\n",
       "      <td>-2014.045000</td>\n",
       "      <td>-945.808000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>368.500000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7.000641e+09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>364.161000</td>\n",
       "      <td>365.004500</td>\n",
       "      <td>289.609500</td>\n",
       "      <td>42.960000</td>\n",
       "      <td>42.660000</td>\n",
       "      <td>32.210000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>480.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>450.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.001232e+09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>495.682000</td>\n",
       "      <td>493.561000</td>\n",
       "      <td>452.091000</td>\n",
       "      <td>128.330000</td>\n",
       "      <td>127.950000</td>\n",
       "      <td>106.280000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>914.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>568.500000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.001814e+09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>703.922000</td>\n",
       "      <td>700.788000</td>\n",
       "      <td>671.150000</td>\n",
       "      <td>357.120000</td>\n",
       "      <td>364.567500</td>\n",
       "      <td>309.980000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1924.000000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>1.990000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>795.500000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.002411e+09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27731.088000</td>\n",
       "      <td>35145.834000</td>\n",
       "      <td>33543.624000</td>\n",
       "      <td>7376.710000</td>\n",
       "      <td>8157.780000</td>\n",
       "      <td>10752.560000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4321.000000</td>\n",
       "      <td>12916.220000</td>\n",
       "      <td>9165.600000</td>\n",
       "      <td>11166.210000</td>\n",
       "      <td>2618.570000</td>\n",
       "      <td>37762.500000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 164 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       mobile_number  loc_og_t2o_mou  std_og_t2o_mou  loc_ic_t2o_mou  \\\n",
       "count   3.001100e+04         29897.0         29897.0         29897.0   \n",
       "mean    7.001223e+09             0.0             0.0             0.0   \n",
       "std     6.846405e+05             0.0             0.0             0.0   \n",
       "min     7.000000e+09             0.0             0.0             0.0   \n",
       "25%     7.000641e+09             0.0             0.0             0.0   \n",
       "50%     7.001232e+09             0.0             0.0             0.0   \n",
       "75%     7.001814e+09             0.0             0.0             0.0   \n",
       "max     7.002411e+09             0.0             0.0             0.0   \n",
       "\n",
       "             arpu_6        arpu_7        arpu_8   onnet_mou_6   onnet_mou_7  \\\n",
       "count  30011.000000  30011.000000  30011.000000  29695.000000  29708.000000   \n",
       "mean     587.284404    589.135427    534.857433    299.184718    307.447285   \n",
       "std      442.722413    462.897814    492.259586    462.202367    483.244786   \n",
       "min    -2258.709000  -2014.045000   -945.808000      0.000000      0.000000   \n",
       "25%      364.161000    365.004500    289.609500     42.960000     42.660000   \n",
       "50%      495.682000    493.561000    452.091000    128.330000    127.950000   \n",
       "75%      703.922000    700.788000    671.150000    357.120000    364.567500   \n",
       "max    27731.088000  35145.834000  33543.624000   7376.710000   8157.780000   \n",
       "\n",
       "        onnet_mou_8      ...          fb_user_6     fb_user_7     fb_user_8  \\\n",
       "count  29073.000000      ...       11397.000000  11662.000000  11754.000000   \n",
       "mean     276.234168      ...           0.897605      0.889813      0.837332   \n",
       "std      471.505518      ...           0.303181      0.313136      0.369078   \n",
       "min        0.000000      ...           0.000000      0.000000      0.000000   \n",
       "25%       32.210000      ...           1.000000      1.000000      1.000000   \n",
       "50%      106.280000      ...           1.000000      1.000000      1.000000   \n",
       "75%      309.980000      ...           1.000000      1.000000      1.000000   \n",
       "max    10752.560000      ...           1.000000      1.000000      1.000000   \n",
       "\n",
       "                aon    aug_vbc_3g    jul_vbc_3g    jun_vbc_3g    sep_vbc_3g  \\\n",
       "count  30011.000000  30011.000000  30011.000000  30011.000000  30011.000000   \n",
       "mean    1264.064776    129.439626    135.127102    121.360548      6.562685   \n",
       "std      975.263117    390.478591    408.024394    389.726031     48.638658   \n",
       "min      180.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%      480.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "50%      914.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "75%     1924.000000      1.600000      1.990000      0.000000      0.000000   \n",
       "max     4321.000000  12916.220000   9165.600000  11166.210000   2618.570000   \n",
       "\n",
       "       Average_amount         Churn  \n",
       "count    30011.000000  30011.000000  \n",
       "mean       696.664356      0.086402  \n",
       "std        488.782088      0.280961  \n",
       "min        368.500000      0.000000  \n",
       "25%        450.000000      0.000000  \n",
       "50%        568.500000      0.000000  \n",
       "75%        795.500000      0.000000  \n",
       "max      37762.500000      1.000000  \n",
       "\n",
       "[8 rows x 164 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mobile_number       False\n",
       "loc_og_t2o_mou       True\n",
       "std_og_t2o_mou       True\n",
       "loc_ic_t2o_mou       True\n",
       "arpu_6              False\n",
       "arpu_7              False\n",
       "arpu_8              False\n",
       "onnet_mou_6          True\n",
       "onnet_mou_7          True\n",
       "onnet_mou_8          True\n",
       "offnet_mou_6         True\n",
       "offnet_mou_7         True\n",
       "offnet_mou_8         True\n",
       "roam_ic_mou_6        True\n",
       "roam_ic_mou_7        True\n",
       "roam_ic_mou_8        True\n",
       "roam_og_mou_6        True\n",
       "roam_og_mou_7        True\n",
       "roam_og_mou_8        True\n",
       "loc_og_t2t_mou_6     True\n",
       "loc_og_t2t_mou_7     True\n",
       "loc_og_t2t_mou_8     True\n",
       "loc_og_t2m_mou_6     True\n",
       "loc_og_t2m_mou_7     True\n",
       "loc_og_t2m_mou_8     True\n",
       "loc_og_t2f_mou_6     True\n",
       "loc_og_t2f_mou_7     True\n",
       "loc_og_t2f_mou_8     True\n",
       "loc_og_t2c_mou_6     True\n",
       "loc_og_t2c_mou_7     True\n",
       "                    ...  \n",
       "arpu_3g_8            True\n",
       "arpu_2g_6            True\n",
       "arpu_2g_7            True\n",
       "arpu_2g_8            True\n",
       "night_pck_user_6     True\n",
       "night_pck_user_7     True\n",
       "night_pck_user_8     True\n",
       "monthly_2g_6        False\n",
       "monthly_2g_7        False\n",
       "monthly_2g_8        False\n",
       "sachet_2g_6         False\n",
       "sachet_2g_7         False\n",
       "sachet_2g_8         False\n",
       "monthly_3g_6        False\n",
       "monthly_3g_7        False\n",
       "monthly_3g_8        False\n",
       "sachet_3g_6         False\n",
       "sachet_3g_7         False\n",
       "sachet_3g_8         False\n",
       "fb_user_6            True\n",
       "fb_user_7            True\n",
       "fb_user_8            True\n",
       "aon                 False\n",
       "aug_vbc_3g          False\n",
       "jul_vbc_3g          False\n",
       "jun_vbc_3g          False\n",
       "sep_vbc_3g          False\n",
       "Average_amount      False\n",
       "Customers           False\n",
       "Churn               False\n",
       "Length: 171, dtype: bool"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mobile_number           0\n",
       "loc_og_t2o_mou        114\n",
       "std_og_t2o_mou        114\n",
       "loc_ic_t2o_mou        114\n",
       "arpu_6                  0\n",
       "arpu_7                  0\n",
       "arpu_8                  0\n",
       "onnet_mou_6           316\n",
       "onnet_mou_7           303\n",
       "onnet_mou_8           938\n",
       "offnet_mou_6          316\n",
       "offnet_mou_7          303\n",
       "offnet_mou_8          938\n",
       "roam_ic_mou_6         316\n",
       "roam_ic_mou_7         303\n",
       "roam_ic_mou_8         938\n",
       "roam_og_mou_6         316\n",
       "roam_og_mou_7         303\n",
       "roam_og_mou_8         938\n",
       "loc_og_t2t_mou_6      316\n",
       "loc_og_t2t_mou_7      303\n",
       "loc_og_t2t_mou_8      938\n",
       "loc_og_t2m_mou_6      316\n",
       "loc_og_t2m_mou_7      303\n",
       "loc_og_t2m_mou_8      938\n",
       "loc_og_t2f_mou_6      316\n",
       "loc_og_t2f_mou_7      303\n",
       "loc_og_t2f_mou_8      938\n",
       "loc_og_t2c_mou_6      316\n",
       "loc_og_t2c_mou_7      303\n",
       "                    ...  \n",
       "arpu_3g_8           18257\n",
