Telecom Churn Prediction Model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Telecom Churn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing the data Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading the libraries and dataset\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "churn_data = pd.read_csv(\"telecom_churn_data.csv\", sep=\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Suppressing Warnings\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "  <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",
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       "      <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",
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       "      <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",
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       "      <th>jun_vbc_3g</th>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6/30/2014</td>\n",
       "      <td>7/31/2014</td>\n",
       "      <td>8/31/2014</td>\n",
       "      <td>9/30/2014</td>\n",
       "      <td>167.690</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1103</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.17</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7001204172</td>\n",
       "      <td>109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6/30/2014</td>\n",
       "      <td>7/31/2014</td>\n",
       "      <td>8/31/2014</td>\n",
       "      <td>9/30/2014</td>\n",
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       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7000142493</td>\n",
       "      <td>109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6/30/2014</td>\n",
       "      <td>7/31/2014</td>\n",
       "      <td>8/31/2014</td>\n",
       "      <td>9/30/2014</td>\n",
       "      <td>261.636</td>\n",
       "      <td>...</td>\n",
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       "<p>5 rows × 226 columns</p>\n",
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      ],
      "text/plain": [
       "   mobile_number  circle_id  loc_og_t2o_mou  std_og_t2o_mou  loc_ic_t2o_mou  \\\n",
       "0     7000842753        109             0.0             0.0             0.0   \n",
       "1     7001865778        109             0.0             0.0             0.0   \n",
       "2     7001625959        109             0.0             0.0             0.0   \n",
       "3     7001204172        109             0.0             0.0             0.0   \n",
       "4     7000142493        109             0.0             0.0             0.0   \n",
       "\n",
       "  last_date_of_month_6 last_date_of_month_7 last_date_of_month_8  \\\n",
       "0            6/30/2014            7/31/2014            8/31/2014   \n",
       "1            6/30/2014            7/31/2014            8/31/2014   \n",
       "2            6/30/2014            7/31/2014            8/31/2014   \n",
       "3            6/30/2014            7/31/2014            8/31/2014   \n",
       "4            6/30/2014            7/31/2014            8/31/2014   \n",
       "\n",
       "  last_date_of_month_9   arpu_6  ...  sachet_3g_9  fb_user_6  fb_user_7  \\\n",
       "0            9/30/2014  197.385  ...            0        1.0        1.0   \n",
       "1            9/30/2014   34.047  ...            0        NaN        1.0   \n",
       "2            9/30/2014  167.690  ...            0        NaN        NaN   \n",
       "3            9/30/2014  221.338  ...            0        NaN        NaN   \n",
       "4            9/30/2014  261.636  ...            0        0.0        NaN   \n",
       "\n",
       "   fb_user_8  fb_user_9   aon  aug_vbc_3g  jul_vbc_3g  jun_vbc_3g  sep_vbc_3g  \n",
       "0        1.0        NaN   968        30.4         0.0      101.20        3.58  \n",
       "1        1.0        NaN  1006         0.0         0.0        0.00        0.00  \n",
       "2        NaN        1.0  1103         0.0         0.0        4.17        0.00  \n",
       "3        NaN        NaN  2491         0.0         0.0        0.00        0.00  \n",
       "4        NaN        NaN  1526         0.0         0.0        0.00        0.00  \n",
       "\n",
       "[5 rows x 226 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# loading the dataset\n",
    "\n",
    "churn_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Understanding the structure of the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "  <thead>\n",
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       "      <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",
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       "      <th>onnet_mou_6</th>\n",
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       "      <th>fb_user_7</th>\n",
       "      <th>fb_user_8</th>\n",
       "      <th>fb_user_9</th>\n",
       "      <th>aon</th>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.001207e+09</td>\n",
       "      <td>109.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>279.154731</td>\n",
       "      <td>261.645069</td>\n",
       "      <td>132.395875</td>\n",
       "      <td>...</td>\n",
       "      <td>0.084581</td>\n",
       "      <td>0.914404</td>\n",
       "      <td>0.908764</td>\n",
       "      <td>0.890808</td>\n",
       "      <td>0.860968</td>\n",
       "      <td>1219.854749</td>\n",
       "      <td>68.170248</td>\n",
       "      <td>66.839062</td>\n",
       "      <td>60.021204</td>\n",
       "      <td>3.299373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.956694e+05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>328.439770</td>\n",
       "      <td>338.156291</td>\n",
       "      <td>344.474791</td>\n",
       "      <td>341.998630</td>\n",
       "      <td>297.207406</td>\n",
       "      <td>...</td>\n",
       "      <td>0.650457</td>\n",
       "      <td>0.279772</td>\n",
       "      <td>0.287950</td>\n",
       "      <td>0.311885</td>\n",
       "      <td>0.345987</td>\n",
       "      <td>954.733842</td>\n",
       "      <td>267.580450</td>\n",
       "      <td>271.201856</td>\n",
       "      <td>253.938223</td>\n",
       "      <td>32.408353</td>\n",
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       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>7.000000e+09</td>\n",
       "      <td>109.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>-945.808000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <th>25%</th>\n",
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       "      <td>109.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>93.411500</td>\n",
       "      <td>86.980500</td>\n",
       "      <td>84.126000</td>\n",
       "      <td>62.685000</td>\n",
       "      <td>7.380000</td>\n",
       "      <td>...</td>\n",
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       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>7.001205e+09</td>\n",
       "      <td>109.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>197.704000</td>\n",
       "      <td>191.640000</td>\n",
       "      <td>192.080000</td>\n",
       "      <td>176.849000</td>\n",
       "      <td>34.310000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>863.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.001812e+09</td>\n",
       "      <td>109.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>371.060000</td>\n",
       "      <td>365.344500</td>\n",
       "      <td>369.370500</td>\n",
       "      <td>353.466500</td>\n",
       "      <td>118.740000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1807.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.002411e+09</td>\n",
       "      <td>109.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27731.088000</td>\n",
       "      <td>35145.834000</td>\n",
       "      <td>33543.624000</td>\n",
       "      <td>38805.617000</td>\n",
       "      <td>7376.710000</td>\n",
       "      <td>...</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4337.000000</td>\n",
       "      <td>12916.220000</td>\n",
       "      <td>9165.600000</td>\n",
       "      <td>11166.210000</td>\n",
       "      <td>2618.570000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 214 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       mobile_number  circle_id  loc_og_t2o_mou  std_og_t2o_mou  \\\n",
       "count   9.999900e+04    99999.0         98981.0         98981.0   \n",
       "mean    7.001207e+09      109.0             0.0             0.0   \n",
       "std     6.956694e+05        0.0             0.0             0.0   \n",
       "min     7.000000e+09      109.0             0.0             0.0   \n",
       "25%     7.000606e+09      109.0             0.0             0.0   \n",
       "50%     7.001205e+09      109.0             0.0             0.0   \n",
       "75%     7.001812e+09      109.0             0.0             0.0   \n",
       "max     7.002411e+09      109.0             0.0             0.0   \n",
       "\n",
       "       loc_ic_t2o_mou        arpu_6        arpu_7        arpu_8        arpu_9  \\\n",
       "count         98981.0  99999.000000  99999.000000  99999.000000  99999.000000   \n",
       "mean              0.0    282.987358    278.536648    279.154731    261.645069   \n",
       "std               0.0    328.439770    338.156291    344.474791    341.998630   \n",
       "min               0.0  -2258.709000  -2014.045000   -945.808000  -1899.505000   \n",
       "25%               0.0     93.411500     86.980500     84.126000     62.685000   \n",
       "50%               0.0    197.704000    191.640000    192.080000    176.849000   \n",
       "75%               0.0    371.060000    365.344500    369.370500    353.466500   \n",
       "max               0.0  27731.088000  35145.834000  33543.624000  38805.617000   \n",
       "\n",
       "        onnet_mou_6  ...   sachet_3g_9     fb_user_6     fb_user_7  \\\n",
       "count  96062.000000  ...  99999.000000  25153.000000  25571.000000   \n",
       "mean     132.395875  ...      0.084581      0.914404      0.908764   \n",
       "std      297.207406  ...      0.650457      0.279772      0.287950   \n",
       "min        0.000000  ...      0.000000      0.000000      0.000000   \n",
       "25%        7.380000  ...      0.000000      1.000000      1.000000   \n",
       "50%       34.310000  ...      0.000000      1.000000      1.000000   \n",
       "75%      118.740000  ...      0.000000      1.000000      1.000000   \n",
       "max     7376.710000  ...     49.000000      1.000000      1.000000   \n",
       "\n",
       "          fb_user_8     fb_user_9           aon    aug_vbc_3g    jul_vbc_3g  \\\n",
       "count  26339.000000  25922.000000  99999.000000  99999.000000  99999.000000   \n",
       "mean       0.890808      0.860968   1219.854749     68.170248     66.839062   \n",
       "std        0.311885      0.345987    954.733842    267.580450    271.201856   \n",
       "min        0.000000      0.000000    180.000000      0.000000      0.000000   \n",
       "25%        1.000000      1.000000    467.000000      0.000000      0.000000   \n",
       "50%        1.000000      1.000000    863.000000      0.000000      0.000000   \n",
       "75%        1.000000      1.000000   1807.500000      0.000000      0.000000   \n",
       "max        1.000000      1.000000   4337.000000  12916.220000   9165.600000   \n",
       "\n",
       "         jun_vbc_3g    sep_vbc_3g  \n",
       "count  99999.000000  99999.000000  \n",
       "mean      60.021204      3.299373  \n",
       "std      253.938223     32.408353  \n",
       "min        0.000000      0.000000  \n",
       "25%        0.000000      0.000000  \n",
       "50%        0.000000      0.000000  \n",
       "75%        0.000000      0.000000  \n",
       "max    11166.210000   2618.570000  \n",
       "\n",
       "[8 rows x 214 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "churn_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Preparation - Cleaning and Manupulating the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "368.5"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating new column that gives the average total recharge for months 6 and 7\n",
    "\n",
    "churn_data['total_recharge_6'] = churn_data['total_rech_amt_6']\n",
    "churn_data['total_recharge_7'] = churn_data['total_rech_amt_7']\n",
    "\n",
    "churn_data['average_total_recharge_6_7'] = (churn_data['total_recharge_6'] + churn_data['total_recharge_7'])/2\n",
    "\n",
    "# Taking the 70th percenile of the value\n",
    "\n",
    "churn_data['average_total_recharge_6_7'].quantile(0.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mobile_number</th>\n",
       "      <th>circle_id</th>\n",
       "      <th>loc_og_t2o_mou</th>\n",
       "      <th>std_og_t2o_mou</th>\n",
       "      <th>loc_ic_t2o_mou</th>\n",
       "      <th>last_date_of_month_6</th>\n",
       "      <th>last_date_of_month_7</th>\n",
       "      <th>last_date_of_month_8</th>\n",
       "      <th>last_date_of_month_9</th>\n",
       "      <th>arpu_6</th>\n",
       "      <th>...</th>\n",
       "      <th>fb_user_8</th>\n",
       "      <th>fb_user_9</th>\n",
       "      <th>aon</th>\n",
       "      <th>aug_vbc_3g</th>\n",
       "      <th>jul_vbc_3g</th>\n",
       "      <th>jun_vbc_3g</th>\n",
       "      <th>sep_vbc_3g</th>\n",
       "      <th>total_recharge_6</th>\n",
       "      <th>total_recharge_7</th>\n",
       "      <th>average_total_recharge_6_7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7000701601</td>\n",
       "      <td>109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6/30/2014</td>\n",
       "      <td>7/31/2014</td>\n",
       "      <td>8/31/2014</td>\n",
       "      <td>9/30/2014</td>\n",
       "      <td>1069.180</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>802</td>\n",
       "      <td>57.74</td>\n",
       "      <td>19.38</td>\n",
       "      <td>18.74</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1580</td>\n",
       "      <td>790</td>\n",
       "      <td>1185.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7001524846</td>\n",
       "      <td>109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6/30/2014</td>\n",
       "      <td>7/31/2014</td>\n",
       "      <td>8/31/2014</td>\n",
       "      <td>9/30/2014</td>\n",
       "      <td>378.721</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>315</td>\n",
       "      <td>21.03</td>\n",
       "      <td>910.65</td>\n",
       "      <td>122.16</td>\n",
       "      <td>0.00</td>\n",
       "      <td>437</td>\n",
       "      <td>601</td>\n",
       "      <td>519.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>7002191713</td>\n",
       "      <td>109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6/30/2014</td>\n",
       "      <td>7/31/2014</td>\n",
       "      <td>8/31/2014</td>\n",
       "      <td>9/30/2014</td>\n",
       "      <td>492.846</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2607</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>507</td>\n",
       "      <td>253</td>\n",
       "      <td>380.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>7000875565</td>\n",
       "      <td>109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6/30/2014</td>\n",
       "      <td>7/31/2014</td>\n",
       "      <td>8/31/2014</td>\n",
       "      <td>9/30/2014</td>\n",
       "      <td>430.975</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>511</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.45</td>\n",
       "      <td>21.89</td>\n",
       "      <td>0.00</td>\n",
       "      <td>570</td>\n",
       "      <td>348</td>\n",
       "      <td>459.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>7000187447</td>\n",
       "      <td>109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6/30/2014</td>\n",
       "      <td>7/31/2014</td>\n",
       "      <td>8/31/2014</td>\n",
       "      <td>9/30/2014</td>\n",
       "      <td>690.008</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>667</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>816</td>\n",
       "      <td>0</td>\n",
       "      <td>408.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>7002124215</td>\n",
       "      <td>109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6/30/2014</td>\n",
       "      <td>7/31/2014</td>\n",
       "      <td>8/31/2014</td>\n",
       "      <td>9/30/2014</td>\n",
       "      <td>514.453</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>720</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>600</td>\n",
       "      <td>680</td>\n",
       "      <td>640.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>7001125315</td>\n",
       "      <td>109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6/30/2014</td>\n",
       "      <td>7/31/2014</td>\n",
       "      <td>8/31/2014</td>\n",
       "      <td>9/30/2014</td>\n",
       "      <td>422.050</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3072</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>440</td>\n",
       "      <td>330</td>\n",
       "      <td>385.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>7000149764</td>\n",
       "      <td>109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6/30/2014</td>\n",
       "      <td>7/31/2014</td>\n",
       "      <td>8/31/2014</td>\n",
       "      <td>9/30/2014</td>\n",
       "      <td>977.020</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1328</td>\n",
       "      <td>358.10</td>\n",
       "      <td>2706.83</td>\n",
       "      <td>1714.67</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1058</td>\n",
       "      <td>2806</td>\n",
       "      <td>1932.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>7000815202</td>\n",
       "      <td>109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6/30/2014</td>\n",
       "      <td>7/31/2014</td>\n",
       "      <td>8/31/2014</td>\n",
       "      <td>9/30/2014</td>\n",
       "      <td>363.987</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1155</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>420</td>\n",
       "      <td>636</td>\n",
       "      <td>528.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>7000721289</td>\n",
       "      <td>109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6/30/2014</td>\n",
       "      <td>7/31/2014</td>\n",
       "      <td>8/31/2014</td>\n",
       "      <td>9/30/2014</td>\n",
       "      <td>482.832</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>496</td>\n",
       "      <td>510</td>\n",
       "      <td>503.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>7000294396</td>\n",
       "      <td>109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6/30/2014</td>\n",
       "      <td>7/31/2014</td>\n",
       "      <td>8/31/2014</td>\n",
       "      <td>9/30/2014</td>\n",
       "      <td>1873.271</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1265</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2317</td>\n",
