1_TelecomChurnCaseStudy_DataCleaning_EDA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <font color='red'>This is the Main file and must be run first before any other files are executed.</font>\n",
    "The complete solution is spred in multiple files:\n",
    "1. File-1: \"Pandarinath_TelecomChurnCaseStudy.ipynb\"\n",
    "        It comprises of cleaning of data and EDA\n",
    "           \n",
    "2. File-2: \"Pandarinath_TelecomChurnCaseStudy_Modelling_PCA_LR.ipynb\"\n",
    "\n",
    "3. File-3: \"Pandarinath_TelecomChurnCaseStudy_Modelling_RFE_LR.ipynb\"\n",
    "\n",
    "4. File-4: \"Pandarinath_TelecomChurnCaseStudy_Modelling_LASSO_DT.ipynb\"\n",
    "\n",
    "5. File-5: \"Pandarinath_TelecomChurnCaseStudy_Modelling_LASSO_RF.ipynb\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <font color='blue'>Telecom Churn Case Study</font>\n",
    "* Institution: IIIT, Bangalore and UpGrad\n",
    "* Course: PG Diploma in Machine Lerning and AI March 2018\n",
    "* Date: 13-Aug-2018\n",
    "* Submitted by:\n",
    "    1. Pandinath Siddineni (ID- APFE187000194)\n",
    "    2. AKNR Chandra Sekhar (ID- APFE187000315)\n",
    "    3. Brajesh Kumar       (ID- APFE187000149)\n",
    "    4. Shweta Tiwari\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color='blue'>Business Goals:</font>\n",
    "1. Retaining high profitable customers is the number one business goal.\n",
    "2. This project is based on the Indian and Southeast Asian market.\n",
    "3. In the Indian and the southeast Asian market, approximately 80% of revenue comes from the top 20% customers (called high-value customers). Thus, if we can reduce churn of the high-value customers, we will be able to reduce significant revenue leakage.\n",
    "4. The business objective is to predict the churn in the last (i.e. the ninth) month using the data (features) from the first three months. To do this task well, understanding the typical customer behaviour during churn will be helpful.\n",
    "\n",
    "### <font color='blue'>Analysis Goals:</font>\n",
    "1. Predict which customers are at high risk of churn\n",
    "2. Build predictive models to identify customers at high risk of churn and identify the main indicators of churn.\n",
    "3. Prepaid is the most common model in India and southeast Asia. Focus on prepaid customers.\n",
    "3. Curn definition used-- \"Usage-based churn: Customers who have not done any usage, either incoming or outgoing - in terms of calls, internet etc. over a period of time.\" In this project, we will use the usage-based definition to define churn.\n",
    "4. In this project, you will define high-value customers based on a certain metric (mentioned later below) and predict churn only on high-value customers.\n",
    "5. especially high-value customers go through  three phases of customer lifecycle: a. The ‘good’ phase, b. The ‘action’ phase, c. The ‘churn’ phase\n",
    "---------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <font color='blue'>PART 1: DATA UNDERSTANDING AND CLEANING</font>\n",
    "\n",
    "1. Understand the properties of loaded dataframe\n",
    "2. Idnetify Uniquness key\n",
    "3. Identify bad colums that has no infromation (all entries are null or same)\n",
    "4. Conver dates to meaningful number of days\n",
    "5. Remove columns with data that does not make much sense for our analysis\n",
    "6. Missing value treatment: replace with '0', mean or median; drop rows; drop columns\n",
    "7. Outlier Treatment\n",
    "8. Write data into a clean data file. This will be used to create master-df for analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import libraries and load datafile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Import required libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "# Until fuction: line seperator\n",
    "def print_ln():\n",
    "    print('-'*80, '\\n')\n",
    "    \n",
    "pd.options.display.float_format = '{:.2f}'.format\n",
    "\n",
    "# Load csv data file\n",
    "telecom_orig = pd.read_csv('telecom_churn_data.csv', low_memory=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataframe Shape:  (99999, 226)\n",
      "-------------------------------------------------------------------------------- \n",
      "\n",
      "Dataframe Info: \n",
      "\n",
      "<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: 172.4+ MB\n",
      "-------------------------------------------------------------------------------- \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
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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>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>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</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>197.38</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>nan</td>\n",
       "      <td>968</td>\n",
       "      <td>30.40</td>\n",
       "      <td>0.00</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>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</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>34.05</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>nan</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>nan</td>\n",
       "      <td>1006</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</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>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</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>167.69</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1103</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</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>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</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>221.34</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>0.00</td>\n",
       "      <td>0.00</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>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</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>261.64</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>1526</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</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            0.00            0.00            0.00   \n",
       "1     7001865778        109            0.00            0.00            0.00   \n",
       "2     7001625959        109            0.00            0.00            0.00   \n",
       "3     7001204172        109            0.00            0.00            0.00   \n",
       "4     7000142493        109            0.00            0.00            0.00   \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  fb_user_7  \\\n",
       "0            9/30/2014  197.38     ...                0       1.00       1.00   \n",
       "1            9/30/2014   34.05     ...                0        nan       1.00   \n",
       "2            9/30/2014  167.69     ...                0        nan        nan   \n",
       "3            9/30/2014  221.34     ...                0        nan        nan   \n",
       "4            9/30/2014  261.64     ...                0       0.00        nan   \n",
       "\n",
       "   fb_user_8  fb_user_9   aon  aug_vbc_3g  jul_vbc_3g  jun_vbc_3g  sep_vbc_3g  \n",
       "0       1.00        nan   968       30.40        0.00      101.20        3.58  \n",
       "1       1.00        nan  1006        0.00        0.00        0.00        0.00  \n",
       "2        nan       1.00  1103        0.00        0.00        4.17        0.00  \n",
       "3        nan        nan  2491        0.00        0.00        0.00        0.00  \n",
       "4        nan        nan  1526        0.00        0.00        0.00        0.00  \n",
       "\n",
       "[5 rows x 226 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Understand the properties of loaded dataframe\n",
    "telecom = telecom_orig.copy()\n",
    "print('Dataframe Shape: ', telecom.shape); print_ln();\n",
    "print(\"Dataframe Info: \\n\"); telecom.info(); print_ln();\n",
    "telecom.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Treating Categorical Variables\n",
    "There are 8 of them and all of these have more than 60% missing values.\n",
    "Missing values can be imputed with '-1' where -1 can be treated as a new category.\n",
    "\n",
    "CATEGORICAL VARIABLES:\n",
    "* night_pck_user_6\n",
    "* night_pck_user_7\n",
    "* night_pck_user_8\n",
    "* night_pck_user_9\n",
    "* fb_user_6\n",
    "* fb_user_7\n",
    "* fb_user_8\n",
    "* fb_user_9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Missing values can be imputed with '-1' \n",
    "# where -1 can be treated as a new category.\n",
    "\n",
    "categorical_vars = [\"night_pck_user_6\", \"night_pck_user_7\", \"night_pck_user_8\", \n",
    "\"night_pck_user_9\", \"fb_user_6\", \"fb_user_7\", \"fb_user_8\", \"fb_user_9\"]\n",
    "\n",
    "for var in categorical_vars:\n",
    "    telecom[var].fillna(value=-1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Create dummy variables for all the six categorial variables\n",
    "night_pck_user_6_s = pd.get_dummies(telecom['night_pck_user_6'],prefix=\"night6\",drop_first=True)\n",
    "night_pck_user_7_s = pd.get_dummies(telecom['night_pck_user_7'],prefix=\"night7\",drop_first=True)\n",
    "night_pck_user_8_s = pd.get_dummies(telecom['night_pck_user_8'],prefix=\"night8\",drop_first=True)\n",
    "\n",
    "fb_user_6_s = pd.get_dummies(telecom['fb_user_6'],prefix=\"fb6\", drop_first=True)\n",
    "fb_user_7_s = pd.get_dummies(telecom['fb_user_7'],prefix=\"fb7\",drop_first=True)\n",
    "fb_user_8_s = pd.get_dummies(telecom['fb_user_8'],prefix=\"fb8\",drop_first=True)\n",
    "\n",
    "telecom = pd.concat([telecom,night_pck_user_6_s],axis=1)\n",
    "telecom = pd.concat([telecom,night_pck_user_7_s],axis=1)\n",
    "telecom = pd.concat([telecom,night_pck_user_8_s],axis=1)\n",
    "\n",
    "telecom = pd.concat([telecom,fb_user_6_s],axis=1)\n",
    "telecom = pd.concat([telecom,fb_user_7_s],axis=1)\n",
    "telecom = pd.concat([telecom,fb_user_8_s],axis=1)\n",
    "\n",
    "# drop all categorial columns\n",
    "telecom = telecom.drop(['fb_user_6', 'fb_user_7', 'fb_user_8', 'night_pck_user_6', 'night_pck_user_7', 'night_pck_user_8'], 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Missing Value Treatment\n",
    "1. Delete: Delete the missing values \n",
    "2. Impute: \n",
    "    - Imputing by a simple statistic: Replace the missing values by another value, commonly the mean, median, mode etc. \n",
    "    - Predictive techniques: Use statistical models such as k-NN, SVM etc. to predict and impute missing values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### COLUMN-WISE: MISSING VALUES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "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",
       "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_9                  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",
       "night6_0.0                 0\n",
       "night6_1.0                 0\n",
       "night7_0.0                 0\n",
       "night7_1.0                 0\n",
       "night8_0.0                 0\n",
       "night8_1.0                 0\n",
       "fb6_0.0                    0\n",
       "fb6_1.0                    0\n",
       "fb7_0.0                    0\n",
       "fb7_1.0                    0\n",
       "fb8_0.0                    0\n",
       "fb8_1.0                    0\n",
       "Length: 232, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# summing up the missing values (column-wise)\n",
    "telecom.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "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",
       "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_9              0.00\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",
       "night6_0.0             0.00\n",
       "night6_1.0             0.00\n",
       "night7_0.0             0.00\n",
       "night7_1.0             0.00\n",
       "night8_0.0             0.00\n",
       "night8_1.0             0.00\n",
       "fb6_0.0                0.00\n",
       "fb6_1.0                0.00\n",
       "fb7_0.0                0.00\n",
       "fb7_1.0                0.00\n",
       "fb8_0.0                0.00\n",
       "fb8_1.0                0.00\n",
       "Length: 232, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Percentage of missing values (column-wise)\n",
    "round(100*(telecom.isnull().sum()/len(telecom.index)), 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date_of_last_rech_data_6   74.85\n",
       "date_of_last_rech_data_7   74.43\n",
       "date_of_last_rech_data_8   73.66\n",
       "date_of_last_rech_data_9   74.08\n",
       "total_rech_data_6          74.85\n",
       "total_rech_data_7          74.43\n",
       "total_rech_data_8          73.66\n",
       "total_rech_data_9          74.08\n",
       "max_rech_data_6            74.85\n",
       "max_rech_data_7            74.43\n",
       "max_rech_data_8            73.66\n",
       "max_rech_data_9            74.08\n",
       "count_rech_2g_6            74.85\n",
       "count_rech_2g_7            74.43\n",
       "count_rech_2g_8            73.66\n",
       "count_rech_2g_9            74.08\n",
       "count_rech_3g_6            74.85\n",
       "count_rech_3g_7            74.43\n",
       "count_rech_3g_8            73.66\n",
       "count_rech_3g_9            74.08\n",
       "av_rech_amt_data_6         74.85\n",
       "av_rech_amt_data_7         74.43\n",
       "av_rech_amt_data_8         73.66\n",
       "av_rech_amt_data_9         74.08\n",
       "arpu_3g_6                  74.85\n",
       "arpu_3g_7                  74.43\n",
       "arpu_3g_8                  73.66\n",
       "arpu_3g_9                  74.08\n",
       "arpu_2g_6                  74.85\n",
       "arpu_2g_7                  74.43\n",
       "arpu_2g_8                  73.66\n",
       "arpu_2g_9                  74.08\n",
       "dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Columns with more than 70% missing values\n",
    "colmns_missing_data = round(100*(telecom.isnull().sum()/len(telecom.index)), 2)\n",