       "arpu_2g_6           18614\n",
       "arpu_2g_7           18349\n",
       "arpu_2g_8           18257\n",
       "night_pck_user_6    18614\n",
       "night_pck_user_7    18349\n",
       "night_pck_user_8    18257\n",
       "monthly_2g_6            0\n",
       "monthly_2g_7            0\n",
       "monthly_2g_8            0\n",
       "sachet_2g_6             0\n",
       "sachet_2g_7             0\n",
       "sachet_2g_8             0\n",
       "monthly_3g_6            0\n",
       "monthly_3g_7            0\n",
       "monthly_3g_8            0\n",
       "sachet_3g_6             0\n",
       "sachet_3g_7             0\n",
       "sachet_3g_8             0\n",
       "fb_user_6           18614\n",
       "fb_user_7           18349\n",
       "fb_user_8           18257\n",
       "aon                     0\n",
       "aug_vbc_3g              0\n",
       "jul_vbc_3g              0\n",
       "jun_vbc_3g              0\n",
       "sep_vbc_3g              0\n",
       "Average_amount          0\n",
       "Customers               0\n",
       "Churn                   0\n",
       "Length: 171, dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mobile_number        0.00\n",
       "loc_og_t2o_mou       0.38\n",
       "std_og_t2o_mou       0.38\n",
       "loc_ic_t2o_mou       0.38\n",
       "arpu_6               0.00\n",
       "arpu_7               0.00\n",
       "arpu_8               0.00\n",
       "onnet_mou_6          1.05\n",
       "onnet_mou_7          1.01\n",
       "onnet_mou_8          3.13\n",
       "offnet_mou_6         1.05\n",
       "offnet_mou_7         1.01\n",
       "offnet_mou_8         3.13\n",
       "roam_ic_mou_6        1.05\n",
       "roam_ic_mou_7        1.01\n",
       "roam_ic_mou_8        3.13\n",
       "roam_og_mou_6        1.05\n",
       "roam_og_mou_7        1.01\n",
       "roam_og_mou_8        3.13\n",
       "loc_og_t2t_mou_6     1.05\n",
       "loc_og_t2t_mou_7     1.01\n",
       "loc_og_t2t_mou_8     3.13\n",
       "loc_og_t2m_mou_6     1.05\n",
       "loc_og_t2m_mou_7     1.01\n",
       "loc_og_t2m_mou_8     3.13\n",
       "loc_og_t2f_mou_6     1.05\n",
       "loc_og_t2f_mou_7     1.01\n",
       "loc_og_t2f_mou_8     3.13\n",
       "loc_og_t2c_mou_6     1.05\n",
       "loc_og_t2c_mou_7     1.01\n",
       "                    ...  \n",
       "arpu_3g_8           60.83\n",
       "arpu_2g_6           62.02\n",
       "arpu_2g_7           61.14\n",
       "arpu_2g_8           60.83\n",
       "night_pck_user_6    62.02\n",
       "night_pck_user_7    61.14\n",
       "night_pck_user_8    60.83\n",
       "monthly_2g_6         0.00\n",
       "monthly_2g_7         0.00\n",
       "monthly_2g_8         0.00\n",
       "sachet_2g_6          0.00\n",
       "sachet_2g_7          0.00\n",
       "sachet_2g_8          0.00\n",
       "monthly_3g_6         0.00\n",
       "monthly_3g_7         0.00\n",
       "monthly_3g_8         0.00\n",
       "sachet_3g_6          0.00\n",
       "sachet_3g_7          0.00\n",
       "sachet_3g_8          0.00\n",
       "fb_user_6           62.02\n",
       "fb_user_7           61.14\n",
       "fb_user_8           60.83\n",
       "aon                  0.00\n",
       "aug_vbc_3g           0.00\n",
       "jul_vbc_3g           0.00\n",
       "jun_vbc_3g           0.00\n",
       "sep_vbc_3g           0.00\n",
       "Average_amount       0.00\n",
       "Customers            0.00\n",
       "Churn                0.00\n",
       "Length: 171, dtype: float64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the percentage of missing values\n",
    "round(100*(df.isnull().sum()/len(df)), 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15005.5"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frac=len(df)*0.5\n",
    "frac"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Removing columns having greater than 50% null values.\n",
    "df=df.dropna(thresh=frac,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30011, 141)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mobile_number            0\n",
       "loc_og_t2o_mou         114\n",
       "std_og_t2o_mou         114\n",
       "loc_ic_t2o_mou         114\n",
       "arpu_6                   0\n",
       "arpu_7                   0\n",
       "arpu_8                   0\n",
       "onnet_mou_6            316\n",
       "onnet_mou_7            303\n",
       "onnet_mou_8            938\n",
       "offnet_mou_6           316\n",
       "offnet_mou_7           303\n",
       "offnet_mou_8           938\n",
       "roam_ic_mou_6          316\n",
       "roam_ic_mou_7          303\n",
       "roam_ic_mou_8          938\n",
       "roam_og_mou_6          316\n",
       "roam_og_mou_7          303\n",
       "roam_og_mou_8          938\n",
       "loc_og_t2t_mou_6       316\n",
       "loc_og_t2t_mou_7       303\n",
       "loc_og_t2t_mou_8       938\n",
       "loc_og_t2m_mou_6       316\n",
       "loc_og_t2m_mou_7       303\n",
       "loc_og_t2m_mou_8       938\n",
       "loc_og_t2f_mou_6       316\n",
       "loc_og_t2f_mou_7       303\n",
       "loc_og_t2f_mou_8       938\n",
       "loc_og_t2c_mou_6       316\n",
       "loc_og_t2c_mou_7       303\n",
       "                      ... \n",
       "date_of_last_rech_8    594\n",
       "last_day_rch_amt_6       0\n",
       "last_day_rch_amt_7       0\n",
       "last_day_rch_amt_8       0\n",
       "vol_2g_mb_6              0\n",
       "vol_2g_mb_7              0\n",
       "vol_2g_mb_8              0\n",
       "vol_3g_mb_6              0\n",
       "vol_3g_mb_7              0\n",
       "vol_3g_mb_8              0\n",
       "monthly_2g_6             0\n",
       "monthly_2g_7             0\n",
       "monthly_2g_8             0\n",
       "sachet_2g_6              0\n",
       "sachet_2g_7              0\n",
       "sachet_2g_8              0\n",
       "monthly_3g_6             0\n",
       "monthly_3g_7             0\n",
       "monthly_3g_8             0\n",
       "sachet_3g_6              0\n",
       "sachet_3g_7              0\n",
       "sachet_3g_8              0\n",
       "aon                      0\n",
       "aug_vbc_3g               0\n",
       "jul_vbc_3g               0\n",
       "jun_vbc_3g               0\n",
       "sep_vbc_3g               0\n",
       "Average_amount           0\n",
       "Customers                0\n",
       "Churn                    0\n",
       "Length: 141, dtype: int64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7         0\n",
       "8         0\n",
       "13        0\n",
       "16        0\n",
       "17        0\n",
       "21        0\n",
       "24        0\n",
       "33        0\n",
       "38        0\n",
       "41        0\n",
       "48        0\n",
       "53        0\n",
       "56        0\n",
       "57        0\n",
       "60        0\n",
       "61        0\n",
       "69        0\n",
       "73        0\n",
       "74        0\n",
       "77       29\n",
       "87        0\n",
       "92        0\n",
       "96        0\n",
       "97        0\n",
       "101       0\n",
       "102       0\n",
       "103       0\n",
       "105       0\n",
       "111      29\n",
       "115       0\n",
       "         ..\n",
       "99887     0\n",
       "99893     0\n",
       "99896     0\n",
       "99899     0\n",
       "99902     0\n",
       "99904     0\n",
       "99921     0\n",
       "99922     0\n",
       "99923     0\n",
       "99924     0\n",
       "99925     0\n",
       "99927     0\n",
       "99929     0\n",
       "99931     0\n",
       "99932     0\n",
       "99935     0\n",
       "99937     0\n",
       "99939     0\n",
       "99941     0\n",
       "99943     0\n",
       "99957     0\n",
       "99959     0\n",
       "99961     0\n",
       "99963     0\n",
       "99965     0\n",
       "99970     0\n",
       "99974     0\n",
       "99986     0\n",
       "99988     0\n",
       "99997     0\n",
       "Length: 30011, dtype: int64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Replacing every null value with median\n",
    "df=df.fillna(df.median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mobile_number            0\n",
       "loc_og_t2o_mou           0\n",
       "std_og_t2o_mou           0\n",
       "loc_ic_t2o_mou           0\n",
       "arpu_6                   0\n",
       "arpu_7                   0\n",
       "arpu_8                   0\n",
       "onnet_mou_6              0\n",
       "onnet_mou_7              0\n",
       "onnet_mou_8              0\n",
       "offnet_mou_6             0\n",
       "offnet_mou_7             0\n",
       "offnet_mou_8             0\n",
       "roam_ic_mou_6            0\n",
       "roam_ic_mou_7            0\n",
       "roam_ic_mou_8            0\n",
       "roam_og_mou_6            0\n",
       "roam_og_mou_7            0\n",
       "roam_og_mou_8            0\n",
       "loc_og_t2t_mou_6         0\n",
       "loc_og_t2t_mou_7         0\n",
       "loc_og_t2t_mou_8         0\n",
       "loc_og_t2m_mou_6         0\n",
       "loc_og_t2m_mou_7         0\n",
       "loc_og_t2m_mou_8         0\n",
       "loc_og_t2f_mou_6         0\n",
       "loc_og_t2f_mou_7         0\n",
       "loc_og_t2f_mou_8         0\n",
       "loc_og_t2c_mou_6         0\n",
       "loc_og_t2c_mou_7         0\n",
       "                      ... \n",
       "date_of_last_rech_8    594\n",
       "last_day_rch_amt_6       0\n",
       "last_day_rch_amt_7       0\n",
       "last_day_rch_amt_8       0\n",