       "      <td>580</td>\n",
       "      <td>1448.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>7002188521</td>\n",
       "      <td>109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6/30/2014</td>\n",
       "      <td>7/31/2014</td>\n",
       "      <td>8/31/2014</td>\n",
       "      <td>9/30/2014</td>\n",
       "      <td>978.077</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>697</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1159</td>\n",
       "      <td>1372</td>\n",
       "      <td>1265.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>7002180748</td>\n",
       "      <td>109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6/30/2014</td>\n",
       "      <td>7/31/2014</td>\n",
       "      <td>8/31/2014</td>\n",
       "      <td>9/30/2014</td>\n",
       "      <td>452.624</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>729</td>\n",
       "      <td>277.97</td>\n",
       "      <td>535.63</td>\n",
       "      <td>1459.45</td>\n",
       "      <td>0.00</td>\n",
       "      <td>528</td>\n",
       "      <td>380</td>\n",
       "      <td>454.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>7001682585</td>\n",
       "      <td>109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6/30/2014</td>\n",
       "      <td>7/31/2014</td>\n",
       "      <td>8/31/2014</td>\n",
       "      <td>9/30/2014</td>\n",
       "      <td>1201.298</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3106</td>\n",
       "      <td>1799.09</td>\n",
       "      <td>3649.39</td>\n",
       "      <td>1892.32</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1389</td>\n",
       "      <td>905</td>\n",
       "      <td>1147.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>7000955945</td>\n",
       "      <td>109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6/30/2014</td>\n",
       "      <td>7/31/2014</td>\n",
       "      <td>8/31/2014</td>\n",
       "      <td>9/30/2014</td>\n",
       "      <td>410.261</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3292</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>430</td>\n",
       "      <td>500</td>\n",
       "      <td>465.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>7000152517</td>\n",
       "      <td>109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6/30/2014</td>\n",
       "      <td>7/31/2014</td>\n",
       "      <td>8/31/2014</td>\n",
       "      <td>9/30/2014</td>\n",
       "      <td>565.516</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1131</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>621</td>\n",
       "      <td>525</td>\n",
       "      <td>573.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>7002393996</td>\n",
       "      <td>109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6/30/2014</td>\n",
       "      <td>7/31/2014</td>\n",
       "      <td>8/31/2014</td>\n",
       "      <td>9/30/2014</td>\n",
       "      <td>809.518</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>607</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>986</td>\n",
       "      <td>806</td>\n",
       "      <td>896.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>7002402996</td>\n",
       "      <td>109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6/30/2014</td>\n",
       "      <td>7/31/2014</td>\n",
       "      <td>8/31/2014</td>\n",
       "      <td>9/30/2014</td>\n",
       "      <td>2401.829</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2283</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2759</td>\n",
       "      <td>2294</td>\n",
       "      <td>2526.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>7001532600</td>\n",
       "      <td>109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6/30/2014</td>\n",
       "      <td>7/31/2014</td>\n",
       "      <td>8/31/2014</td>\n",
       "      <td>9/30/2014</td>\n",
       "      <td>324.449</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>444</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>356</td>\n",
       "      <td>424</td>\n",
       "      <td>390.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>7001328263</td>\n",
       "      <td>109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6/30/2014</td>\n",
       "      <td>7/31/2014</td>\n",
       "      <td>8/31/2014</td>\n",
       "      <td>9/30/2014</td>\n",
       "      <td>30.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1894</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1154</td>\n",
       "      <td>577.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>7001728145</td>\n",
       "      <td>109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6/30/2014</td>\n",
       "      <td>7/31/2014</td>\n",
       "      <td>8/31/2014</td>\n",
       "      <td>9/30/2014</td>\n",
       "      <td>623.635</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3201</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>750</td>\n",
       "      <td>888</td>\n",
       "      <td>819.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>7001606600</td>\n",
       "      <td>109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6/30/2014</td>\n",
       "      <td>7/31/2014</td>\n",
       "      <td>8/31/2014</td>\n",
       "      <td>9/30/2014</td>\n",
       "      <td>465.794</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3226</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>628</td>\n",
       "      <td>554</td>\n",
       "      <td>591.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>7001480390</td>\n",
       "      <td>109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6/30/2014</td>\n",
       "      <td>7/31/2014</td>\n",
       "      <td>8/31/2014</td>\n",
       "      <td>9/30/2014</td>\n",
       "      <td>676.999</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>970</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1033</td>\n",
       "      <td>832</td>\n",
       "      <td>932.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>7000589828</td>\n",
       "      <td>109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6/30/2014</td>\n",
       "      <td>7/31/2014</td>\n",
       "      <td>8/31/2014</td>\n",
       "      <td>9/30/2014</td>\n",
       "      <td>374.863</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>502</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>432</td>\n",
       "      <td>328</td>\n",
       "      <td>380.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>7001746121</td>\n",
       "      <td>109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6/30/2014</td>\n",
       "      <td>7/31/2014</td>\n",
       "      <td>8/31/2014</td>\n",
       "      <td>9/30/2014</td>\n",
       "      <td>512.524</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>359</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>716</td>\n",
       "      <td>526</td>\n",
       "      <td>621.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>7001631060</td>\n",
       "      <td>109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6/30/2014</td>\n",
       "      <td>7/31/2014</td>\n",
       "      <td>8/31/2014</td>\n",
       "      <td>9/30/2014</td>\n",
       "      <td>451.375</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1727</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>421</td>\n",
       "      <td>427</td>\n",
       "      <td>424.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>7002236431</td>\n",
       "      <td>109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6/30/2014</td>\n",
       "      <td>7/31/2014</td>\n",
       "      <td>8/31/2014</td>\n",
       "      <td>9/30/2014</td>\n",
       "      <td>883.401</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>902</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1025</td>\n",
       "      <td>1110</td>\n",
       "      <td>1067.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>7000208415</td>\n",
       "      <td>109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6/30/2014</td>\n",
       "      <td>7/31/2014</td>\n",
       "      <td>8/31/2014</td>\n",
       "      <td>9/30/2014</td>\n",
       "      <td>659.587</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>467</td>\n",
       "      <td>65.85</td>\n",
       "      <td>6.46</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>710</td>\n",
       "      <td>605</td>\n",
       "      <td>657.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>7001300706</td>\n",
       "      <td>109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6/30/2014</td>\n",
       "      <td>7/31/2014</td>\n",
       "      <td>8/31/2014</td>\n",
       "      <td>9/30/2014</td>\n",
       "      <td>596.301</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>332</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>704</td>\n",
       "      <td>178</td>\n",
       "      <td>441.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>7001453769</td>\n",
       "      <td>109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6/30/2014</td>\n",
       "      <td>7/31/2014</td>\n",
       "      <td>8/31/2014</td>\n",
       "      <td>9/30/2014</td>\n",
       "      <td>1173.256</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2954</td>\n",
       "      <td>253.66</td>\n",
       "      <td>0.00</td>\n",
       "      <td>48.55</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1111</td>\n",
       "      <td>588</td>\n",
       "      <td>849.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99887</th>\n",
       "      <td>7001271496</td>\n",
       "      <td>109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6/30/2014</td>\n",
       "      <td>7/31/2014</td>\n",
       "      <td>8/31/2014</td>\n",
       "      <td>9/30/2014</td>\n",
       "      <td>816.531</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>485</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1000</td>\n",
       "      <td>761</td>\n",
       "      <td>880.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99893</th>\n",
       "      <td>7001438347</td>\n",
       "      <td>109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6/30/2014</td>\n",
       "      <td>7/31/2014</td>\n",
       "      <td>8/31/2014</td>\n",
       "      <td>9/30/2014</td>\n",
       "      <td>547.602</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3003</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>629</td>\n",
       "      <td>530</td>\n",
       "      <td>579.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99896</th>\n",
       "      <td>7002362186</td>\n",
       "      <td>109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6/30/2014</td>\n",
       "      <td>7/31/2014</td>\n",
       "      <td>8/31/2014</td>\n",
       "      <td>9/30/2014</td>\n",
       "      <td>504.271</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1843</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>640</td>\n",
       "      <td>588</td>\n",
       "      <td>614.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99899</th>\n",
       "      <td>7001632563</td>\n",
       "      <td>109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6/30/2014</td>\n",
       "      <td>7/31/2014</td>\n",
       "      <td>8/31/2014</td>\n",
       "      <td>9/30/2014</td>\n",
       "      <td>976.749</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3409</td>\n",
       "      <td>750.31</td>\n",
       "      <td>1056.00</td>\n",
       "      <td>869.22</td>\n",
       "      <td>0.00</td>\n",
       "      <td>504</td>\n",
       "      <td>1380</td>\n",
       "      <td>942.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99902</th>\n",
       "      <td>7002142544</td>\n",
       "      <td>109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6/30/2014</td>\n",
       "      <td>7/31/2014</td>\n",
       "      <td>8/31/2014</td>\n",
       "      <td>9/30/2014</td>\n",
       "      <td>441.209</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1404</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>454</td>\n",
       "      <td>384</td>\n",
       "      <td>419.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99904</th>\n",
       "      <td>7000649339</td>\n",
       "      <td>109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6/30/2014</td>\n",
       "      <td>7/31/2014</td>\n",
       "      <td>8/31/2014</td>\n",
       "      <td>9/30/2014</td>\n",
       "      <td>435.504</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1698</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>550</td>\n",
       "      <td>560</td>\n",
       "      <td>555.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99921</th>\n",
       "      <td>7000157295</td>\n",
       "      <td>109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6/30/2014</td>\n",
       "      <td>7/31/2014</td>\n",
       "      <td>8/31/2014</td>\n",
       "      <td>9/30/2014</td>\n",
       "      <td>79.066</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>560</td>\n",
       "      <td>219.42</td>\n",
       "      <td>764.34</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.51</td>\n",
       "      <td>90</td>\n",
       "      <td>649</td>\n",
       "      <td>369.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99922</th>\n",
       "      <td>7001602180</td>\n",
       "      <td>109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6/30/2014</td>\n",
       "      <td>7/31/2014</td>\n",
       "      <td>8/31/2014</td>\n",
       "      <td>9/30/2014</td>\n",
       "      <td>458.049</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>507</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>518</td>\n",
       "      <td>775</td>\n",
       "      <td>646.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99923</th>\n",
       "      <td>7000571771</td>\n",
       "      <td>109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6/30/2014</td>\n",
       "      <td>7/31/2014</td>\n",
       "      <td>8/31/2014</td>\n",
       "      <td>9/30/2014</td>\n",
       "      <td>919.526</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>198</td>\n",
       "      <td>1164.67</td>\n",
       "      <td>1320.06</td>\n",
       "      <td>1447.87</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1096</td>\n",
       "      <td>1132</td>\n",
       "      <td>1114.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99924</th>\n",
       "      <td>7001581208</td>\n",
       "      <td>109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6/30/2014</td>\n",
       "      <td>7/31/2014</td>\n",
       "      <td>8/31/2014</td>\n",
       "      <td>9/30/2014</td>\n",
       "      <td>457.827</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3104</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>550</td>\n",
       "      <td>450</td>\n",
       "      <td>500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99925</th>\n",
       "      <td>7000877443</td>\n",
       "      <td>109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6/30/2014</td>\n",
       "      <td>7/31/2014</td>\n",
       "      <td>8/31/2014</td>\n",
       "      <td>9/30/2014</td>\n",
       "      <td>137.643</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>485</td>\n",
       "      <td>323.56</td>\n",
       "      <td>86.55</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>177</td>\n",
       "      <td>1147</td>\n",
       "      <td>662.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99927</th>\n",
       "      <td>7001570091</td>\n",
       "      <td>109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6/30/2014</td>\n",
       "      <td>7/31/2014</td>\n",
       "      <td>8/31/2014</td>\n",
       "      <td>9/30/2014</td>\n",
       "      <td>474.220</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2333</td>\n",
       "      <td>150.56</td>\n",
       "      <td>296.70</td>\n",
       "      <td>95.89</td>\n",
       "      <td>0.00</td>\n",
       "      <td>589</td>\n",
       "      <td>512</td>\n",
       "      <td>550.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99929</th>\n",
       "      <td>7002128793</td>\n",
       "      <td>109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6/30/2014</td>\n",
       "      <td>7/31/2014</td>\n",
       "      <td>8/31/2014</td>\n",
       "      <td>9/30/2014</td>\n",
       "      <td>418.140</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2349</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>548</td>\n",
       "      <td>548</td>\n",
       "      <td>548.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99931</th>\n",
       "      <td>7001904289</td>\n",
       "      <td>109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6/30/2014</td>\n",
       "      <td>7/31/2014</td>\n",
       "      <td>8/31/2014</td>\n",
       "      <td>9/30/2014</td>\n",
       "      <td>897.341</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2894</td>\n",
       "      <td>252.13</td>\n",
       "      <td>383.62</td>\n",
       "      <td>349.81</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1099</td>\n",
       "      <td>250</td>\n",
       "      <td>674.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99932</th>\n",
       "      <td>7000018772</td>\n",
       "      <td>109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6/30/2014</td>\n",
       "      <td>7/31/2014</td>\n",
       "      <td>8/31/2014</td>\n",
       "      <td>9/30/2014</td>\n",
       "      <td>419.691</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>180</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>466</td>\n",
       "      <td>435</td>\n",
       "      <td>450.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99935</th>\n",
       "      <td>7002109027</td>\n",
       "      <td>109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6/30/2014</td>\n",
       "      <td>7/31/2014</td>\n",
       "      <td>8/31/2014</td>\n",
       "      <td>9/30/2014</td>\n",
       "      <td>552.795</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1929</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>681</td>\n",
       "      <td>340</td>\n",
       "      <td>510.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99937</th>\n",
       "      <td>7000534033</td>\n",
       "      <td>109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6/30/2014</td>\n",
       "      <td>7/31/2014</td>\n",
       "      <td>8/31/2014</td>\n",
       "      <td>9/30/2014</td>\n",
       "      <td>351.397</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>530</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>446</td>\n",
       "      <td>576</td>\n",
       "      <td>511.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99939</th>\n",
       "      <td>7001631916</td>\n",
       "      <td>109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6/30/2014</td>\n",
       "      <td>7/31/2014</td>\n",
       "      <td>8/31/2014</td>\n",