    "colmns_missing_data[colmns_missing_data >= 70]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count   25153.00\n",
      "mean        2.46\n",
      "std         2.79\n",
      "min         1.00\n",
      "25%         1.00\n",
      "50%         1.00\n",
      "75%         3.00\n",
      "max        61.00\n",
      "Name: total_rech_data_6, dtype: float64\n",
      "-------------------------------------------------------------------------------- \n",
      "\n",
      "count   25571.00\n",
      "mean        2.67\n",
      "std         3.03\n",
      "min         1.00\n",
      "25%         1.00\n",
      "50%         1.00\n",
      "75%         3.00\n",
      "max        54.00\n",
      "Name: total_rech_data_7, dtype: float64\n",
      "-------------------------------------------------------------------------------- \n",
      "\n",
      "count   25153.00\n",
      "mean      192.60\n",
      "std       192.65\n",
      "min         1.00\n",
      "25%        82.00\n",
      "50%       154.00\n",
      "75%       252.00\n",
      "max      7546.00\n",
      "Name: av_rech_amt_data_6, dtype: float64\n",
      "-------------------------------------------------------------------------------- \n",
      "\n",
      "count   25571.00\n",
      "mean      200.98\n",
      "std       196.79\n",
      "min         0.50\n",
      "25%        92.00\n",
      "50%       154.00\n",
      "75%       252.00\n",
      "max      4365.00\n",
      "Name: av_rech_amt_data_7, dtype: float64\n",
      "-------------------------------------------------------------------------------- \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Treat missing values\n",
    "# Columns used for filtering high-value customers, can't be dropped\n",
    "# total_rech_data_6 \n",
    "# total_rech_data_7 \n",
    "# av_rech_amt_data_6 \n",
    "# av_rech_amt_data_7 \n",
    "\n",
    "# All these columns has more than 70% missing values\n",
    "\n",
    "print(telecom.total_rech_data_6.describe()); print_ln()\n",
    "print(telecom.total_rech_data_7.describe()); print_ln()\n",
    "print(telecom.av_rech_amt_data_6.describe()); print_ln()\n",
    "print(telecom.av_rech_amt_data_7.describe()); print_ln()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([1.4539e+04, 8.0550e+03, 1.8810e+03, 4.6500e+02, 3.0400e+02,\n",
       "        1.4100e+02, 4.4000e+01, 4.4000e+01, 3.8000e+01, 1.3000e+01,\n",
       "        1.3000e+01, 1.0000e+01, 7.0000e+00, 6.0000e+00, 4.0000e+00,\n",
       "        1.0000e+00, 0.0000e+00, 2.0000e+00, 2.0000e+00, 0.0000e+00,\n",
       "        0.0000e+00, 1.0000e+00, 0.0000e+00, 0.0000e+00, 1.0000e+00]),\n",
       " array([5.00000e-01, 1.75080e+02, 3.49660e+02, 5.24240e+02, 6.98820e+02,\n",
       "        8.73400e+02, 1.04798e+03, 1.22256e+03, 1.39714e+03, 1.57172e+03,\n",
       "        1.74630e+03, 1.92088e+03, 2.09546e+03, 2.27004e+03, 2.44462e+03,\n",
       "        2.61920e+03, 2.79378e+03, 2.96836e+03, 3.14294e+03, 3.31752e+03,\n",
       "        3.49210e+03, 3.66668e+03, 3.84126e+03, 4.01584e+03, 4.19042e+03,\n",
       "        4.36500e+03]),\n",
       " <a list of 25 Patch objects>)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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1LmhubU2tC5pb2+7W1YTgJklzrZdPuHe/hCRJUo96Wflyv4QkSVKPOgpf7peQJEmaG36x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\nkiQVZPElSZJU0P8BDMX/S7YyuKsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let us look at the distribution to find out the value to fill\n",
    "# total_rech_data_6\n",
    "plt.figure(1)\n",
    "fig, ax = plt.subplots(figsize=(10,10))\n",
    "plt.subplot(2,2,1)\n",
    "plt.title(\"total_rech_data_6\")\n",
    "plt.hist(telecom.total_rech_data_6.dropna(),bins=25)\n",
    "\n",
    "# total_rech_data_7\n",
    "plt.subplot(2,2,2)\n",
    "plt.title(\"total_rech_data_7\")\n",
    "plt.hist(telecom.total_rech_data_7.dropna(),bins=25)\n",
    "\n",
    "# av_rech_amt_data_6\n",
    "plt.subplot(2,2,3)\n",
    "plt.title(\"av_rech_amt_data_6\")\n",
    "plt.hist(telecom.av_rech_amt_data_6.dropna(),bins=25)\n",
    "\n",
    "# total_rech_data_7\n",
    "plt.subplot(2,2,4)\n",
    "plt.title(\"av_rech_amt_data_7\")\n",
    "plt.hist(telecom.av_rech_amt_data_7.dropna(),bins=25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Variables are numeric and distribution resemble the 'power law'. \n",
    "# The minimum value is 1, and not zero, \n",
    "# The rows with missing values also have the 'dates of recharge' columns \n",
    "# missing -- indicating that no recharge was done in this month. \n",
    "# Thus, missing values can be imputed with 0.\n",
    "\n",
    "telecom.total_rech_data_6.fillna(value=0, inplace=True)\n",
    "telecom.total_rech_data_7.fillna(value=0, inplace=True)\n",
    "telecom.total_rech_data_8.fillna(value=0, inplace=True)\n",
    "telecom.total_rech_data_9.fillna(value=0, inplace=True)\n",
    "\n",
    "telecom.av_rech_amt_data_6.fillna(value=0, inplace=True)\n",
    "telecom.av_rech_amt_data_7.fillna(value=0, inplace=True)\n",
    "telecom.av_rech_amt_data_8.fillna(value=0, inplace=True)\n",
    "telecom.av_rech_amt_data_9.fillna(value=0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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",
      "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_9              0.00\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",
      "night6_0.0             0.00\n",
      "night6_1.0             0.00\n",
      "night7_0.0             0.00\n",
      "night7_1.0             0.00\n",
      "night8_0.0             0.00\n",
      "night8_1.0             0.00\n",
      "fb6_0.0                0.00\n",
      "fb6_1.0                0.00\n",
      "fb7_0.0                0.00\n",
      "fb7_1.0                0.00\n",
      "fb8_0.0                0.00\n",
      "fb8_1.0                0.00\n",
      "Length: 232, dtype: float64\n",
      "-------------------------------------------------------------------------------- \n",
      "\n",
      "date_of_last_rech_data_6   74.85\n",
      "date_of_last_rech_data_7   74.43\n",
      "date_of_last_rech_data_8   73.66\n",
      "date_of_last_rech_data_9   74.08\n",
      "max_rech_data_6            74.85\n",
      "max_rech_data_7            74.43\n",
      "max_rech_data_8            73.66\n",
      "max_rech_data_9            74.08\n",
      "count_rech_2g_6            74.85\n",
      "count_rech_2g_7            74.43\n",
      "count_rech_2g_8            73.66\n",
      "count_rech_2g_9            74.08\n",
      "count_rech_3g_6            74.85\n",
      "count_rech_3g_7            74.43\n",
      "count_rech_3g_8            73.66\n",
      "count_rech_3g_9            74.08\n",
      "arpu_3g_6                  74.85\n",
      "arpu_3g_7                  74.43\n",
      "arpu_3g_8                  73.66\n",
      "arpu_3g_9                  74.08\n",
      "arpu_2g_6                  74.85\n",
      "arpu_2g_7                  74.43\n",
      "arpu_2g_8                  73.66\n",
      "arpu_2g_9                  74.08\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Column analysis % of missing values\n",
    "print(round(100*(telecom.isnull().sum()/len(telecom.index)), 2)); print_ln()\n",
    "\n",
    "# Columns with more than 70% missing values\n",
    "colmns_missing_data = round(100*(telecom.isnull().sum()/len(telecom.index)), 2)\n",
    "print(colmns_missing_data[colmns_missing_data >= 70])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of columns droped here  =  24\n",
      "Dataframe Shape:  (99999, 208)\n"
     ]
    }
   ],
   "source": [
    "drop_missing_values_columns = colmns_missing_data[colmns_missing_data > 70].index\n",
    "telecom.drop(drop_missing_values_columns, axis=1, inplace=True)\n",
    "\n",
    "print('Number of columns droped here  = ', len(drop_missing_values_columns))\n",
    "print('Dataframe Shape: ', telecom.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filter High-Value Customers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# New DERIVED Columns for Total Data Recharge Amount\n",
    "\n",
    "telecom[\"total_rech_data_amt_6\"] = telecom[\"total_rech_data_6\"]*telecom['av_rech_amt_data_6']\n",
    "telecom[\"total_rech_data_amt_7\"] = telecom[\"total_rech_data_7\"]*telecom['av_rech_amt_data_7']\n",
    "telecom[\"total_rech_data_amt_8\"] = telecom[\"total_rech_data_8\"]*telecom['av_rech_amt_data_8']\n",
    "telecom[\"total_rech_data_amt_9\"] = telecom[\"total_rech_data_9\"]*telecom['av_rech_amt_data_9']\n",
    "\n",
    "# We can drop total_rech_data_* and av_rech_amt_data_*\n",
    "drop_data_columns = [\"total_rech_data_6\", \"total_rech_data_7\", \"total_rech_data_8\", \"total_rech_data_9\", \n",
    "                'av_rech_amt_data_6', 'av_rech_amt_data_7', 'av_rech_amt_data_8', 'av_rech_amt_data_9']\n",
    "telecom.drop(drop_data_columns, axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "70 percentile of first two months avg recharge amount:  478.0\n",
      "Dataframe Shape:  (30001, 204)\n"
     ]
    }
   ],
   "source": [
    "# Filter high-value customers\n",
    "avg_rech_amt_6_7 = (telecom[\"total_rech_amt_6\"] + telecom[\"total_rech_data_amt_6\"] + \n",
    "                      telecom[\"total_rech_amt_7\"] + telecom[\"total_rech_data_amt_7\"]) / 2.0\n",
    "amont_70_pc = np.percentile(avg_rech_amt_6_7, 70.0)\n",
    "print('70 percentile of first two months avg recharge amount: ', amont_70_pc)\n",
    "\n",
    "telecom = telecom[avg_rech_amt_6_7 >= amont_70_pc]\n",
    "print('Dataframe Shape: ', telecom.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Identify Curn & Tag Churners"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of columns droped here  =  48\n",
      "Dataframe Shape:  (30001, 157)\n",
      "-------------------------------------------------------------------------------- \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Identify Churn\n",
    "X = telecom[\"total_ic_mou_9\"] + telecom[\"total_og_mou_9\"] + telecom[\"vol_2g_mb_9\"] + telecom[\"vol_3g_mb_9\"]\n",
    "telecom[\"churn\"] = np.where(X, 0, 1)\n",
    "#telecom[\"churn\"].head(10)\n",
    "\n",
    "# Columns to be dropped: all columns ending with \"_9\"\n",
    "drop_fourth_month_columns = [hdr for hdr in list(telecom) if hdr.endswith(\"_9\")]\n",
    "drop_fourth_month_columns += [\"sep_vbc_3g\"]\n",
    "telecom.drop(drop_fourth_month_columns, axis=1, inplace=True)\n",
    "\n",
    "print('Number of columns droped here  = ', len(drop_fourth_month_columns))\n",
    "print('Dataframe Shape: ', telecom.shape); print_ln();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Identify columns that have no varience & Drop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataframe Unique Value Count is ZERO (all null values): \n",
      " Series([], dtype: int64)\n",
      "-------------------------------------------------------------------------------- \n",
      "\n",
      "Dataframe Unique Value Count is ONE (all same values): \n",
      " last_date_of_month_7    1\n",
      "circle_id               1\n",
      "loc_og_t2o_mou          1\n",
      "std_og_t2o_mou          1\n",
      "loc_ic_t2o_mou          1\n",
      "last_date_of_month_6    1\n",
      "std_ic_t2o_mou_6        1\n",
      "last_date_of_month_8    1\n",
      "std_ic_t2o_mou_7        1\n",
      "std_og_t2c_mou_6        1\n",
      "std_og_t2c_mou_7        1\n",
      "std_og_t2c_mou_8        1\n",
      "std_ic_t2o_mou_8        1\n",
      "dtype: int64\n",
      "-------------------------------------------------------------------------------- \n",
      "\n",
      "Dataframe Shape:  (30001, 144)\n",
      "-------------------------------------------------------------------------------- \n",
      "\n",
      "Number of columns dropped here  =  13\n",
      "Dataframe Shape:  (30001, 144)\n",
      "-------------------------------------------------------------------------------- \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Identify columns that have no varience\n",
    "telecom_unique_count = telecom.nunique().sort_values(ascending=False)\n",
    "#print(\"Dataframe Unique Values: \\n\", telecom_unique_count); print_ln()\n",
    "\n",
    "# Identify bad colums that has no infromation (all entries are NA or same)\n",
    "# Find columns with all NULL entries and add to drop_columns list\n",
    "telecom_unique_count_is_zero = telecom_unique_count[telecom_unique_count == 0]\n",
    "print(\"Dataframe Unique Value Count is ZERO (all null values): \\n\", telecom_unique_count_is_zero); print_ln();\n",
    "drop_no_varience_columns = list(telecom_unique_count_is_zero.index)\n",
    "\n",
    "# Find columns with all same entries and add to drop_columns list\n",
    "telecom_unique_count_is_one = telecom_unique_count[telecom_unique_count == 1]\n",
    "print(\"Dataframe Unique Value Count is ONE (all same values): \\n\", telecom_unique_count_is_one); print_ln();\n",
    "drop_no_varience_columns += list(telecom_unique_count_is_one.index)\n",
    "\n",
    "telecom.drop(drop_no_varience_columns, axis=1, inplace=True)\n",
    "\n",
    "print('Dataframe Shape: ', telecom.shape); print_ln();\n",
    "print('Number of columns dropped here  = ', len(drop_no_varience_columns))\n",
    "print('Dataframe Shape: ', telecom.shape); print_ln();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mobile_number</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>offnet_mou_6</th>\n",
       "      <th>offnet_mou_7</th>\n",
       "      <th>offnet_mou_8</th>\n",
       "      <th>...</th>\n",
       "      <th>fb6_0.0</th>\n",
       "      <th>fb6_1.0</th>\n",
       "      <th>fb7_0.0</th>\n",
       "      <th>fb7_1.0</th>\n",
       "      <th>fb8_0.0</th>\n",
       "      <th>fb8_1.0</th>\n",
       "      <th>total_rech_data_amt_6</th>\n",
       "      <th>total_rech_data_amt_7</th>\n",
       "      <th>total_rech_data_amt_8</th>\n",
       "      <th>churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7000842753</td>\n",
       "      <td>197.38</td>\n",
       "      <td>214.82</td>\n",
       "      <td>213.80</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.00</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>252.00</td>\n",