       "vol_2g_mb_6              0\n",
       "vol_2g_mb_7              0\n",
       "vol_2g_mb_8              0\n",
       "vol_3g_mb_6              0\n",
       "vol_3g_mb_7              0\n",
       "vol_3g_mb_8              0\n",
       "monthly_2g_6             0\n",
       "monthly_2g_7             0\n",
       "monthly_2g_8             0\n",
       "sachet_2g_6              0\n",
       "sachet_2g_7              0\n",
       "sachet_2g_8              0\n",
       "monthly_3g_6             0\n",
       "monthly_3g_7             0\n",
       "monthly_3g_8             0\n",
       "sachet_3g_6              0\n",
       "sachet_3g_7              0\n",
       "sachet_3g_8              0\n",
       "aon                      0\n",
       "aug_vbc_3g               0\n",
       "jul_vbc_3g               0\n",
       "jun_vbc_3g               0\n",
       "sep_vbc_3g               0\n",
       "Average_amount           0\n",
       "Customers                0\n",
       "Churn                    0\n",
       "Length: 141, dtype: int64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#CHecking if the columns have any null values.\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7        0\n",
       "8        0\n",
       "13       0\n",
       "16       0\n",
       "17       0\n",
       "21       0\n",
       "24       0\n",
       "33       0\n",
       "38       0\n",
       "41       0\n",
       "48       0\n",
       "53       0\n",
       "56       0\n",
       "57       0\n",
       "60       0\n",
       "61       0\n",
       "69       0\n",
       "73       0\n",
       "74       0\n",
       "77       0\n",
       "87       0\n",
       "92       0\n",
       "96       0\n",
       "97       0\n",
       "101      0\n",
       "102      0\n",
       "103      0\n",
       "105      0\n",
       "111      0\n",
       "115      0\n",
       "        ..\n",
       "99887    0\n",
       "99893    0\n",
       "99896    0\n",
       "99899    0\n",
       "99902    0\n",
       "99904    0\n",
       "99921    0\n",
       "99922    0\n",
       "99923    0\n",
       "99924    0\n",
       "99925    0\n",
       "99927    0\n",
       "99929    0\n",
       "99931    0\n",
       "99932    0\n",
       "99935    0\n",
       "99937    0\n",
       "99939    0\n",
       "99941    0\n",
       "99943    0\n",
       "99957    0\n",
       "99959    0\n",
       "99961    0\n",
       "99963    0\n",
       "99965    0\n",
       "99970    0\n",
       "99974    0\n",
       "99986    0\n",
       "99988    0\n",
       "99997    0\n",
       "Length: 30011, dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.64016527273333"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Seeing Churn percentage,to see how many have churned.\n",
    "churn = (sum(df['Churn'])/len(df))*100\n",
    "churn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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>mobile_number</th>\n",
       "      <th>loc_og_t2o_mou</th>\n",
       "      <th>std_og_t2o_mou</th>\n",
       "      <th>loc_ic_t2o_mou</th>\n",
       "      <th>arpu_6</th>\n",
       "      <th>arpu_7</th>\n",
       "      <th>arpu_8</th>\n",
       "      <th>onnet_mou_6</th>\n",
       "      <th>onnet_mou_7</th>\n",
       "      <th>onnet_mou_8</th>\n",
       "      <th>...</th>\n",
       "      <th>sachet_3g_7</th>\n",
       "      <th>sachet_3g_8</th>\n",
       "      <th>aon</th>\n",
       "      <th>aug_vbc_3g</th>\n",
       "      <th>jul_vbc_3g</th>\n",
       "      <th>jun_vbc_3g</th>\n",
       "      <th>sep_vbc_3g</th>\n",
       "      <th>Average_amount</th>\n",
       "      <th>Customers</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7000701601</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1069.180</td>\n",
       "      <td>1349.850</td>\n",
       "      <td>3171.480</td>\n",
       "      <td>57.84</td>\n",
       "      <td>54.68</td>\n",
       "      <td>52.29</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>802</td>\n",
       "      <td>57.74</td>\n",
       "      <td>19.38</td>\n",
       "      <td>18.74</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1185.0</td>\n",
       "      <td>High Value</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7001524846</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>378.721</td>\n",
       "      <td>492.223</td>\n",
       "      <td>137.362</td>\n",
       "      <td>413.69</td>\n",
       "      <td>351.03</td>\n",
       "      <td>35.08</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>315</td>\n",
       "      <td>21.03</td>\n",
       "      <td>910.65</td>\n",
       "      <td>122.16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>519.0</td>\n",
       "      <td>High Value</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>7002191713</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>492.846</td>\n",
       "      <td>205.671</td>\n",
       "      <td>593.260</td>\n",
       "      <td>501.76</td>\n",
       "      <td>108.39</td>\n",
       "      <td>534.24</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2607</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>380.0</td>\n",
       "      <td>High Value</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>7000875565</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>430.975</td>\n",
       "      <td>299.869</td>\n",
       "      <td>187.894</td>\n",
       "      <td>50.51</td>\n",
       "      <td>74.01</td>\n",
       "      <td>70.61</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>511</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.45</td>\n",
       "      <td>21.89</td>\n",
       "      <td>0.0</td>\n",
       "      <td>459.0</td>\n",
       "      <td>High Value</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>7000187447</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>690.008</td>\n",
       "      <td>18.980</td>\n",
       "      <td>25.499</td>\n",
       "      <td>1185.91</td>\n",
       "      <td>9.28</td>\n",
       "      <td>7.79</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>667</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>408.0</td>\n",
       "      <td>High Value</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 141 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    mobile_number  loc_og_t2o_mou  std_og_t2o_mou  loc_ic_t2o_mou    arpu_6  \\\n",
       "7      7000701601             0.0             0.0             0.0  1069.180   \n",
       "8      7001524846             0.0             0.0             0.0   378.721   \n",
       "13     7002191713             0.0             0.0             0.0   492.846   \n",
       "16     7000875565             0.0             0.0             0.0   430.975   \n",
       "17     7000187447             0.0             0.0             0.0   690.008   \n",
       "\n",
       "      arpu_7    arpu_8  onnet_mou_6  onnet_mou_7  onnet_mou_8  ...    \\\n",
       "7   1349.850  3171.480        57.84        54.68        52.29  ...     \n",
       "8    492.223   137.362       413.69       351.03        35.08  ...     \n",
       "13   205.671   593.260       501.76       108.39       534.24  ...     \n",
       "16   299.869   187.894        50.51        74.01        70.61  ...     \n",
       "17    18.980    25.499      1185.91         9.28         7.79  ...     \n",
       "\n",
       "    sachet_3g_7  sachet_3g_8   aon  aug_vbc_3g  jul_vbc_3g  jun_vbc_3g  \\\n",
       "7             0            0   802       57.74       19.38       18.74   \n",
       "8             0            0   315       21.03      910.65      122.16   \n",
       "13            0            0  2607        0.00        0.00        0.00   \n",
       "16            0            0   511        0.00        2.45       21.89   \n",
       "17            0            0   667        0.00        0.00        0.00   \n",
       "\n",
       "    sep_vbc_3g  Average_amount   Customers  Churn  \n",
       "7          0.0          1185.0  High Value      1  \n",
       "8          0.0           519.0  High Value      0  \n",
       "13         0.0           380.0  High Value      0  \n",
       "16         0.0           459.0  High Value      0  \n",
       "17         0.0           408.0  High Value      0  \n",
       "\n",
       "[5 rows x 141 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mobile_number</th>\n",
       "      <th>loc_og_t2o_mou</th>\n",
       "      <th>std_og_t2o_mou</th>\n",
       "      <th>loc_ic_t2o_mou</th>\n",
       "      <th>arpu_6</th>\n",
       "      <th>arpu_7</th>\n",
       "      <th>arpu_8</th>\n",
       "      <th>onnet_mou_6</th>\n",
       "      <th>onnet_mou_7</th>\n",
       "      <th>onnet_mou_8</th>\n",
       "      <th>...</th>\n",
       "      <th>sachet_3g_6</th>\n",
       "      <th>sachet_3g_7</th>\n",
       "      <th>sachet_3g_8</th>\n",
       "      <th>aon</th>\n",
       "      <th>aug_vbc_3g</th>\n",
       "      <th>jul_vbc_3g</th>\n",
       "      <th>jun_vbc_3g</th>\n",
       "      <th>sep_vbc_3g</th>\n",
       "      <th>Average_amount</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3.001100e+04</td>\n",
       "      <td>30011.0</td>\n",
       "      <td>30011.0</td>\n",
       "      <td>30011.0</td>\n",
       "      <td>30011.000000</td>\n",
       "      <td>30011.000000</td>\n",
       "      <td>30011.000000</td>\n",
       "      <td>30011.000000</td>\n",