       "      <td>9/30/2014</td>\n",
       "      <td>398.308</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2229</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>456</td>\n",
       "      <td>946</td>\n",
       "      <td>701.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99941</th>\n",
       "      <td>7002281862</td>\n",
       "      <td>109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6/30/2014</td>\n",
       "      <td>7/31/2014</td>\n",
       "      <td>8/31/2014</td>\n",
       "      <td>9/30/2014</td>\n",
       "      <td>565.598</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1661</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>775</td>\n",
       "      <td>920</td>\n",
       "      <td>847.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99943</th>\n",
       "      <td>7000899330</td>\n",
       "      <td>109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6/30/2014</td>\n",
       "      <td>7/31/2014</td>\n",
       "      <td>8/31/2014</td>\n",
       "      <td>9/30/2014</td>\n",
       "      <td>189.320</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>224</td>\n",
       "      <td>113.05</td>\n",
       "      <td>181.91</td>\n",
       "      <td>406.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>351</td>\n",
       "      <td>565</td>\n",
       "      <td>458.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99957</th>\n",
       "      <td>7002108597</td>\n",
       "      <td>109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6/30/2014</td>\n",
       "      <td>7/31/2014</td>\n",
       "      <td>8/31/2014</td>\n",
       "      <td>9/30/2014</td>\n",
       "      <td>1090.075</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1214</td>\n",
       "      <td>1729.06</td>\n",
       "      <td>1151.35</td>\n",
       "      <td>0.00</td>\n",
       "      <td>235.08</td>\n",
       "      <td>1376</td>\n",
       "      <td>672</td>\n",
       "      <td>1024.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99959</th>\n",
       "      <td>7001792051</td>\n",
       "      <td>109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6/30/2014</td>\n",
       "      <td>7/31/2014</td>\n",
       "      <td>8/31/2014</td>\n",
       "      <td>9/30/2014</td>\n",
       "      <td>268.845</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1923</td>\n",
       "      <td>64.43</td>\n",
       "      <td>65.21</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>252</td>\n",
       "      <td>692</td>\n",
       "      <td>472.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99961</th>\n",
       "      <td>7000992757</td>\n",
       "      <td>109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6/30/2014</td>\n",
       "      <td>7/31/2014</td>\n",
       "      <td>8/31/2014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>283.572</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2168</td>\n",
       "      <td>0.00</td>\n",
       "      <td>41.99</td>\n",
       "      <td>112.64</td>\n",
       "      <td>0.00</td>\n",
       "      <td>220</td>\n",
       "      <td>596</td>\n",
       "      <td>408.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99963</th>\n",
       "      <td>7000484880</td>\n",
       "      <td>109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6/30/2014</td>\n",
       "      <td>7/31/2014</td>\n",
       "      <td>8/31/2014</td>\n",
       "      <td>9/30/2014</td>\n",
       "      <td>583.505</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>763</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>696</td>\n",
       "      <td>683</td>\n",
       "      <td>689.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99965</th>\n",
       "      <td>7000519799</td>\n",
       "      <td>109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6/30/2014</td>\n",
       "      <td>7/31/2014</td>\n",
       "      <td>8/31/2014</td>\n",
       "      <td>9/30/2014</td>\n",
       "      <td>292.930</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>464</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>336</td>\n",
       "      <td>736</td>\n",
       "      <td>536.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99970</th>\n",
       "      <td>7001386760</td>\n",
       "      <td>109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6/30/2014</td>\n",
       "      <td>7/31/2014</td>\n",
       "      <td>8/31/2014</td>\n",
       "      <td>9/30/2014</td>\n",
       "      <td>261.269</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1022</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>337</td>\n",
       "      <td>447</td>\n",
       "      <td>392.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99974</th>\n",
       "      <td>7001453306</td>\n",
       "      <td>109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6/30/2014</td>\n",
       "      <td>7/31/2014</td>\n",
       "      <td>8/31/2014</td>\n",
       "      <td>9/30/2014</td>\n",
       "      <td>414.303</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3054</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>520</td>\n",
       "      <td>410</td>\n",
       "      <td>465.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99986</th>\n",
       "      <td>7001729035</td>\n",
       "      <td>109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6/30/2014</td>\n",
       "      <td>7/31/2014</td>\n",
       "      <td>8/31/2014</td>\n",
       "      <td>9/30/2014</td>\n",
       "      <td>644.973</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2696</td>\n",
       "      <td>497.45</td>\n",
       "      <td>598.67</td>\n",
       "      <td>604.08</td>\n",
       "      <td>0.00</td>\n",
       "      <td>567</td>\n",
       "      <td>1130</td>\n",
       "      <td>848.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99988</th>\n",
       "      <td>7002111859</td>\n",
       "      <td>109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6/30/2014</td>\n",
       "      <td>7/31/2014</td>\n",
       "      <td>8/31/2014</td>\n",
       "      <td>9/30/2014</td>\n",
       "      <td>312.558</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>328</td>\n",
       "      <td>104.73</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>380</td>\n",
       "      <td>554</td>\n",
       "      <td>467.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99997</th>\n",
       "      <td>7000498689</td>\n",
       "      <td>109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6/30/2014</td>\n",
       "      <td>7/31/2014</td>\n",
       "      <td>8/31/2014</td>\n",
       "      <td>9/30/2014</td>\n",
       "      <td>322.991</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>478</td>\n",
       "      <td>1445.74</td>\n",
       "      <td>1151.03</td>\n",
       "      <td>1173.18</td>\n",
       "      <td>0.00</td>\n",
       "      <td>381</td>\n",
       "      <td>358</td>\n",
       "      <td>369.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30011 rows × 229 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       mobile_number  circle_id  loc_og_t2o_mou  std_og_t2o_mou  \\\n",
       "7         7000701601        109             0.0             0.0   \n",
       "8         7001524846        109             0.0             0.0   \n",
       "13        7002191713        109             0.0             0.0   \n",
       "16        7000875565        109             0.0             0.0   \n",
       "17        7000187447        109             0.0             0.0   \n",
       "21        7002124215        109             0.0             0.0   \n",
       "24        7001125315        109             0.0             0.0   \n",
       "33        7000149764        109             0.0             0.0   \n",
       "38        7000815202        109             0.0             0.0   \n",
       "41        7000721289        109             0.0             0.0   \n",
       "48        7000294396        109             0.0             0.0   \n",
       "53        7002188521        109             0.0             0.0   \n",
       "56        7002180748        109             0.0             0.0   \n",
       "57        7001682585        109             0.0             0.0   \n",
       "60        7000955945        109             0.0             0.0   \n",
       "61        7000152517        109             0.0             0.0   \n",
       "69        7002393996        109             0.0             0.0   \n",
       "73        7002402996        109             0.0             0.0   \n",
       "74        7001532600        109             0.0             0.0   \n",
       "77        7001328263        109             0.0             0.0   \n",
       "87        7001728145        109             0.0             0.0   \n",
       "92        7001606600        109             0.0             0.0   \n",
       "96        7001480390        109             0.0             0.0   \n",
       "97        7000589828        109             0.0             0.0   \n",
       "101       7001746121        109             0.0             0.0   \n",
       "102       7001631060        109             0.0             0.0   \n",
       "103       7002236431        109             0.0             0.0   \n",
       "105       7000208415        109             0.0             0.0   \n",
       "111       7001300706        109             0.0             0.0   \n",
       "115       7001453769        109             0.0             0.0   \n",
       "...              ...        ...             ...             ...   \n",
       "99887     7001271496        109             0.0             0.0   \n",
       "99893     7001438347        109             0.0             0.0   \n",
       "99896     7002362186        109             0.0             0.0   \n",
       "99899     7001632563        109             0.0             0.0   \n",
       "99902     7002142544        109             0.0             0.0   \n",
       "99904     7000649339        109             0.0             0.0   \n",
       "99921     7000157295        109             0.0             0.0   \n",
       "99922     7001602180        109             0.0             0.0   \n",
       "99923     7000571771        109             0.0             0.0   \n",
       "99924     7001581208        109             0.0             0.0   \n",
       "99925     7000877443        109             0.0             0.0   \n",
       "99927     7001570091        109             0.0             0.0   \n",
       "99929     7002128793        109             0.0             0.0   \n",
       "99931     7001904289        109             0.0             0.0   \n",
       "99932     7000018772        109             0.0             0.0   \n",
       "99935     7002109027        109             0.0             0.0   \n",
       "99937     7000534033        109             0.0             0.0   \n",
       "99939     7001631916        109             0.0             0.0   \n",
       "99941     7002281862        109             0.0             0.0   \n",
       "99943     7000899330        109             0.0             0.0   \n",
       "99957     7002108597        109             0.0             0.0   \n",
       "99959     7001792051        109             0.0             0.0   \n",
       "99961     7000992757        109             0.0             0.0   \n",
       "99963     7000484880        109             0.0             0.0   \n",
       "99965     7000519799        109             0.0             0.0   \n",
       "99970     7001386760        109             0.0             0.0   \n",
       "99974     7001453306        109             0.0             0.0   \n",
       "99986     7001729035        109             0.0             0.0   \n",
       "99988     7002111859        109             0.0             0.0   \n",
       "99997     7000498689        109             0.0             0.0   \n",
       "\n",
       "       loc_ic_t2o_mou last_date_of_month_6 last_date_of_month_7  \\\n",
       "7                 0.0            6/30/2014            7/31/2014   \n",
       "8                 0.0            6/30/2014            7/31/2014   \n",
       "13                0.0            6/30/2014            7/31/2014   \n",
       "16                0.0            6/30/2014            7/31/2014   \n",
       "17                0.0            6/30/2014            7/31/2014   \n",
       "21                0.0            6/30/2014            7/31/2014   \n",
       "24                0.0            6/30/2014            7/31/2014   \n",
       "33                0.0            6/30/2014            7/31/2014   \n",
       "38                0.0            6/30/2014            7/31/2014   \n",
       "41                0.0            6/30/2014            7/31/2014   \n",
       "48                0.0            6/30/2014            7/31/2014   \n",
       "53                0.0            6/30/2014            7/31/2014   \n",
       "56                0.0            6/30/2014            7/31/2014   \n",
       "57                0.0            6/30/2014            7/31/2014   \n",
       "60                0.0            6/30/2014            7/31/2014   \n",
       "61                0.0            6/30/2014            7/31/2014   \n",
       "69                0.0            6/30/2014            7/31/2014   \n",
       "73                0.0            6/30/2014            7/31/2014   \n",
       "74                0.0            6/30/2014            7/31/2014   \n",
       "77                0.0            6/30/2014            7/31/2014   \n",
       "87                0.0            6/30/2014            7/31/2014   \n",
       "92                0.0            6/30/2014            7/31/2014   \n",
       "96                0.0            6/30/2014            7/31/2014   \n",
       "97                0.0            6/30/2014            7/31/2014   \n",
       "101               0.0            6/30/2014            7/31/2014   \n",
       "102               0.0            6/30/2014            7/31/2014   \n",
       "103               0.0            6/30/2014            7/31/2014   \n",
       "105               0.0            6/30/2014            7/31/2014   \n",
       "111               0.0            6/30/2014            7/31/2014   \n",
       "115               0.0            6/30/2014            7/31/2014   \n",
       "...               ...                  ...                  ...   \n",
       "99887             0.0            6/30/2014            7/31/2014   \n",
       "99893             0.0            6/30/2014            7/31/2014   \n",
       "99896             0.0            6/30/2014            7/31/2014   \n",
       "99899             0.0            6/30/2014            7/31/2014   \n",
       "99902             0.0            6/30/2014            7/31/2014   \n",
       "99904             0.0            6/30/2014            7/31/2014   \n",
       "99921             0.0            6/30/2014            7/31/2014   \n",
       "99922             0.0            6/30/2014            7/31/2014   \n",
       "99923             0.0            6/30/2014            7/31/2014   \n",
       "99924             0.0            6/30/2014            7/31/2014   \n",
       "99925             0.0            6/30/2014            7/31/2014   \n",
       "99927             0.0            6/30/2014            7/31/2014   \n",
       "99929             0.0            6/30/2014            7/31/2014   \n",
       "99931             0.0            6/30/2014            7/31/2014   \n",
       "99932             0.0            6/30/2014            7/31/2014   \n",
       "99935             0.0            6/30/2014            7/31/2014   \n",
       "99937             0.0            6/30/2014            7/31/2014   \n",
       "99939             0.0            6/30/2014            7/31/2014   \n",
       "99941             0.0            6/30/2014            7/31/2014   \n",
       "99943             0.0            6/30/2014            7/31/2014   \n",
       "99957             0.0            6/30/2014            7/31/2014   \n",
       "99959             0.0            6/30/2014            7/31/2014   \n",
       "99961             0.0            6/30/2014            7/31/2014   \n",
       "99963             0.0            6/30/2014            7/31/2014   \n",
       "99965             0.0            6/30/2014            7/31/2014   \n",
       "99970             0.0            6/30/2014            7/31/2014   \n",
       "99974             0.0            6/30/2014            7/31/2014   \n",
       "99986             0.0            6/30/2014            7/31/2014   \n",
       "99988             0.0            6/30/2014            7/31/2014   \n",
       "99997             0.0            6/30/2014            7/31/2014   \n",
       "\n",
       "      last_date_of_month_8 last_date_of_month_9    arpu_6  ...  fb_user_8  \\\n",
       "7                8/31/2014            9/30/2014  1069.180  ...        NaN   \n",
       "8                8/31/2014            9/30/2014   378.721  ...        1.0   \n",
       "13               8/31/2014            9/30/2014   492.846  ...        1.0   \n",
       "16               8/31/2014            9/30/2014   430.975  ...        NaN   \n",
       "17               8/31/2014            9/30/2014   690.008  ...        NaN   \n",
       "21               8/31/2014            9/30/2014   514.453  ...        NaN   \n",
       "24               8/31/2014            9/30/2014   422.050  ...        NaN   \n",
       "33               8/31/2014            9/30/2014   977.020  ...        1.0   \n",
       "38               8/31/2014            9/30/2014   363.987  ...        NaN   \n",
       "41               8/31/2014            9/30/2014   482.832  ...        NaN   \n",
       "48               8/31/2014            9/30/2014  1873.271  ...        NaN   \n",
       "53               8/31/2014            9/30/2014   978.077  ...        0.0   \n",
       "56               8/31/2014            9/30/2014   452.624  ...        1.0   \n",
       "57               8/31/2014            9/30/2014  1201.298  ...        1.0   \n",
       "60               8/31/2014            9/30/2014   410.261  ...        NaN   \n",
       "61               8/31/2014            9/30/2014   565.516  ...        1.0   \n",
       "69               8/31/2014            9/30/2014   809.518  ...        NaN   \n",
       "73               8/31/2014            9/30/2014  2401.829  ...        NaN   \n",
       "74               8/31/2014            9/30/2014   324.449  ...        NaN   \n",
       "77               8/31/2014            9/30/2014    30.000  ...        NaN   \n",
       "87               8/31/2014            9/30/2014   623.635  ...        1.0   \n",
       "92               8/31/2014            9/30/2014   465.794  ...        1.0   \n",
       "96               8/31/2014            9/30/2014   676.999  ...        NaN   \n",
       "97               8/31/2014            9/30/2014   374.863  ...        NaN   \n",