       "      <td>252.00</td>\n",
       "      <td>252.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7000701601</td>\n",
       "      <td>1069.18</td>\n",
       "      <td>1349.85</td>\n",
       "      <td>3171.48</td>\n",
       "      <td>57.84</td>\n",
       "      <td>54.68</td>\n",
       "      <td>52.29</td>\n",
       "      <td>453.43</td>\n",
       "      <td>567.16</td>\n",
       "      <td>325.91</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7001524846</td>\n",
       "      <td>378.72</td>\n",
       "      <td>492.22</td>\n",
       "      <td>137.36</td>\n",
       "      <td>413.69</td>\n",
       "      <td>351.03</td>\n",
       "      <td>35.08</td>\n",
       "      <td>94.66</td>\n",
       "      <td>80.63</td>\n",
       "      <td>136.48</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>354.00</td>\n",
       "      <td>207.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>7002124215</td>\n",
       "      <td>514.45</td>\n",
       "      <td>597.75</td>\n",
       "      <td>637.76</td>\n",
       "      <td>102.41</td>\n",
       "      <td>132.11</td>\n",
       "      <td>85.14</td>\n",
       "      <td>757.93</td>\n",
       "      <td>896.68</td>\n",
       "      <td>983.39</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>7000887461</td>\n",
       "      <td>74.35</td>\n",
       "      <td>193.90</td>\n",
       "      <td>366.97</td>\n",
       "      <td>48.96</td>\n",
       "      <td>50.66</td>\n",
       "      <td>33.58</td>\n",
       "      <td>85.41</td>\n",
       "      <td>89.36</td>\n",
       "      <td>205.89</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>712.00</td>\n",
       "      <td>540.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 144 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    mobile_number  arpu_6  arpu_7  arpu_8  onnet_mou_6  onnet_mou_7  \\\n",
       "0      7000842753  197.38  214.82  213.80          nan          nan   \n",
       "7      7000701601 1069.18 1349.85 3171.48        57.84        54.68   \n",
       "8      7001524846  378.72  492.22  137.36       413.69       351.03   \n",
       "21     7002124215  514.45  597.75  637.76       102.41       132.11   \n",
       "23     7000887461   74.35  193.90  366.97        48.96        50.66   \n",
       "\n",
       "    onnet_mou_8  offnet_mou_6  offnet_mou_7  offnet_mou_8  ...    fb6_0.0  \\\n",
       "0          0.00           nan           nan          0.00  ...          0   \n",
       "7         52.29        453.43        567.16        325.91  ...          0   \n",
       "8         35.08         94.66         80.63        136.48  ...          0   \n",
       "21        85.14        757.93        896.68        983.39  ...          0   \n",
       "23        33.58         85.41         89.36        205.89  ...          0   \n",
       "\n",
       "    fb6_1.0  fb7_0.0  fb7_1.0  fb8_0.0  fb8_1.0  total_rech_data_amt_6  \\\n",
       "0         1        0        1        0        1                 252.00   \n",
       "7         0        0        0        0        0                   0.00   \n",
       "8         0        0        1        0        1                   0.00   \n",
       "21        0        0        0        0        0                   0.00   \n",
       "23        0        0        1        0        1                   0.00   \n",
       "\n",
       "    total_rech_data_amt_7  total_rech_data_amt_8  churn  \n",
       "0                  252.00                 252.00      1  \n",
       "7                    0.00                   0.00      1  \n",
       "8                  354.00                 207.00      0  \n",
       "21                   0.00                   0.00      0  \n",
       "23                 712.00                 540.00      0  \n",
       "\n",
       "[5 rows x 144 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "telecom.head(5) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ROW-WISE: MISSING VALUES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rows with all NULL values = 0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0        54\n",
       "77       27\n",
       "91        1\n",
       "111      27\n",
       "188      28\n",
       "191       1\n",
       "358      27\n",
       "364      27\n",
       "414      28\n",
       "423      27\n",
       "527      27\n",
       "539      54\n",
       "588       1\n",
       "603       2\n",
       "679       1\n",
       "687      82\n",
       "690      28\n",
       "723      28\n",
       "788      27\n",
       "845      28\n",
       "895      27\n",
       "933      27\n",
       "934      27\n",
       "1138     54\n",
       "1255     27\n",
       "1374      1\n",
       "1489     54\n",
       "1500     55\n",
       "1524     28\n",
       "1555     56\n",
       "         ..\n",
       "98790    55\n",
       "98823    28\n",
       "98838     1\n",
       "98872    28\n",
       "98943    81\n",
       "98962    27\n",
       "98971     1\n",
       "99000    27\n",
       "99034    27\n",
       "99070    27\n",
       "99135    81\n",
       "99142    81\n",
       "99195    27\n",
       "99224     2\n",
       "99246    27\n",
       "99296    27\n",
       "99313     1\n",
       "99323    27\n",
       "99338    81\n",
       "99391    27\n",
       "99398    28\n",
       "99436    28\n",
       "99515     1\n",
       "99611    27\n",
       "99625    27\n",
       "99672     1\n",
       "99700    27\n",
       "99790    55\n",
       "99827    27\n",
       "99851    56\n",
       "Length: 1838, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sum it up to check how many rows have all missing values\n",
    "print(\"Rows with all NULL values =\",  telecom.isnull().all(axis=1).sum())\n",
    "\n",
    "# sum of misisng values in each row\n",
    "rows_missing_data = telecom.isnull().sum(axis=1)\n",
    "rows_missing_data[rows_missing_data > 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rows with >= 27 missing values = 1497\n",
      "     % of rows with >= 27 missing values = 4.989833672210926 %\n",
      "Dataframe Shape after dropping rows:  (28504, 144)\n",
      "-------------------------------------------------------------------------------- \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# small percentage of rows has more than or equal to 27 missing values\n",
    "drop_rows = telecom.isnull().sum(axis=1) >= 27\n",
    "print(\"Number of rows with >= 27 missing values = {}\".format(drop_rows.sum()))\n",
    "print(\"     % of rows with >= 27 missing values = {} %\".format(drop_rows.sum()/telecom.shape[0]*100))\n",
    "\n",
    "# drop rows with missing values greater >= 27\n",
    "telecom = telecom[telecom.isnull().sum(axis=1) < 27]\n",
    "print('Dataframe Shape after dropping rows: ', telecom.shape); print_ln();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "91       1\n",
      "191      1\n",
      "588      1\n",
      "603      2\n",
      "679      1\n",
      "1374     1\n",
      "1913     1\n",
      "2777     1\n",
      "3757     1\n",
      "4284     1\n",
      "4814     1\n",
      "4934     1\n",
      "5187     1\n",
      "5449     1\n",
      "5798     1\n",
      "5926     1\n",
      "7304     1\n",
      "7399     1\n",
      "7567     1\n",
      "7662     1\n",
      "7914     1\n",
      "8118     1\n",
      "8159     1\n",
      "8281     1\n",
      "8454     1\n",
      "8680     1\n",
      "8979     1\n",
      "9012     1\n",
      "9027     1\n",
      "9068     1\n",
      "        ..\n",
      "93165    1\n",
      "93483    1\n",
      "93755    1\n",
      "93769    1\n",
      "94057    1\n",
      "94089    1\n",
      "94241    1\n",
      "94719    1\n",
      "94970    1\n",
      "95531    1\n",
      "95638    1\n",
      "95709    1\n",
      "96053    1\n",
      "96387    1\n",
      "96403    1\n",
      "96480    1\n",
      "96522    1\n",
      "96524    1\n",
      "96715    2\n",
      "97077    1\n",
      "97158    1\n",
      "97926    1\n",
      "98753    1\n",
      "98789    1\n",
      "98838    1\n",
      "98971    1\n",
      "99224    2\n",
      "99313    1\n",
      "99515    1\n",
      "99672    1\n",
      "Length: 341, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "rows_missing_data = telecom.isnull().sum(axis=1)\n",
    "#print(rows_missing_data)\n",
    "print(rows_missing_data[rows_missing_data > 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date_of_last_rech_6   0.12\n",
       "date_of_last_rech_7   0.18\n",
       "date_of_last_rech_8   0.97\n",
       "dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Missing Values Treatment: continue\n",
    "# look at the COLUMN summary again\n",
    "df = round(100*(telecom.isnull().sum()/len(telecom.index)), 2)\n",
    "df[df > 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: The remaining missing values are in date columns, before imputing convert them into appropreate values for analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fix Dates & convert to meaningful numbers\n",
    "1. Convert date_of_last_rech_6 --> rech_b4_days_to_month_end_6 (voice rechanrged before number of days to month end)\n",
    "2. convert date_of_last_rech_data_6 --> rech_b4_days_to_month_end_data_6 (data rechanrged before number of days to month end)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date_of_last_rech_6</th>\n",
       "      <th>date_of_last_rech_7</th>\n",
       "      <th>date_of_last_rech_8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6/27/2014</td>\n",
       "      <td>7/25/2014</td>\n",
       "      <td>8/26/2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>6/25/2014</td>\n",
       "      <td>7/31/2014</td>\n",
       "      <td>8/30/2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>6/30/2014</td>\n",
       "      <td>7/31/2014</td>\n",
       "      <td>8/31/2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>6/18/2014</td>\n",
       "      <td>7/7/2014</td>\n",
       "      <td>8/24/2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>6/30/2014</td>\n",
       "      <td>7/30/2014</td>\n",
       "      <td>8/26/2014</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   date_of_last_rech_6 date_of_last_rech_7 date_of_last_rech_8\n",
       "7            6/27/2014           7/25/2014           8/26/2014\n",
       "8            6/25/2014           7/31/2014           8/30/2014\n",
       "21           6/30/2014           7/31/2014           8/31/2014\n",
       "23           6/18/2014            7/7/2014           8/24/2014\n",
       "33           6/30/2014           7/30/2014           8/26/2014"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# covert date columns to python datetime format\n",
    "date_vars = [\"date_of_last_rech_6\",  \"date_of_last_rech_7\", \"date_of_last_rech_8\"]\n",
    "telecom[date_vars].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 28504 entries, 7 to 99997\n",
      "Data columns (total 3 columns):\n",
      "date_of_last_rech_6    28471 non-null datetime64[ns]\n",
      "date_of_last_rech_7    28452 non-null datetime64[ns]\n",
      "date_of_last_rech_8    28228 non-null datetime64[ns]\n",
      "dtypes: datetime64[ns](3)\n",
      "memory usage: 2.1 MB\n",
      "None\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date_of_last_rech_6</th>\n",
       "      <th>date_of_last_rech_7</th>\n",
       "      <th>date_of_last_rech_8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2014-06-27</td>\n",
       "      <td>2014-07-25</td>\n",
       "      <td>2014-08-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2014-06-25</td>\n",
       "      <td>2014-07-31</td>\n",
       "      <td>2014-08-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2014-06-30</td>\n",
       "      <td>2014-07-31</td>\n",
       "      <td>2014-08-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2014-06-18</td>\n",
       "      <td>2014-07-07</td>\n",
       "      <td>2014-08-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2014-06-30</td>\n",
       "      <td>2014-07-30</td>\n",
       "      <td>2014-08-26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   date_of_last_rech_6 date_of_last_rech_7 date_of_last_rech_8\n",
       "7           2014-06-27          2014-07-25          2014-08-26\n",
       "8           2014-06-25          2014-07-31          2014-08-30\n",
       "21          2014-06-30          2014-07-31          2014-08-31\n",
       "23          2014-06-18          2014-07-07          2014-08-24\n",
       "33          2014-06-30          2014-07-30          2014-08-26"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert to datetime\n",
    "for col in date_vars:\n",
    "    telecom[col] = pd.to_datetime(telecom[col])\n",
    "\n",
    "print(telecom[date_vars].info())\n",
    "telecom[date_vars].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 28504 entries, 7 to 99997\n",
      "Data columns (total 3 columns):\n",
      "rech_days_left_6    28471 non-null float64\n",
      "rech_days_left_7    28452 non-null float64\n",
      "rech_days_left_8    28228 non-null float64\n",
      "dtypes: float64(3)\n",
      "memory usage: 2.1 MB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# Create new days columns, instead of date\n",
    "import datetime\n",
    "last_date_of_month_6 = datetime.datetime.strptime(\"30-06-2014\", \"%d-%m-%Y\")\n",
    "last_date_of_month_7 = datetime.datetime.strptime(\"31-07-2014\", \"%d-%m-%Y\")\n",
    "last_date_of_month_8 = datetime.datetime.strptime(\"31-08-2014\", \"%d-%m-%Y\")\n",
    "\n",
    "telecom[\"rech_days_left_6\"]      = (last_date_of_month_6 - telecom.date_of_last_rech_6).astype('timedelta64[D]')\n",
    "telecom[\"rech_days_left_7\"]      = (last_date_of_month_7 - telecom.date_of_last_rech_7).astype('timedelta64[D]')\n",
    "telecom[\"rech_days_left_8\"]      = (last_date_of_month_8 - telecom.date_of_last_rech_8).astype('timedelta64[D]')\n",
    "\n",
    "day_columns = [\"rech_days_left_6\", \"rech_days_left_7\", \"rech_days_left_8\"]\n",