       "      <td>30011.000000</td>\n",
       "      <td>30011.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>30011.000000</td>\n",
       "      <td>30011.000000</td>\n",
       "      <td>30011.000000</td>\n",
       "      <td>30011.000000</td>\n",
       "      <td>30011.000000</td>\n",
       "      <td>30011.000000</td>\n",
       "      <td>30011.000000</td>\n",
       "      <td>30011.000000</td>\n",
       "      <td>30011.000000</td>\n",
       "      <td>30011.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.001223e+09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>587.284404</td>\n",
       "      <td>589.135427</td>\n",
       "      <td>534.857433</td>\n",
       "      <td>297.385708</td>\n",
       "      <td>305.635027</td>\n",
       "      <td>270.922215</td>\n",
       "      <td>...</td>\n",
       "      <td>0.137916</td>\n",
       "      <td>0.150212</td>\n",
       "      <td>0.140415</td>\n",
       "      <td>1264.064776</td>\n",
       "      <td>129.439626</td>\n",
       "      <td>135.127102</td>\n",
       "      <td>121.360548</td>\n",
       "      <td>6.562685</td>\n",
       "      <td>696.664356</td>\n",
       "      <td>0.086402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.846405e+05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>442.722413</td>\n",
       "      <td>462.897814</td>\n",
       "      <td>492.259586</td>\n",
       "      <td>460.093114</td>\n",
       "      <td>481.133789</td>\n",
       "      <td>465.019618</td>\n",
       "      <td>...</td>\n",
       "      <td>0.875528</td>\n",
       "      <td>0.969286</td>\n",
       "      <td>0.974727</td>\n",
       "      <td>975.263117</td>\n",
       "      <td>390.478591</td>\n",
       "      <td>408.024394</td>\n",
       "      <td>389.726031</td>\n",
       "      <td>48.638658</td>\n",
       "      <td>488.782088</td>\n",
       "      <td>0.280961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>7.000000e+09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2258.709000</td>\n",
       "      <td>-2014.045000</td>\n",
       "      <td>-945.808000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>368.500000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7.000641e+09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>364.161000</td>\n",
       "      <td>365.004500</td>\n",
       "      <td>289.609500</td>\n",
       "      <td>43.510000</td>\n",
       "      <td>43.260000</td>\n",
       "      <td>33.985000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>480.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>450.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.001232e+09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>495.682000</td>\n",
       "      <td>493.561000</td>\n",
       "      <td>452.091000</td>\n",
       "      <td>128.330000</td>\n",
       "      <td>127.950000</td>\n",
       "      <td>106.280000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>914.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>568.500000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.001814e+09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>703.922000</td>\n",
       "      <td>700.788000</td>\n",
       "      <td>671.150000</td>\n",
       "      <td>353.310000</td>\n",
       "      <td>359.925000</td>\n",
       "      <td>297.735000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1924.000000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>1.990000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>795.500000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.002411e+09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27731.088000</td>\n",
       "      <td>35145.834000</td>\n",
       "      <td>33543.624000</td>\n",
       "      <td>7376.710000</td>\n",
       "      <td>8157.780000</td>\n",
       "      <td>10752.560000</td>\n",
       "      <td>...</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>4321.000000</td>\n",
       "      <td>12916.220000</td>\n",
       "      <td>9165.600000</td>\n",
       "      <td>11166.210000</td>\n",
       "      <td>2618.570000</td>\n",
       "      <td>37762.500000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 137 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       mobile_number  loc_og_t2o_mou  std_og_t2o_mou  loc_ic_t2o_mou  \\\n",
       "count   3.001100e+04         30011.0         30011.0         30011.0   \n",
       "mean    7.001223e+09             0.0             0.0             0.0   \n",
       "std     6.846405e+05             0.0             0.0             0.0   \n",
       "min     7.000000e+09             0.0             0.0             0.0   \n",
       "25%     7.000641e+09             0.0             0.0             0.0   \n",
       "50%     7.001232e+09             0.0             0.0             0.0   \n",
       "75%     7.001814e+09             0.0             0.0             0.0   \n",
       "max     7.002411e+09             0.0             0.0             0.0   \n",
       "\n",
       "             arpu_6        arpu_7        arpu_8   onnet_mou_6   onnet_mou_7  \\\n",
       "count  30011.000000  30011.000000  30011.000000  30011.000000  30011.000000   \n",
       "mean     587.284404    589.135427    534.857433    297.385708    305.635027   \n",
       "std      442.722413    462.897814    492.259586    460.093114    481.133789   \n",
       "min    -2258.709000  -2014.045000   -945.808000      0.000000      0.000000   \n",
       "25%      364.161000    365.004500    289.609500     43.510000     43.260000   \n",
       "50%      495.682000    493.561000    452.091000    128.330000    127.950000   \n",
       "75%      703.922000    700.788000    671.150000    353.310000    359.925000   \n",
       "max    27731.088000  35145.834000  33543.624000   7376.710000   8157.780000   \n",
       "\n",
       "        onnet_mou_8      ...        sachet_3g_6   sachet_3g_7   sachet_3g_8  \\\n",
       "count  30011.000000      ...       30011.000000  30011.000000  30011.000000   \n",
       "mean     270.922215      ...           0.137916      0.150212      0.140415   \n",
       "std      465.019618      ...           0.875528      0.969286      0.974727   \n",
       "min        0.000000      ...           0.000000      0.000000      0.000000   \n",
       "25%       33.985000      ...           0.000000      0.000000      0.000000   \n",
       "50%      106.280000      ...           0.000000      0.000000      0.000000   \n",
       "75%      297.735000      ...           0.000000      0.000000      0.000000   \n",
       "max    10752.560000      ...          29.000000     35.000000     41.000000   \n",
       "\n",
       "                aon    aug_vbc_3g    jul_vbc_3g    jun_vbc_3g    sep_vbc_3g  \\\n",
       "count  30011.000000  30011.000000  30011.000000  30011.000000  30011.000000   \n",
       "mean    1264.064776    129.439626    135.127102    121.360548      6.562685   \n",
       "std      975.263117    390.478591    408.024394    389.726031     48.638658   \n",
       "min      180.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%      480.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "50%      914.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "75%     1924.000000      1.600000      1.990000      0.000000      0.000000   \n",
       "max     4321.000000  12916.220000   9165.600000  11166.210000   2618.570000   \n",
       "\n",
       "       Average_amount         Churn  \n",
       "count    30011.000000  30011.000000  \n",
       "mean       696.664356      0.086402  \n",
       "std        488.782088      0.280961  \n",
       "min        368.500000      0.000000  \n",
       "25%        450.000000      0.000000  \n",
       "50%        568.500000      0.000000  \n",
       "75%        795.500000      0.000000  \n",
       "max      37762.500000      1.000000  \n",
       "\n",
       "[8 rows x 137 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Deleting all categorical variables\n",
    "del df['date_of_last_rech_6']\n",
    "del df['date_of_last_rech_7']\n",
    "del df['date_of_last_rech_8']\n",
    "del df['Customers']\n",
    "del df['mobile_number']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loc_og_t2o_mou</th>\n",
       "      <th>std_og_t2o_mou</th>\n",
       "      <th>loc_ic_t2o_mou</th>\n",
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       "      <th>offnet_mou_6</th>\n",
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       "      <th>sep_vbc_3g</th>\n",
       "      <th>Average_amount</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
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       "      <td>30011.000000</td>\n",
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       "      <td>30011.000000</td>\n",
       "      <td>30011.000000</td>\n",
       "      <td>30011.000000</td>\n",
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       "      <td>30011.000000</td>\n",
       "      <td>30011.000000</td>\n",
       "      <td>30011.000000</td>\n",
       "      <td>30011.000000</td>\n",
       "      <td>30011.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>587.284404</td>\n",
       "      <td>589.135427</td>\n",
       "      <td>534.857433</td>\n",
       "      <td>297.385708</td>\n",
       "      <td>305.635027</td>\n",
       "      <td>270.922215</td>\n",
       "      <td>420.944380</td>\n",
       "      <td>...</td>\n",
       "      <td>0.137916</td>\n",