       "101              8/31/2014            9/30/2014   512.524  ...        1.0   \n",
       "102              8/31/2014            9/30/2014   451.375  ...        0.0   \n",
       "103              8/31/2014            9/30/2014   883.401  ...        NaN   \n",
       "105              8/31/2014            9/30/2014   659.587  ...        1.0   \n",
       "111              8/31/2014            9/30/2014   596.301  ...        NaN   \n",
       "115              8/31/2014            9/30/2014  1173.256  ...        1.0   \n",
       "...                    ...                  ...       ...  ...        ...   \n",
       "99887            8/31/2014            9/30/2014   816.531  ...        NaN   \n",
       "99893            8/31/2014            9/30/2014   547.602  ...        NaN   \n",
       "99896            8/31/2014            9/30/2014   504.271  ...        NaN   \n",
       "99899            8/31/2014            9/30/2014   976.749  ...        1.0   \n",
       "99902            8/31/2014            9/30/2014   441.209  ...        1.0   \n",
       "99904            8/31/2014            9/30/2014   435.504  ...        NaN   \n",
       "99921            8/31/2014            9/30/2014    79.066  ...        1.0   \n",
       "99922            8/31/2014            9/30/2014   458.049  ...        NaN   \n",
       "99923            8/31/2014            9/30/2014   919.526  ...        1.0   \n",
       "99924            8/31/2014            9/30/2014   457.827  ...        NaN   \n",
       "99925            8/31/2014            9/30/2014   137.643  ...        1.0   \n",
       "99927            8/31/2014            9/30/2014   474.220  ...        1.0   \n",
       "99929            8/31/2014            9/30/2014   418.140  ...        1.0   \n",
       "99931            8/31/2014            9/30/2014   897.341  ...        NaN   \n",
       "99932            8/31/2014            9/30/2014   419.691  ...        NaN   \n",
       "99935            8/31/2014            9/30/2014   552.795  ...        1.0   \n",
       "99937            8/31/2014            9/30/2014   351.397  ...        NaN   \n",
       "99939            8/31/2014            9/30/2014   398.308  ...        NaN   \n",
       "99941            8/31/2014            9/30/2014   565.598  ...        NaN   \n",
       "99943            8/31/2014            9/30/2014   189.320  ...        NaN   \n",
       "99957            8/31/2014            9/30/2014  1090.075  ...        1.0   \n",
       "99959            8/31/2014            9/30/2014   268.845  ...        1.0   \n",
       "99961            8/31/2014                  NaN   283.572  ...        NaN   \n",
       "99963            8/31/2014            9/30/2014   583.505  ...        NaN   \n",
       "99965            8/31/2014            9/30/2014   292.930  ...        NaN   \n",
       "99970            8/31/2014            9/30/2014   261.269  ...        NaN   \n",
       "99974            8/31/2014            9/30/2014   414.303  ...        NaN   \n",
       "99986            8/31/2014            9/30/2014   644.973  ...        1.0   \n",
       "99988            8/31/2014            9/30/2014   312.558  ...        1.0   \n",
       "99997            8/31/2014            9/30/2014   322.991  ...        1.0   \n",
       "\n",
       "       fb_user_9   aon  aug_vbc_3g  jul_vbc_3g  jun_vbc_3g  sep_vbc_3g  \\\n",
       "7            NaN   802       57.74       19.38       18.74        0.00   \n",
       "8            NaN   315       21.03      910.65      122.16        0.00   \n",
       "13           NaN  2607        0.00        0.00        0.00        0.00   \n",
       "16           NaN   511        0.00        2.45       21.89        0.00   \n",
       "17           NaN   667        0.00        0.00        0.00        0.00   \n",
       "21           NaN   720        0.00        0.00        0.00        0.00   \n",
       "24           NaN  3072        0.00        0.00        0.00        0.00   \n",
       "33           1.0  1328      358.10     2706.83     1714.67        0.00   \n",
       "38           0.0  1155        0.00        0.00        0.00        0.00   \n",
       "41           NaN  2010        0.00        0.00        0.00        0.00   \n",
       "48           NaN  1265        0.00        0.00        0.00        0.00   \n",
       "53           0.0   697        0.00        0.00        0.00        0.00   \n",
       "56           1.0   729      277.97      535.63     1459.45        0.00   \n",
       "57           1.0  3106     1799.09     3649.39     1892.32        0.00   \n",
       "60           NaN  3292        0.00        0.00        0.00        0.00   \n",
       "61           1.0  1131        0.00        0.00        0.00        0.00   \n",
       "69           NaN   607        0.00        0.00        0.00        0.00   \n",
       "73           NaN  2283        0.00        0.00        0.00        0.00   \n",
       "74           NaN   444        0.00        0.00        0.00        0.00   \n",
       "77           NaN  1894        0.00        0.00        0.00        0.00   \n",
       "87           1.0  3201        0.00        0.00        0.00        0.00   \n",
       "92           1.0  3226        0.00        0.00        0.00        0.00   \n",
       "96           NaN   970        0.00        0.00        0.00        0.00   \n",
       "97           NaN   502        0.00        0.00        0.00        0.00   \n",
       "101          1.0   359        0.00        0.00        0.00        0.00   \n",
       "102          0.0  1727        0.00        0.00        0.00        0.00   \n",
       "103          NaN   902        0.00        0.00        0.00        0.00   \n",
       "105          1.0   467       65.85        6.46        0.00        0.00   \n",
       "111          NaN   332        0.00        0.00        0.00        0.00   \n",
       "115          NaN  2954      253.66        0.00       48.55        0.00   \n",
       "...          ...   ...         ...         ...         ...         ...   \n",
       "99887        NaN   485        0.00        0.00        0.00        0.00   \n",
       "99893        NaN  3003        0.00        0.00        0.00        0.00   \n",
       "99896        NaN  1843        0.00        0.00        0.00        0.00   \n",
       "99899        1.0  3409      750.31     1056.00      869.22        0.00   \n",
       "99902        NaN  1404        0.00        0.00        0.00        0.00   \n",
       "99904        NaN  1698        0.00        0.00        0.00        0.00   \n",
       "99921        NaN   560      219.42      764.34        0.00        0.51   \n",
       "99922        0.0   507        0.00        0.00        0.00        0.00   \n",
       "99923        NaN   198     1164.67     1320.06     1447.87        0.00   \n",
       "99924        NaN  3104        0.00        0.00        0.00        0.00   \n",
       "99925        1.0   485      323.56       86.55        0.00        0.00   \n",
       "99927        1.0  2333      150.56      296.70       95.89        0.00   \n",
       "99929        1.0  2349        0.00        0.00        0.00        0.00   \n",
       "99931        1.0  2894      252.13      383.62      349.81        0.00   \n",
       "99932        NaN   180        0.00        0.00        0.00        0.00   \n",
       "99935        1.0  1929        0.00        0.00        0.00        0.00   \n",
       "99937        NaN   530        0.00        0.00        0.00        0.00   \n",
       "99939        NaN  2229        0.00        0.00        0.00        0.00   \n",
       "99941        NaN  1661        0.00        0.00        0.00        0.00   \n",
       "99943        NaN   224      113.05      181.91      406.02        0.00   \n",
       "99957        1.0  1214     1729.06     1151.35        0.00      235.08   \n",
       "99959        1.0  1923       64.43       65.21        0.00        0.00   \n",
       "99961        NaN  2168        0.00       41.99      112.64        0.00   \n",
       "99963        NaN   763        0.00        0.00        0.00        0.00   \n",
       "99965        NaN   464        0.00        0.00        0.00        0.00   \n",
       "99970        0.0  1022        0.00        0.00        0.00        0.00   \n",
       "99974        NaN  3054        0.00        0.00        0.00        0.00   \n",
       "99986        1.0  2696      497.45      598.67      604.08        0.00   \n",
       "99988        1.0   328      104.73        0.00        0.00        0.00   \n",
       "99997        1.0   478     1445.74     1151.03     1173.18        0.00   \n",
       "\n",
       "       total_recharge_6  total_recharge_7  average_total_recharge_6_7  \n",
       "7                  1580               790                      1185.0  \n",
       "8                   437               601                       519.0  \n",
       "13                  507               253                       380.0  \n",
       "16                  570               348                       459.0  \n",
       "17                  816                 0                       408.0  \n",
       "21                  600               680                       640.0  \n",
       "24                  440               330                       385.0  \n",
       "33                 1058              2806                      1932.0  \n",
       "38                  420               636                       528.0  \n",
       "41                  496               510                       503.0  \n",
       "48                 2317               580                      1448.5  \n",
       "53                 1159              1372                      1265.5  \n",
       "56                  528               380                       454.0  \n",
       "57                 1389               905                      1147.0  \n",
       "60                  430               500                       465.0  \n",
       "61                  621               525                       573.0  \n",
       "69                  986               806                       896.0  \n",
       "73                 2759              2294                      2526.5  \n",
       "74                  356               424                       390.0  \n",
       "77                    0              1154                       577.0  \n",
       "87                  750               888                       819.0  \n",
       "92                  628               554                       591.0  \n",
       "96                 1033               832                       932.5  \n",
       "97                  432               328                       380.0  \n",
       "101                 716               526                       621.0  \n",
       "102                 421               427                       424.0  \n",
       "103                1025              1110                      1067.5  \n",
       "105                 710               605                       657.5  \n",
       "111                 704               178                       441.0  \n",
       "115                1111               588                       849.5  \n",
       "...                 ...               ...                         ...  \n",
       "99887              1000               761                       880.5  \n",
       "99893               629               530                       579.5  \n",
       "99896               640               588                       614.0  \n",
       "99899               504              1380                       942.0  \n",
       "99902               454               384                       419.0  \n",
       "99904               550               560                       555.0  \n",
       "99921                90               649                       369.5  \n",
       "99922               518               775                       646.5  \n",
       "99923              1096              1132                      1114.0  \n",
       "99924               550               450                       500.0  \n",
       "99925               177              1147                       662.0  \n",
       "99927               589               512                       550.5  \n",
       "99929               548               548                       548.0  \n",
       "99931              1099               250                       674.5  \n",
       "99932               466               435                       450.5  \n",
       "99935               681               340                       510.5  \n",
       "99937               446               576                       511.0  \n",
       "99939               456               946                       701.0  \n",
       "99941               775               920                       847.5  \n",
       "99943               351               565                       458.0  \n",
       "99957              1376               672                      1024.0  \n",
       "99959               252               692                       472.0  \n",
       "99961               220               596                       408.0  \n",
       "99963               696               683                       689.5  \n",
       "99965               336               736                       536.0  \n",
       "99970               337               447                       392.0  \n",
       "99974               520               410                       465.0  \n",
       "99986               567              1130                       848.5  \n",
       "99988               380               554                       467.0  \n",
       "99997               381               358                       369.5  \n",
       "\n",
       "[30011 rows x 229 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The threshold value for high-value customers is 368.5 and now filtering the data using this value\n",
    "\n",
    "churn_data = churn_data.loc[churn_data['average_total_recharge_6_7'] >= 368.5]\n",
    "churn_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mobile_number</th>\n",
       "      <th>vol_2g_mb_6</th>\n",
       "      <th>vol_3g_mb_6</th>\n",
       "      <th>vol_2g_mb_7</th>\n",
       "      <th>vol_3g_mb_7</th>\n",
       "      <th>vol_2g_mb_8</th>\n",
       "      <th>vol_3g_mb_8</th>\n",
       "      <th>vol_2g_mb_9</th>\n",
       "      <th>vol_3g_mb_9</th>\n",
       "      <th>total_og_mou_6</th>\n",
       "      <th>total_og_mou_7</th>\n",
       "      <th>total_og_mou_8</th>\n",
       "      <th>total_og_mou_9</th>\n",
       "      <th>total_ic_mou_6</th>\n",
       "      <th>total_ic_mou_7</th>\n",
       "      <th>total_ic_mou_8</th>\n",
       "      <th>total_ic_mou_9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7000701601</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>487.53</td>\n",
       "      <td>609.24</td>\n",
       "      <td>350.16</td>\n",
       "      <td>0.00</td>\n",
       "      <td>626.46</td>\n",
       "      <td>558.04</td>\n",
       "      <td>428.74</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7001524846</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>356.0</td>\n",
       "      <td>750.95</td>\n",
       "      <td>0.03</td>\n",
       "      <td>11.94</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>508.36</td>\n",
       "      <td>431.66</td>\n",
       "      <td>171.56</td>\n",
       "      <td>142.18</td>\n",
       "      <td>103.86</td>\n",
       "      <td>28.49</td>\n",
       "      <td>16.54</td>\n",
       "      <td>34.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>7002191713</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>907.09</td>\n",
       "      <td>192.41</td>\n",
       "      <td>1015.26</td>\n",
       "      <td>446.09</td>\n",
       "      <td>968.61</td>\n",
       "      <td>172.58</td>\n",
       "      <td>1144.53</td>\n",
       "      <td>631.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>7000875565</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>346.81</td>\n",
       "      <td>286.01</td>\n",
       "      <td>233.38</td>\n",
       "      <td>255.74</td>\n",
       "      <td>364.86</td>\n",
       "      <td>381.56</td>\n",
       "      <td>294.46</td>\n",
       "      <td>334.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>7000187447</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1249.53</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.00</td>\n",
       "      <td>631.08</td>\n",
       "      <td>91.88</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0.00</td>\n",
       "      <td>87.13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mobile_number  vol_2g_mb_6  vol_3g_mb_6  vol_2g_mb_7  vol_3g_mb_7  \\\n",
       "7      7000701601          0.0          0.0          0.0         0.00   \n",
       "8      7001524846          0.0          0.0        356.0       750.95   \n",
       "13     7002191713          0.0          0.0          0.0         0.00   \n",
       "16     7000875565          0.0          0.0          0.0         0.00   \n",
       "17     7000187447          0.0          0.0          0.0         0.00   \n",
       "\n",
       "    vol_2g_mb_8  vol_3g_mb_8  vol_2g_mb_9  vol_3g_mb_9  total_og_mou_6  \\\n",
       "7          0.00         0.00          0.0          0.0          487.53   \n",
       "8          0.03        11.94          0.0          0.0          508.36   \n",
       "13         0.02         0.00          0.0          0.0          907.09   \n",
       "16         0.00         0.00          0.0          0.0          346.81   \n",
       "17         0.00         0.00          0.0          0.0         1249.53   \n",
       "\n",
       "    total_og_mou_7  total_og_mou_8  total_og_mou_9  total_ic_mou_6  \\\n",
       "7           609.24          350.16            0.00          626.46   \n",
       "8           431.66          171.56          142.18          103.86   \n",
       "13          192.41         1015.26          446.09          968.61   \n",
       "16          286.01          233.38          255.74          364.86   \n",
       "17            0.81            0.00          631.08           91.88   \n",
       "\n",
       "    total_ic_mou_7  total_ic_mou_8  total_ic_mou_9  \n",
       "7           558.04          428.74            0.00  \n",
       "8            28.49           16.54           34.91  \n",
       "13          172.58         1144.53          631.86  \n",
       "16          381.56          294.46          334.56  \n",
       "17            2.31            0.00           87.13  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We get around 30k high-value customers\n",
    "# Now we create a new dataframe called \"churn\" containing only useful values that are more indicative of churn\n",
    "\n",
    "churn = churn_data[[\"mobile_number\",'vol_2g_mb_6','vol_3g_mb_6','vol_2g_mb_7','vol_3g_mb_7','vol_2g_mb_8','vol_3g_mb_8'\n",