    "#print(telecom[day_columns].head(10))\n",
    "print(telecom[day_columns].info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of columns droped here  =  3\n",
      "Dataframe Shape:  (28504, 144)\n"
     ]
    }
   ],
   "source": [
    "# Drop all old date columns: add dates columns to drop_column list\n",
    "telecom.drop(date_vars, axis=1, inplace=True)\n",
    "telecom1 = telecom.copy()\n",
    "\n",
    "print('Number of columns droped here  = ', len(date_vars))\n",
    "print('Dataframe Shape: ', telecom.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rech_days_left_6   0.12\n",
       "rech_days_left_7   0.18\n",
       "rech_days_left_8   0.97\n",
       "dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Missing Values Treatment: continue\n",
    "# look at the COLUMN summary again\n",
    "df = round(100*(telecom.isnull().sum()/len(telecom.index)), 2)\n",
    "df[df > 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rech_days_left_6</th>\n",
       "      <th>rech_days_left_7</th>\n",
       "      <th>rech_days_left_8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>28471.00</td>\n",
       "      <td>28452.00</td>\n",
       "      <td>28228.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.39</td>\n",
       "      <td>3.49</td>\n",
       "      <td>4.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.49</td>\n",
       "      <td>4.29</td>\n",
       "      <td>5.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>6.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>29.00</td>\n",
       "      <td>30.00</td>\n",
       "      <td>30.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       rech_days_left_6  rech_days_left_7  rech_days_left_8\n",
       "count          28471.00          28452.00          28228.00\n",
       "mean               3.39              3.49              4.15\n",
       "std                4.49              4.29              5.10\n",
       "min                0.00              0.00              0.00\n",
       "25%                0.00              0.00              1.00\n",
       "50%                2.00              2.00              3.00\n",
       "75%                4.00              5.00              6.00\n",
       "max               29.00             30.00             30.00"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "telecom[['rech_days_left_6', 'rech_days_left_7', 'rech_days_left_8']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Total Number of missing values in the data frame = 0\n",
      "Dataframe Shape:  (28504, 144)\n",
      "-------------------------------------------------------------------------------- \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Mean & Median are different: imputing with median value for recharge days left \n",
    "telecom.loc[np.isnan(telecom['rech_days_left_6']), ['rech_days_left_6']] = telecom['rech_days_left_6'].median()\n",
    "telecom.loc[np.isnan(telecom['rech_days_left_7']), ['rech_days_left_7']] = telecom['rech_days_left_7'].median()\n",
    "telecom.loc[np.isnan(telecom['rech_days_left_8']), ['rech_days_left_8']] = telecom['rech_days_left_8'].median()\n",
    "\n",
    "#round(100*(telecom.isnull().sum()/len(telecom.index)), 2)\n",
    "print(\"\\nTotal Number of missing values in the data frame = {}\".format(telecom.isnull().sum().sum()))\n",
    "print('Dataframe Shape: ', telecom.shape); print_ln();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Oulier Assessment\n",
    "- Use data distribution to find outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\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>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>offnet_mou_6</th>\n",
       "      <th>offnet_mou_7</th>\n",
       "      <th>offnet_mou_8</th>\n",
       "      <th>...</th>\n",
       "      <th>fb7_1.0</th>\n",
       "      <th>fb8_0.0</th>\n",
       "      <th>fb8_1.0</th>\n",
       "      <th>total_rech_data_amt_6</th>\n",
       "      <th>total_rech_data_amt_7</th>\n",
       "      <th>total_rech_data_amt_8</th>\n",
       "      <th>churn</th>\n",
       "      <th>rech_days_left_6</th>\n",
       "      <th>rech_days_left_7</th>\n",
       "      <th>rech_days_left_8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>28504.00</td>\n",
       "      <td>28504.00</td>\n",
       "      <td>28504.00</td>\n",
       "      <td>28504.00</td>\n",
       "      <td>28504.00</td>\n",
       "      <td>28504.00</td>\n",
       "      <td>28504.00</td>\n",
       "      <td>28504.00</td>\n",
       "      <td>28504.00</td>\n",
       "      <td>28504.00</td>\n",
       "      <td>...</td>\n",
       "      <td>28504.00</td>\n",
       "      <td>28504.00</td>\n",
       "      <td>28504.00</td>\n",
       "      <td>28504.00</td>\n",
       "      <td>28504.00</td>\n",
       "      <td>28504.00</td>\n",
       "      <td>28504.00</td>\n",
       "      <td>28504.00</td>\n",
       "      <td>28504.00</td>\n",
       "      <td>28504.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7001213864.75</td>\n",
       "      <td>560.38</td>\n",
       "      <td>570.63</td>\n",
       "      <td>526.06</td>\n",
       "      <td>265.25</td>\n",
       "      <td>275.93</td>\n",
       "      <td>244.73</td>\n",
       "      <td>379.89</td>\n",
       "      <td>389.85</td>\n",
       "      <td>350.23</td>\n",
       "      <td>...</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.48</td>\n",
       "      <td>444.79</td>\n",
       "      <td>527.56</td>\n",
       "      <td>469.51</td>\n",
       "      <td>0.06</td>\n",
       "      <td>3.39</td>\n",
       "      <td>3.48</td>\n",
       "      <td>4.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>687776.04</td>\n",
       "      <td>448.96</td>\n",
       "      <td>480.08</td>\n",
       "      <td>500.75</td>\n",
       "      <td>458.72</td>\n",
       "      <td>482.67</td>\n",
       "      <td>464.02</td>\n",
       "      <td>479.25</td>\n",
       "      <td>502.60</td>\n",
       "      <td>485.65</td>\n",
       "      <td>...</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1417.12</td>\n",
       "      <td>1481.60</td>\n",
       "      <td>1644.91</td>\n",
       "      <td>0.23</td>\n",
       "      <td>4.49</td>\n",
       "      <td>4.29</td>\n",
       "      <td>5.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>7000000074.00</td>\n",
       "      <td>-2258.71</td>\n",
       "      <td>-2014.05</td>\n",
       "      <td>-945.81</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7000622950.00</td>\n",
       "      <td>317.36</td>\n",
       "      <td>322.60</td>\n",
       "      <td>254.76</td>\n",
       "      <td>20.14</td>\n",
       "      <td>19.53</td>\n",
       "      <td>14.84</td>\n",
       "      <td>80.73</td>\n",
       "      <td>80.35</td>\n",
       "      <td>60.49</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7001217677.00</td>\n",
       "      <td>485.17</td>\n",
       "      <td>488.90</td>\n",
       "      <td>442.79</td>\n",
       "      <td>89.98</td>\n",
       "      <td>89.91</td>\n",
       "      <td>74.09</td>\n",
       "      <td>231.55</td>\n",
       "      <td>231.82</td>\n",
       "      <td>199.88</td>\n",
       "      <td>...</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>92.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>25.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7001806580.25</td>\n",
       "      <td>698.91</td>\n",
       "      <td>705.00</td>\n",
       "      <td>672.45</td>\n",
       "      <td>298.76</td>\n",
       "      <td>304.89</td>\n",
       "      <td>257.41</td>\n",
       "      <td>494.37</td>\n",
       "      <td>506.47</td>\n",
       "      <td>457.43</td>\n",
       "      <td>...</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>392.00</td>\n",
       "      <td>575.00</td>\n",
       "      <td>342.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>6.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90%</th>\n",
       "      <td>7002165664.10</td>\n",
       "      <td>987.58</td>\n",
       "      <td>1001.34</td>\n",
       "      <td>991.09</td>\n",
       "      <td>760.84</td>\n",
       "      <td>801.71</td>\n",
       "      <td>688.28</td>\n",
       "      <td>898.93</td>\n",
       "      <td>931.20</td>\n",
       "      <td>844.92</td>\n",
       "      <td>...</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1051.40</td>\n",
       "      <td>1374.00</td>\n",
       "      <td>1118.80</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>9.00</td>\n",
       "      <td>10.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95%</th>\n",
       "      <td>7002285365.20</td>\n",
       "      <td>1232.63</td>\n",
       "      <td>1268.47</td>\n",
       "      <td>1267.39</td>\n",
       "      <td>1135.71</td>\n",
       "      <td>1206.17</td>\n",
       "      <td>1101.55</td>\n",
       "      <td>1256.09</td>\n",
       "      <td>1292.28</td>\n",
       "      <td>1187.34</td>\n",
       "      <td>...</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2025.00</td>\n",
       "      <td>2424.00</td>\n",
       "      <td>2268.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>13.00</td>\n",
       "      <td>15.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99%</th>\n",
       "      <td>7002385436.73</td>\n",
       "      <td>1955.15</td>\n",
       "      <td>1999.46</td>\n",
       "      <td>1989.92</td>\n",
       "      <td>2146.08</td>\n",
       "      <td>2220.47</td>\n",
       "      <td>2175.25</td>\n",
       "      <td>2301.03</td>\n",
       "      <td>2434.54</td>\n",
       "      <td>2219.38</td>\n",
       "      <td>...</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>5129.73</td>\n",
       "      <td>6219.16</td>\n",
       "      <td>5749.82</td>\n",
       "      <td>1.00</td>\n",
       "      <td>19.00</td>\n",
       "      <td>18.00</td>\n",
       "      <td>25.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7002410905.00</td>\n",
       "      <td>27731.09</td>\n",
       "      <td>35145.83</td>\n",
       "      <td>33543.62</td>\n",
       "      <td>7376.71</td>\n",
       "      <td>8157.78</td>\n",
       "      <td>10752.56</td>\n",
       "      <td>8362.36</td>\n",
       "      <td>9667.13</td>\n",
       "      <td>14007.34</td>\n",
       "      <td>...</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>105644.00</td>\n",
       "      <td>55080.00</td>\n",
       "      <td>89106.50</td>\n",
       "      <td>1.00</td>\n",
       "      <td>29.00</td>\n",
       "      <td>30.00</td>\n",
       "      <td>30.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11 rows × 144 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       mobile_number   arpu_6   arpu_7   arpu_8  onnet_mou_6  onnet_mou_7  \\\n",
       "count       28504.00 28504.00 28504.00 28504.00     28504.00     28504.00   \n",
       "mean   7001213864.75   560.38   570.63   526.06       265.25       275.93   \n",
       "std        687776.04   448.96   480.08   500.75       458.72       482.67   \n",
       "min    7000000074.00 -2258.71 -2014.05  -945.81         0.00         0.00   \n",
       "25%    7000622950.00   317.36   322.60   254.76        20.14        19.53   \n",
       "50%    7001217677.00   485.17   488.90   442.79        89.98        89.91   \n",
       "75%    7001806580.25   698.91   705.00   672.45       298.76       304.89   \n",
       "90%    7002165664.10   987.58  1001.34   991.09       760.84       801.71   \n",
       "95%    7002285365.20  1232.63  1268.47  1267.39      1135.71      1206.17   \n",
       "99%    7002385436.73  1955.15  1999.46  1989.92      2146.08      2220.47   \n",
       "max    7002410905.00 27731.09 35145.83 33543.62      7376.71      8157.78   \n",
       "\n",
       "       onnet_mou_8  offnet_mou_6  offnet_mou_7  offnet_mou_8  \\\n",
       "count     28504.00      28504.00      28504.00      28504.00   \n",
       "mean        244.73        379.89        389.85        350.23   \n",
       "std         464.02        479.25        502.60        485.65   \n",
       "min           0.00          0.00          0.00          0.00   \n",
       "25%          14.84         80.73         80.35         60.49   \n",
       "50%          74.09        231.55        231.82        199.88   \n",
       "75%         257.41        494.37        506.47        457.43   \n",
       "90%         688.28        898.93        931.20        844.92   \n",
       "95%        1101.55       1256.09       1292.28       1187.34   \n",
       "99%        2175.25       2301.03       2434.54       2219.38   \n",
       "max       10752.56       8362.36       9667.13      14007.34   \n",
       "\n",
       "             ...         fb7_1.0  fb8_0.0  fb8_1.0  total_rech_data_amt_6  \\\n",
       "count        ...        28504.00 28504.00 28504.00               28504.00   \n",
       "mean         ...            0.52     0.06     0.48                 444.79   \n",
       "std          ...            0.50     0.23     0.50                1417.12   \n",
       "min          ...            0.00     0.00     0.00                   0.00   \n",
       "25%          ...            0.00     0.00     0.00                   0.00   \n",
       "50%          ...            1.00     0.00     0.00                  92.00   \n",
       "75%          ...            1.00     0.00     1.00                 392.00   \n",
       "90%          ...            1.00     0.00     1.00                1051.40   \n",
       "95%          ...            1.00     1.00     1.00                2025.00   \n",
       "99%          ...            1.00     1.00     1.00                5129.73   \n",
       "max          ...            1.00     1.00     1.00              105644.00   \n",
       "\n",
       "       total_rech_data_amt_7  total_rech_data_amt_8    churn  \\\n",
       "count               28504.00               28504.00 28504.00   \n",
       "mean                  527.56                 469.51     0.06   \n",
       "std                  1481.60                1644.91     0.23   \n",
       "min                     0.00                   0.00     0.00   \n",