       "      <td>0.150212</td>\n",
       "      <td>0.140415</td>\n",
       "      <td>1264.064776</td>\n",
       "      <td>129.439626</td>\n",
       "      <td>135.127102</td>\n",
       "      <td>121.360548</td>\n",
       "      <td>6.562685</td>\n",
       "      <td>696.664356</td>\n",
       "      <td>0.086402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>442.722413</td>\n",
       "      <td>462.897814</td>\n",
       "      <td>492.259586</td>\n",
       "      <td>460.093114</td>\n",
       "      <td>481.133789</td>\n",
       "      <td>465.019618</td>\n",
       "      <td>468.816296</td>\n",
       "      <td>...</td>\n",
       "      <td>0.875528</td>\n",
       "      <td>0.969286</td>\n",
       "      <td>0.974727</td>\n",
       "      <td>975.263117</td>\n",
       "      <td>390.478591</td>\n",
       "      <td>408.024394</td>\n",
       "      <td>389.726031</td>\n",
       "      <td>48.638658</td>\n",
       "      <td>488.782088</td>\n",
       "      <td>0.280961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2258.709000</td>\n",
       "      <td>-2014.045000</td>\n",
       "      <td>-945.808000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>368.500000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>364.161000</td>\n",
       "      <td>365.004500</td>\n",
       "      <td>289.609500</td>\n",
       "      <td>43.510000</td>\n",
       "      <td>43.260000</td>\n",
       "      <td>33.985000</td>\n",
       "      <td>142.825000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>480.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>450.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>495.682000</td>\n",
       "      <td>493.561000</td>\n",
       "      <td>452.091000</td>\n",
       "      <td>128.330000</td>\n",
       "      <td>127.950000</td>\n",
       "      <td>106.280000</td>\n",
       "      <td>285.960000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>914.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>568.500000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>703.922000</td>\n",
       "      <td>700.788000</td>\n",
       "      <td>671.150000</td>\n",
       "      <td>353.310000</td>\n",
       "      <td>359.925000</td>\n",
       "      <td>297.735000</td>\n",
       "      <td>523.125000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1924.000000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>1.990000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>795.500000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27731.088000</td>\n",
       "      <td>35145.834000</td>\n",
       "      <td>33543.624000</td>\n",
       "      <td>7376.710000</td>\n",
       "      <td>8157.780000</td>\n",
       "      <td>10752.560000</td>\n",
       "      <td>8362.360000</td>\n",
       "      <td>...</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>4321.000000</td>\n",
       "      <td>12916.220000</td>\n",
       "      <td>9165.600000</td>\n",
       "      <td>11166.210000</td>\n",
       "      <td>2618.570000</td>\n",
       "      <td>37762.500000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 136 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       loc_og_t2o_mou  std_og_t2o_mou  loc_ic_t2o_mou        arpu_6  \\\n",
       "count         30011.0         30011.0         30011.0  30011.000000   \n",
       "mean              0.0             0.0             0.0    587.284404   \n",
       "std               0.0             0.0             0.0    442.722413   \n",
       "min               0.0             0.0             0.0  -2258.709000   \n",
       "25%               0.0             0.0             0.0    364.161000   \n",
       "50%               0.0             0.0             0.0    495.682000   \n",
       "75%               0.0             0.0             0.0    703.922000   \n",
       "max               0.0             0.0             0.0  27731.088000   \n",
       "\n",
       "             arpu_7        arpu_8   onnet_mou_6   onnet_mou_7   onnet_mou_8  \\\n",
       "count  30011.000000  30011.000000  30011.000000  30011.000000  30011.000000   \n",
       "mean     589.135427    534.857433    297.385708    305.635027    270.922215   \n",
       "std      462.897814    492.259586    460.093114    481.133789    465.019618   \n",
       "min    -2014.045000   -945.808000      0.000000      0.000000      0.000000   \n",
       "25%      365.004500    289.609500     43.510000     43.260000     33.985000   \n",
       "50%      493.561000    452.091000    128.330000    127.950000    106.280000   \n",
       "75%      700.788000    671.150000    353.310000    359.925000    297.735000   \n",
       "max    35145.834000  33543.624000   7376.710000   8157.780000  10752.560000   \n",
       "\n",
       "       offnet_mou_6      ...        sachet_3g_6   sachet_3g_7   sachet_3g_8  \\\n",
       "count  30011.000000      ...       30011.000000  30011.000000  30011.000000   \n",
       "mean     420.944380      ...           0.137916      0.150212      0.140415   \n",
       "std      468.816296      ...           0.875528      0.969286      0.974727   \n",
       "min        0.000000      ...           0.000000      0.000000      0.000000   \n",
       "25%      142.825000      ...           0.000000      0.000000      0.000000   \n",
       "50%      285.960000      ...           0.000000      0.000000      0.000000   \n",
       "75%      523.125000      ...           0.000000      0.000000      0.000000   \n",
       "max     8362.360000      ...          29.000000     35.000000     41.000000   \n",
       "\n",
       "                aon    aug_vbc_3g    jul_vbc_3g    jun_vbc_3g    sep_vbc_3g  \\\n",
       "count  30011.000000  30011.000000  30011.000000  30011.000000  30011.000000   \n",
       "mean    1264.064776    129.439626    135.127102    121.360548      6.562685   \n",
       "std      975.263117    390.478591    408.024394    389.726031     48.638658   \n",
       "min      180.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%      480.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "50%      914.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "75%     1924.000000      1.600000      1.990000      0.000000      0.000000   \n",
       "max     4321.000000  12916.220000   9165.600000  11166.210000   2618.570000   \n",
       "\n",
       "       Average_amount         Churn  \n",
       "count    30011.000000  30011.000000  \n",
       "mean       696.664356      0.086402  \n",
       "std        488.782088      0.280961  \n",
       "min        368.500000      0.000000  \n",
       "25%        450.000000      0.000000  \n",
       "50%        568.500000      0.000000  \n",
       "75%        795.500000      0.000000  \n",
       "max      37762.500000      1.000000  \n",
       "\n",
       "[8 rows x 136 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "  <thead>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99887</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99893</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99896</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99899</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99902</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99904</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99921</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99922</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99923</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99924</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99925</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99927</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99929</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99931</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99932</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99935</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99937</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99939</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99941</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99943</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99957</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99959</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99961</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99963</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99965</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99970</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99974</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99986</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99988</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99997</th>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30011 rows × 0 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: []\n",
       "Index: [7, 8, 13, 16, 17, 21, 24, 33, 38, 41, 48, 53, 56, 57, 60, 61, 69, 73, 74, 77, 87, 92, 96, 97, 101, 102, 103, 105, 111, 115, 122, 124, 131, 133, 134, 143, 146, 147, 149, 154, 157, 158, 165, 170, 171, 173, 175, 176, 177, 184, 185, 188, 191, 195, 199, 203, 204, 206, 208, 213, 218, 223, 225, 226, 235, 241, 244, 245, 246, 252, 253, 255, 256, 259, 264, 266, 270, 274, 278, 281, 283, 284, 290, 292, 293, 294, 295, 301, 302, 304, 310, 319, 320, 321, 333, 337, 339, 345, 346, 356, ...]\n",
       "\n",
       "[30011 rows x 0 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We have removed every categorical variables, now we have only numeric variables\n",