    "                ,'vol_2g_mb_9','vol_3g_mb_9',\"total_og_mou_6\",\"total_og_mou_7\",\"total_og_mou_8\",\n",
    "               \"total_og_mou_9\",\"total_ic_mou_6\",\"total_ic_mou_7\",\"total_ic_mou_8\",\"total_ic_mou_9\"]]\n",
    "\n",
    "churn.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mobile_number     0\n",
       "vol_2g_mb_6       0\n",
       "vol_3g_mb_6       0\n",
       "vol_2g_mb_7       0\n",
       "vol_3g_mb_7       0\n",
       "vol_2g_mb_8       0\n",
       "vol_3g_mb_8       0\n",
       "vol_2g_mb_9       0\n",
       "vol_3g_mb_9       0\n",
       "total_og_mou_6    0\n",
       "total_og_mou_7    0\n",
       "total_og_mou_8    0\n",
       "total_og_mou_9    0\n",
       "total_ic_mou_6    0\n",
       "total_ic_mou_7    0\n",
       "total_ic_mou_8    0\n",
       "total_ic_mou_9    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for null values\n",
    "\n",
    "churn.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tag churners and remove attributes of the churn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To tag users as churn or not churn, using functions to determine whether users received incoming calls, made outgoing calls,\n",
    "# used 2g and 3g data\n",
    "\n",
    "def total_ic_mou_9(x):\n",
    "    if x == 0:\n",
    "        return \"No Usage\"\n",
    "    else:\n",
    "        return \"Active\"\n",
    "    \n",
    "churn['IC_mou_9'] = churn['total_ic_mou_9'].apply(total_ic_mou_9)\n",
    "\n",
    "def total_og_mou_9(x):\n",
    "    if x == 0:\n",
    "        return \"No Usage\"\n",
    "    else:\n",
    "        return \"Active\"\n",
    "    \n",
    "churn['OG_mou_9'] = churn['total_og_mou_9'].apply(total_og_mou_9)\n",
    "\n",
    "def vol_2g_mb_9 (x):\n",
    "    if x == 0:\n",
    "        return \"No Usage\"\n",
    "    else:\n",
    "        return \"Active\"\n",
    "    \n",
    "churn['2G_mb_9'] = churn['vol_2g_mb_9'].apply(vol_2g_mb_9)\n",
    "\n",
    "def vol_3g_mb_9 (x):\n",
    "    if x == 0:\n",
    "        return \"No Usage\"\n",
    "    else:\n",
    "        return \"Active\"\n",
    "    \n",
    "churn['3G_mb_9'] = churn['vol_3g_mb_9'].apply(vol_3g_mb_9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a new function to tag churned users\n",
    "\n",
    "def f(row):\n",
    "    if row[\"IC_mou_9\"] == \"No Usage\" and row[\"OG_mou_9\"] == \"No Usage\" and row[\"2G_mb_9\"] == \"No Usage\" and row[\"3G_mb_9\"] == \"No Usage\":\n",
    "        return \"1\"\n",
    "    else:\n",
    "        return \"0\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# checking to see churn rate in a new column \"Churned\"\n",
    "\n",
    "churn['Churned'] = churn.apply(f, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\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>vol_2g_mb_6</th>\n",
       "      <th>vol_3g_mb_6</th>\n",
       "      <th>vol_2g_mb_7</th>\n",
       "      <th>vol_3g_mb_7</th>\n",
       "      <th>vol_2g_mb_8</th>\n",
       "      <th>vol_3g_mb_8</th>\n",
       "      <th>vol_2g_mb_9</th>\n",
       "      <th>vol_3g_mb_9</th>\n",
       "      <th>total_og_mou_6</th>\n",
       "      <th>...</th>\n",
       "      <th>total_og_mou_9</th>\n",
       "      <th>total_ic_mou_6</th>\n",
       "      <th>total_ic_mou_7</th>\n",
       "      <th>total_ic_mou_8</th>\n",
       "      <th>total_ic_mou_9</th>\n",
       "      <th>IC_mou_9</th>\n",
       "      <th>OG_mou_9</th>\n",
       "      <th>2G_mb_9</th>\n",
       "      <th>3G_mb_9</th>\n",
       "      <th>Churned</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7000701601</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>487.53</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>626.46</td>\n",
       "      <td>558.04</td>\n",
       "      <td>428.74</td>\n",
       "      <td>0.00</td>\n",
       "      <td>No Usage</td>\n",
       "      <td>No Usage</td>\n",
       "      <td>No Usage</td>\n",
       "      <td>No Usage</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7001524846</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>356.0</td>\n",
       "      <td>750.95</td>\n",
       "      <td>0.03</td>\n",
       "      <td>11.94</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>508.36</td>\n",
       "      <td>...</td>\n",
       "      <td>142.18</td>\n",
       "      <td>103.86</td>\n",
       "      <td>28.49</td>\n",
       "      <td>16.54</td>\n",
       "      <td>34.91</td>\n",
       "      <td>Active</td>\n",
       "      <td>Active</td>\n",
       "      <td>No Usage</td>\n",
       "      <td>No Usage</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>7002191713</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>907.09</td>\n",
       "      <td>...</td>\n",
       "      <td>446.09</td>\n",
       "      <td>968.61</td>\n",
       "      <td>172.58</td>\n",
       "      <td>1144.53</td>\n",
       "      <td>631.86</td>\n",
       "      <td>Active</td>\n",
       "      <td>Active</td>\n",
       "      <td>No Usage</td>\n",
       "      <td>No Usage</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>7000875565</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>346.81</td>\n",
       "      <td>...</td>\n",
       "      <td>255.74</td>\n",
       "      <td>364.86</td>\n",
       "      <td>381.56</td>\n",
       "      <td>294.46</td>\n",
       "      <td>334.56</td>\n",
       "      <td>Active</td>\n",
       "      <td>Active</td>\n",
       "      <td>No Usage</td>\n",
       "      <td>No Usage</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>7000187447</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1249.53</td>\n",
       "      <td>...</td>\n",
       "      <td>631.08</td>\n",
       "      <td>91.88</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0.00</td>\n",
       "      <td>87.13</td>\n",
       "      <td>Active</td>\n",
       "      <td>Active</td>\n",
       "      <td>No Usage</td>\n",
       "      <td>No Usage</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    mobile_number  vol_2g_mb_6  vol_3g_mb_6  vol_2g_mb_7  vol_3g_mb_7  \\\n",
       "7      7000701601          0.0          0.0          0.0         0.00   \n",
       "8      7001524846          0.0          0.0        356.0       750.95   \n",
       "13     7002191713          0.0          0.0          0.0         0.00   \n",
       "16     7000875565          0.0          0.0          0.0         0.00   \n",
       "17     7000187447          0.0          0.0          0.0         0.00   \n",
       "\n",
       "    vol_2g_mb_8  vol_3g_mb_8  vol_2g_mb_9  vol_3g_mb_9  total_og_mou_6  ...  \\\n",
       "7          0.00         0.00          0.0          0.0          487.53  ...   \n",
       "8          0.03        11.94          0.0          0.0          508.36  ...   \n",
       "13         0.02         0.00          0.0          0.0          907.09  ...   \n",
       "16         0.00         0.00          0.0          0.0          346.81  ...   \n",
       "17         0.00         0.00          0.0          0.0         1249.53  ...   \n",
       "\n",
       "    total_og_mou_9  total_ic_mou_6  total_ic_mou_7  total_ic_mou_8  \\\n",
       "7             0.00          626.46          558.04          428.74   \n",
       "8           142.18          103.86           28.49           16.54   \n",
       "13          446.09          968.61          172.58         1144.53   \n",
       "16          255.74          364.86          381.56          294.46   \n",
       "17          631.08           91.88            2.31            0.00   \n",
       "\n",
       "    total_ic_mou_9  IC_mou_9  OG_mou_9   2G_mb_9   3G_mb_9 Churned  \n",
       "7             0.00  No Usage  No Usage  No Usage  No Usage       1  \n",
       "8            34.91    Active    Active  No Usage  No Usage       0  \n",
       "13          631.86    Active    Active  No Usage  No Usage       0  \n",
       "16          334.56    Active    Active  No Usage  No Usage       0  \n",
       "17           87.13    Active    Active  No Usage  No Usage       0  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking to see update\n",
    "\n",
    "churn.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.64016527273333"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculating the Churn Rate\n",
    "\n",
    "churn[\"Churned\"]= churn[\"Churned\"].astype(int)\n",
    "\n",
    "churn_rate = (sum(churn['Churned'])/len(churn['Churned'].index))*100\n",
    "churn_rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['mobile_number', 'vol_2g_mb_6', 'vol_3g_mb_6', 'vol_2g_mb_7',\n",
       "       'vol_3g_mb_7', 'vol_2g_mb_8', 'vol_3g_mb_8', 'vol_2g_mb_9',\n",
       "       'vol_3g_mb_9', 'total_og_mou_6', 'total_og_mou_7', 'total_og_mou_8',\n",
       "       'total_og_mou_9', 'total_ic_mou_6', 'total_ic_mou_7', 'total_ic_mou_8',\n",
       "       'total_ic_mou_9', 'IC_mou_9', 'OG_mou_9', '2G_mb_9', '3G_mb_9',\n",
       "       'Churned'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting the list of columns\n",
    "\n",
    "churn.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mobile_number</th>\n",
       "      <th>vol_2g_mb_6</th>\n",
       "      <th>vol_3g_mb_6</th>\n",
       "      <th>vol_2g_mb_7</th>\n",
       "      <th>vol_3g_mb_7</th>\n",
       "      <th>vol_2g_mb_8</th>\n",
       "      <th>vol_3g_mb_8</th>\n",
       "      <th>total_og_mou_6</th>\n",
       "      <th>total_og_mou_7</th>\n",
       "      <th>total_og_mou_8</th>\n",
       "      <th>total_ic_mou_6</th>\n",
       "      <th>total_ic_mou_7</th>\n",
       "      <th>total_ic_mou_8</th>\n",
       "      <th>IC_mou_9</th>\n",
       "      <th>OG_mou_9</th>\n",
       "      <th>2G_mb_9</th>\n",
       "      <th>3G_mb_9</th>\n",
       "      <th>Churned</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7000701601</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>487.53</td>\n",
       "      <td>609.24</td>\n",
       "      <td>350.16</td>\n",
       "      <td>626.46</td>\n",
       "      <td>558.04</td>\n",
       "      <td>428.74</td>\n",
       "      <td>No Usage</td>\n",
       "      <td>No Usage</td>\n",
       "      <td>No Usage</td>\n",
       "      <td>No Usage</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7001524846</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>356.0</td>\n",
       "      <td>750.95</td>\n",
       "      <td>0.03</td>\n",
       "      <td>11.94</td>\n",
       "      <td>508.36</td>\n",
       "      <td>431.66</td>\n",
       "      <td>171.56</td>\n",
       "      <td>103.86</td>\n",
       "      <td>28.49</td>\n",
       "      <td>16.54</td>\n",
       "      <td>Active</td>\n",
       "      <td>Active</td>\n",
       "      <td>No Usage</td>\n",
       "      <td>No Usage</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>7002191713</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>907.09</td>\n",
       "      <td>192.41</td>\n",
       "      <td>1015.26</td>\n",
       "      <td>968.61</td>\n",
       "      <td>172.58</td>\n",
       "      <td>1144.53</td>\n",
       "      <td>Active</td>\n",
       "      <td>Active</td>\n",
       "      <td>No Usage</td>\n",
       "      <td>No Usage</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>7000875565</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>346.81</td>\n",
       "      <td>286.01</td>\n",
       "      <td>233.38</td>\n",
       "      <td>364.86</td>\n",
       "      <td>381.56</td>\n",
       "      <td>294.46</td>\n",
       "      <td>Active</td>\n",
       "      <td>Active</td>\n",
       "      <td>No Usage</td>\n",
       "      <td>No Usage</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>7000187447</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1249.53</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.00</td>\n",
       "      <td>91.88</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Active</td>\n",
       "      <td>Active</td>\n",
       "      <td>No Usage</td>\n",
       "      <td>No Usage</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mobile_number  vol_2g_mb_6  vol_3g_mb_6  vol_2g_mb_7  vol_3g_mb_7  \\\n",
       "7      7000701601          0.0          0.0          0.0         0.00   \n",
       "8      7001524846          0.0          0.0        356.0       750.95   \n",
       "13     7002191713          0.0          0.0          0.0         0.00   \n",
       "16     7000875565          0.0          0.0          0.0         0.00   \n",
       "17     7000187447          0.0          0.0          0.0         0.00   \n",
       "\n",
       "    vol_2g_mb_8  vol_3g_mb_8  total_og_mou_6  total_og_mou_7  total_og_mou_8  \\\n",
       "7          0.00         0.00          487.53          609.24          350.16   \n",
       "8          0.03        11.94          508.36          431.66          171.56   \n",
       "13         0.02         0.00          907.09          192.41         1015.26   \n",
       "16         0.00         0.00          346.81          286.01          233.38   \n",
       "17         0.00         0.00         1249.53            0.81            0.00   \n",
       "\n",
       "    total_ic_mou_6  total_ic_mou_7  total_ic_mou_8  IC_mou_9  OG_mou_9  \\\n",
       "7           626.46          558.04          428.74  No Usage  No Usage   \n",
       "8           103.86           28.49           16.54    Active    Active   \n",
       "13          968.61          172.58         1144.53    Active    Active   \n",
       "16          364.86          381.56          294.46    Active    Active   \n",
       "17           91.88            2.31            0.00    Active    Active   \n",
       "\n",
       "     2G_mb_9   3G_mb_9  Churned  \n",
       "7   No Usage  No Usage        1  \n",
       "8   No Usage  No Usage        0  \n",
       "13  No Usage  No Usage        0  \n",
       "16  No Usage  No Usage        0  \n",
       "17  No Usage  No Usage        0  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dropping month 9 related columns\n",
    "\n",
    "churn = churn.drop(columns = ['vol_2g_mb_9','vol_3g_mb_9','total_og_mou_9','total_ic_mou_9'])\n",
    "\n",
    "churn.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting to 1 and 0\n",
    "\n",
    "churn['OG_mou_9'] = churn['OG_mou_9'].map({'No Usage': 0, 'Active': 1})\n",
    "churn['IC_mou_9'] = churn['IC_mou_9'].map({'No Usage': 0, 'Active': 1})\n",
    "churn['2G_mb_9'] = churn['2G_mb_9'].map({'No Usage': 0, 'Active': 1})\n",
    "churn['3G_mb_9'] = churn['3G_mb_9'].map({'No Usage': 0, 'Active': 1})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Conducting further data cleaning after tagging high-value customers as churn 1 or churn 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reusing updated dataset in new dataframe called \"df_1\" \n",
    "\n",
    "df_1 = churn_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mobile_number</th>\n",
       "      <th>circle_id</th>\n",
       "      <th>loc_og_t2o_mou</th>\n",
       "      <th>std_og_t2o_mou</th>\n",
       "      <th>loc_ic_t2o_mou</th>\n",
       "      <th>last_date_of_month_6</th>\n",
       "      <th>last_date_of_month_7</th>\n",
       "      <th>last_date_of_month_8</th>\n",
       "      <th>last_date_of_month_9</th>\n",
       "      <th>arpu_6</th>\n",
       "      <th>...</th>\n",
       "      <th>fb_user_8</th>\n",
       "      <th>fb_user_9</th>\n",
       "      <th>aon</th>\n",
       "      <th>aug_vbc_3g</th>\n",
       "      <th>jul_vbc_3g</th>\n",
       "      <th>jun_vbc_3g</th>\n",
       "      <th>sep_vbc_3g</th>\n",
       "      <th>total_recharge_6</th>\n",
       "      <th>total_recharge_7</th>\n",
       "      <th>average_total_recharge_6_7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7000701601</td>\n",
       "      <td>109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6/30/2014</td>\n",
       "      <td>7/31/2014</td>\n",
       "      <td>8/31/2014</td>\n",
       "      <td>9/30/2014</td>\n",
       "      <td>1069.180</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>802</td>\n",
       "      <td>57.74</td>\n",
       "      <td>19.38</td>\n",
       "      <td>18.74</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1580</td>\n",
       "      <td>790</td>\n",
       "      <td>1185.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7001524846</td>\n",
       "      <td>109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6/30/2014</td>\n",
       "      <td>7/31/2014</td>\n",
       "      <td>8/31/2014</td>\n",
       "      <td>9/30/2014</td>\n",
       "      <td>378.721</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>315</td>\n",
       "      <td>21.03</td>\n",
       "      <td>910.65</td>\n",
       "      <td>122.16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>437</td>\n",
       "      <td>601</td>\n",
       "      <td>519.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>7002191713</td>\n",
       "      <td>109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6/30/2014</td>\n",
       "      <td>7/31/2014</td>\n",
       "      <td>8/31/2014</td>\n",
       "      <td>9/30/2014</td>\n",
       "      <td>492.846</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2607</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>507</td>\n",
       "      <td>253</td>\n",
       "      <td>380.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>7000875565</td>\n",
       "      <td>109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6/30/2014</td>\n",
       "      <td>7/31/2014</td>\n",
       "      <td>8/31/2014</td>\n",
       "      <td>9/30/2014</td>\n",
       "      <td>430.975</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>511</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.45</td>\n",
       "      <td>21.89</td>\n",
       "      <td>0.0</td>\n",
       "      <td>570</td>\n",
       "      <td>348</td>\n",
       "      <td>459.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>7000187447</td>\n",
       "      <td>109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6/30/2014</td>\n",
       "      <td>7/31/2014</td>\n",
       "      <td>8/31/2014</td>\n",
       "      <td>9/30/2014</td>\n",
       "      <td>690.008</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>667</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>816</td>\n",
       "      <td>0</td>\n",
       "      <td>408.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 229 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    mobile_number  circle_id  loc_og_t2o_mou  std_og_t2o_mou  loc_ic_t2o_mou  \\\n",
       "7      7000701601        109             0.0             0.0             0.0   \n",
       "8      7001524846        109             0.0             0.0             0.0   \n",
       "13     7002191713        109             0.0             0.0             0.0   \n",
       "16     7000875565        109             0.0             0.0             0.0   \n",
       "17     7000187447        109             0.0             0.0             0.0   \n",
       "\n",