       "25%                     0.00                   0.00     0.00   \n",
       "50%                   100.00                  25.00     0.00   \n",
       "75%                   575.00                 342.00     0.00   \n",
       "90%                  1374.00                1118.80     0.00   \n",
       "95%                  2424.00                2268.00     1.00   \n",
       "99%                  6219.16                5749.82     1.00   \n",
       "max                 55080.00               89106.50     1.00   \n",
       "\n",
       "       rech_days_left_6  rech_days_left_7  rech_days_left_8  \n",
       "count          28504.00          28504.00          28504.00  \n",
       "mean               3.39              3.48              4.14  \n",
       "std                4.49              4.29              5.08  \n",
       "min                0.00              0.00              0.00  \n",
       "25%                0.00              0.00              1.00  \n",
       "50%                2.00              2.00              3.00  \n",
       "75%                4.00              5.00              6.00  \n",
       "90%               10.00              9.00             10.00  \n",
       "95%               14.00             13.00             15.00  \n",
       "99%               19.00             18.00             25.00  \n",
       "max               29.00             30.00             30.00  \n",
       "\n",
       "[11 rows x 144 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking outliers at 25%,50%,75%,90%,95% and 99%\n",
    "telecom.describe(percentiles=[.25,.5,.75,.90,.95,.99])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "* Most of the outliers are on the revenue data and the model being built is on usage of voice and data.\n",
    "* Hence there is no need to treat these outliers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <font color='blue'>SUMMARY PART 1: DATA UNDERSTANDING AND CLEANING</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original Dataframe Shape:  (99999, 226)\n",
      "-------------------------------------------------------------------------------- \n",
      "\n",
      "Original Dataframe Info: \n",
      "\n",
      "<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: 172.4+ MB\n",
      "-------------------------------------------------------------------------------- \n",
      "\n",
      "Original Dataframe Nulls: 3594931\n",
      "-------------------------------------------------------------------------------- \n",
      "\n",
      "Cleaned Dataframe Shape:  (28504, 144)\n",
      "-------------------------------------------------------------------------------- \n",
      "\n",
      "Cleaned Dataframe Info: \n",
      "\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 28504 entries, 7 to 99997\n",
      "Columns: 144 entries, mobile_number to rech_days_left_8\n",
      "dtypes: float64(105), int32(1), int64(26), uint8(12)\n",
      "memory usage: 30.4 MB\n",
      "-------------------------------------------------------------------------------- \n",
      "\n",
      "Cleaned Dataframe Nulls: 0\n",
      "-------------------------------------------------------------------------------- \n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('Original Dataframe Shape: ', telecom_orig.shape); print_ln();\n",
    "print(\"Original Dataframe Info: \\n\"); telecom_orig.info(); print_ln();\n",
    "print(\"Original Dataframe Nulls:\", telecom_orig.isnull().sum().sum()); print_ln();\n",
    "\n",
    "print('Cleaned Dataframe Shape: ', telecom.shape); print_ln();\n",
    "print(\"Cleaned Dataframe Info: \\n\"); telecom.info(); print_ln();\n",
    "print(\"Cleaned Dataframe Nulls:\", telecom.isnull().sum().sum()); print_ln();\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save the cleaned data in new file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# write treated telecom file\n",
    "telecom.to_csv(\"telecom_churn_data_clean.csv\", sep=',', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <font color='blue'>PART 2: EXPLORATORY DATA ANALYSIS (EDA)</font>\n",
    "1. Univariate \n",
    "2. Bivatiate Analysis\n",
    "3. Check imbalance in data-set (dependent variable)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# # Load csv data file\n",
    "# telecom_orig = pd.read_csv('telecom_churn_data_clean_cat.csv', low_memory=False)\n",
    "# telecom = telecom_orig.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dependent Variable: check data imbalance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Churn Count     = 1596\n",
      "Total Not-Churn Count = 26908\n",
      "Churn Rate            = 5.60%\n",
      "-------------------------------------------------------------------------------- \n",
      "\n"
     ]
    }
   ],
   "source": [
    "#checking whether data is imbalanced or not\n",
    "churn  = telecom[\"churn\"].value_counts()\n",
    "churn_rate = churn[1]/(churn[1] + churn[0])\n",
    "\n",
    "print(\"Total Churn Count     = {}\".format(churn[1]))\n",
    "print(\"Total Not-Churn Count = {}\".format(churn[0]))\n",
    "print(\"Churn Rate            = {:.2f}%\".format(churn_rate*100))\n",
    "print_ln()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "OBSERVATION: Data is highly imbalanced, we need to balance it before we can create the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Univariate Analysis on  'total_rech_num_6', 'total_rech_num_7', 'total_rech_num_8'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_not_churn = telecom[telecom[\"churn\"] == 0]\n",
    "df_churn     = telecom[telecom[\"churn\"] == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean:\n",
      "11.826036866359447\n",
      "14.31704260651629\n",
      "median:\n",
      "9.0\n",
      "12.0\n",
      "Quantiles:\n",
      "[ 0.  6.  9. 14.]\n",
      "[ 0.  8. 12. 18.]\n"
     ]
    }
   ],
   "source": [
    "#claculation of mean for churn or non churn\n",
    "print(\"Mean:\")\n",
    "print(np.mean(df_not_churn[\"total_rech_num_6\"]))\n",
    "print(np.mean(df_churn[\"total_rech_num_6\"]))\n",
    "print(\"median:\")\n",
    "print(np.median(df_not_churn[\"total_rech_num_6\"]))\n",
    "print(np.median(df_churn[\"total_rech_num_6\"]))\n",
    "\n",
    "print(\"Quantiles:\")\n",
    "print(np.percentile(df_not_churn[\"total_rech_num_6\"],np.arange(0,100,25)))\n",
    "print(np.percentile(df_churn[\"total_rech_num_6\"],np.arange(0,100,25)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bkumar5\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1633: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n",
      "C:\\Users\\bkumar5\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1f8abe67ba8>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sic/jJZ7MPSktIiLu5KpAtR0KG/JNV6i9sSRej5dYMlbK5omIyDjmqkB1Cq1Q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\ngg==\n",
      "text/plain": [
       "<Figure size 473.625x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#feature importance of total_rech_num_6\n",
    "#now use histogram or probability density function for univiraite analysis\n",
    "#one variable analysis\n",
    "#this is important to know feature importance in classification\n",
    "sns.set_style('whitegrid')\n",
    "sns.FacetGrid(telecom,hue='churn',size=6).map(sns.distplot,\"total_rech_num_6\").add_legend()\n",
    "\n",
    "#its difficult to know about the churning rate based on total_rech_num_6 feature as overlaped data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean:\n",
      "11.875464545859968\n",
      "13.43170426065163\n",
      "median:\n",
      "9.0\n",
      "10.0\n",
      "Quantiles:\n",
      "[ 0.  6.  9. 15.]\n",
      "[ 0.  7. 10. 17.]\n"
     ]
    }
   ],
   "source": [
    "print(\"Mean:\")\n",
    "print(np.mean(df_not_churn[\"total_rech_num_7\"]))\n",
    "print(np.mean(df_churn[\"total_rech_num_7\"]))\n",
    "print(\"median:\")\n",
    "print(np.median(df_not_churn[\"total_rech_num_7\"]))\n",
    "print(np.median(df_churn[\"total_rech_num_7\"]))\n",
    "\n",
    "print(\"Quantiles:\")\n",
    "print(np.percentile(df_not_churn[\"total_rech_num_7\"],np.arange(0,100,25)))\n",
    "print(np.percentile(df_churn[\"total_rech_num_7\"],np.arange(0,100,25)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bkumar5\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1633: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n",
      "C:\\Users\\bkumar5\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1f8abe639b0>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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s7wMsR4faELQh69eQr4hIlQmHw8Tj8cL3juNg28WnHBUNVMdxeOyxx3jkkUfo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\n",
      "text/plain": [
       "<Figure size 473.625x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#feature importance of total_rech_data_7\n",
    "#now use histogram or probability density function for univiraite analysis\n",
    "#one variable analysis\n",
    "#this is important to know feature importance in classification\n",
    "sns.set_style('whitegrid')\n",
    "sns.FacetGrid(telecom,hue='churn',size=6).map(sns.distplot,\"total_rech_num_7\").add_legend()\n",
    "\n",
    "#its difficult to know about the churning rate based on total_rech_data_7 feature as overlaped data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean:\n",
      "10.523561766017542\n",
      "7.021303258145363\n",
      "median:\n",
      "8.0\n",
      "5.0\n",
      "Quantiles:\n",
      "[ 0.  5.  8. 13.]\n",
      "[0. 3. 5. 9.]\n"
     ]
    }
   ],
   "source": [
    "print(\"Mean:\")\n",
    "print(np.mean(df_not_churn[\"total_rech_num_8\"]))\n",
    "print(np.mean(df_churn[\"total_rech_num_8\"]))\n",
    "print(\"median:\")\n",
    "print(np.median(df_not_churn[\"total_rech_num_8\"]))\n",
    "print(np.median(df_churn[\"total_rech_num_8\"]))\n",
    "\n",
    "print(\"Quantiles:\")\n",
    "print(np.percentile(df_not_churn[\"total_rech_num_8\"],np.arange(0,100,25)))\n",
    "print(np.percentile(df_churn[\"total_rech_num_8\"],np.arange(0,100,25)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bkumar5\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1633: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n",
      "C:\\Users\\bkumar5\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1f8aba9fef0>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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y9TFQiYioM+sHanTIV+i97VCNOdSOhzs0NHHIl4iIOrN+oEYXJaGXQ77OroZ8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\n",
      "text/plain": [
       "<Figure size 473.625x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#feature importance of total_rech_amt_8\n",
    "#now use histogram or probability density function for univiraite analysis\n",
    "#one variable analysis\n",
    "#this is important to know feature importance in classification\n",
    "sns.FacetGrid(telecom,hue='churn',size=6).map(sns.distplot,\"total_rech_num_8\").add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1f8b7032b00>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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UakbsP3JWdnv/OAktdbPDxVvkZOphMnC+sqhafLj3BD7YcwJFfvUtlISL4pbc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\nNEmjEEIIoUkahRBCCE3/B4KVEc7kQ2laAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 401.625x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.FacetGrid(telecom,hue='churn',size=5).map(plt.scatter,\"total_rech_num_6\",\"total_rech_num_7\").add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1f8b729bb38>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HWVlZ1NeP5Tm0IhFlc3l4ZKRwaGwNffZpJhb1YZRAupnFd/sPojKdg0Gn3Qxz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\nCGGVFAohhBBWSaEQQghh1f8DHSP5MW5mIycAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 401.625x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.FacetGrid(telecom,hue='churn',size=5).map(plt.scatter,\"total_rech_num_7\",\"total_rech_num_8\").add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1f8b732e198>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KtbW16NmzJ4qKijB8+HA4HOJdMmLee+89PPnkkwCA3/zmN7j77ruRkZGBl156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\nCJGWJAohhBBp/QUa89yG3ey1LwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 401.625x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.FacetGrid(telecom,hue='churn',size=5).map(plt.scatter,\"total_rech_num_6\",\"total_rech_num_8\").add_legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Univariate Analysis on 'total_rech_amt_6', 'total_rech_amt_7', 'total_rech_amt_8',"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean:\n",
      "656.1418537237996\n",
      "786.3007518796992\n",
      "median:\n",
      "564.0\n",
      "669.0\n",
      "Quantiles:\n",
      "[  0. 366. 564. 820.]\n",
      "[  0. 450. 669. 984.]\n"
     ]
    }
   ],
   "source": [
    "print(\"Mean:\")\n",
    "print(np.mean(df_not_churn[\"total_rech_amt_6\"]))\n",
    "print(np.mean(df_churn[\"total_rech_amt_6\"]))\n",
    "print(\"median:\")\n",
    "print(np.median(df_not_churn[\"total_rech_amt_6\"]))\n",
    "print(np.median(df_churn[\"total_rech_amt_6\"]))\n",
    "\n",
    "print(\"Quantiles:\")\n",
    "print(np.percentile(df_not_churn[\"total_rech_amt_6\"],np.arange(0,100,25)))\n",
    "print(np.percentile(df_churn[\"total_rech_amt_6\"],np.arange(0,100,25)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bkumar5\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1633: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n",
      "C:\\Users\\bkumar5\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1f8b7393a20>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1pgFDZQoAiJU2TN1ut6ZMmSJJmjRpkpqbmyPXtm3bpsmTJys/P18lJSUqKyvT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\n/wOrvizJP9MY9QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 473.625x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#feature importance of total_rech_num_6\n",
    "#now use histogram or probability density function for univiraite analysis\n",
    "#one variable analysis\n",