    "df.loc[:, df.dtypes == object]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7     1\n",
       "8     0\n",
       "13    0\n",
       "16    0\n",
       "17    0\n",
       "Name: Churn, dtype: int64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Putting feature variable to X\n",
    "X = df.drop(['Churn'],axis=1)\n",
    "\n",
    "# Putting response variable to y\n",
    "y = df['Churn']\n",
    "\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting the data into train and test\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y, train_size=0.7,test_size=0.3,random_state=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(21007, 135)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "loc_og_t2o_mou        0\n",
       "std_og_t2o_mou        0\n",
       "loc_ic_t2o_mou        0\n",
       "arpu_6                0\n",
       "arpu_7                0\n",
       "arpu_8                0\n",
       "onnet_mou_6           0\n",
       "onnet_mou_7           0\n",
       "onnet_mou_8           0\n",
       "offnet_mou_6          0\n",
       "offnet_mou_7          0\n",
       "offnet_mou_8          0\n",
       "roam_ic_mou_6         0\n",
       "roam_ic_mou_7         0\n",
       "roam_ic_mou_8         0\n",
       "roam_og_mou_6         0\n",
       "roam_og_mou_7         0\n",
       "roam_og_mou_8         0\n",
       "loc_og_t2t_mou_6      0\n",
       "loc_og_t2t_mou_7      0\n",
       "loc_og_t2t_mou_8      0\n",
       "loc_og_t2m_mou_6      0\n",
       "loc_og_t2m_mou_7      0\n",
       "loc_og_t2m_mou_8      0\n",
       "loc_og_t2f_mou_6      0\n",
       "loc_og_t2f_mou_7      0\n",
       "loc_og_t2f_mou_8      0\n",
       "loc_og_t2c_mou_6      0\n",
       "loc_og_t2c_mou_7      0\n",
       "loc_og_t2c_mou_8      0\n",
       "                     ..\n",
       "max_rech_amt_6        0\n",
       "max_rech_amt_7        0\n",
       "max_rech_amt_8        0\n",
       "last_day_rch_amt_6    0\n",
       "last_day_rch_amt_7    0\n",
       "last_day_rch_amt_8    0\n",
       "vol_2g_mb_6           0\n",
       "vol_2g_mb_7           0\n",
       "vol_2g_mb_8           0\n",
       "vol_3g_mb_6           0\n",
       "vol_3g_mb_7           0\n",
       "vol_3g_mb_8           0\n",
       "monthly_2g_6          0\n",
       "monthly_2g_7          0\n",
       "monthly_2g_8          0\n",
       "sachet_2g_6           0\n",
       "sachet_2g_7           0\n",
       "sachet_2g_8           0\n",
       "monthly_3g_6          0\n",
       "monthly_3g_7          0\n",
       "monthly_3g_8          0\n",
       "sachet_3g_6           0\n",
       "sachet_3g_7           0\n",
       "sachet_3g_8           0\n",
       "aon                   0\n",
       "aug_vbc_3g            0\n",
       "jul_vbc_3g            0\n",
       "jun_vbc_3g            0\n",
       "sep_vbc_3g            0\n",
       "Average_amount        0\n",
       "Length: 135, dtype: int64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Improting the PCA module\n",
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(svd_solver='randomized', random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PCA(copy=True, iterated_power='auto', n_components=None, random_state=42,\n",
       "  svd_solver='randomized', tol=0.0, whiten=False)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Doing the PCA on the train data\n",
    "pca.fit(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  9.79901464e-17,   0.00000000e+00,   8.32667268e-17, ...,\n",
       "         -3.38289844e-02,  -5.45617096e-04,   1.42359997e-01],\n",
       "       [  1.97024637e-17,  -5.55111512e-17,  -3.33066907e-16, ...,\n",
       "          1.67923208e-01,   5.40224777e-03,   1.53747454e-01],\n",
       "       [ -6.59940552e-18,   1.94289029e-16,  -3.33066907e-16, ...,\n",
       "         -3.75384840e-02,   5.50217897e-04,   9.89619334e-02],\n",
       "       ..., \n",
       "       [ -0.00000000e+00,  -4.47253571e-02,  -6.47597978e-02, ...,\n",
       "          5.20417043e-18,  -1.79435460e-17,  -3.74102777e-04],\n",
       "       [ -1.00000000e+00,   8.62029812e-23,   8.40230339e-24, ...,\n",
       "          1.69191778e-16,  -1.90682425e-14,   1.85639099e-16],\n",
       "       [ -0.00000000e+00,  -2.93363169e-02,   3.52936763e-02, ...,\n",
       "         -2.60208521e-18,   2.36212417e-15,  -4.94965114e-01]])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.components_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "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>Feature</th>\n",
       "      <th>PC1</th>\n",
       "      <th>PC2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>total_og_mou_7</td>\n",
       "      <td>0.352327</td>\n",
       "      <td>0.010681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>total_og_mou_8</td>\n",
       "      <td>0.330511</td>\n",
       "      <td>0.038572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>std_og_mou_7</td>\n",
       "      <td>0.305819</td>\n",
       "      <td>-0.036340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>std_og_mou_8</td>\n",
       "      <td>0.283607</td>\n",
       "      <td>-0.013709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>total_og_mou_6</td>\n",
       "      <td>0.283171</td>\n",
       "      <td>-0.004369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>std_og_mou_6</td>\n",
       "      <td>0.248531</td>\n",
       "      <td>-0.047589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>offnet_mou_7</td>\n",
       "      <td>0.179238</td>\n",
       "      <td>0.025061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>onnet_mou_7</td>\n",
       "      <td>0.174269</td>\n",
       "      <td>-0.009012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>onnet_mou_8</td>\n",
       "      <td>0.167835</td>\n",
       "      <td>0.004004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>offnet_mou_8</td>\n",
       "      <td>0.164774</td>\n",
       "      <td>0.037909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>total_rech_amt_8</td>\n",
       "      <td>0.158594</td>\n",
       "      <td>0.178136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>total_rech_amt_7</td>\n",
       "      <td>0.157934</td>\n",
       "      <td>0.168488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>std_og_t2m_mou_7</td>\n",
       "      <td>0.157783</td>\n",
       "      <td>-0.011355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>std_og_t2t_mou_7</td>\n",
       "      <td>0.148386</td>\n",
       "      <td>-0.025732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>offnet_mou_6</td>\n",
       "      <td>0.147647</td>\n",
       "      <td>0.018211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>std_og_t2m_mou_8</td>\n",
       "      <td>0.142454</td>\n",
       "      <td>0.000248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>Average_amount</td>\n",
       "      <td>0.142360</td>\n",
       "      <td>0.153747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>std_og_t2t_mou_8</td>\n",
       "      <td>0.141553</td>\n",
       "      <td>-0.014168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>onnet_mou_6</td>\n",
       "      <td>0.139690</td>\n",
       "      <td>-0.016998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>arpu_8</td>\n",
       "      <td>0.134706</td>\n",
       "      <td>0.151844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>arpu_7</td>\n",
       "      <td>0.133008</td>\n",
       "      <td>0.143280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>std_og_t2m_mou_6</td>\n",
       "      <td>0.128650</td>\n",
       "      <td>-0.017324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>total_rech_amt_6</td>\n",
       "      <td>0.126786</td>\n",
       "      <td>0.139007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>std_og_t2t_mou_6</td>\n",
       "      <td>0.120122</td>\n",
       "      <td>-0.030940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>arpu_6</td>\n",
       "      <td>0.103949</td>\n",
       "      <td>0.118637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>loc_og_mou_8</td>\n",
       "      <td>0.040701</td>\n",
       "      <td>0.047706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>loc_og_mou_7</td>\n",
       "      <td>0.040417</td>\n",
       "      <td>0.044009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>loc_og_mou_6</td>\n",
       "      <td>0.029902</td>\n",
       "      <td>0.040708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>loc_og_t2t_mou_8</td>\n",
       "      <td>0.024763</td>\n",
       "      <td>0.017306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>loc_og_t2t_mou_7</td>\n",
       "      <td>0.024703</td>\n",
       "      <td>0.015501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>loc_og_t2f_mou_8</td>\n",
       "      <td>-0.000277</td>\n",
       "      <td>0.001261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>loc_og_t2f_mou_7</td>\n",
       "      <td>-0.000298</td>\n",
       "      <td>0.001350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>roam_ic_mou_7</td>\n",
       "      <td>-0.000304</td>\n",
       "      <td>0.002463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>loc_og_t2f_mou_6</td>\n",
       "      <td>-0.000336</td>\n",
       "      <td>0.001250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>max_rech_amt_6</td>\n",
       "      <td>-0.000496</td>\n",
       "      <td>0.039898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>sep_vbc_3g</td>\n",
       "      <td>-0.000546</td>\n",
       "      <td>0.005402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>isd_ic_mou_8</td>\n",
       "      <td>-0.000548</td>\n",
       "      <td>0.002909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>isd_ic_mou_7</td>\n",
       "      <td>-0.000568</td>\n",
       "      <td>0.003349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>isd_ic_mou_6</td>\n",
       "      <td>-0.001151</td>\n",
       "      <td>0.002367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>loc_ic_t2f_mou_8</td>\n",
       "      <td>-0.001251</td>\n",
       "      <td>0.002350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>loc_ic_t2f_mou_7</td>\n",
       "      <td>-0.001444</td>\n",
       "      <td>0.002373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>loc_ic_t2t_mou_6</td>\n",
       "      <td>-0.001613</td>\n",
       "      <td>0.006991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>loc_ic_t2f_mou_6</td>\n",
       "      <td>-0.001629</td>\n",
       "      <td>0.002261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>loc_ic_t2m_mou_7</td>\n",
       "      <td>-0.002185</td>\n",
       "      <td>0.023669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>loc_ic_t2m_mou_8</td>\n",
       "      <td>-0.002504</td>\n",
       "      <td>0.023408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>total_ic_mou_6</td>\n",
       "      <td>-0.002804</td>\n",
       "      <td>0.034312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>loc_ic_mou_8</td>\n",
       "      <td>-0.003159</td>\n",
       "      <td>0.036224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>loc_ic_mou_7</td>\n",
       "      <td>-0.003476</td>\n",
       "      <td>0.035411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>loc_ic_t2m_mou_6</td>\n",
       "      <td>-0.006293</td>\n",
       "      <td>0.020583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>vol_2g_mb_8</td>\n",
       "      <td>-0.008908</td>\n",
       "      <td>0.008774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>loc_ic_mou_6</td>\n",
       "      <td>-0.009601</td>\n",
       "      <td>0.029993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>vol_2g_mb_7</td>\n",
       "      <td>-0.011885</td>\n",
       "      <td>0.007868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>vol_2g_mb_6</td>\n",
       "      <td>-0.014460</td>\n",
       "      <td>0.013421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>aug_vbc_3g</td>\n",
       "      <td>-0.030706</td>\n",
       "      <td>0.169468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>jun_vbc_3g</td>\n",
       "      <td>-0.033829</td>\n",
       "      <td>0.167923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>jul_vbc_3g</td>\n",
       "      <td>-0.036516</td>\n",
       "      <td>0.183502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>vol_3g_mb_8</td>\n",
       "      <td>-0.073752</td>\n",
       "      <td>0.501181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>vol_3g_mb_6</td>\n",
       "      <td>-0.082158</td>\n",
       "      <td>0.456028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>vol_3g_mb_7</td>\n",
       "      <td>-0.085300</td>\n",
       "      <td>0.500050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>aon</td>\n",
       "      <td>-0.098191</td>\n",
       "      <td>0.042870</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>135 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Feature       PC1       PC2\n",
       "58     total_og_mou_7  0.352327  0.010681\n",
       "59     total_og_mou_8  0.330511  0.038572\n",
       "46       std_og_mou_7  0.305819 -0.036340\n",
       "47       std_og_mou_8  0.283607 -0.013709\n",
       "57     total_og_mou_6  0.283171 -0.004369\n",
       "45       std_og_mou_6  0.248531 -0.047589\n",
       "10       offnet_mou_7  0.179238  0.025061\n",
       "7         onnet_mou_7  0.174269 -0.009012\n",
       "8         onnet_mou_8  0.167835  0.004004\n",
       "11       offnet_mou_8  0.164774  0.037909\n",
       "104  total_rech_amt_8  0.158594  0.178136\n",
       "103  total_rech_amt_7  0.157934  0.168488\n",
       "37   std_og_t2m_mou_7  0.157783 -0.011355\n",
       "34   std_og_t2t_mou_7  0.148386 -0.025732\n",
       "9        offnet_mou_6  0.147647  0.018211\n",
       "38   std_og_t2m_mou_8  0.142454  0.000248\n",
       "134    Average_amount  0.142360  0.153747\n",
       "35   std_og_t2t_mou_8  0.141553 -0.014168\n",
       "6         onnet_mou_6  0.139690 -0.016998\n",
       "5              arpu_8  0.134706  0.151844\n",
       "4              arpu_7  0.133008  0.143280\n",
       "36   std_og_t2m_mou_6  0.128650 -0.017324\n",
       "102  total_rech_amt_6  0.126786  0.139007\n",
       "33   std_og_t2t_mou_6  0.120122 -0.030940\n",
       "3              arpu_6  0.103949  0.118637\n",
       "32       loc_og_mou_8  0.040701  0.047706\n",
       "31       loc_og_mou_7  0.040417  0.044009\n",
       "30       loc_og_mou_6  0.029902  0.040708\n",
       "20   loc_og_t2t_mou_8  0.024763  0.017306\n",
       "19   loc_og_t2t_mou_7  0.024703  0.015501\n",
       "..                ...       ...       ...\n",
       "26   loc_og_t2f_mou_8 -0.000277  0.001261\n",
       "25   loc_og_t2f_mou_7 -0.000298  0.001350\n",
       "13      roam_ic_mou_7 -0.000304  0.002463\n",
       "24   loc_og_t2f_mou_6 -0.000336  0.001250\n",
       "105    max_rech_amt_6 -0.000496  0.039898\n",
       "133        sep_vbc_3g -0.000546  0.005402\n",
       "95       isd_ic_mou_8 -0.000548  0.002909\n",
       "94       isd_ic_mou_7 -0.000568  0.003349\n",
       "93       isd_ic_mou_6 -0.001151  0.002367\n",
       "68   loc_ic_t2f_mou_8 -0.001251  0.002350\n",
       "67   loc_ic_t2f_mou_7 -0.001444  0.002373\n",
       "60   loc_ic_t2t_mou_6 -0.001613  0.006991\n",
       "66   loc_ic_t2f_mou_6 -0.001629  0.002261\n",
       "64   loc_ic_t2m_mou_7 -0.002185  0.023669\n",
       "65   loc_ic_t2m_mou_8 -0.002504  0.023408\n",
       "87     total_ic_mou_6 -0.002804  0.034312\n",
       "71       loc_ic_mou_8 -0.003159  0.036224\n",
       "70       loc_ic_mou_7 -0.003476  0.035411\n",
       "63   loc_ic_t2m_mou_6 -0.006293  0.020583\n",
       "113       vol_2g_mb_8 -0.008908  0.008774\n",
       "69       loc_ic_mou_6 -0.009601  0.029993\n",
       "112       vol_2g_mb_7 -0.011885  0.007868\n",
       "111       vol_2g_mb_6 -0.014460  0.013421\n",
       "130        aug_vbc_3g -0.030706  0.169468\n",
       "132        jun_vbc_3g -0.033829  0.167923\n",
       "131        jul_vbc_3g -0.036516  0.183502\n",
       "116       vol_3g_mb_8 -0.073752  0.501181\n",
       "114       vol_3g_mb_6 -0.082158  0.456028\n",
       "115       vol_3g_mb_7 -0.085300  0.500050\n",
       "129               aon -0.098191  0.042870\n",
       "\n",
       "[135 rows x 3 columns]"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#From pca components we can see which variables affect the churn most\n",
    "colnames = list(X_train.columns)\n",
    "pcs_df = pd.DataFrame({'PC1':pca.components_[0],'PC2':pca.components_[1], 'Feature':colnames})\n",
    "pcs_df.sort_values(ascending=False,by=['PC1','PC2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "fig = plt.figure(figsize = (16,10))\n",
    "plt.scatter(pcs_df.PC1, pcs_df.PC2)\n",
    "plt.xlabel('Principal Component 1')\n",
    "plt.ylabel('Principal Component 2')\n",
    "for i, txt in enumerate(pcs_df.Feature):\n",
    "    plt.annotate(txt, (pcs_df.PC1[i],pcs_df.PC2[i]))\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  2.49048433e-01,   1.69607016e-01,   1.08660709e-01,\n",
       "         7.11631200e-02,   6.10920267e-02,   5.52394034e-02,\n",
       "         4.68318008e-02,   3.29958999e-02,   2.58651397e-02,\n",
       "         2.23963368e-02,   1.65283316e-02,   1.45282560e-02,\n",
       "         1.28034154e-02,   1.21818545e-02,   1.13445354e-02,\n",
       "         8.94195880e-03,   7.63339758e-03,   6.93585166e-03,\n",
       "         6.64853826e-03,   5.19492441e-03,   4.89451895e-03,\n",
       "         4.61035567e-03,   4.25950551e-03,   3.90663328e-03,\n",
       "         3.30568816e-03,   2.84684150e-03,   2.67589575e-03,\n",
       "         2.65620915e-03,   2.20644859e-03,   2.20213265e-03,\n",
       "         1.92965931e-03,   1.69711356e-03,   1.61279849e-03,\n",
       "         1.45320241e-03,   1.22198192e-03,   1.03090594e-03,\n",
       "         9.57980361e-04,   8.92576492e-04,   8.45093323e-04,\n",
       "         8.08002540e-04,   7.40618734e-04,   6.95801895e-04,\n",
       "         6.75593335e-04,   5.95772016e-04,   5.67564302e-04,\n",
       "         5.44232422e-04,   5.06810328e-04,   4.61990292e-04,\n",
       "         4.41651837e-04,   4.30154956e-04,   4.04346174e-04,\n",
       "         2.57023225e-04,   2.34626076e-04,   1.78395800e-04,\n",
       "         1.75785598e-04,   1.52427325e-04,   1.44283866e-04,\n",
       "         1.37141052e-04,   1.24349184e-04,   1.07099993e-04,\n",
       "         1.00805703e-04,   8.32420108e-05,   7.05461573e-05,\n",
       "         6.09494939e-05,   5.83959643e-05,   5.16530911e-05,\n",
       "         4.96216021e-05,   4.41888174e-05,   3.55764314e-05,\n",
       "         3.14472524e-05,   2.79325671e-05,   2.12744001e-05,\n",
       "         1.61988610e-05,   1.52895402e-05,   1.44588184e-05,\n",