       "   last_date_of_month_6 last_date_of_month_7 last_date_of_month_8  \\\n",
       "7             6/30/2014            7/31/2014            8/31/2014   \n",
       "8             6/30/2014            7/31/2014            8/31/2014   \n",
       "13            6/30/2014            7/31/2014            8/31/2014   \n",
       "16            6/30/2014            7/31/2014            8/31/2014   \n",
       "17            6/30/2014            7/31/2014            8/31/2014   \n",
       "\n",
       "   last_date_of_month_9    arpu_6  ...  fb_user_8  fb_user_9   aon  \\\n",
       "7             9/30/2014  1069.180  ...        NaN        NaN   802   \n",
       "8             9/30/2014   378.721  ...        1.0        NaN   315   \n",
       "13            9/30/2014   492.846  ...        1.0        NaN  2607   \n",
       "16            9/30/2014   430.975  ...        NaN        NaN   511   \n",
       "17            9/30/2014   690.008  ...        NaN        NaN   667   \n",
       "\n",
       "    aug_vbc_3g  jul_vbc_3g  jun_vbc_3g  sep_vbc_3g  total_recharge_6  \\\n",
       "7        57.74       19.38       18.74         0.0              1580   \n",
       "8        21.03      910.65      122.16         0.0               437   \n",
       "13        0.00        0.00        0.00         0.0               507   \n",
       "16        0.00        2.45       21.89         0.0               570   \n",
       "17        0.00        0.00        0.00         0.0               816   \n",
       "\n",
       "    total_recharge_7  average_total_recharge_6_7  \n",
       "7                790                      1185.0  \n",
       "8                601                       519.0  \n",
       "13               253                       380.0  \n",
       "16               348                       459.0  \n",
       "17                 0                       408.0  \n",
       "\n",
       "[5 rows x 229 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking to see df_1\n",
    "\n",
    "df_1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mobile_number</th>\n",
       "      <th>total_og_mou_6</th>\n",
       "      <th>total_og_mou_7</th>\n",
       "      <th>total_og_mou_8</th>\n",
       "      <th>total_og_mou_9</th>\n",
       "      <th>total_ic_mou_6</th>\n",
       "      <th>total_ic_mou_7</th>\n",
       "      <th>total_ic_mou_8</th>\n",
       "      <th>total_ic_mou_9</th>\n",
       "      <th>loc_og_t2c_mou_6</th>\n",
       "      <th>...</th>\n",
       "      <th>total_rech_amt_8</th>\n",
       "      <th>total_rech_amt_9</th>\n",
       "      <th>total_rech_data_6</th>\n",
       "      <th>total_rech_data_7</th>\n",
       "      <th>total_rech_data_8</th>\n",
       "      <th>total_rech_data_9</th>\n",
       "      <th>av_rech_amt_data_6</th>\n",
       "      <th>av_rech_amt_data_7</th>\n",
       "      <th>av_rech_amt_data_8</th>\n",
       "      <th>av_rech_amt_data_9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7000701601</td>\n",
       "      <td>487.53</td>\n",
       "      <td>609.24</td>\n",
       "      <td>350.16</td>\n",
       "      <td>0.00</td>\n",
       "      <td>626.46</td>\n",
       "      <td>558.04</td>\n",
       "      <td>428.74</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3638</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7001524846</td>\n",
       "      <td>508.36</td>\n",
       "      <td>431.66</td>\n",
       "      <td>171.56</td>\n",
       "      <td>142.18</td>\n",
       "      <td>103.86</td>\n",
       "      <td>28.49</td>\n",
       "      <td>16.54</td>\n",
       "      <td>34.91</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>120</td>\n",
       "      <td>186</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>177.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>7002191713</td>\n",
       "      <td>907.09</td>\n",
       "      <td>192.41</td>\n",
       "      <td>1015.26</td>\n",
       "      <td>446.09</td>\n",
       "      <td>968.61</td>\n",
       "      <td>172.58</td>\n",
       "      <td>1144.53</td>\n",
       "      <td>631.86</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>717</td>\n",
       "      <td>353</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>69.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>7000875565</td>\n",
       "      <td>346.81</td>\n",
       "      <td>286.01</td>\n",
       "      <td>233.38</td>\n",
       "      <td>255.74</td>\n",
       "      <td>364.86</td>\n",
       "      <td>381.56</td>\n",
       "      <td>294.46</td>\n",
       "      <td>334.56</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>160</td>\n",
       "      <td>220</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>7000187447</td>\n",
       "      <td>1249.53</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.00</td>\n",
       "      <td>631.08</td>\n",
       "      <td>91.88</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0.00</td>\n",
       "      <td>87.13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>30</td>\n",
       "      <td>335</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 53 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    mobile_number  total_og_mou_6  total_og_mou_7  total_og_mou_8  \\\n",
       "7      7000701601          487.53          609.24          350.16   \n",
       "8      7001524846          508.36          431.66          171.56   \n",
       "13     7002191713          907.09          192.41         1015.26   \n",
       "16     7000875565          346.81          286.01          233.38   \n",
       "17     7000187447         1249.53            0.81            0.00   \n",
       "\n",
       "    total_og_mou_9  total_ic_mou_6  total_ic_mou_7  total_ic_mou_8  \\\n",
       "7             0.00          626.46          558.04          428.74   \n",
       "8           142.18          103.86           28.49           16.54   \n",
       "13          446.09          968.61          172.58         1144.53   \n",
       "16          255.74          364.86          381.56          294.46   \n",
       "17          631.08           91.88            2.31            0.00   \n",
       "\n",
       "    total_ic_mou_9  loc_og_t2c_mou_6  ...  total_rech_amt_8  total_rech_amt_9  \\\n",
       "7             0.00               0.0  ...              3638                 0   \n",
       "8            34.91               0.0  ...               120               186   \n",
       "13          631.86               0.0  ...               717               353   \n",
       "16          334.56               0.0  ...               160               220   \n",
       "17           87.13               0.0  ...                30               335   \n",
       "\n",
       "    total_rech_data_6  total_rech_data_7  total_rech_data_8  \\\n",
       "7                 NaN                NaN                NaN   \n",
       "8                 NaN                2.0                3.0   \n",
       "13                NaN                NaN                3.0   \n",
       "16                NaN                NaN                NaN   \n",
       "17                NaN                NaN                NaN   \n",
       "\n",
       "    total_rech_data_9  av_rech_amt_data_6  av_rech_amt_data_7  \\\n",
       "7                 NaN                 NaN                 NaN   \n",
       "8                 NaN                 NaN               177.0   \n",
       "13                NaN                 NaN                 NaN   \n",
       "16                NaN                 NaN                 NaN   \n",
       "17                NaN                 NaN                 NaN   \n",
       "\n",
       "    av_rech_amt_data_8  av_rech_amt_data_9  \n",
       "7                  NaN                 NaN  \n",
       "8                 69.0                 NaN  \n",
       "13                69.0                 NaN  \n",
       "16                 NaN                 NaN  \n",
       "17                 NaN                 NaN  \n",
       "\n",
       "[5 rows x 53 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Keeping only the columns that would be a predictor of churn based on relevance\n",
    "\n",
    "df_1 = df_1[[\"mobile_number\",\"total_og_mou_6\",\"total_og_mou_7\",\"total_og_mou_8\",\"total_og_mou_9\",\"total_ic_mou_6\",\n",
    "                    \"total_ic_mou_7\",\"total_ic_mou_8\",\"total_ic_mou_9\",\"loc_og_t2c_mou_6\",\"loc_og_t2c_mou_7\",\"loc_og_t2c_mou_8\",\n",
    "                    \"loc_og_t2c_mou_9\",\"std_og_t2c_mou_6\",\"std_og_t2c_mou_7\",\"std_og_t2c_mou_8\",\"std_og_t2c_mou_9\",\"loc_og_t2m_mou_6\",\n",
    "                    \"loc_og_t2m_mou_7\",\"loc_og_t2m_mou_8\",\"loc_og_t2m_mou_9\",\"std_og_t2m_mou_6\",\"std_og_t2m_mou_7\",\"std_og_t2m_mou_8\",\n",
    "                    \"std_og_t2m_mou_9\",\"loc_ic_t2m_mou_6\",\"loc_ic_t2m_mou_7\",\"loc_ic_t2m_mou_8\",\"loc_ic_t2m_mou_9\",\"std_ic_t2m_mou_6\",\n",
    "                    \"std_ic_t2m_mou_7\",\"std_ic_t2m_mou_8\",\"std_ic_t2m_mou_9\",\"vol_2g_mb_6\",\"vol_2g_mb_7\",\"vol_2g_mb_8\",\"vol_2g_mb_9\",\"vol_3g_mb_6\",\"vol_3g_mb_7\",\"vol_3g_mb_8\",\n",
    "                    \"vol_3g_mb_9\",'total_rech_amt_6','total_rech_amt_7','total_rech_amt_8','total_rech_amt_9','total_rech_data_6','total_rech_data_7'\n",
    "                    ,'total_rech_data_8','total_rech_data_9','av_rech_amt_data_6','av_rech_amt_data_7','av_rech_amt_data_8','av_rech_amt_data_9']]\n",
    "\n",
    "df_1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe thead 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>total_og_mou_6</th>\n",
       "      <th>total_og_mou_7</th>\n",
       "      <th>total_og_mou_8</th>\n",
       "      <th>total_og_mou_9</th>\n",
       "      <th>total_ic_mou_6</th>\n",
       "      <th>total_ic_mou_7</th>\n",
       "      <th>total_ic_mou_8</th>\n",
       "      <th>total_ic_mou_9</th>\n",
       "      <th>vol_2g_mb_6</th>\n",
       "      <th>...</th>\n",
       "      <th>total_t2m_mou_8</th>\n",
       "      <th>total_t2m_mou_9</th>\n",
       "      <th>total_ic_t2m_mou_6</th>\n",
       "      <th>total_ic_t2m_mou_7</th>\n",
       "      <th>total_ic_t2m_mou_8</th>\n",
       "      <th>total_ic_t2m_mou_9</th>\n",
       "      <th>total_og_t2c_mou_6</th>\n",
       "      <th>total_og_t2c_mou_7</th>\n",
       "      <th>total_og_t2c_mou_8</th>\n",
       "      <th>total_og_t2c_mou_9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7000701601</td>\n",
       "      <td>487.53</td>\n",
       "      <td>609.24</td>\n",
       "      <td>350.16</td>\n",
       "      <td>0.00</td>\n",
       "      <td>626.46</td>\n",
       "      <td>558.04</td>\n",
       "      <td>428.74</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>211.42</td>\n",
       "      <td>NaN</td>\n",
       "      <td>309.00</td>\n",
       "      <td>348.48</td>\n",
       "      <td>262.52</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7001524846</td>\n",
       "      <td>508.36</td>\n",
       "      <td>431.66</td>\n",
       "      <td>171.56</td>\n",
       "      <td>142.18</td>\n",
       "      <td>103.86</td>\n",
       "      <td>28.49</td>\n",
       "      <td>16.54</td>\n",
       "      <td>34.91</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>126.23</td>\n",
       "      <td>108.71</td>\n",
       "      <td>80.01</td>\n",
       "      <td>18.06</td>\n",
       "      <td>16.13</td>\n",
       "      <td>30.87</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.15</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>7002191713</td>\n",
       "      <td>907.09</td>\n",
       "      <td>192.41</td>\n",
       "      <td>1015.26</td>\n",
       "      <td>446.09</td>\n",
       "      <td>968.61</td>\n",
       "      <td>172.58</td>\n",
       "      <td>1144.53</td>\n",
       "      <td>631.86</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>451.40</td>\n",
       "      <td>204.25</td>\n",
       "      <td>451.01</td>\n",
       "      <td>48.32</td>\n",
       "      <td>513.09</td>\n",
       "      <td>256.30</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>7000875565</td>\n",
       "      <td>346.81</td>\n",
       "      <td>286.01</td>\n",
       "      <td>233.38</td>\n",
       "      <td>255.74</td>\n",
       "      <td>364.86</td>\n",
       "      <td>381.56</td>\n",
       "      <td>294.46</td>\n",
       "      <td>334.56</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>146.57</td>\n",
       "      <td>215.28</td>\n",
       "      <td>245.49</td>\n",
       "      <td>222.77</td>\n",
       "      <td>224.09</td>\n",
       "      <td>202.44</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>7000187447</td>\n",
       "      <td>1249.53</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.00</td>\n",
       "      <td>631.08</td>\n",
       "      <td>91.88</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0.00</td>\n",
       "      <td>87.13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>87.89</td>\n",
       "      <td>48.69</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0.00</td>\n",
       "      <td>45.49</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 41 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    mobile_number  total_og_mou_6  total_og_mou_7  total_og_mou_8  \\\n",
       "7      7000701601          487.53          609.24          350.16   \n",
       "8      7001524846          508.36          431.66          171.56   \n",
       "13     7002191713          907.09          192.41         1015.26   \n",
       "16     7000875565          346.81          286.01          233.38   \n",
       "17     7000187447         1249.53            0.81            0.00   \n",
       "\n",
       "    total_og_mou_9  total_ic_mou_6  total_ic_mou_7  total_ic_mou_8  \\\n",
       "7             0.00          626.46          558.04          428.74   \n",
       "8           142.18          103.86           28.49           16.54   \n",
       "13          446.09          968.61          172.58         1144.53   \n",
       "16          255.74          364.86          381.56          294.46   \n",
       "17          631.08           91.88            2.31            0.00   \n",
       "\n",
       "    total_ic_mou_9  vol_2g_mb_6  ...  total_t2m_mou_8  total_t2m_mou_9  \\\n",
       "7             0.00          0.0  ...           211.42              NaN   \n",
       "8            34.91          0.0  ...           126.23           108.71   \n",
       "13          631.86          0.0  ...           451.40           204.25   \n",
       "16          334.56          0.0  ...           146.57           215.28   \n",
       "17           87.13          0.0  ...             0.00            87.89   \n",
       "\n",
       "    total_ic_t2m_mou_6  total_ic_t2m_mou_7  total_ic_t2m_mou_8  \\\n",
       "7               309.00              348.48              262.52   \n",
       "8                80.01               18.06               16.13   \n",
       "13              451.01               48.32              513.09   \n",
       "16              245.49              222.77              224.09   \n",
       "17               48.69                2.31                0.00   \n",
       "\n",
       "    total_ic_t2m_mou_9  total_og_t2c_mou_6  total_og_t2c_mou_7  \\\n",
       "7                  NaN                 0.0                 0.0   \n",
       "8                30.87                 0.0                 0.0   \n",
       "13              256.30                 0.0                 0.0   \n",
       "16              202.44                 0.0                 0.0   \n",
       "17               45.49                 0.0                 0.0   \n",
       "\n",
       "    total_og_t2c_mou_8  total_og_t2c_mou_9  \n",
       "7                 0.00                 NaN  \n",
       "8                 7.15                 0.0  \n",
       "13                0.49                 0.0  \n",
       "16                0.00                 0.0  \n",
       "17                0.00                 0.0  \n",
       "\n",
       "[5 rows x 41 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The idea here is to see whether a user was in frequent contact with a competitor which would indicate if they are planning\n",
    "# to switch. Also, to see the calls made to the original operator which is indicative of complaints or follow-ups.\n",
    "\n",
    "# Aggregating local outgoing and std ourgoing to t2m (competitor) for months 6, 7, 8 and 9\n",
    "\n",
    "df_1['total_t2m_mou_6'] = df_1.loc_og_t2m_mou_6 + df_1.std_og_t2m_mou_6\n",
    "df_1['total_t2m_mou_7'] = df_1.loc_og_t2m_mou_7 + df_1.std_og_t2m_mou_7\n",
    "df_1['total_t2m_mou_8'] = df_1.loc_og_t2m_mou_8 + df_1.std_og_t2m_mou_8\n",
    "df_1['total_t2m_mou_9'] = df_1.loc_og_t2m_mou_9 + df_1.std_og_t2m_mou_9\n",
    "\n",
    "# Aggregating local incoming and std incoming from t2m (competitor) for months 6, 7, 8 and 9\n",
    "\n",
    "df_1['total_ic_t2m_mou_6'] = df_1.loc_ic_t2m_mou_6 + df_1.std_ic_t2m_mou_6\n",
    "df_1['total_ic_t2m_mou_7'] = df_1.loc_ic_t2m_mou_7 + df_1.std_ic_t2m_mou_7\n",
    "df_1['total_ic_t2m_mou_8'] = df_1.loc_ic_t2m_mou_8 + df_1.std_ic_t2m_mou_8\n",
    "df_1['total_ic_t2m_mou_9'] = df_1.loc_ic_t2m_mou_9 + df_1.std_ic_t2m_mou_9\n",
    "\n",
    "# Aggregating local outgoing and std outgoing to t2c (self) for months 6, 7, 8 and 9\n",
    "\n",
    "df_1['total_og_t2c_mou_6'] = df_1.loc_og_t2c_mou_6 + df_1.std_og_t2c_mou_6\n",
    "df_1['total_og_t2c_mou_7'] = df_1.loc_og_t2c_mou_7 + df_1.std_og_t2c_mou_7\n",
    "df_1['total_og_t2c_mou_8'] = df_1.loc_og_t2c_mou_8 + df_1.std_og_t2c_mou_8\n",
    "df_1['total_og_t2c_mou_9'] = df_1.loc_og_t2c_mou_9 + df_1.std_og_t2c_mou_9\n",
    "\n",
    "# Dropping redundant columns\n",
    "\n",
    "df_1 = df_1.drop(columns = ['loc_og_t2m_mou_6','std_og_t2m_mou_6','loc_og_t2m_mou_7','std_og_t2m_mou_7',\n",
    "                              'loc_og_t2m_mou_8','std_og_t2m_mou_8','loc_og_t2m_mou_9','std_og_t2m_mou_9'\n",
    "                              ,'loc_ic_t2m_mou_6','std_ic_t2m_mou_6','loc_ic_t2m_mou_7','std_ic_t2m_mou_7',\n",
    "                              'loc_ic_t2m_mou_8','std_ic_t2m_mou_8','loc_ic_t2m_mou_9','std_ic_t2m_mou_9',\n",
    "                              'loc_og_t2c_mou_6','std_og_t2c_mou_6','loc_og_t2c_mou_7','std_og_t2c_mou_7',\n",
    "                              'loc_og_t2c_mou_8','std_og_t2c_mou_8','loc_og_t2c_mou_9','std_og_t2c_mou_9'])\n",
    "\n",
    "df_1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mobile_number             0\n",
       "total_og_mou_6            0\n",
       "total_og_mou_7            0\n",
       "total_og_mou_8            0\n",
       "total_og_mou_9            0\n",
       "total_ic_mou_6            0\n",
       "total_ic_mou_7            0\n",
       "total_ic_mou_8            0\n",
       "total_ic_mou_9            0\n",
       "vol_2g_mb_6               0\n",
       "vol_2g_mb_7               0\n",
       "vol_2g_mb_8               0\n",
       "vol_2g_mb_9               0\n",