    "#this is important to know feature importance in classification\n",
    "sns.FacetGrid(telecom,hue='churn',size=6).map(sns.distplot,\"total_rech_amt_6\").add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean:\n",
      "668.5358257767207\n",
      "733.1115288220551\n",
      "median:\n",
      "570.0\n",
      "616.0\n",
      "Quantiles:\n",
      "[  0. 366. 570. 833.]\n",
      "[  0.   382.75 616.   939.25]\n"
     ]
    }
   ],
   "source": [
    "print(\"Mean:\")\n",
    "print(np.mean(df_not_churn[\"total_rech_amt_7\"]))\n",
    "print(np.mean(df_churn[\"total_rech_amt_7\"]))\n",
    "print(\"median:\")\n",
    "print(np.median(df_not_churn[\"total_rech_amt_7\"]))\n",
    "print(np.median(df_churn[\"total_rech_amt_7\"]))\n",
    "\n",
    "print(\"Quantiles:\")\n",
    "print(np.percentile(df_not_churn[\"total_rech_amt_7\"],np.arange(0,100,25)))\n",
    "print(np.percentile(df_churn[\"total_rech_amt_7\"],np.arange(0,100,25)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bkumar5\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1633: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n",
      "C:\\Users\\bkumar5\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1f8b744a3c8>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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dmWIsYjGLEh/Y2GBrzlRERPJTmNI9ZxpL3Mc0Pszr0WpeEREpiMKU7jCNRuOreEtKwHEM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\n",
      "text/plain": [
       "<Figure size 473.625x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#feature importance of total_rech_num_6\n",
    "#now use histogram or probability density function for univiraite analysis\n",
    "#one variable analysis\n",
    "#this is important to know feature importance in classification\n",
    "sns.FacetGrid(telecom,hue='churn',size=6).map(sns.distplot,\"total_rech_amt_7\").add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean:\n",
      "619.713691095585\n",
      "352.98809523809524\n",
      "median:\n",
      "518.0\n",
      "190.0\n",
      "Quantiles:\n",
      "[  0. 288. 518. 800.]\n",
      "[  0.    16.5  190.   506.25]\n"
     ]
    }
   ],
   "source": [
    "print(\"Mean:\")\n",
    "print(np.mean(df_not_churn[\"total_rech_amt_8\"]))\n",
    "print(np.mean(df_churn[\"total_rech_amt_8\"]))\n",
    "print(\"median:\")\n",
    "print(np.median(df_not_churn[\"total_rech_amt_8\"]))\n",
    "print(np.median(df_churn[\"total_rech_amt_8\"]))\n",
    "\n",
    "print(\"Quantiles:\")\n",
    "print(np.percentile(df_not_churn[\"total_rech_amt_8\"],np.arange(0,100,25)))\n",
    "print(np.percentile(df_churn[\"total_rech_amt_8\"],np.arange(0,100,25)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bkumar5\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1633: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n",
      "C:\\Users\\bkumar5\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1f8b721fba8>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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X69W9996rX/ziFxo0aJC2bdumm2++WdOmTet2mdzbMmXMFAD6pZUrV6qgoEBVVVW68cYb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\n",
      "text/plain": [
       "<Figure size 473.625x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#feature importance of total_rech_num_6\n",
    "#now use histogram or probability density function for univiraite analysis\n",
    "#one variable analysis\n",
    "#this is important to know feature importance in classification\n",
    "sns.FacetGrid(telecom,hue='churn',size=6).map(sns.distplot,\"total_rech_amt_8\").add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1f8b76d0d30>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MiIicT1FIjB07FmPHjlW5FCIicjWKQuLpp59GWVkZqqurIUkSrFYr8vPz1a6NiIicTFFI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\nMSSIiEjW/w/tMZKgz3Qu3AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 401.625x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.FacetGrid(telecom,hue='churn',size=5).map(plt.scatter,\"total_rech_amt_6\",\"total_rech_amt_7\").add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1f8b7918240>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TL+/D3FV78OTL+7B+x7ew22Xnn+flizbHZxD5HyaJdmo8yK1524WzO3a7Xcb6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\n/5zAMpchKmDcAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 401.625x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.FacetGrid(telecom,hue='churn',size=5).map(plt.scatter,\"total_rech_amt_6\",\"total_rech_amt_8\").add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1f8b7971c88>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2MO1qPoTdLmLL3y/j5s9hQFjDkIjtMYRXORERQ8IVs9WGknJbnUtZ66s956Co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\n",
      "text/plain": [
       "<Figure size 401.625x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.FacetGrid(telecom,hue='churn',size=5).map(plt.scatter,\"total_rech_amt_7\",\"total_rech_amt_8\").add_legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Univariate Analysis on max_rech_amt_6', 'max_rech_amt_7', 'max_rech_amt_8',"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean:\n",
      "168.3110227441653\n",
      "158.79761904761904\n",
      "median:\n",
      "120.0\n",
      "110.0\n",
      "Quantiles:\n",
      "[  0. 110. 120. 200.]\n",
      "[  0. 100. 110. 154.]\n"
     ]
    }
   ],
   "source": [
    "print(\"Mean:\")\n",
    "print(np.mean(df_not_churn[\"max_rech_amt_6\"]))\n",
    "print(np.mean(df_churn[\"max_rech_amt_6\"]))\n",
    "print(\"median:\")\n",
    "print(np.median(df_not_churn[\"max_rech_amt_6\"]))\n",
    "print(np.median(df_churn[\"max_rech_amt_6\"]))\n",
    "\n",
    "print(\"Quantiles:\")\n",
    "print(np.percentile(df_not_churn[\"max_rech_amt_6\"],np.arange(0,100,25)))\n",
    "print(np.percentile(df_churn[\"max_rech_amt_6\"],np.arange(0,100,25)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bkumar5\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1633: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n",
      "C:\\Users\\bkumar5\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1f8cdffff98>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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jR1NfX59yvxUrVnSp2O7QfgKSiR4QLiIinWnRBhvqTEVExI7C1Eb7/NQ1UxERSUVhaiN5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\nMsYYY3HiZsoYY4zFiZspY4wxFqc/AKt8VBl3bM1lAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 473.625x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.FacetGrid(telecom,hue='churn',size=6).map(sns.distplot,\"max_rech_amt_6\").add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean:\n",
      "171.94440315147912\n",
      "159.51629072681703\n",
      "median:\n",
      "130.0\n",
      "110.0\n",
      "Quantiles:\n",
      "[  0. 110. 130. 200.]\n",
      "[  0.  98. 110. 154.]\n"
     ]
    }
   ],
   "source": [
    "print(\"Mean:\")\n",
    "print(np.mean(df_not_churn[\"max_rech_amt_7\"]))\n",
    "print(np.mean(df_churn[\"max_rech_amt_7\"]))\n",
    "print(\"median:\")\n",
    "print(np.median(df_not_churn[\"max_rech_amt_7\"]))\n",
    "print(np.median(df_churn[\"max_rech_amt_7\"]))\n",
    "\n",
    "print(\"Quantiles:\")\n",
    "print(np.percentile(df_not_churn[\"max_rech_amt_7\"],np.arange(0,100,25)))\n",
    "print(np.percentile(df_churn[\"max_rech_amt_7\"],np.arange(0,100,25)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bkumar5\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1633: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n",
      "C:\\Users\\bkumar5\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1f8b7336b00>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sUG33jjPGSgxVi0REJI8oUF0GgzHJnZKA9C3cDApUERHJToGakt7LN/mnz0re\n2c7YClQREclOgeoyAMZKB6pNskJ11OUrIiI5UKCmZa5DTXX5YsWHqD0iIpJPFKiuZJx6KtRUoKrL\nV0REcqBATXEXnLrLULvHUNXlKyIiOVCgulJjqCk+T4VqtLuDiIhkoUDtQ7rL15dAezuIiEg2ClSX\nIbMK9bmzfC07rkAVEZGsFKgeFt4uX7/7IIGjm4yLiEgWCtSUHuOkqS5fy1aXr4iIZKdAdfWsQb2T\nkhSoIiKSTdZAdRyHefPmMX36dBoaGtixY0fG8aamJqZNm0Z9fT0rV64EoLW1lTvuuIOZM2dy9913\n09nZmfF9X/rSl/jZz342yKcycN2zeHvr8o2TUJeviIhkkTVQV6xYQTQapbGxkTlz5rBo0aL0sZaW\nFpYuXcqyZctYsmQJixcvJhqN8tRTTzF16lSeffZZrr76ahobG9Ofeeyxxzh27NipOZsBSjiA1WNS\nUqrL15fAqEIVEZEssgZqMBhk0qRJAIwfP55169alj61du5YJEyYQCASorKykurqajRs3Znxm8uTJ\nrF69GoCXXnoJy7KYPHnyqTiXAUvEj69QU3v5qstXRERy4c/2hlAoREVFRfq5z+cjHo/j9/sJhUJU\nVlamj5WXlxMKhTJeLy8vp729nc2bN/PLX/6S73//+zz55JM5NzAYDPbnfPr9fTuO7aQrYgEGDLS2\nHu4+6NhYdpzde/YRtAa3HbkY7HM/VfKlnZA/bc2XdkL+tDVf2gn509bBbmddXd2gft/pljVQKyoq\nCIfD6eeO4+D3+3s9Fg6HqaysTL9eUlJCOBxmxIgRvPDCCxw4cIAvfOEL7Nmzh6KiIs4///ys1epg\nXuBgMHjc9x3Z2kFbewIOBrEsGD36zPQx65AffAnOPvuc0/4Pure2Dkf50k7In7bmSzshf9qaL+2E\n/GlrvrTzdMoaqLW1taxcuZJbbrmFNWvWUFNTkz42btw4HnvsMSKRCNFolK1bt1JTU0NtbS2vvvoq\n06ZNo7m5mbq6Or785S+nP/f4449TVVU1bLp+E+kbylgZr1vGpy5fERHJSdZAnTJlCqtWrWLGjBkY\nY1i4cCFPP/001dXV3HTTTTQ0NDBz5kyMMcyePZvi4mJmzZrF3LlzaWpqYtSoUTz66KOn41z6zTEO\nRzqPEYuXAaZHnCYD1fJFtLGDiIhklTVQbdtm/vz5Ga+NHTs2/bi+vp76+vqM41VVVSxZsqTP7/yn\nf/qn/rZz0LWED/PU2//N+oOb+auq693iNDNSbeMDn7YeFBGR7LIG6gfR/vaDzP3tQ3TGuwDoiHVx\n/NYOYOHHsg3xhBJVREROrCBRj1r+AAAgAElEQVR3Slp3cBOd8S4uHVUNQDyRvOep1bNCdZfOxBKx\n09tAERHJOwUZqF3xKADVI88H6N4JqccgqmVSgRpHRETkRAoyUKOJZKCWBUoBiDsJLOv4SUmpCjXq\nKFBFROTECjJQu+IRAMqLkoGaSM866tnlmxxijqvLV0REsijIQI26Xb7lgTIAEu6ko74q1LiTOG1t\nExGR/FSQgdrldvmWF7mBapzk5vhWH5OS1OUrIiJZFNSymTXHNnJkawc7j+4GYEvrNqC7y7dnhZq6\n40zc6bvLd8O2VjZsP8y0Gy4f/AaLiEjeKMgKNeZ24Rb7AgA4JkFv61DTXb6m9y7fvW37eejNR3jm\n5Tdo74iemsaKiEheKMhAjbtduAFfEeAum7HA6tHla1nJy+P0MYb6fusOuuwj2BVHiUQ1zioiUsgK\nM1ATcSwsitxATVWox01KcgM10ccdxtOTlSxDLK7dlERECllhBqqTwG/78KUqUNP7xg42qeO9h2XC\nDVTLcojGVaGKiBSyAg3UOH6fP12BOu4s3+O2HrSt7uN9fE/yjYZYTBWqiEghK9xAtbsD1eCGYc+t\nB60TV6jdXb6OunxFRApcQQZqzIlTZPs9Xb6pZTOZierjxGOoCdMdqOryFREpbAUZqKkxVNt2AxUH\nMD33dUgfN9m6fDUpSUSk4BVcoDrGwTEOftufrkgNTnLZTM8xVDdhHXoPy9RdaCzbIaYKVUSkoBVc\noKbGPf22D8uysC3bHUPtZWOHLGOokZi7g5JliGpSkohIQSu8QHWrSr+d3HXRlw7U4zd2yOwSPl40\ndZ9USxWqiEihK7xAdTIDNV2h9tzVAfC5AdvXGGp3hapZviIiha7gAjV155ginxuoto3B0GuXb84V\nqiGqQBURKWgFF6jeMVRIdvliOVi9TkrqsU61h1jcnZRkOURj6vIVESlkBRiofXT59iLd5dtnhep2\n+dpaNiMiUugUqJYNdu9hmF6HmtOkJAWqiEghK7hATY+hegPVSoXh8Xv5GmP1GahxT6BqpyQRkcJW\ncIHacwzVG6g9J/paFuBYfW/s4L19m9ahiogUtMIL1B7rUC1sLDs1w7fnDcYBY0OfFap7+zZbXb4i\nIoWu8AK1Z4XquQS9VqjGxljHL6lJfpe6fEVEJKkAA9WtUH3dFWpaL5s7YPqeBdx9txnN8hURKXSF\nG6h2L4HaW6IayzNpqed36X6oIiKSVHCB2nOWb+9lqUeOFao2dhARKWwFF6g9x1At4x1D7btC3X84\nzL88/hovv7MrfchxA9VShSoiUvD82d/ywbGjpYvDRR0AvL+rnSI7QqgjceIi1ST3+r3vqVUcOtrJ\n6JEl3HjdhYAbqBZgG01KEhEpcAVXoTokgy+1Ty8me5cvlsOho50AdHTGPN/lVqWqUEVECl7hBarb\nTWuT7PIlS5evhYVlG75wy1X4fTbhLk+gprt8tbGDiEihK7hATZgENnb3zcSzVKhlZclL9Km/uYSK\n0iLCnfH0sfRkJdshEo/39nERESkQBReojklgW77uF8yJl80UB5LH406cshI/HZ4K1XiW08Q0hioi\nUtAKL1DpO1B7q1VT3cBxJ0FZaRFhzxiqdzlN3Ikd91kRESkcBReoCZPAhydQHU+MWsdHamryUtyJ\nU1FSRDSenIDkGAc8WxJqlq+ISGEruEB1jJNRoRpz4kuQ2kkpWaEmVxl1dMVIOJkBmjAJEk7ve/6K\niMgHX0EFqsHgkMDn7fJ1Ttzl661Qy0uKAAh3xrq3HUx/2GgcVUSkgBVYoCbHPDMrVG+M9r5sBpK3\nfSsvdQO1K0YskTlmqt2SREQKW0HtlJTaiMF7yzaTpULN6PItKQJflI7OOJGeq2RsR/v5iogUsIKs\nUH1W9+8RJnHiCtXb5dtu7aW07mX+1PJnOroimW/ULdxERApa1grVcRweeOABNm3aRCAQYMGCBVx0\n0UXp401NTSxbtgy/38+sWbO44YYbaG1t5Z577qGrq4sxY8bw0EMPUVpayjPPPMOvfvUrAD7+8Y/z\n1a9+9dSdWW/nku7y9VSoWScldS+bidkhAFo6DtERvaTHG9XlKyJSyLJWqCtWrCAajdLY2MicOXNY\ntGhR+lhLSwtLly5l2bJlLFmyhMWLFxONRnnqqaeYOnUqzz77LFdffTWNjY3s2rWLX/ziFyxbtozG\nxkZef/11Nm7ceEpPrientzFUJ0ugeipUy5fs0u2MRuiM9lh3qlu4iYgUtKyBGgwGmTRpEgDjx49n\n3bp16WNr165lwoQJBAIBKisrqa6uZuPGjRmfmTx5MqtXr+acc87hP//zP/H5fNi2TTwep7i4+BSd\nVu/SXb54A7W7m7e3vXxtPIHqdwM1FqUzGs14n2U7xBKqUEVEClXWLt9QKERFRUX6uc/nIx6P4/f7\nCYVCVFZWpo+Vl5cTCoUyXi8vL6e9vZ2ioiJGjx6NMYbvfve7XH311VxyySXH/byegsHgQM6rV6kK\nte1YjM07w1SN6SIW6Q7BSDRCa+vhjM90krzLzKYtmzl09AAArW1H2LB5S+aXWw7r1m8gfKhk0No7\nmOd+KuVLOyF/2pov7YT8aWu+tBPyp62D3c66urpB/b7TLWugVlRUEA6H088dx8Hv9/d6LBwOU1lZ\nmX69pKSEcDjMiBEjAIhEInzzm9+kvLycb33rWzk1cDAv8NL3Xwags72cA9srOP+Ccnz2ofQGgsXF\nxYwuPzPjM5GuMHTARZdcxNE9UdgJvoCfs887D7ZAciKTActwyaWXUXfl2YPS1mAwmBd/ufKlnZA/\nbc2XdkL+tDVf2gn509Z8aefplLXLt7a2lubmZgDWrFlDTU1N+ti4ceMIBoNEIhHa29vZunUrNTU1\n1NbW8uqrrwLQ3NxMXV0dxhi+8pWvcMUVVzB//nx8Pl+vP+9USnX5JmLJn33wADiJE9++zfYsm3Gs\nZJdvNBEjEkuOoRZZRe6HNSlJRKSQZa1Qp0yZwqpVq5gxYwbGGBYuXMjTTz9NdXU1N910Ew0NDcyc\nORNjDLNnz6a4uJhZs2Yxd+5cmpqaGDVqFI8++igrVqzg7bffJhqN8tprrwHwz//8z0yYMOGUn2RK\nqsvXibuBuh9M6Ylv35a6zVvcieOYZIjGEjG63ED1W0XETDS5bEb3RBURKVhZA9W2bebPn5/x2tix\nY9OP6+vrqa+vzzheVVXFkiVLMl6bMmUKf/rTn06mrSctFagJN1BD7RY+bALpd5x4UlLUvaNM1OkO\n1CJfgE4njGU72iBfRKSAFeTGDqkuX+iuVqGPnZKs7i7fSDy5mUPCxIjEk4FabLtxrC5fEZGCVlCB\nmqpQTdxH5QiDZRnIupdvd4UacffvjZs40Vhy78Fin7v0xzKqUEVEClhhBmrCR3EJjK4i4wbjvbGt\n7p2SUhWqQ6K7QvV7KlSNoYqIFKyCCtRUly+OD78fzj6HjAr1xJvjx4kk3M0crAThSDJci/3eClWB\nKiJSqAoqUB2SXbLG8eHzQ9WY7go06cTLZqJxN1DtBCF3c/z29uR3WrbDph2tvPTGdl56Y/spOgMR\nERmuCipQMypUHxQVQe1HPJeglxI1vWwmEafLrVAt26GjK/m4yDMpKZ4wp6ztIiIyvBVUoDo9unwB\nystPvJev1UeFGo64gepLbexgSDjq8hURKVSFF6jGAmPjcwPVynIJUrd6izqx7jFUO5HeHL/YTo2h\nOiRUoYqIFKyCClSDg+XeacafDtQsOyW5xztind2v2SY9HhvwBdzXHFWoIiIFrKAC1cHBMj0C1bLS\nodlrl69boXZEOzNf9yXXoQbSY6hGFaqISAEr2ED1eTZdPFG3b2qWbzjakXnADVS/3b05fsJRoIqI\nFKqCClSDA05mhQrepTO9TUpKvhaOZQZqqkL1W90Valw3GBcRKVgFE6jGuOOeqQrVc/e4VIXa22hq\nalLS8RXq8bdvU5eviEjhKphATTjuPruJ4yvU1Dhpb1JhG471GEP1JyvUPV3bk89th1AsNEitFRGR\nfFMwgZpe8uIkTzkjUDlBl687ackxTuZ73S5fn5UMaMs2GPX4iogUrMIJVHdTBuP4AIPt6fJNTTyy\n+lhBY9vdl6kiUJ58bzpQk8mcXDYzyI0WEZG8UTiB6laoJpHcJckbntYJJiUlP9T9uu24k5DcMVTb\nrVCxDFqGKiJSuAonUFO3Xov7MpbMQPbdkrzHi32lydfs5ASkdHVrOwpUEZECVjiB6laoTtyXMX4K\n3TN5+9ozyfYcKbHLjvushQWWAlVEpJAVTqDGuwPVu2QGTjwpCTJnARfbJT0+aycrWNuQmkgsIiKF\np3ACNT3L9/gKNesG+Z7jGRWqSY6/2paFZTkYY2GM1qKKiBSiwglUdww1NSnJq7vLt68Ktfv1tvgR\n7yfdz9lgJYNU2w+KiBSmAgrU7gr1+ElJ2e44032Z/KnN8D2fsywbrOQAqnZLEhEpTIUTqInudajH\ndfmmx0h7D1bbM4aa3moQsIynsnUr1LhmJomIFKTCCdQTVKipWbx91aneCrbI8laotvt5VagiIoWu\ncAI1kRxDpZcx1HSXbl87JWV0+XoqVMvq/jMVqKpQRUQKUuEEajy5s9FAunwtz6Sl1FaDAH5f96Qk\ngwEMu7q2kDBaPyMiUmj82d/ywZCa5XuiSUnZunxty9e91SDdFartTkqyRx3knc53KQ45vPxOEddd\ndQ4jygO9fqeIiHywFE6FeoJ1qPYJbt/mPW7jy+j+TW876E5KsouT90zd23aQ7/3sXZav3DJIrRcR\nkeGucALVc7eZfu+U5F4mn2V3bzWIt7J1g7U4ec/UtlgbADv2t9OVqoxFROQDrXACNVWhnmBSUp9d\nvlZ3ly94b/dmZxxPBWpHInmj8W1Hd3D783fzx73rBuUcRERk+CqcQI1HSM4bsk/Q5dvHOlS6u3yT\n73dvKu5dNgNYgWSgRgkDcMzsB2DToa2Dcg4iIjJ8FU6gJqJgfIDVS4Wa205JPiszUG3Ls1MS3RVq\nwk7+mQrYlvDhkz8BEREZ1gonUONRcNxg7GPZTL+7fL2TkgDL5y6X8cXBjqcDNhWoHbFOlgSX0dp5\n9ORPSEREhpXCCdREFBwftm2we5x1+n6nVpYuX6tnl6+VcdyrqspKV6gHO5KB+upf/sBv3n+VlX9Z\n3a+2t3dEtem+iMgwVziBGo8mZ/j2svLWyrJsJnXclx5DtTP+tHoJ4isvL0tXqEc6jxFLxPg/b6wB\nYHfb/pzbvetAO1/49m+0BEdEZJgrnEBNRHud4QtQ7qskYJdQ7qvs9bPejR2ge3KS1aPrF8BEkzcg\nrxwdxfLH069vO3SAw5FDAGw/vDf9esIxvPGnvcQTDgknwSvb3qAz1pU+/tu3dhCLO7y1LvcQFhGR\n068gdkpyHIdYIoZxKijq5Ywr/CO57oyP9/n5nhVpz8rUW6E6oZH4RnfxfutfMr7jZyvfxSpJLqfZ\n33EAxzjYls2Kt3fyxP+3hr//f67kgpo2nnr7v/lzyxY+6ruGeMLhleBuALbsOUxHV4yykiJERGT4\nKYgKNZq6dVvCf9ymDrnoOcvX18eyGQAnPBKAo06yonQ6KoBkN296FrCJc7gjeaPyt9cn3/fi6u1s\nbEkur3l125scjBwmuOEAR0MRAmccITDhdyx/t7n/jT+FHOPwXtsmjnW1DXVTRESG3Ac6UMPRDl7Y\n8BuORtqTLzjHr0HNxXHrUNN/Zu6UBN2BGkocSz5vHw3AUXYn5zy5c4t2t+0nGkvw3vstABw+1sV7\nu98HwGB45fAf+P07uwA45+o9WLbh9T39m8x0qv2fd1/jpYOv8dz6Xw91U0REhtwHusv3zy1beHbt\nC+llKybR+6SkbI5bNpPu8j1+UpLTUZkMTfelRNto/GfvhIpkGwKxKqKBQ6zfs51jLQHMJW9yrf+v\nWffnCPs793Fm4GwCdgnbOnYQ2/MuZ5xTSUs82e3bmthLS/gwZ5Wf2f+TGGQ79rXx7Nsv4xsFa/dv\nGOrmiIgMuQ90hXpF1Vgsy+LNXX9MvtDLxvi5GOkfzeiiMZxRlAyyvnZKMgkb4kVYTnH6s07oDIyx\n0mtUz/GNBWDj/l38atNKfCMPU3rhTq64wr1JeSLAmOLzk9X0pe/BBWsB8LdVA/DKX97q/wmcAsub\nN2CPTFbX+0IHtXmFDDnHOCx45fv8+J1nh7opUqA+0IE6oriCK6vG0h5NbgXY17KZbIp9pVxZOYFS\nXzngXZeaOSnJxEoACz/F7vt82E4gPfMX4IpRV2AM7G3fz87IJgC2HNvARZclZwR3tIzk0K5K4u/X\ngeMjYh9jhH8UI6OXYRyLFzcP/TjqsVCEVduDWLbB6SoFoPFPv2DF1tdYsfW1IW6dFJKjXW10ubPi\n1x3YxNoDG1ix9XUO6hc8GQIf6ECNxBIc2jGy+4UBVqg99axQU3+aaDJIA3YyQIt9JZSXW5hIMnRs\nfLTb+7GiZbRbBzBFHVjGJpKI8t6xNwE4sucM3t9kET82mrM6rmNU0VlcXHYlZ55RhHP0LELxY+w8\nuof2jigbtrVy6GgHxpzeTR9++9YOzKjk0p+ig1cCsO3wPkLRMC9teYU1+9af1vYUkn3tB+mIdQ51\nM4ZMcO+feP/wdgAOdxxh9q8f4L7ffYdoIsZv30/+smkw+sVOhkTWeHEchwceeIBNmzYRCARYsGAB\nF110Ufp4U1MTy5Ytw+/3M2vWLG644QZaW1u555576OrqYsyYMTz00EOUlpb2+t5TrW3fKBjrPkn4\n8A9glm9P6THU9E5J3goVSn3FhB0otksJVEBXJPV6ObZt4XPKSVjJe6deaK5jp/U2bZEQtvFzRnkZ\nYy41BEpaGTNmNFALQMkoSGw/D9/ogyx+7RkOvjOOjuLdFF28HsKjuKTkQ8yc9FE+VH0BPjvzJDfu\naGXr7mOEOqMc6wxx5QVn89Frz6W4qP8XoysW5edrm/Gde5hLR11EcWQE6yMl7DUHWPH+ag52tPCd\n5h/xLx+dzTUXns/rO/7Ah86+Iudx31AkTGlRyXHnUCgSTqLPX5De3r2GR1f/iNGlZ/CN6+/i0tHV\nx73HMQ6Q/R6/Q6Wtq52AP0CJP/nL58aW9xlRXMF5I84hnojzi02/48zSUUy++KMc6mjlu6//kKqy\nUXz9Y1/kj3v/xGNvLCHgK2L+jffw/J9/TTjWSTjWyU/+2Mg7e9dSPfJ8DnceYeVfVlN/zVT8A+mS\nEhmgrH/bVqxYQTQapbGxkTVr1rBo0SJ+8IMfANDS0sLSpUt5/vnniUQizJw5k+uvv56nnnqKqVOn\nMm3aNH70ox/R2NjIrbfe2ut7A4HAKTu54iIfC7/4Ce556S1MSfuAu3x7sul9cpKJFmNZhtJAMXRB\niV1K2Vlw5EiyQi1zu4zLfOW004KJFVE1YiSHohV0JEJUFo3gmo8kw7m1NfP/VP1FUBofQ/TQueyt\n2o1zyRECpWEwNow4xHZWsvCtlVhv2lxaeQWTLvgY24/u5vW/rKGrMzm2a49oxS4N89s/jsT8+nIu\nvqiIo+wlEa7AtFVRekYIp/wQY8rOpvbca+n0H2Lt/g2Yjkqs0BgCo1vZHllH9LzkLwPV9ofZV7QX\n6+iZJIr3cLCjBaezHErD/PvLP6Ci3EcHRwjYASaf/QlKA0Xs69rJBSPP4doxV3Koo5W/HN1OVemZ\nnFd5Lq9sf5139r/LiOJK/urc6xhVcgYJJ0FV2WjOqzyHtkiIAx0HGVlcybkV5xCKhjgQPkipv5yq\n4ipCsTCtkVbOKKlgRNFo9h1tZfex/UQSEbAcjh4+yqbEUc4oHsnoklFEnC6ORo9QESjjrPIz6Up0\ncjB8iIBdwqjAaNqjYVo6DlEZKOOCkWeTsKPsDx2k2C7hzJIqIk4XrV2HKQuUcmZxFcciIQ51HGJE\nSTnnjzibY10h9rTtp8RXyvmV59BlOtnXvp+AXUJVyVl0xMPs79hPRaCMcmsUb+z5A2sOv0MZ5fxd\nUQeVJeXsbNtJRaCCYquEZRub8OHjcMdR/nXFw1x1xrWMKq2ketS5XFB5Pu/sXcNru1dR7C9m3Jhr\nOKvsTOKJBCOKR3Jh5QV0JTrY2b6LEl8J1ZXVHO1qY0fbDiqLy7j8zLG0dhxj06EtlAfKueasGtpj\n7fzp4EaK7RKuOvNKQrE21h/eQFlRKR+q+hAt4cOs2rmKlR3v8r/Ov44DoRZW736b8qIyJl74MVo6\nW3hlxyrKi8r4+AWTOdCxn+Zdr1NaVMrNl/wtfzm6nT8eWINt2dx86U1sOryFvxzdDsCbO9ey7eh2\nWruOsOPobv71t4+wP3yAgB0glojx7ZWP0Rnv5OIRF3EscoyX/7IKgL8+72PsbdtP857XWbH5bT56\nQS2dkTh7W6OU72ilrNhPeWkRjgMdkRidkTh+n01xkY8iv43jmPQ2n8VFPoqKbBIJQ1c0TizuUOS3\nKQn48dkWXdEEXdE4lmVREvBRHPARizt0RuLEYg7FAR+lxX4sCzq64nRG4ti2RVmJn9KAn65ognBn\njFjcoawk2S5jYP+RKOu2HiJQ5KOitIiSYj/hzhihjhhxx6GyLEBFaRHxhEN7R5RQR4zSEj8jygIU\nB3y0d8RoC0cwDoyoCDCiPEA05tAWjhDuilNe4mdEeTGBIptQZ4xwZwzbsigvLaKitAjLsjDG4BiD\n45D+Bc/ns7Ati+372lj/l8Os2XCIdfv/zDWXnsnVl4zWGnlyCNRgMMikSZMAGD9+POvWdd/bc+3a\ntUyYMIFAIEAgEKC6upqNGzcSDAa58847AZg8eTKLFy/mwgsv7PW948aNO0WnlnTh2ZXccPl1vLxr\n5aB1+fa1DtWKF1NamuzqhWSFes65YI8u5f0w6THYkaUVtCfADp1Dydk2VZzLzs4tVPhH9vLTuo0e\nbbFz24coCcSwRxwiYBVz1Yg6wGLX0X0cae8gEWhnq7WBrRvdmbdl4C9LPrSMzQjrbI5VHIDL3mFX\n6ovPSP431ZF4NLKDzdvfzvzhZUAXmLifeMvF3HrF33BB+dnsP7yXqpIzOcweTLSE86MfJVG2k4Pl\n79MBxA+fgxl5iBX7fpn+qncOwAubf9XrOTodFRyLd7Fi+8oTXouBenfb29nfNIRMtJg2fzvPbvzZ\n8ccci+jmOrAdApeuZd2RNXAE2Ov9fICI1cWq3W+eVDt+ufXFjOcrdryc8fyVHa+nH+/au4e39waT\nP99YWJZhY+vm5POEj6N2Gz/b2AiAEykhFu/kuU0vJJ+HRuAURXlx6++A5N8Xu7iD4P53AYjtvhyr\ntJ1d7MYYiG6pxS4NYS7cjHEsNr5+IVbJmRTX/BGT8PH00qNYAYuScfDjV1/iyS0t3Y1+6eBJXZPT\nKz/aumnPFp57eQuVZQGenveJAfV6fZBkjZdQKERFRUX6uc/nIx6P4/f7CYVCVFZ2b9dXXl5OKBTK\neL28vJz29vY+35tNMBjs1wn15iPFY/nIZWPhspP+KteFwEd7PP8rz/dfCPyvHsc9z0f3drzHTk2j\nLzzup076a+CvAY7v6mPMVTm1fFBcmXpwmE9fNt59/FeeN1wK/E3y4aBdcwGgJvWgdihbcer0/PvS\n8/nlqQeTk3/0vB5XpJ67Dz4yeE2TE1u3ds2gfE9dXd2gfM9QyBqoFRUVhMPh9HPHcfC7ZV7PY+Fw\nmMrKyvTrJSUlhMNhRowY0ed7TySfL6yIiBSWrDMXamtraW5Ozp5bs2YNNTXpXwkZN24cwWCQSCRC\ne3s7W7dupaamhtraWl599VUAmpubqaur6/O9IiIiHwSWybLmIjXLd/PmzRhjWLhwIc3NzVRXV3PT\nTTfR1NREY2MjxhjuvPNObr75Zg4dOsTcuXMJh8OMGjWKRx99lLKysl7fKyIi8kGQNVBFREQku+G5\nWE1ERCTPKFBFREQGgQJVRERkEBTEvlzZtk8cKp/5zGfSS4cuuOACpk+fzr//+7/j8/mYOHEiX/3q\nV4e07e+99x6PPPIIS5cuZceOHdx7771YlsXll1/Ot771LWzb5oknnuCVV17B7/fzzW9+k3HjxvX5\n3tPV1vXr13PXXXdx8cUXA/C5z32OW265ZUjbGovF+OY3v8mePXuIRqPMmjWLyy67bFhe097aes45\n5wy7a5pIJPi3f/s3tm3bhs/n46GHHsIYMyyvaW9tbW9vH3bXNOXw4cNMmzaNn/zkJ/j9/mF5TYcl\nUwB+85vfmLlz5xpjjHn33XfNXXfdNcQtMqarq8t8+tOfznjtU5/6lNmxY4dxHMd86UtfMuvWrRuy\ntv/oRz8yU6dONZ/97GeNMcbceeed5s033zTGGHP//feb3/72t2bdunWmoaHBOI5j9uzZY6ZNm9bn\ne09nW5uamsySJUsy3jPUbX3uuefMggULjDHGtLa2mo9//OPD9pr21tbheE1/97vfmXvvvdcYY8yb\nb75p7rrrrmF7TXtr63C8psYYE41GzVe+8hXziU98wrz//vvD9poORwXxq8OJtk8cKhs3bqSzs5M7\n7riD22+/nT/84Q9Eo1Gqq6uxLIuJEyfyxhtvDFnbq6urefzxx9PP169fz1/9VXI3pMmTJ7N69WqC\nwSATJ07EsizOO+88EokEra2tvb73dLZ13bp1vPLKK3z+85/nm9/8JqFQaMjb+slPfpKvf/3r6ec+\nn2/YXtPe2jocr+nf/u3f8uCDDwKwd+9eqqqqhu017a2tw/GaAnznO99hxowZjBkzBhje/+4PNwUR\nqH1tnziUSkpK+OIXv8iSJUv49re/zX333UdpaWn6uHfLxqFo+80335zeEQuSG2Sn7vvaV9tSr/f2\n3tPZ1nHjxvEv//Iv/M///A8XXnghTz755JC3tby8nIqKCkKhEF/72te4++67h+017a2tw/GaAvj9\nfubOncuDDz7IzTffPGyvaW9tHY7XdPny5YwePTr9SzwM73/3h5uCCNQTbZ84VC655BI+9alPYVkW\nl1xyCZWVlRw9ejR9vACi+78AAAdZSURBVK8tG4eq7d5xkGzbSfb23tNpypQpXHvttenHf/7zn4dF\nW/ft28ftt9/Opz/9af7u7/5uWF/Tnm0drtcUkhXVb37zG+6//34ikchxP3u4tLNnWydOnDjsrunz\nzz/P6tWraWhoYMOGDcydO5fW1tbjfvZQt3O4KohAPdH2iUPlueeeY9GiRQAcOHCAzs5OysrK2Llz\nJ8YYXn/9da677rph0/arr76at956C0huJ5lq2+uvv47jOOzduxfHcRg9enSv7z2dvvjFL7J27VoA\n3njjDa655pohb+uhQ4e44447+MY3vsH//t//Gxi+17S3tg7Ha/rCCy/wH//xHwCUlpZiWRbXXnvt\nsLymvbX1q1/96rC7pv/zP//DT3/6U5YuXcpVV13Fd77zHSZPnjwsr+lwVBA7JfW2feLYsWOzf/AU\nikaj3HfffezduxfLsrjnnnuwbZuFCxeSSCSYOHEis2fPHtK27969m3/+53+mqamJbdu2cf/99xOL\nxbj00ktZsGABPp+Pxx9/nObmZhzH4b777uO6667r872nq63r16/nwQcfpKioiKqqKh588EEqKiqG\ntK0LFizgxRdf5NJLL02/9q//+q8sWLBg2F3T3tp699138/DDDw+ra9rR0cF9993HoUOHiMfj/MM/\n/ANjx44dln9Pe2vrueeeO+z+nno1NDTwwAMPYNv2sLymw1FBBKqIiMipVhBdviIiIqeaAlVERGQQ\nKFBFREQGgQJVRERkEChQRUREBoECVUREZBAoUEWGmRtvvJHdu3eflp/V3t7OP/7jP57wPRs2bODT\nn/50+r+TJk1i6tSpp6V9IvmkIG7fJiK9O3bsGBs2bDjhe6666ip+/vOfA9DZ2clnP/tZHnjggdPQ\nOpH8okCVgvHWW2/xwx/+kKKiInbv3s2NN95IWVkZK1asAOBHP/oRL730Ej//+c/p7OykqKiIRx99\nlNLSUqZNm8ZPf/pTLrzwQm677TbmzJnD3/zN3/T5cx5++GEcx+Hyyy9n3rx5zJ8/ny1btpBIJPiH\nf/gHpk6dSiQS4dvf/jbBYJCioiK+8pWvcMsttwDw5JNPsmHDBjo7O/nud7/Lhz/84T7Pa/PmzTz4\n4IN0dHTQ2trKl7/8ZT73uc/x+OOPs3fvXrZv305rayuzZs3ijTfe4L333uPKK6/ke9/7HgsWLODg\nwYP84z/+I08++WTWa/gf//EffOQjHym4LeVEcnK67xcnMlTefPNNM2HCBLN3717T0dFhxo8fb372\ns58ZY4y59957zTPPPGO+8IUvmM7OTmOMMY899piZP3++MSZ5j9C///u/N4sXLzb3339/1p9TV1dn\n2trajDHGPPzww+a//uu/jDHGtLe3m1tvvdXs3LnT/PjHPzZf//rXTSKRMAcPHjS33HKLiUQi5oYb\nbjD/+Z//aYwxZunSpeaf/umfTvjzFixYYFavXm2MMWbnzp1m/Pjxxhhjvv/975tp06aZWCxm3nrr\nLXPllVeaLVu2mFgsZqZMmWI2bNhgdu3aZW644Yacrl9bW5v52Mc+Zg4dOpTT+0UKjSpUKSg1NTWc\ne+65AIwaNYqPfexjAJx33nm0tbXx6KOP8qtf/Yrt27fz2muvcdVVVwFw22238eKLL/J//+//5Ze/\n/GXWn5O6gxDA6tWr6erq4vnnnwf+//bu3qV1KIzj+BdJUZwKpRQqKNKOOnQQCr4sZpAihS4WimL/\nAFuyCHZwEgdxcdXBVXCroFDaTXRRQRCXdhKEFkFBBKlCG4frDYpv7TXT7e8zJeck55xMDzk5Oc+f\nfV0rlQonJyfMzMzQ1dWF3+9nf3/fud80TQDC4TCFQuHbvpaWljg8PGRzc5Nyuczj46NTNzo6imEY\nBINB/H4/4XAYgEAgwP39/bsUXD/Z29vDNE18Pl/L94h0Ei1Kko7i8Xjenb/duLtarZJMJnl4eGBi\nYoJEIoH9utX109MTtVqNRqNBrVb7sZ+enh7nuNlssr6+Tj6fJ5/Ps7u7y/j4OIZhOLkjAa6urnh+\nfn43rrf1X7Esi2KxSCgUwrKsL5/3t2n/SqWSMyUtIh8poIq8uri4YGBggHQ6zfDwMKVSiUajAcDG\nxgbRaJRcLkcul3PKWxGNRtnZ2QHg5uaGeDxOtVplZGSEg4MDbNvm9vaW2dlZJ6C24+joiGw2i2ma\nTqq/VsdnGEZLCett2+by8pJIJNL2+EQ6hQKqyKuxsTGazSaxWIxEIsHg4CDX19ecn59TKBSwLIup\nqSm8Xi/b29stt7uwsEC9Xmd6epr5+XkWFxfp7+8nlUrR29tLPB4nnU6zvLzc1hTsX5lMhlQqRSwW\n4+zsjL6+vpZ/u/H5fASDQebm5r697u7uDo/HQ3d3d9vjE+kUSt8mIiLiAi1KEvkHp6enrKysfFq3\ntbVFIBBwtb+1tTWOj48/lA8NDbG6uvrr9uv1Oslk8tO6bDbL5OTkr/sQ+d/pDVVERMQF+oYqIiLi\nAgVUERERFyigioiIuEABVURExAUv/UjTDb8Nyo0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 473.625x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.FacetGrid(telecom,hue='churn',size=6).map(sns.distplot,\"max_rech_amt_7\").add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean:\n",
      "166.4161959268619\n",
      "111.0983709273183\n",
      "median:\n",
      "144.0\n",
      "56.0\n",
      "Quantiles:\n",
      "[  0.  70. 144. 198.]\n",
      "[  0.  10.  56. 130.]\n"
     ]
    }
   ],
   "source": [
    "print(\"Mean:\")\n",
    "print(np.mean(df_not_churn[\"max_rech_amt_8\"]))\n",
    "print(np.mean(df_churn[\"max_rech_amt_8\"]))\n",
    "print(\"median:\")\n",
    "print(np.median(df_not_churn[\"max_rech_amt_8\"]))\n",
    "print(np.median(df_churn[\"max_rech_amt_8\"]))\n",
    "\n",
    "print(\"Quantiles:\")\n",
    "print(np.percentile(df_not_churn[\"max_rech_amt_8\"],np.arange(0,100,25)))\n",
    "print(np.percentile(df_churn[\"max_rech_amt_8\"],np.arange(0,100,25)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bkumar5\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1633: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n",
      "C:\\Users\\bkumar5\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1f8b73365c0>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SOO9UlhTsbg6jQz9qPdepmUG6/eAR8wFDtlq9yWPcOIZKRET2HFSh2m89KMfb\nwBE9DADxdajmc7nGUBf/9j38YuWHPXTFRETUmzimQrXGULO0fBPjqhEjZD6Q1vK138t3/bZmlHvd\nPXPBRETUqzimQs21l2+iQg0bneYDqctmbCpUIQRCUR0xjbs9EBGRgwI12fLNEqgwx0wjhtnyhZCz\n7OWbXqHGNHPWbzTGQCUiIgcFqnUeapYxVEmSoEou6yxUkbpsxmr5CoiU7AxFzNfGNL0Hr5qIiHoL\n5wRqjmUzAKDKKWOhKZOS5JQDxvWUCjUcNX8fW75ERAQ4KFBztXyjEUCPSskH0nZKyn7aTDheoeqG\ngK4zVImInM4xgapb61C7fuSDB4BwOPm9sNnLN/U81FBUs75mlUpERM4JVGG/bEaLmeOmliynzWRu\nPZioUAEgykAlInI8xwRqrpavpgFIDVSRcsC4zTrUUCQ5GYkTk4iIyDGBmqvlq2kA9JQ9Lgyly05J\nUsYYaoQtXyIiSuG4QM22sYOupbd8RVqFGn9cNpC682AomqxKozFWqERETuecQM2xl69ZoWYfQ5W7\nMYbKCpWIiBwXqHK2SUlalklJqbN8hQRIBsIpbV4GKhERpXJMoBrxlq+aZVKSnnVSUvJbSZIhyQY6\nw8kQTWv5clISEZHjOSZQkxVqllm+OtImJaUe3wbE276SgWA4Zj2WtmyG+/kSETme4wLVbgw1veWb\n3MsXiAeqbKAzlFqhsuVLRERJjgnURMs3M1ANAzB0KX1SUsppM0B8qU2OCpXrUImIyDGBatfy1eNZ\nmLZsJmVSEmAGqjmGmhqoqctmWKESETmd4wI1s0LVE4VmjklJsiQDsp4xKYkVKhERJTkmUO1avloi\nF9N2SpKTB4sjvp9vzpYvK1QiIqdzTKDatXwTgZq5DhUZFaokCwRDUeux9GUzDFQiIqdzTKAaInuF\nmmj5SolAFRIAGXLmshkAoWjU2i0pbS9fbj1IROR4jglUzYhXqDYt31J3ifmFMJ9PnZQkxX9GpOyW\nlHbaDA8YJyJyPMcEqmHoZus2dbYRkoFanghUo+stST3CLRjSoBsC0ZiOshJz3JWzfImIyDGBqgsj\n66YOiZavr7TU/CJeocqZGzsA1tKZRLu3wusGwK0HiYjIUYGqZz26LVGhVpR5IAwZhp5o+SZfI6VW\nqOEYQpH0QI2xQiUicjzHBKphZK9QNatCdUPE3IDmMh/IMikJ8Q3yw/EZvuVl8UDlLF8iIsdT87+k\nb9CFkXVj/MROSb4yF6IfNmRt+VoVqmwgGEpWqOVs+RIRUZyDAlXPXaGWuSFC5Vl/VoYZxJJkjqGG\nM1u+rFCJiBzPMYFqtnztx1CjmjPJAAAgAElEQVTLy1xpj6dNSkp8LRv4cGszdh/qAAAcPtoJADjY\nEuyBKyYiot7EMWOoZss3+yxfSRLwuJSMNm/yNYkKFZKBSEy3KlKPS4EkmWFNRETO5qBAtW/5Kqq5\nkYPHnVLBZh1D1RHVdGszfJcqQ5FlaPHdk4iIyLkcE6i5Wr5qvPHtdiVvR7atByXJQDSlQnWpMhRF\ngq4zUImInC7vGKphGJg/fz62bt0Kt9uNhQsXYuTIkdbzjY2NWLFiBVRVxZw5czBp0iS0trbi9ttv\nRzgcxqBBg7B48WKUlpbiqaeewosvvggA+PznP4/vfe97PffJMuRq+ZbE93TwuFIDN/08VPMLA9GY\nAU1PBqoqS9DZ8iUicry8FeqqVasQjUaxcuVK3Hbbbbjvvvus55qbm7F8+XKsWLECy5Ytw9KlSxGN\nRvHoo49i2rRpePrppzF27FisXLkSe/fuxQsvvIAVK1Zg5cqVeOutt7Bly5Ye/XCpdKNry1eIZMsX\nANwpgZq2sQOSGztE0ipUBYois0IlIqL8Farf78eECRMAAOPGjcPGjRut5zZs2IDx48fD7XbD7Xaj\npqYGW7Zsgd/vx+zZswEAEydOxNKlS/G1r30Nv/nNb6AoZmhpmgaPx5P3Av1+/wl9sEwxPYZwKGz9\nvtbWo/E1qNUQRhS79+yGFotYrz948ACOlx4FAHTCnMWrqAYCx0I4qpjnora0HIaha4hqomjXeabq\n65+v2Hi/CsP7VZi+er8aGhpO9yWclLyBGggE4PP5rO8VRYGmaVBVFYFAAOXlybWbXq8XgUAg7XGv\n14uOjg64XC5UVVVBCIEHHngAY8eOxahRo/JeYLFusPj0SZT7ytHQ0IDnP1mHqqoBiITN50pLXRhZ\nMxI7juzFobZWAMCwYcOgoB0AIEUN7A8AqioBkoLSUh+AVowYPgwf79uNSCDS6/8i5OL3+/v05ys2\n3q/C8H4VhvfrzJW35evz+RAMJtdZGoYBNT6LJ/O5YDCI8vLytMeDwSAqKioAAJFIBLfffjuCwSDu\nueeeon6QfAzDgGpzuHjWlm/K6xKzfBVVIBoz0mb5qjInJRERUTcCtb6+Hk1NTQCAdevWoba21nqu\nrq4Ofr8fkUgEHR0d2LFjB2pra1FfX4/Vq1cDAJqamtDQ0AAhBL7zne/gM5/5DBYsWGC1fk8FQxgQ\nELZnoSZm+XrSxlC7njZjBqqOaMYsX0MI6Fw6Q0TkaHlbvlOmTMGaNWtwww03QAiBRYsW4cknn0RN\nTQ0mT56MWbNmYebMmRBC4NZbb4XH48GcOXMwb948NDY2orKyEkuWLMGqVavw3nvvIRqN4s033wQA\n/OAHP8D48eN7/EMmNl7IXDajZwRq6rIZSQIQz8hEEMuKgADQGTZ/0KWY61ABIKbpUNyO2XiKiIgy\n5E0AWZaxYMGCtMdGjx5tfT1jxgzMmDEj7fnq6mosW7Ys7bEpU6bgo48+OplrPWG6iAeqnL1CTbR8\nUzd2SK1QE7N8ZcVM2GAoBlUxDytXFCn+uwzA3SOXT0REvYAjNnbQhTnmma/la7dsJlmhmsEcimhw\nqeZjqmL+GeUG+UREjuaIQO1uy9djMynJ2ilJToZmIlCV+JZK0RiPcCMicjJHBKpVoWa2fOMZmH1j\nh657+UpycuKRFahKYgyVFSoRkZM5JFATFWr6x81ZoWbbyzelQnVnVKgMVCIiZ3NEoNq1fLuOoabO\n8k3dyzf+c2ktX/MxNT4pKaqx5UtE5GSOCFTNruXb3Y0drO+SoanGK9TE74zFWKESETmZIwLVMMwg\nzDcpSZYka2w0fQxVggQJQuo6KYkVKhERAQ4JVLsxVLNCFUjdtCkxjpo6hgqYbV8hJUMzOcuXk5KI\niMgpgRofQ83W8lXU9PB02wSqBDm9QlUSs3zjk5LY8iUicjRHBKoRH0NVs7R81Yy9ohK7JUkZiSpL\nMgRSK1TzddY6VLZ8iYgczRGbzyZavnKW02bcGUeyNpw3CK3Hw5AzAxUydKFZ32fulMSWLxGRszkj\nUA37MdTSsvTXjjyrAiPPqujyOyRJhiHMLQdjmpGysQMrVCIicljLN3VzfMMAhJC6tHztmBWqbk1a\n6jIpiWOoRESO5ohAtVq+KWOomZs65CNLZsvX3SVQExUqA5WIyMmcEahZWr6ZmzrkI0OGgIDLZQZo\n1zFUtnyJiJzMEYGareWbualDPokN8t3xM0+tWb4K9/IlIiKHBGq2lq+eOGlGyfYTXSU2yHe7zO95\nfBsREaVyyCzfxNaDXSvUbrd84z87dFAZ2juACq9ZqvL4NiIiApwSqFlavlqBFaoUr1BHDfeh7uzh\n1uMqj28jIiI4peVrZGn5JirU7rZ84xVqIpwTEhUq16ESETmbIwLVyLI5vjWGWsAsXyBLoFpjqKxQ\niYiczBGBao2hyl0nJandbflaFaqW9niiQtXY8iUicjRnBGqiQs2ybKbQCtXIqFBlyTyMnC1fIiJn\nc0SgJlu+J7FsxqZClSQJiiJxpyQiIodzRKAmWr5ytjHUbs/yNV+YOYYKmJVvjOtQiYgczRmBarV8\nu+7l2/11qObkIx1al+cUReKyGSIih3NEoOac5VvgTknZKlRVkdnyJSJyOEcEataWb4HrUKX4+Ksh\nslSossTN8YmIHM4hOyV1bfnqOqAoAvFOLrYE1uX8HYkKVcs2hqpIiIQZqERETuaoCjWz5dvd6hRI\nVrfZK1SZGzsQETmcMwLVOm0mveXb3QlJQHIv3+xjqBI03YBhiJO7UCIi6rUcEaiGzU5JJ1KhZq5D\nNX9v/MQZnVUqEZFTOSJQ9YxZvkKcRKAi+xgqwBNniIiczBGBGjPMqlKVzR6vEIAQUkEtXzm+sUPM\niHZ5zqpQubkDEZFjOSJQw1oEAFDi8gAADN2sKAupUEuUUgDA8djRLs+p8QqVa1GJiJzLGYEaCwMA\nStUSAIBumAHY3ZNmAECRVPiUfjiWJVBl6wg3VqhERE7ljEBNVKhqRoVa4CrcSlc1QkYQYT2U9ria\nOMKNk5KIiBzLEYEa0sJwKS5rlq9hFN7yBYD+rmoAwLFYS9rjCitUIiLHc0SghrWIVZ0CgH6CFWoi\nUNtiLYgYYbzTugqdWod1yDjHUImInMsRWw+GYxGUpgTqiVaole5khbqlYx0+Dvjhkt1Q5M8A4LIZ\nIiInc0iFGkZJfEIScGKzfAGgn1oFwAzUXaGtAIDm6EFrDJXLZoiInKvPV6hCCIQ0mwq1wE/vkt3w\nKf3QHD0ITcQAAC2RgxgaD2a2fImInKvPV6gxQ4MhDGsNKpAcQy1k2UxCpavaClOX5EZURBCT2833\n4hFuRESO1ecDNbEGtRgtXyA5MUmChAsqLgEAdCrmrF+OoRIROVffD9SMNagAYMRzr9CWL5AM1CEl\nNRheMhoAEMQRAOARbkREDtbnAzWkpe+SBKQsmzmBCnVYyUj0U6vwT+WXoMo9CBJkHBV7AQAfH/4E\nq3a8efIXTUREvU6fn5SUuY8vcOKTkgCgTC3HdUNvsr73KuUIaO2ArEPnECoRkWP1/Qo1lqXlexIV\naqZytR8gCchl7dB0HjBORORUfT5Qw1piUlLKLN8T3NghG69aAQCQSjsQizFQiYicygGBalaopRmz\nfGVZQC7Cp/fI5rFukieM4+2clERE5FSOCdTMMdRiVKdAMlBdpWG0tnIQlYjIqfp8oIZi2Wf5FitQ\n3bIZ1GppGO0dBjSNbV8iIidyzizfjElJbnfhv2tLYF2Xx2RJhlvywHCHIATQdoxVKhGRE/X9ClXL\nslNSEVu+AOBRSqHJYQACrW0MVCIiJ+rzgWpNSoqPocY0A0JIJ7QG1Y5HLgEgAFeEgUpE5FB9P1Az\n1qFGohqA4iyZSXDLZvUre0IMVCIih+r7gZqx9WAoYgbeiZw0Y8ea6VsWQlsbl84QETlRnw/UkBaB\nJElwKS4AQDhRoRa95QuUVkTRdkyHrpuhGopoaA9Gi/dGRER0xurzgRrWIihVSyBJ5u5IoUjxW76J\nCtXtC8MwgINHgwCA//nNO7j5Z69D01m1EhH1dX1/2UwsnLZkJtwDY6iJClVym+3lvYcDiER1bPr0\nKABg885WXDimunhvSEREZ5y+H6haBD63N/l9fAy1mC1fRVKhQEFYBAAAr76/B4FQzHr+g82HGahE\nRH1cn2/5hrT0CrUnWr6SJJlrUSWzQj3S1olte9rgLVHhdil4f/Ph4r0ZERGdkfp0oBqGgageS9vH\ntycmJQHm0hkDGmRXFDsPtCOmGRg7agAuOrcaew934HBrZ3HfkIiIzih9OlCzbTvYE8tmgOTEpNJ+\nZpUqARg7qgoXnz8YgNn2JSKivssRgZq6MX5PTEoCgJJ4oHoqzHHUkUMq4Ctz4+LzGKhERE7QpwM1\nlOVw8XCkZ1q+/VwD4l80AwAuHG1+P6iqDDVnlWPDJy2IxLLvohSKaHj7owPYeeB42uNv/GMf5i55\nHW0d4eJeLBERFV2fnuWbPAs1WaH2xKQkAPAq5fDIpYi6mvGVq87BoP7l1nMXnzcYz77xCT76pMVq\nAQPAtj1taFy1DR9uPYKoZsBb6sL/+4MvYHBVGY60deLRP65DKKLjnY8O4l8/N6q4F0xEREXVtyvU\nWJYKNRpfNlPkQJUkCVWuQdChIeI5kvZcIkT/tnYXYpq5ycOW3a348a/W4N1Nh3BWtRdfqB+OYCiG\nH//mNfx1y+u484/LEFbaAAD/2Jr++4iI6MzTpwM12xhqqIdavgAwwG0G5+7ObWmPnz+qCueO6I/3\nPj6EH/3vGvi3HMb8x99GOKrj6s+OxBevOAdjR1XhrKEChw5r+O2bTQhUfIzSse9h4JAY1m9vOW27\nLcU0HYbBQ9OJiPLp4y3fHGOoRa5QAaBc7Q+X5Mae0HZs6hgIVXLhKmMEVEXB/NmX4pd//AfeXteC\n+b9+B5IETL6kBmOG94chdKxpfRk4+yBK+wsI31GIkBcoDSI07C2E2y7F//fixxg60AcAuOays4t/\n8Vl0hmOYc/9rOKtKwyWXnJK3JCLqtfp4oKafhQokWr4Ccg/U5pIkoco9GIcje/Fu26sAgO3Pf4Ra\n34X4x/G3EPVEMPaiidjxcQk+VzcEn6mpBAC8f+wNbA9+ZP4SH4BQOUZJl0AuO4RPOz+Ga9RH2H34\nbAwd6ENU0/HTJ9/F5+qGYlLDiOJ/iBR/f3c32ss2o3PgVvz+fRVfu+TqHn0/IqLerE8HaiiWbR2q\nBkUR1mb5xVZTOgY+pRyK7MLR6GEcie7Hkdb9kKFAkiTs9LyKS74wEedVnA8A+DS4GZs6PkA/tQpj\nfBdCM2IorfRClmQAIxAzYtiL7dh5aDMuw1D8Y8sR+LccwfrtzagbU40B/Up75HPouoFn/7EG6oit\nkCTghU/+DK9XwOfx4qrRE3rkPYmIerM+PYYaM8z9dMtcZujsPdyBg0eDUNSeGxN0yW4MLhmBavdZ\nqPXW4ZyysRjoHoqL+l2Gfyq/GC7JA//xJjx78An87fAf8MbRF6BKLlw58MvwyCXwquXxMDV9tupK\nwJARqvoIu5oP48NtzXCpMkIRHU+8sOmErvHNdfvxxj/25XzNs+99gNDg96FIKlzHaiBkHX/f9g6E\n4HgqkVPtPdyBjs7kkZRCCDzx50349n2rbJcFOkneCtUwDMyfPx9bt26F2+3GwoULMXLkSOv5xsZG\nrFixAqqqYs6cOZg0aRJaW1tx++23IxwOY9CgQVi8eDFKS0uzvrYnXVFzCTRDR231aHR0RnHvE+8i\nEtUx8pxOmL3VniVJEs4qGYGzkGzNjuv3OewJfYIjkX1o11oxxDMS9f2vQKWrGocjXUNuf3gXfNGz\nESj5FK+FnoK7zoMLqv8JzTur0LRuH66+bCTqxgy0Xh+Oath7uAMbdxzFuu3N2HWgHZMahuNr15wH\nVZHx9MtbseKVrQCAPYfaMetfz4ckSYhpOg40BzF8cDk2N2/DH3ctB2Qdl/imYn+gBbuPdaKl/2E0\n+t/Am29I6Fdqrq8dPbw/amsq4St19fj97A2EECf8Hx26oaMzFkK5p+f/bpIzaboBVUmvo3YfbAdg\nbkYDmH+H3/7oIN5ctx9fnHAOxo4aACEE/vTGDjz14ib093nww/9zKc4bWYXfv7QFf3rjE5wztF+X\n3+tEksjzb//f//53vPbaa7jvvvuwbt06PPbYY/jf//1fAEBzczNuvPFGPPPMM4hEIpg5cyaeeeYZ\nPPDAAxg7diymT5+Oxx9/HG63G1OnTs36Wrfbbfvefr8fDQ0NJ/0hNd3APY+/jQ2ftOArk8/FJx1v\no6pqwEn/3pMR0UMQEChRyvK+9vhxA+t27Ybsa4Na3g6hmq1sI1yGsvAI1Azqh2PBMNrbDXS0CxjR\nEohoCaCrKFHdCMU01JxVhiFnufDBjk9RPiACCRLaj8sYP2oEBnorseajfQga7Sgb3ALddwiGITAq\n9gVcee6lWLvnVezZ60Og8kPIvuMwIiUwAv2BmBuABBgqhpYPxoXDR+LswdU4q7Ic3hIXjnVE0Noe\nga4bqOpXigH9ShCNGWhrDyHQGYOvzI2qilKUuBUcC0TQ1hGGLEvo53OjwutGZziGY4EIQhENvjIV\n/X1uRDQd+48EcPBoEGUlKoYN9GJAv1IcPNaGXS2H0B7tgNtjoMJbgjK1DO3BCFo7O1DqVtHfW4bB\n5QPgQTnCIUCWFAwoL0NleSl0XSAYiiEc1VDiUeAtdUGRJXSGNXOYQAZKPSo8HhXRqI5gJAZdM+Dx\nqIihE/s7DmD9oc3Y2rYVkpBw7oBzMKZqFIb7RsCNUiiSCm+JG15PCXRDIBSJIhLTIaQYmiOHsa31\nE/zj0AZ0akEM9JyFC6rH4p/OOhcDSwciEtWh6QIlLgWlJS7IkoRQREc0pkNVZHjcCjwuBZGYhnBE\nh6YbKC1R4XErMAyBcERDOKbBpSgo8ShQFQnhqG5N0PN4FJS4FMR0A+GohmhMh8etosRtDlOEoxrC\nEQ2SDJS4VXhUGRFNRziqQdcF3C4ZpR4VhhCIRHWEohpc8etyqzJCUfO6DGGgxKNa13osEEEwpKH5\n8D7Uja1FiUdFNKYhHNXREe3A0chRaEYMsiSjRPai0lMJX0kJStwqhpUPQSisozOsQVUleEtd8LgU\nBEIxtAeiiGkGKnxu9PN6IIRAR2cUnWENHreC8jI33C4Z7cEojnWY/y71L/egwutGOKrjWEcEwXAM\nFWVu9PN5oCgS2trDaOuIwKXKqKooQYXXjfZgFEePhxEKa6is8GBAv1IYQqC5rRNHj4fhLXVhUGUZ\nfKUuNB8L4XBrEDFN4KwBZRhcVYZAKIb9RwJoORbCgP6lGD7Qh9ISFXsOtWP3oQ4osoSzh1Rg2CAf\nDrV0YvveNrQcD0OOHcPky+sghMDGHUexdU8b+vs8uGD0AIwYXI6NO47i/Y8Pob0zivG1g3Dx+YNx\nuLUTr32wBxs+acE5w/rhyoYRGFLtxfNNO7B+ewsAoG5MNf71c2fj1ff3pu3sNuXSGsR0A2/496HC\n60agMwpZlnF53VCs/nAfhgzw4v7vXYHKihI4Xd4K1e/3Y8IEc8xs3Lhx2Lhxo/Xchg0bMH78eLjd\nbrjdbtTU1GDLli3w+/2YPXs2AGDixIlYunQpRowYkfW1dXV1PfTRkv66dic2fNKCz15wFr5+zfmY\n//+/3ePvmY9H6f7YZ0WFDPnoKEQPjsJFlxnQS4+iOXoALeIIwiVbsS0KwAVgAODK8t8JpQCa4/9z\njwEi8cfdlcAm8TEQADAKcAPQABghL2K7z0fZ+W5sCawDANSO8WDTR5dCHrodEe9uyAMOpb3HEezA\nq8fXAumbPfWsdgBn2BJdI2L+n8rGlo+xseXjgn5WxNwwQlU44juM1/cfwuv7X+uJSzwj/f2DFwt6\nfWxvLbSD5/TQ1Zz5/vpBU5fH/vb2rrTvVUXCjn3H8cfXtluP1ZxVjp0H2vHrfcn/H7/o3GpIkLBu\nezM2fNJiPfavnxuFla9sxSvv7QEAfKamEj/8P5di18F2PLD8A6z+cB+qKkqwYPZlDNO4vIEaCATg\n8yVbUIqiQNM0qKqKQCCA8vLkjkBerxeBQCDtca/Xi46ODtvX5uP3+wv6QNkMKwPmzxwOAPjww3/g\n38aMO+nfeapN/PfU70YCqO/ZN7ww5esqs2V99YjEexOdZmNO9wX0Zv2yPvr/XDI8/YHYQXzzCxUA\nKqyHdm43523c8eXkjm/7d23B/l3Fu7pidCVPl7yB6vP5EAwGre8Nw4CqqlmfCwaDKC8vtx4vKSlB\nMBhERUWF7Wtz6c03loiInCXvKHJ9fT2amsz2wrp161BbW2s9V1dXB7/fj0gkgo6ODuzYsQO1tbWo\nr6/H6tWrAQBNTU1oaGiwfS0REVFfkHdSUmKW77Zt2yCEwKJFi9DU1ISamhpMnjwZjY2NWLlyJYQQ\nmD17Nq6++mq0tLRg3rx5CAaDqKysxJIlS1BWVpb1tURERH1B3kAlIiKi/LhwiIiIqAgYqEREREXA\nQCUiIiqCPr05fqZ82yg60fr16/Gzn/0My5cvx+7du3HnnXdCkiSce+65uOeeeyDLMn75y1/ijTfe\ngKqq+OEPf4i6ujrb1/ZVsVgMP/zhD7F//35Eo1HMmTMHY8aM4f3KQdd1/PjHP8bOnTuhKAoWL14M\nIQTvWQ5Hjx7F9OnT8cQTT0BVVd6r3kY4yMsvvyzmzZsnhBDiww8/FN/+9rdP8xWdXo8//riYNm2a\n+MpXviKEEGL27NninXfeEUIIcffdd4u///3vYuPGjWLWrFnCMAyxf/9+MX36dNvX9mV//OMfxcKF\nC4UQQrS2torPf/7zvF95vPLKK+LOO+8UQgjxzjvviG9/+9u8ZzlEo1Hxne98R/zLv/yL+OSTT3iv\neiFH/SdMrm0UnaimpgYPP/yw9f2mTZtw6aWXAjC3jFy7di38fj+uuOIKSJKEoUOHQtd1tLa2Zn1t\nX3bNNdfg+9//vvW9oii8X3lcddVVuPfeewEABw4cQHV1Ne9ZDvfffz9uuOEGDBo0CAD/feyNHBWo\ndtsoOtXVV19t7XoFmKdMJM6JTd0yMvWeJR7P9tq+zOv1wufzIRAIYO7cubjlllt4v7pBVVXMmzcP\n9957L66++mreMxvPPvssqqqqrP/gB/jvY2/kqEDNtY0iIW3MJd+Wkdle29cdPHgQ3/jGN/Bv//Zv\n+OIXv8j71U33338/Xn75Zdx9992IRCLW47xnSc888wzWrl2LWbNmYfPmzZg3bx5aW1ut53mvegdH\nBWqubRQJGDt2LN59910A5paRF198Merr6/HWW2/BMAwcOHAAhmGgqqoq62v7spaWFtx444244447\n8O//bp5UwPuV23PPPYfHHnsMAFBaWgpJknDBBRfwnmXx+9//Hr/73e+wfPlynH/++bj//vsxceJE\n3qtexlE7JWXbRnH06NGn+7JOq3379uEHP/gBGhsbsXPnTtx9992IxWI455xzsHDhQiiKgocffhhN\nTU0wDAN33XUXLr74YtvX9lULFy7E3/72N5xzTvLIsB/96EdYuHAh75eNzs5O3HXXXWhpaYGmabjp\nppswevRo/h3LY9asWZg/fz5kWea96mUcFahEREQ9xVEtXyIiop7CQCUiIioCBioREVERMFCJiIiK\ngIFKRERUBAxUIiKiImCgEp1hrrzySuzbt++UvFdHRwe++93v5n3dpk2bcN111+FLX/oSZs+ejfb2\n9lNwdUS9CwOVyMGOHz+OzZs3533dT3/6U8ydOxcvvPACRo0ahWXLlp2CqyPqXbiRLTnGu+++i1/9\n6ldwuVzYt28frrzySpSVlWHVqlUAgMcffxwvvfQSnn/+eYRCIbhcLixZsgSlpaWYPn06fve732HE\niBG47rrrcNttt+ELX/iC7fs8+OCDMAwD5557Ln7yk59gwYIF2L59O3Rdx0033YRp06YhEongf/7n\nf+D3++FyufCd73wH1157LQDgkUcewebNmxEKhfDAAw/goosusv1c27Ztw7333ovOzk60trbiW9/6\nFr761a/i4YcfxoEDB7Br1y60trZizpw5ePvtt7F+/Xqcd955+PnPf46FCxfiyJEj+O53v4tHHnnE\n9j0Mw7D2kA2FQujXr98J/lMg6sNOy6FxRKfBO++8I8aPHy8OHDggOjs7xbhx48Qf/vAHIYQQd955\np3jqqafEN7/5TREKhYQQQjz00ENiwYIFQgjzPNSvf/3rYunSpeLuu+/O+z4NDQ2ivb1dCCHEgw8+\nKH77298KIYTo6OgQU6dOFXv27BG//vWvxfe//32h67o4cuSIuPbaa0UkEhGTJk0Sv/nNb4QQQixf\nvlzcfPPNOd9v4cKFYu3atUIIIfbs2SPGjRsnhBDiF7/4hZg+fbqIxWLi3XffFeedd57Yvn27iMVi\nYsqUKWLz5s1i7969YtKkSXnv3YcffijGjx8vLr/8cnHVVVeJ1tbWvD9D5DSsUMlRamtrMWTIEABA\nZWUlLrvsMgDA0KFD0d7ejiVLluDFF1/Erl278Oabb+L8888HAFx33XX429/+hj//+c/4y1/+kvd9\nRo0ahfLycgDA2rVrEQ6H8cwzzwAw97jdvn073n//fcyYMQOyLGPgwIF48cUXrZ+/6qqrAABjxozB\nyy+/nPO97rzzTrz55pt47LHHsG3bNnR2dlrPXX755VBVFUOHDsXAgQMxZswYAMDgwYNx/PjxtKPA\n7ITDYfzoRz/CU089hbq6Ojz55JOYN28eHn/88bw/S+QkHEMlR3G5XGnfp24gfvDgQVx//fXo6OjA\nxIkT8eUvfxkivtV1JBLBoUOHoOs6Dh06lPd9SkpKrK8Nw8CDDz6I559/Hs8//zwaGxsxYcIEqKpq\nnWEJALt370Y0Gk27rtTn7dxyyy145ZVXMHr0aNxyyy22n/dEjyrctm0bPB4P6urqAADXX3893nvv\nvRP6XUR9GQOVKO6jjzOk+rcAAAGjSURBVD7CyJEj8R//8R+48MILsWrVKui6DgB46KGH8NnPfhZ3\n3XUX7rrrLuvx7vjsZz+LP/zhDwCAI0eO4Etf+hIOHjyISy65BH/9618hhMDRo0fx9a9/3QrUQqxZ\nswZz587FVVddZR1P2N3rU1UVmqblfM3IkSNx6NAhfPrppwCAV199FRdeeGHB10nU17HlSxR3xRVX\nYMuWLbj22mshhMAll1yC7du3Y926dXj55ZfxwgsvwOfz4U9/+hOeeOIJ3HTTTd36vd/73vcwf/58\nTJs2Dbqu44477kBNTQ1mzpyJhQsX4ktf+hIA4O677+5WCzbTzTffjJkzZ8Lj8eC8887DsGHDur3s\nZsCAARg6dChmzZqF5cuXZ31Nv379sHjxYtxyyy0QQmDAgAFYtGhRwddJ1Nfx+DYiIqIiYIVKdAI+\n+OAD3HvvvVmfe/zxxzF48OCivt/999+PtWvXdnn8ggsuwE9/+tOT/v3hcBjXX3991ufmzp2LyZMn\nn/R7EPV1rFCJiIiKgJOSiIiIioCBSkREVAQMVCIioiJgoBIRERXB/wXOffMySxn6dgAAAABJRU5E\nrkJggg==\n",
      "text/plain": [
       "<Figure size 473.625x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.FacetGrid(telecom,hue='churn',size=6).map(sns.distplot,\"max_rech_amt_8\").add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1f8b7539748>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kyBEmTpzIhx9+yMmTJ9tVd5pMJsrLG2eePnr0KCkpKQAd9hd3RcDfZH/9619Z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\nJUEIIUTQ/wM24FRXOGHp5AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 401.625x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.FacetGrid(telecom,hue='churn',size=5).map(plt.scatter,\"max_rech_amt_6\",\"max_rech_amt_7\").add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1f8ce009518>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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I/ut+5JFH8LOf/Qzl5eX45S9/iW3btqGwsDCcsUWEv2YWuU0pXFcXlm5+FzW2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\n",
      "text/plain": [
       "<Figure size 401.625x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.FacetGrid(telecom,hue='churn',size=5).map(plt.scatter,\"max_rech_amt_7\",\"max_rech_amt_8\").add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1f8ce3d6a90>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Mn/3sZ6ioqMB1112HnTt3YuzYsaGMjQSBZzGSWFGR/0lOLBDrBdOkd06AEu6x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\njDFBXCQYY4wJ+gPJK97P9YJqugAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 401.625x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.FacetGrid(telecom,hue='churn',size=5).map(plt.scatter,\"max_rech_amt_6\",\"max_rech_amt_8\").add_legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: due to space & size we did not include all univariate and bivariate analysis in this file."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlation Matrix: Bivariate Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f8ce476358>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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//elPKC8vBwC0tLScdd3f1xEWFoakpCQAQEREBJxOJw4dOoQxY8bAYunKWJKa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oJwBwzagem5to6rTVUqpyb9Iwe5PeXOJav0gZ9+vSW1zHbVb3le//vEAZv0e4R5Ku3wCADr9Q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2LS5ARh+LH/+z6Zx/A1xFxnIcbo5xfJtPoNF/cHyib++dPNwvD7o2iL+mjHXcLw1vfFdYx8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\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f3x/+/v6YPHky5s+fD7PZDK1Wi+XLlwMAPvzwQ6xZswZVVVVITExE7969hWUVFxcjMTER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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "telecom2 = telecom.drop(['mobile_number', 'churn'], axis=1)\n",
    "\n",
    "# Let's see the correlation matrix \n",
    "plt.figure(figsize = (20,20))        # Size of the figure\n",
    "sns.heatmap(telecom2.corr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HIGHLY CORRELATED FEATURES IN DATA SET:36\n",
      "\n",
      "['onnet_mou_8', 'loc_og_t2t_mou_8', 'loc_og_t2m_mou_8', 'loc_og_t2f_mou_7', 'loc_og_mou_6', 'loc_og_mou_7', 'loc_og_mou_8', 'std_og_t2t_mou_6', 'std_og_t2t_mou_7', 'std_og_t2t_mou_8', 'std_og_t2m_mou_6', 'std_og_t2m_mou_7', 'std_og_t2m_mou_8', 'isd_og_mou_7', 'isd_og_mou_8', 'total_og_mou_6', 'total_og_mou_7', 'total_og_mou_8', 'loc_ic_t2t_mou_7', 'loc_ic_t2t_mou_8', 'loc_ic_t2m_mou_8', 'loc_ic_mou_6', 'loc_ic_mou_7', 'loc_ic_mou_8', 'std_ic_mou_6', 'std_ic_mou_7', 'std_ic_mou_8', 'total_ic_mou_6', 'total_ic_mou_7', 'total_ic_mou_8', 'total_rech_amt_6', 'total_rech_amt_7', 'total_rech_amt_8', 'fb6_1.0', 'fb7_1.0', 'fb8_1.0']\n"
     ]
    }
   ],
   "source": [
    "# Create correlation matrix\n",
    "corr_matrix = telecom2.corr().abs()\n",
    "\n",
    "# Select upper triangle of correlation matrix\n",
    "upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(np.bool))\n",
    "\n",
    "# Find index of feature columns with correlation greater than 0.80\n",
    "high_corr_features = [column for column in upper.columns if any(upper[column] > 0.80)]\n",
    "\n",
    "print(\"HIGHLY CORRELATED FEATURES IN DATA SET:{}\\n\\n{}\".format(len(high_corr_features), high_corr_features))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* OBSERVATION: 38 highly correlated features (> 80%) found in the data set.\n",
    "* NOTE: We are not dropping these columns manually rather using feature reduction methods to do the job. We worked with RFE, LASSO and PCA."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <font color='blue'>SUMMARY PART 2: EXPLORATORY DATA ANALYSIS (EDA)</font>\n",
    "\n",
    "OBSERVATIONS\n",
    "1. Data is higly imbalanced only 5.6% data is churn & remaining is non_churn\n",
    "2. From univariate analysis some relationship is emerging to the churn, but no clear relationship found.\n",
    "3. There are many highly correlated features, found 38 features with more then 0.8 correlation.\n",
    "\n",
    "NEXT STEPS:\n",
    "1. Need to balance the data, apply suitable data-balancing method\n",
    "2. Features are highly correlated, use feture reduction methods before modelling."
   ]
  }
 ],
 "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",
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 },
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