       "         1.17786152e-05,   1.14656339e-05,   1.00707077e-05,\n",
       "         8.91839164e-06,   8.16005290e-06,   7.38503955e-06,\n",
       "         5.76635466e-06,   4.71899428e-06,   4.51097973e-06,\n",
       "         3.41135614e-06,   2.17548068e-06,   1.88299933e-06,\n",
       "         1.83629595e-06,   1.56637216e-06,   1.32498957e-06,\n",
       "         9.78803568e-07,   5.07592108e-07,   4.95648325e-07,\n",
       "         3.91444805e-07,   3.10110188e-07,   1.91196407e-07,\n",
       "         1.31853898e-07,   9.60658854e-08,   6.07763616e-08,\n",
       "         3.24696366e-08,   3.11331534e-08,   1.52047304e-08,\n",
       "         1.32322022e-08,   7.74015155e-09,   6.94086631e-09,\n",
       "         5.29092085e-09,   3.87083085e-09,   3.63184202e-09,\n",
       "         2.58113916e-09,   1.07468918e-09,   1.54065294e-12,\n",
       "         9.87209213e-13,   7.14221995e-13,   6.92922848e-13,\n",
       "         6.42902570e-13,   6.03396909e-13,   5.59931103e-13,\n",
       "         5.45692112e-13,   4.44153840e-13,   4.29035058e-13,\n",
       "         4.17946002e-13,   4.11214416e-13,   3.84706783e-13,\n",
       "         3.70516894e-13,   3.45588397e-13,   2.46232062e-33,\n",
       "         2.46232062e-33,   2.46232062e-33,   2.46232062e-33,\n",
       "         2.46232062e-33,   2.46232062e-33,   2.46232062e-33,\n",
       "         2.46232062e-33,   4.76003013e-34,   3.25308792e-34])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Making the screeplot - plotting the cumulative variance against the number of components\n",
    "%matplotlib inline\n",
    "fig = plt.figure(figsize = (12,8))\n",
    "plt.plot(np.cumsum(pca.explained_variance_ratio_))\n",
    "plt.xlabel('number of components')\n",
    "plt.ylabel('cumulative explained variance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Using incremental PCA for efficiency - saves a lot of time on larger datasets let's take 30 components\n",
    "from sklearn.decomposition import IncrementalPCA\n",
    "pca_final = IncrementalPCA(n_components=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(21007, 30)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train_pca = pca_final.fit_transform(X_train)\n",
    "df_train_pca.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating correlation matrix for the principal components\n",
    "corrmat = np.corrcoef(df_train_pca.transpose())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xf5dca30>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting the correlation matrix\n",
    "%matplotlib inline\n",
    "plt.figure(figsize = (20,10))\n",
    "sns.heatmap(corrmat,annot = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.00900566621716\n",
      "-0.00476309556502\n"
     ]
    }
   ],
   "source": [
    "corrmat_nodiag = corrmat - np.diagflat(corrmat.diagonal())\n",
    "print(corrmat_nodiag.max())\n",
    "print(corrmat_nodiag.min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9004, 30)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Applying selected components to the test data - 30 components\n",
    "df_test_pca = pca_final.transform(X_test)\n",
    "df_test_pca.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Training the model on the train data\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import metrics\n",
    "\n",
    "learner_pca = LogisticRegression()\n",
    "model_pca = learner_pca.fit(df_train_pca,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.89'"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Making prediction on the test data\n",
    "pred_probs_test = model_pca.predict_proba(df_test_pca)[:,1]\n",
    "\"{:2.2}\".format(metrics.roc_auc_score(y_test, pred_probs_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Now consider only 20 components\n",
    "from sklearn.decomposition import IncrementalPCA\n",
    "pca_final2 = IncrementalPCA(n_components=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(21007, 20)"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train_pca2 = pca_final2.fit_transform(X_train)\n",
    "df_train_pca2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating correlation matrix for the principal components\n",
    "corrmat = np.corrcoef(df_train_pca2.transpose())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x14b539f0>"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting the correlation matrix\n",
    "%matplotlib inline\n",
    "plt.figure(figsize = (20,10))\n",
    "sns.heatmap(corrmat,annot = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.00307813623631\n",
      "-0.0063841758336\n"
     ]
    }
   ],
   "source": [
    "corrmat_nodiag = corrmat - np.diagflat(corrmat.diagonal())\n",
    "print(corrmat_nodiag.max())\n",
    "print(corrmat_nodiag.min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9004, 20)"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Applying selected components to the test data - 20 components\n",
    "df_test_pca2 = pca_final2.transform(X_test)\n",
    "df_test_pca2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.87'"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Training the model on the train data\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import metrics\n",
    "\n",
    "learner_pca2 = LogisticRegression()\n",
    "model_pca2 = learner_pca2.fit(df_train_pca2,y_train)\n",
    "#Making prediction on the test data\n",
    "pred_probs_test2 = model_pca2.predict_proba(df_test_pca2)[:,1]\n",
    "\"{:2.2}\".format(metrics.roc_auc_score(y_test, pred_probs_test2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca_final3 = IncrementalPCA(n_components=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(21007, 10)"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train_pca3 = pca_final3.fit_transform(X_train)\n",
    "df_train_pca3.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating correlation matrix for the principal components\n",
    "corrmat = np.corrcoef(df_train_pca3.transpose())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xf5a1910>"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting the correlation matrix\n",
    "%matplotlib inline\n",
    "plt.figure(figsize = (20,10))\n",
    "sns.heatmap(corrmat,annot = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9004, 10)"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Applying selected components to the test data - 20 components\n",
    "df_test_pca3 = pca_final3.transform(X_test)\n",
    "df_test_pca3.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.86'"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Training the model on the train data\n",
    "\n",
    "learner_pca3 = LogisticRegression()\n",
    "model_pca3 = learner_pca3.fit(df_train_pca3,y_train)\n",
    "#Making prediction on the test data\n",
    "pred_probs_test3 = model_pca3.predict_proba(df_test_pca3)[:,1]\n",
    "\"{:2.2}\".format(metrics.roc_auc_score(y_test, pred_probs_test3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Now consider only 5 components\n",
    "from sklearn.decomposition import IncrementalPCA\n",
    "pca_final4 = IncrementalPCA(n_components=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(21007, 5)"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train_pca4 = pca_final4.fit_transform(X_train)\n",
    "df_train_pca4.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating correlation matrix for the principal components\n",
    "corrmat = np.corrcoef(df_train_pca4.transpose())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x36be8f90>"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting the correlation matrix\n",
    "%matplotlib inline\n",
    "plt.figure(figsize = (20,10))\n",
    "sns.heatmap(corrmat,annot = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9004, 5)"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Applying selected components to the test data - 20 components\n",
    "df_test_pca4 = pca_final4.transform(X_test)\n",
    "df_test_pca4.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.75'"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Training the model on the train data\n",
    "\n",
    "learner_pca4 = LogisticRegression()\n",
    "model_pca4 = learner_pca3.fit(df_train_pca4,y_train)\n",
    "#Making prediction on the test data\n",
    "pred_probs_test4 = model_pca4.predict_proba(df_test_pca4)[:,1]\n",
    "\"{:2.2}\".format(metrics.roc_auc_score(y_test, pred_probs_test4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From above we can see that having 10 components would help us have more roc_auc_score hence learner_pca3 is the model that can be used than all the other models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From pca components we can see the strong indicators of churn\n",
    "total_og_mou_7\n",
    "total_og_mou_8\n",
    "offnet_mou_7\n",
    "onnet_mou_8\n",
    "offnet_mou_8\n",
    "are the strong affectors of churn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When the outgoing calls in 7th and 8th month,offnet in 7th and 8th,and onnet in 8th month are very less or are not done, then we can predict that the customer can churn\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Recommendations to prevent churn:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1.To do marketing in 8th and 9th months, when we see the values of columns(indicators) are zero(0)\n",
    "2.To gives discounts and offers to the people who are about to churn(in 9th month)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}