       "vol_3g_mb_6               0\n",
       "vol_3g_mb_7               0\n",
       "vol_3g_mb_8               0\n",
       "vol_3g_mb_9               0\n",
       "total_rech_amt_6          0\n",
       "total_rech_amt_7          0\n",
       "total_rech_amt_8          0\n",
       "total_rech_amt_9          0\n",
       "total_rech_data_6     18614\n",
       "total_rech_data_7     18349\n",
       "total_rech_data_8     18257\n",
       "total_rech_data_9     18550\n",
       "av_rech_amt_data_6    18614\n",
       "av_rech_amt_data_7    18349\n",
       "av_rech_amt_data_8    18257\n",
       "av_rech_amt_data_9    18550\n",
       "total_t2m_mou_6         316\n",
       "total_t2m_mou_7         303\n",
       "total_t2m_mou_8         938\n",
       "total_t2m_mou_9        1704\n",
       "total_ic_t2m_mou_6      316\n",
       "total_ic_t2m_mou_7      303\n",
       "total_ic_t2m_mou_8      938\n",
       "total_ic_t2m_mou_9     1704\n",
       "total_og_t2c_mou_6      316\n",
       "total_og_t2c_mou_7      303\n",
       "total_og_t2c_mou_8      938\n",
       "total_og_t2c_mou_9     1704\n",
       "dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for missing values\n",
    "\n",
    "df_1.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mobile_number            0\n",
       "total_og_mou_6           0\n",
       "total_og_mou_7           0\n",
       "total_og_mou_8           0\n",
       "total_og_mou_9           0\n",
       "total_ic_mou_6           0\n",
       "total_ic_mou_7           0\n",
       "total_ic_mou_8           0\n",
       "total_ic_mou_9           0\n",
       "vol_2g_mb_6              0\n",
       "vol_2g_mb_7              0\n",
       "vol_2g_mb_8              0\n",
       "vol_2g_mb_9              0\n",
       "vol_3g_mb_6              0\n",
       "vol_3g_mb_7              0\n",
       "vol_3g_mb_8              0\n",
       "vol_3g_mb_9              0\n",
       "total_rech_amt_6         0\n",
       "total_rech_amt_7         0\n",
       "total_rech_amt_8         0\n",
       "total_rech_amt_9         0\n",
       "total_t2m_mou_6        316\n",
       "total_t2m_mou_7        303\n",
       "total_t2m_mou_8        938\n",
       "total_t2m_mou_9       1704\n",
       "total_ic_t2m_mou_6     316\n",
       "total_ic_t2m_mou_7     303\n",
       "total_ic_t2m_mou_8     938\n",
       "total_ic_t2m_mou_9    1704\n",
       "total_og_t2c_mou_6     316\n",
       "total_og_t2c_mou_7     303\n",
       "total_og_t2c_mou_8     938\n",
       "total_og_t2c_mou_9    1704\n",
       "dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dropping missing columns which cannot be imputed nor are required for modelling\n",
    "\n",
    "df_1 = df_1.drop(columns = [\"total_rech_data_6\",\"total_rech_data_7\",\"total_rech_data_8\",\n",
    "                            \"total_rech_data_9\",\"av_rech_amt_data_6\",\"av_rech_amt_data_7\",\n",
    "                            \"av_rech_amt_data_8\",\"av_rech_amt_data_9\"])\n",
    "df_1.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 27991 entries, 8 to 99997\n",
      "Data columns (total 33 columns):\n",
      "mobile_number         27991 non-null int64\n",
      "total_og_mou_6        27991 non-null float64\n",
      "total_og_mou_7        27991 non-null float64\n",
      "total_og_mou_8        27991 non-null float64\n",
      "total_og_mou_9        27991 non-null float64\n",
      "total_ic_mou_6        27991 non-null float64\n",
      "total_ic_mou_7        27991 non-null float64\n",
      "total_ic_mou_8        27991 non-null float64\n",
      "total_ic_mou_9        27991 non-null float64\n",
      "vol_2g_mb_6           27991 non-null float64\n",
      "vol_2g_mb_7           27991 non-null float64\n",
      "vol_2g_mb_8           27991 non-null float64\n",
      "vol_2g_mb_9           27991 non-null float64\n",
      "vol_3g_mb_6           27991 non-null float64\n",
      "vol_3g_mb_7           27991 non-null float64\n",
      "vol_3g_mb_8           27991 non-null float64\n",
      "vol_3g_mb_9           27991 non-null float64\n",
      "total_rech_amt_6      27991 non-null int64\n",
      "total_rech_amt_7      27991 non-null int64\n",
      "total_rech_amt_8      27991 non-null int64\n",
      "total_rech_amt_9      27991 non-null int64\n",
      "total_t2m_mou_6       27991 non-null float64\n",
      "total_t2m_mou_7       27991 non-null float64\n",
      "total_t2m_mou_8       27991 non-null float64\n",
      "total_t2m_mou_9       27991 non-null float64\n",
      "total_ic_t2m_mou_6    27991 non-null float64\n",
      "total_ic_t2m_mou_7    27991 non-null float64\n",
      "total_ic_t2m_mou_8    27991 non-null float64\n",
      "total_ic_t2m_mou_9    27991 non-null float64\n",
      "total_og_t2c_mou_6    27991 non-null float64\n",
      "total_og_t2c_mou_7    27991 non-null float64\n",
      "total_og_t2c_mou_8    27991 non-null float64\n",
      "total_og_t2c_mou_9    27991 non-null float64\n",
      "dtypes: float64(28), int64(5)\n",
      "memory usage: 7.3 MB\n"
     ]
    }
   ],
   "source": [
    "# Removing missing rows and will not remove significant amount of data\n",
    "\n",
    "df_1.dropna(inplace=True)\n",
    "df_1.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adding Churned column to df_1\n",
    "\n",
    "df_1[\"Churned\"] = churn[\"Churned\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Normalization phase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normalizing continuous features\n",
    "\n",
    "a = df_1[[\"total_og_mou_6\",\"total_og_mou_7\",\"total_og_mou_8\",\"total_og_mou_9\",\n",
    "              \"total_ic_mou_6\",\"total_ic_mou_7\",\"total_ic_mou_8\",\"total_ic_mou_9\",\"vol_2g_mb_6\",\n",
    "              \"vol_2g_mb_7\",\"vol_2g_mb_8\",\"vol_2g_mb_9\",\"vol_3g_mb_6\",\"vol_3g_mb_7\",\"vol_3g_mb_8\",\n",
    "              \"vol_3g_mb_9\",\"total_rech_amt_6\",\"total_rech_amt_7\",\"total_rech_amt_8\",\"total_rech_amt_9\",\n",
    "              \"total_t2m_mou_6\",\"total_t2m_mou_7\",\"total_t2m_mou_8\",\"total_t2m_mou_9\",\"total_ic_t2m_mou_6\",\n",
    "              \"total_ic_t2m_mou_7\",\"total_ic_t2m_mou_8\",\"total_ic_t2m_mou_9\",\"total_og_t2c_mou_6\",\n",
    "              \"total_og_t2c_mou_7\",\"total_og_t2c_mou_8\",\"total_og_t2c_mou_9\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mobile_number</th>\n",
       "      <th>Churned</th>\n",
       "      <th>total_og_mou_6</th>\n",
       "      <th>total_og_mou_7</th>\n",
       "      <th>total_og_mou_8</th>\n",
       "      <th>total_og_mou_9</th>\n",
       "      <th>total_ic_mou_6</th>\n",
       "      <th>total_ic_mou_7</th>\n",
       "      <th>total_ic_mou_8</th>\n",
       "      <th>total_ic_mou_9</th>\n",
       "      <th>...</th>\n",
       "      <th>total_t2m_mou_8</th>\n",
       "      <th>total_t2m_mou_9</th>\n",
       "      <th>total_ic_t2m_mou_6</th>\n",
       "      <th>total_ic_t2m_mou_7</th>\n",
       "      <th>total_ic_t2m_mou_8</th>\n",
       "      <th>total_ic_t2m_mou_9</th>\n",
       "      <th>total_og_t2c_mou_6</th>\n",
       "      <th>total_og_t2c_mou_7</th>\n",
       "      <th>total_og_t2c_mou_8</th>\n",
       "      <th>total_og_t2c_mou_9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7001524846</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.272106</td>\n",
       "      <td>-0.423303</td>\n",
       "      <td>-0.701383</td>\n",
       "      <td>-0.695009</td>\n",
       "      <td>-0.582344</td>\n",
       "      <td>-0.796348</td>\n",
       "      <td>-0.806959</td>\n",
       "      <td>-0.723987</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.504644</td>\n",
       "      <td>-0.512118</td>\n",
       "      <td>-0.465246</td>\n",
       "      <td>-0.731527</td>\n",
       "      <td>-0.707056</td>\n",
       "      <td>-0.645319</td>\n",
       "      <td>-0.228954</td>\n",
       "      <td>-0.206234</td>\n",
       "      <td>0.713078</td>\n",
       "      <td>-0.235027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>7002191713</td>\n",
       "      <td>0</td>\n",
       "      <td>0.343251</td>\n",
       "      <td>-0.775251</td>\n",
       "      <td>0.526533</td>\n",
       "      <td>-0.231534</td>\n",
       "      <td>1.834518</td>\n",
       "      <td>-0.407443</td>\n",
       "      <td>2.281981</td>\n",
       "      <td>0.929430</td>\n",
       "      <td>...</td>\n",
       "      <td>0.186172</td>\n",
       "      <td>-0.294179</td>\n",
       "      <td>1.040290</td>\n",
       "      <td>-0.609718</td>\n",
       "      <td>1.267214</td>\n",
       "      <td>0.311284</td>\n",
       "      <td>-0.228954</td>\n",
       "      <td>-0.206234</td>\n",
       "      <td>-0.170316</td>\n",
       "      <td>-0.235027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>7000875565</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.521425</td>\n",
       "      <td>-0.637561</td>\n",
       "      <td>-0.611411</td>\n",
       "      <td>-0.521826</td>\n",
       "      <td>0.147116</td>\n",
       "      <td>0.156604</td>\n",
       "      <td>-0.045890</td>\n",
       "      <td>0.105976</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.461432</td>\n",
       "      <td>-0.269018</td>\n",
       "      <td>0.206280</td>\n",
       "      <td>0.092514</td>\n",
       "      <td>0.119106</td>\n",
       "      <td>0.082732</td>\n",
       "      <td>-0.228954</td>\n",
       "      <td>-0.206234</td>\n",
       "      <td>-0.235310</td>\n",
       "      <td>-0.235027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>7000187447</td>\n",
       "      <td>0</td>\n",
       "      <td>0.871735</td>\n",
       "      <td>-1.057104</td>\n",
       "      <td>-0.951071</td>\n",
       "      <td>0.050583</td>\n",
       "      <td>-0.615827</td>\n",
       "      <td>-0.867009</td>\n",
       "      <td>-0.852253</td>\n",
       "      <td>-0.579349</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.772817</td>\n",
       "      <td>-0.559612</td>\n",
       "      <td>-0.592344</td>\n",
       "      <td>-0.794927</td>\n",
       "      <td>-0.771135</td>\n",
       "      <td>-0.583279</td>\n",
       "      <td>-0.228954</td>\n",
       "      <td>-0.206234</td>\n",
       "      <td>-0.235310</td>\n",
       "      <td>-0.235027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>7002124215</td>\n",
       "      <td>0</td>\n",
       "      <td>0.271102</td>\n",
       "      <td>0.455104</td>\n",
       "      <td>0.604077</td>\n",
       "      <td>0.661286</td>\n",
       "      <td>-0.293830</td>\n",
       "      <td>-0.517267</td>\n",
       "      <td>-0.137409</td>\n",
       "      <td>-0.121007</td>\n",
       "      <td>...</td>\n",
       "      <td>1.312995</td>\n",
       "      <td>1.224213</td>\n",
       "      <td>-0.035458</td>\n",
       "      <td>-0.349918</td>\n",
       "      <td>0.146160</td>\n",
       "      <td>0.112436</td>\n",
       "      <td>-0.228954</td>\n",
       "      <td>-0.206234</td>\n",
       "      <td>-0.235310</td>\n",
       "      <td>-0.235027</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    mobile_number  Churned  total_og_mou_6  total_og_mou_7  total_og_mou_8  \\\n",
       "8      7001524846        0       -0.272106       -0.423303       -0.701383   \n",
       "13     7002191713        0        0.343251       -0.775251        0.526533   \n",
       "16     7000875565        0       -0.521425       -0.637561       -0.611411   \n",
       "17     7000187447        0        0.871735       -1.057104       -0.951071   \n",
       "21     7002124215        0        0.271102        0.455104        0.604077   \n",
       "\n",
       "    total_og_mou_9  total_ic_mou_6  total_ic_mou_7  total_ic_mou_8  \\\n",
       "8        -0.695009       -0.582344       -0.796348       -0.806959   \n",
       "13       -0.231534        1.834518       -0.407443        2.281981   \n",
       "16       -0.521826        0.147116        0.156604       -0.045890   \n",
       "17        0.050583       -0.615827       -0.867009       -0.852253   \n",
       "21        0.661286       -0.293830       -0.517267       -0.137409   \n",
       "\n",
       "    total_ic_mou_9  ...  total_t2m_mou_8  total_t2m_mou_9  total_ic_t2m_mou_6  \\\n",
       "8        -0.723987  ...        -0.504644        -0.512118           -0.465246   \n",
       "13        0.929430  ...         0.186172        -0.294179            1.040290   \n",
       "16        0.105976  ...        -0.461432        -0.269018            0.206280   \n",
       "17       -0.579349  ...        -0.772817        -0.559612           -0.592344   \n",
       "21       -0.121007  ...         1.312995         1.224213           -0.035458   \n",
       "\n",
       "    total_ic_t2m_mou_7  total_ic_t2m_mou_8  total_ic_t2m_mou_9  \\\n",
       "8            -0.731527           -0.707056           -0.645319   \n",
       "13           -0.609718            1.267214            0.311284   \n",
       "16            0.092514            0.119106            0.082732   \n",
       "17           -0.794927           -0.771135           -0.583279   \n",
       "21           -0.349918            0.146160            0.112436   \n",
       "\n",
       "    total_og_t2c_mou_6  total_og_t2c_mou_7  total_og_t2c_mou_8  \\\n",
       "8            -0.228954           -0.206234            0.713078   \n",
       "13           -0.228954           -0.206234           -0.170316   \n",
       "16           -0.228954           -0.206234           -0.235310   \n",
       "17           -0.228954           -0.206234           -0.235310   \n",
       "21           -0.228954           -0.206234           -0.235310   \n",
       "\n",
       "    total_og_t2c_mou_9  \n",
       "8            -0.235027  \n",
       "13           -0.235027  \n",
       "16           -0.235027  \n",
       "17           -0.235027  \n",
       "21           -0.235027  \n",
       "\n",
       "[5 rows x 34 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normalized_a=(a-a.mean())/a.std()\n",
    "\n",
    "df_1 = df_1.drop([\"total_og_mou_6\",\"total_og_mou_7\",\"total_og_mou_8\",\"total_og_mou_9\",\n",
    "              \"total_ic_mou_6\",\"total_ic_mou_7\",\"total_ic_mou_8\",\"total_ic_mou_9\",\"vol_2g_mb_6\",\n",
    "              \"vol_2g_mb_7\",\"vol_2g_mb_8\",\"vol_2g_mb_9\",\"vol_3g_mb_6\",\"vol_3g_mb_7\",\"vol_3g_mb_8\",\n",
    "              \"vol_3g_mb_9\",\"total_rech_amt_6\",\"total_rech_amt_7\",\"total_rech_amt_8\",\"total_rech_amt_9\",\n",
    "              \"total_t2m_mou_6\",\"total_t2m_mou_7\",\"total_t2m_mou_8\",\"total_t2m_mou_9\",\"total_ic_t2m_mou_6\",\n",
    "              \"total_ic_t2m_mou_7\",\"total_ic_t2m_mou_8\",\"total_ic_t2m_mou_9\",\"total_og_t2c_mou_6\",\n",
    "              \"total_og_t2c_mou_7\",\"total_og_t2c_mou_8\",\"total_og_t2c_mou_9\"], 1)\n",
    "\n",
    "df_1 = pd.concat([df_1,normalized_a],axis=1)\n",
    "df_1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Building\n",
    "Splitting our data into a training set and a test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8     0\n",
       "13    0\n",
       "16    0\n",
       "17    0\n",
       "21    0\n",
       "Name: Churned, dtype: int32"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train-test split\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Putting feature variable to X\n",
    "X = df_1.drop(['Churned','mobile_number'],axis=1)\n",
    "\n",
    "# Putting response variable to y\n",
    "y = df_1['Churned']\n",
    "\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X,y, train_size=0.7,test_size=0.3,random_state=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(19593, 32)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PCA Projection to 2D\n",
    "We have 32 variables after creating our dummy variables for our categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing required library\n",
    "\n",
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(svd_solver='randomized', random_state=np.random)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PCA(copy=True, iterated_power='auto', n_components=None,\n",
       "    random_state=<module 'numpy.random' from 'C:\\\\Users\\\\Neerja\\\\Anaconda3\\\\lib\\\\site-packages\\\\numpy\\\\random\\\\__init__.py'>,\n",
       "    svd_solver='randomized', tol=0.0, whiten=False)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.fit(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.17346943,  0.19856885,  0.20948586, ...,  0.01249857,\n",
       "         0.01682108,  0.01948512],\n",
       "       [ 0.22435888,  0.26859195,  0.25353467, ...,  0.03235669,\n",
       "         0.03512953,  0.03371994],\n",
       "       [-0.07443501, -0.07744391, -0.05099894, ...,  0.01372448,\n",
       "         0.01338589,  0.01826646],\n",
       "       ...,\n",
       "       [ 0.0263536 , -0.0129594 , -0.11759754, ..., -0.00425992,\n",
       "         0.0071254 , -0.00472652],\n",
       "       [ 0.1466359 , -0.39332282,  0.49799189, ...,  0.01347986,\n",
       "        -0.01856409,  0.00995693],\n",
       "       [-0.03617127,  0.11227238, -0.13140881, ..., -0.00123754,\n",
       "         0.00139521, -0.00192438]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.components_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot the principal components\n",
    "- We'll plot original features on the first 2 principal components as axes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Feature</th>\n",
       "      <th>PC1</th>\n",
       "      <th>PC2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>total_og_mou_6</td>\n",
       "      <td>0.173469</td>\n",
       "      <td>0.224359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>total_og_mou_7</td>\n",
       "      <td>0.198569</td>\n",
       "      <td>0.268592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>total_og_mou_8</td>\n",
       "      <td>0.209486</td>\n",
       "      <td>0.253535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>total_og_mou_9</td>\n",
       "      <td>0.207958</td>\n",
       "      <td>0.234703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>total_ic_mou_6</td>\n",
       "      <td>0.229194</td>\n",
       "      <td>-0.255782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>total_ic_mou_7</td>\n",
       "      <td>0.247148</td>\n",
       "      <td>-0.255885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>total_ic_mou_8</td>\n",
       "      <td>0.258702</td>\n",
       "      <td>-0.252459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>total_ic_mou_9</td>\n",
       "      <td>0.245246</td>\n",
       "      <td>-0.229138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>vol_2g_mb_6</td>\n",
       "      <td>-0.024587</td>\n",
       "      <td>-0.049752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>vol_2g_mb_7</td>\n",
       "      <td>-0.025988</td>\n",
       "      <td>-0.046530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>vol_2g_mb_8</td>\n",
       "      <td>-0.018919</td>\n",
       "      <td>-0.043895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>vol_2g_mb_9</td>\n",
       "      <td>-0.012580</td>\n",
       "      <td>-0.040318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>vol_3g_mb_6</td>\n",
       "      <td>0.014124</td>\n",
       "      <td>-0.059392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>vol_3g_mb_7</td>\n",
       "      <td>0.016606</td>\n",
       "      <td>-0.059136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>vol_3g_mb_8</td>\n",
       "      <td>0.025819</td>\n",
       "      <td>-0.056901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>vol_3g_mb_9</td>\n",
       "      <td>0.031381</td>\n",
       "      <td>-0.055791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>total_rech_amt_6</td>\n",
       "      <td>0.196549</td>\n",
       "      <td>0.144613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>total_rech_amt_7</td>\n",
       "      <td>0.212789</td>\n",
       "      <td>0.170327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>total_rech_amt_8</td>\n",
       "      <td>0.221399</td>\n",
       "      <td>0.160484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>total_rech_amt_9</td>\n",
       "      <td>0.216508</td>\n",
       "      <td>0.139134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>total_t2m_mou_6</td>\n",
       "      <td>0.185106</td>\n",
       "      <td>0.179449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>total_t2m_mou_7</td>\n",
       "      <td>0.205862</td>\n",
       "      <td>0.213055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>total_t2m_mou_8</td>\n",
       "      <td>0.213235</td>\n",
       "      <td>0.200520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>total_t2m_mou_9</td>\n",
       "      <td>0.216874</td>\n",
       "      <td>0.189224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>total_ic_t2m_mou_6</td>\n",
       "      <td>0.232788</td>\n",
       "      <td>-0.246697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>total_ic_t2m_mou_7</td>\n",
       "      <td>0.253290</td>\n",
       "      <td>-0.250865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>total_ic_t2m_mou_8</td>\n",
       "      <td>0.258861</td>\n",
       "      <td>-0.245382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>total_ic_t2m_mou_9</td>\n",
       "      <td>0.252053</td>\n",
       "      <td>-0.230331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>total_og_t2c_mou_6</td>\n",
       "      <td>0.009229</td>\n",
       "      <td>0.018072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>total_og_t2c_mou_7</td>\n",
       "      <td>0.012499</td>\n",
       "      <td>0.032357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>total_og_t2c_mou_8</td>\n",
       "      <td>0.016821</td>\n",
       "      <td>0.035130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>total_og_t2c_mou_9</td>\n",
       "      <td>0.019485</td>\n",
       "      <td>0.033720</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Feature       PC1       PC2\n",
       "0       total_og_mou_6  0.173469  0.224359\n",
       "1       total_og_mou_7  0.198569  0.268592\n",
       "2       total_og_mou_8  0.209486  0.253535\n",
       "3       total_og_mou_9  0.207958  0.234703\n",
       "4       total_ic_mou_6  0.229194 -0.255782\n",
       "5       total_ic_mou_7  0.247148 -0.255885\n",
       "6       total_ic_mou_8  0.258702 -0.252459\n",
       "7       total_ic_mou_9  0.245246 -0.229138\n",
       "8          vol_2g_mb_6 -0.024587 -0.049752\n",
       "9          vol_2g_mb_7 -0.025988 -0.046530\n",
       "10         vol_2g_mb_8 -0.018919 -0.043895\n",
       "11         vol_2g_mb_9 -0.012580 -0.040318\n",
       "12         vol_3g_mb_6  0.014124 -0.059392\n",
       "13         vol_3g_mb_7  0.016606 -0.059136\n",
       "14         vol_3g_mb_8  0.025819 -0.056901\n",
       "15         vol_3g_mb_9  0.031381 -0.055791\n",
       "16    total_rech_amt_6  0.196549  0.144613\n",
       "17    total_rech_amt_7  0.212789  0.170327\n",
       "18    total_rech_amt_8  0.221399  0.160484\n",
       "19    total_rech_amt_9  0.216508  0.139134\n",
       "20     total_t2m_mou_6  0.185106  0.179449\n",
       "21     total_t2m_mou_7  0.205862  0.213055\n",
       "22     total_t2m_mou_8  0.213235  0.200520\n",
       "23     total_t2m_mou_9  0.216874  0.189224\n",
       "24  total_ic_t2m_mou_6  0.232788 -0.246697\n",
       "25  total_ic_t2m_mou_7  0.253290 -0.250865\n",
       "26  total_ic_t2m_mou_8  0.258861 -0.245382\n",
       "27  total_ic_t2m_mou_9  0.252053 -0.230331\n",
       "28  total_og_t2c_mou_6  0.009229  0.018072\n",
       "29  total_og_t2c_mou_7  0.012499  0.032357\n",
       "30  total_og_t2c_mou_8  0.016821  0.035130\n",
       "31  total_og_t2c_mou_9  0.019485  0.033720"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "colnames = list(X_train.columns)\n",
    "pcs_df = pd.DataFrame({'Feature':colnames,'PC1':pca.components_[0],'PC2':pca.components_[1]})\n",
    "pcs_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "fig = plt.figure(figsize = (16,16))\n",
    "plt.scatter(pcs_df.PC1, pcs_df.PC2)\n",
    "plt.xlabel('Principal Component 1')\n",
    "plt.ylabel('Principal Component 2')\n",
    "for i, txt in enumerate(pcs_df.Feature):\n",
    "    plt.annotate(txt, (pcs_df.PC1[i],pcs_df.PC2[i]))\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.21847731, 0.15466204, 0.11634149, 0.08652313, 0.06399781,\n",
       "       0.0562584 , 0.04010582, 0.02679078, 0.02563471, 0.02270237,\n",
       "       0.02022413, 0.0191824 , 0.01666992, 0.01477698, 0.01456959,\n",
       "       0.01378792, 0.01224162, 0.0103067 , 0.01019799, 0.00900234,\n",
       "       0.00799141, 0.00715402, 0.00640123, 0.00612131, 0.00448749,\n",
       "       0.003947  , 0.00360733, 0.0026331 , 0.00202261, 0.00121469,\n",
       "       0.00114729, 0.00081903])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the cumulative variance against the number of components\n",
    "%matplotlib inline\n",
    "fig = plt.figure(figsize = (12,8))\n",
    "plt.plot(np.cumsum(pca.explained_variance_ratio_))\n",
    "plt.xlabel('number of components')\n",
    "plt.ylabel('cumulative explained variance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Looks like 18 components are enough to describe 95% of the variance in the dataset\n",
    "- We'll choose 18 components for our modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import IncrementalPCA\n",
    "pca_final = IncrementalPCA(n_components=18)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Basis transformation - getting the data onto our PCs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(19593, 18)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train_pca = pca_final.fit_transform(X_train)\n",
    "df_train_pca.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating correlation matrix for the principal components\n",
    "\n",
    "corr_1 = np.corrcoef(df_train_pca.transpose())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Creating correlation matrix for the principal components - we expect little to no correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating correlation matrix for the principal components\n",
    "corrmat = np.corrcoef(df_train_pca.transpose())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x226a94b6f28>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting the correlation matrix\n",
    "%matplotlib inline\n",
    "plt.figure(figsize = (20,10))\n",
    "sns.heatmap(corrmat,annot = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8398, 18)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Applying selected components to the test data - 18 components\n",
    "\n",
    "df_test_pca = pca_final.transform(X_test)\n",
    "df_test_pca.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic Regression Model for predicting churn of high-value customers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import metrics\n",
    "\n",
    "learner_pca = LogisticRegression()\n",
    "model_pca = learner_pca.fit(df_train_pca,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_roc( actual, probs ):\n",
    "    fpr, tpr, thresholds = metrics.roc_curve( actual, probs,\n",
    "                                              drop_intermediate = False )\n",
    "    auc_score = metrics.roc_auc_score( actual, probs )\n",
    "    plt.figure(figsize=(6, 6))\n",
    "    plt.plot( fpr, tpr, label='ROC curve (area = %0.2f)' % auc_score )\n",
    "    plt.plot([0, 1], [0, 1], 'k--')\n",
    "    plt.xlim([0.0, 1.0])\n",
    "    plt.ylim([0.0, 1.05])\n",
    "    plt.xlabel('False Positive Rate or [1 - True Negative Rate]')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.title('Receiver operating characteristic')\n",
    "    plt.legend(loc=\"lower right\")\n",
    "    plt.show()\n",
    "\n",
    "    return fpr, tpr, thresholds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.98'"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_probs_test = model_pca.predict_proba(df_test_pca)[:,1]\n",
    "\"{:2.2}\".format(metrics.roc_auc_score(y_test, pred_probs_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The Logistic Regression model gives us an AUC score of 0.98"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision Tree Model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create Decision Tree classifer object\n",
    "clf = DecisionTreeClassifier()\n",
    "\n",
    "# Train Decision Tree Classifer\n",
    "clf = clf.fit(X_train,y_train)\n",
    "\n",
    "#Predict the response for test dataset\n",
    "y_pred = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Evaluating Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 1.0\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We got a classification rate of 100%, considered as good accuracy. \n",
    "This is because the parameters for the decision tree model were already tunned up!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing Decision Trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_cols = X.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['total_og_mou_6', 'total_og_mou_7', 'total_og_mou_8', 'total_og_mou_9',\n",
       "       'total_ic_mou_6', 'total_ic_mou_7', 'total_ic_mou_8', 'total_ic_mou_9',\n",
       "       'vol_2g_mb_6', 'vol_2g_mb_7', 'vol_2g_mb_8', 'vol_2g_mb_9',\n",
       "       'vol_3g_mb_6', 'vol_3g_mb_7', 'vol_3g_mb_8', 'vol_3g_mb_9',\n",
       "       'total_rech_amt_6', 'total_rech_amt_7', 'total_rech_amt_8',\n",
       "       'total_rech_amt_9', 'total_t2m_mou_6', 'total_t2m_mou_7',\n",
       "       'total_t2m_mou_8', 'total_t2m_mou_9', 'total_ic_t2m_mou_6',\n",
       "       'total_ic_t2m_mou_7', 'total_ic_t2m_mou_8', 'total_ic_t2m_mou_9',\n",
       "       'total_og_t2c_mou_6', 'total_og_t2c_mou_7', 'total_og_t2c_mou_8',\n",
       "       'total_og_t2c_mou_9'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Bhishaj\\Anaconda3\\lib\\site-packages\\sklearn\\externals\\six.py:31: DeprecationWarning: The module is deprecated in version 0.21 and will be removed in version 0.23 since we've dropped support for Python 2.7. Please rely on the official version of six (https://pypi.org/project/six/).\n",
      "  \"(https://pypi.org/project/six/).\", DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import export_graphviz\n",
    "from sklearn.externals.six import StringIO  \n",
    "from IPython.display import Image  \n",
    "import pydotplus\n",
    "\n",
    "dot_data = StringIO()\n",
    "export_graphviz(clf, out_file=dot_data,  \n",
    "                filled=True, rounded=True,\n",
    "                special_characters=True,feature_names = feature_cols,class_names=['0','1'])\n",
    "graph = pydotplus.graph_from_dot_data(dot_data.getvalue())  \n",
    "graph.write_png('churns.png')\n",
    "Image(graph.create_png())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.17346943,  0.19856885,  0.20948586, ...,  0.01249857,\n",
       "         0.01682108,  0.01948512],\n",
       "       [ 0.22435888,  0.26859195,  0.25353467, ...,  0.03235669,\n",
       "         0.03512953,  0.03371994],\n",
       "       [-0.07443501, -0.07744391, -0.05099894, ...,  0.01372448,\n",
       "         0.01338589,  0.01826646],\n",
       "       ...,\n",
       "       [ 0.0263536 , -0.0129594 , -0.11759754, ..., -0.00425992,\n",
       "         0.0071254 , -0.00472652],\n",
       "       [ 0.1466359 , -0.39332282,  0.49799189, ...,  0.01347986,\n",
       "        -0.01856409,  0.00995693],\n",
       "       [-0.03617127,  0.11227238, -0.13140881, ..., -0.00123754,\n",
       "         0.00139521, -0.00192438]])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.components_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To decrease churn of high-value customers, the company can :-\n",
    "# 1. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Shape of passed values is (32, 32), indices imply (2, 32)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals\\managers.py\u001b[0m in \u001b[0;36mcreate_block_manager_from_blocks\u001b[1;34m(blocks, axes)\u001b[0m\n\u001b[0;32m   1652\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1653\u001b[1;33m         \u001b[0mmgr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mBlockManager\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mblocks\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxes\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1654\u001b[0m         \u001b[0mmgr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_consolidate_inplace\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals\\managers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, blocks, axes, do_integrity_check)\u001b[0m\n\u001b[0;32m    113\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mdo_integrity_check\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 114\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_verify_integrity\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    115\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals\\managers.py\u001b[0m in \u001b[0;36m_verify_integrity\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    310\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mblock\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_verify_integrity\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mblock\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mmgr_shape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 311\u001b[1;33m                 \u001b[0mconstruction_error\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtot_items\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mblock\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    312\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mtot_items\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals\\managers.py\u001b[0m in \u001b[0;36mconstruction_error\u001b[1;34m(tot_items, block_shape, axes, e)\u001b[0m\n\u001b[0;32m   1690\u001b[0m     raise ValueError(\"Shape of passed values is {0}, indices imply {1}\".format(\n\u001b[1;32m-> 1691\u001b[1;33m         passed, implied))\n\u001b[0m\u001b[0;32m   1692\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Shape of passed values is (32, 32), indices imply (2, 32)",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-49-dbe50395e40c>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdecomposition\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mPCA\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpca\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcomponents_\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mindex\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'PC1'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'PC2'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, data, index, columns, dtype, copy)\u001b[0m\n\u001b[0;32m    422\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    423\u001b[0m                 mgr = init_ndarray(data, index, columns, dtype=dtype,\n\u001b[1;32m--> 424\u001b[1;33m                                    copy=copy)\n\u001b[0m\u001b[0;32m    425\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    426\u001b[0m         \u001b[1;31m# For data is list-like, or Iterable (will consume into list)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals\\construction.py\u001b[0m in \u001b[0;36minit_ndarray\u001b[1;34m(values, index, columns, dtype, copy)\u001b[0m\n\u001b[0;32m    165\u001b[0m         \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmaybe_infer_to_datetimelike\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    166\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 167\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mcreate_block_manager_from_blocks\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    168\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    169\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals\\managers.py\u001b[0m in \u001b[0;36mcreate_block_manager_from_blocks\u001b[1;34m(blocks, axes)\u001b[0m\n\u001b[0;32m   1658\u001b[0m         \u001b[0mblocks\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mb\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'values'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mb\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mb\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mblocks\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1659\u001b[0m         \u001b[0mtot_items\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mb\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mb\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mblocks\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1660\u001b[1;33m         \u001b[0mconstruction_error\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtot_items\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mblocks\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxes\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1661\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1662\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals\\managers.py\u001b[0m in \u001b[0;36mconstruction_error\u001b[1;34m(tot_items, block_shape, axes, e)\u001b[0m\n\u001b[0;32m   1689\u001b[0m         \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Empty data passed with indices specified.\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1690\u001b[0m     raise ValueError(\"Shape of passed values is {0}, indices imply {1}\".format(\n\u001b[1;32m-> 1691\u001b[1;33m         passed, implied))\n\u001b[0m\u001b[0;32m   1692\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1693\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Shape of passed values is (32, 32), indices imply (2, 32)"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import pylab as pl\n",
    "from sklearn import datasets\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "pd.DataFrame(pca.components_,columns=X_train.columns,index = ['PC1','PC2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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