Telcom_churn_class/Telecom_Case_Study.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ouYpd7xLRJ6H"
   },
   "source": [
    "# **Telecom Churn Case Study**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lrEvYjg6RYVX"
   },
   "source": [
    "**Problem statement** - In the telecom industry, customers are able to choose from multiple service providers and actively switch from one operator to another. In this highly competitive market, the telecommunications industry experiences an average of 15-25% annual churn rate. Given the fact that it costs 5-10 times more to acquire a new customer than to retain an existing one, `customer retention has now become even more important than customer acquisition`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rVUuP5EmA-q-"
   },
   "source": [
    "## Importing required Libraries\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "w5ECf68xA4hN"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QQ2nB_3fBdev"
   },
   "source": [
    "## Importing Scikit Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "oFKZUsYV8ubH"
   },
   "outputs": [],
   "source": [
    "import statsmodels.api as sm\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV, KFold\n",
    "\n",
    "from sklearn.feature_selection import RFE\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import confusion_matrix, precision_recall_curve, \\\n",
    "                            auc, roc_auc_score, roc_curve, recall_score,\\\n",
    "                            accuracy_score\n",
    "\n",
    "# PCA libraries\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.decomposition import IncrementalPCA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bIYrOeSukj4E"
   },
   "source": [
    "## Load dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "ys2GM-XO8ubI"
   },
   "outputs": [],
   "source": [
    "# Load the dataset\n",
    "churn_df = pd.read_csv('telecom_churn_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 224
    },
    "id": "cFuuNhdq8ubJ",
    "outputId": "6d3c60ab-a74d-4faa-9aef-556d0b3822d7"
   },
   "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",
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       "      <th>last_date_of_month_9</th>\n",
       "      <th>arpu_6</th>\n",
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       "      <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",
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       "      <td>1103</td>\n",
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       "      <td>4.17</td>\n",
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       "      <th>3</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>8/31/2014</td>\n",
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       "      <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",
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       "<p>5 rows × 226 columns</p>\n",
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       "   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]"
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     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "churn_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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     "base_uri": "https://localhost:8080/",
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    "id": "mNVR1TVNKGa2",
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       "      <th>count</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>278.536648</td>\n",
       "      <td>279.154731</td>\n",
       "      <td>261.645069</td>\n",
       "      <td>132.395875</td>\n",
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       "      <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",
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       "      <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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       "      <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>-2014.045000</td>\n",
       "      <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",
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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",
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       "      <td>1.000000</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <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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       "      <th>75%</th>\n",
       "      <td>7.001812e+09</td>\n",
       "      <td>109.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>371.060000</td>\n",
       "      <td>365.344500</td>\n",
       "      <td>369.370500</td>\n",
       "      <td>353.466500</td>\n",
       "      <td>118.740000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1807.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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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_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "ODMIzWR5UvMq"
   },
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns', 250)\n",
    "pd.set_option('display.max_rows', 250)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "g4jVgjnFSjjK",
    "outputId": "573ce3ee-324e-4941-fbd1-1a6e3511ce22"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mobile_number</th>\n",
       "      <td>99999.0</td>\n",
       "      <td>7.001207e+09</td>\n",
       "      <td>695669.386290</td>\n",
       "      <td>7.000000e+09</td>\n",
       "      <td>7.000606e+09</td>\n",
       "      <td>7.001205e+09</td>\n",
       "      <td>7.001812e+09</td>\n",
       "      <td>7.002411e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>circle_id</th>\n",
       "      <td>99999.0</td>\n",
       "      <td>1.090000e+02</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.090000e+02</td>\n",
       "      <td>1.090000e+02</td>\n",
       "      <td>1.090000e+02</td>\n",
       "      <td>1.090000e+02</td>\n",
       "      <td>1.090000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loc_og_t2o_mou</th>\n",
       "      <td>98981.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_og_t2o_mou</th>\n",
       "      <td>98981.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loc_ic_t2o_mou</th>\n",
       "      <td>98981.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>arpu_6</th>\n",
       "      <td>99999.0</td>\n",
       "      <td>2.829874e+02</td>\n",
       "      <td>328.439770</td>\n",
       "      <td>-2.258709e+03</td>\n",
       "      <td>9.341150e+01</td>\n",
       "      <td>1.977040e+02</td>\n",
       "      <td>3.710600e+02</td>\n",
       "      <td>2.773109e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>arpu_7</th>\n",
       "      <td>99999.0</td>\n",
       "      <td>2.785366e+02</td>\n",
       "      <td>338.156291</td>\n",
       "      <td>-2.014045e+03</td>\n",
       "      <td>8.698050e+01</td>\n",
       "      <td>1.916400e+02</td>\n",
       "      <td>3.653445e+02</td>\n",
       "      <td>3.514583e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>arpu_8</th>\n",
       "      <td>99999.0</td>\n",
       "      <td>2.791547e+02</td>\n",
       "      <td>344.474791</td>\n",
       "      <td>-9.458080e+02</td>\n",
       "      <td>8.412600e+01</td>\n",
       "      <td>1.920800e+02</td>\n",
       "      <td>3.693705e+02</td>\n",
       "      <td>3.354362e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>arpu_9</th>\n",
       "      <td>99999.0</td>\n",
       "      <td>2.616451e+02</td>\n",
       "      <td>341.998630</td>\n",
       "      <td>-1.899505e+03</td>\n",
       "      <td>6.268500e+01</td>\n",
       "      <td>1.768490e+02</td>\n",
       "      <td>3.534665e+02</td>\n",
       "      <td>3.880562e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>onnet_mou_6</th>\n",
       "      <td>96062.0</td>\n",
       "      <td>1.323959e+02</td>\n",
       "      <td>297.207406</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>7.380000e+00</td>\n",
       "      <td>3.431000e+01</td>\n",
       "      <td>1.187400e+02</td>\n",
       "      <td>7.376710e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>onnet_mou_7</th>\n",
       "      <td>96140.0</td>\n",
       "      <td>1.336708e+02</td>\n",
       "      <td>308.794148</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>6.660000e+00</td>\n",
       "      <td>3.233000e+01</td>\n",
       "      <td>1.155950e+02</td>\n",
       "      <td>8.157780e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>onnet_mou_8</th>\n",
       "      <td>94621.0</td>\n",
       "      <td>1.330181e+02</td>\n",
       "      <td>308.951589</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>6.460000e+00</td>\n",
       "      <td>3.236000e+01</td>\n",
       "      <td>1.158600e+02</td>\n",
       "      <td>1.075256e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>onnet_mou_9</th>\n",
       "      <td>92254.0</td>\n",
       "      <td>1.303023e+02</td>\n",
       "      <td>308.477668</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>5.330000e+00</td>\n",
       "      <td>2.984000e+01</td>\n",
       "      <td>1.121300e+02</td>\n",
       "      <td>1.042746e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>offnet_mou_6</th>\n",
       "      <td>96062.0</td>\n",
       "      <td>1.979356e+02</td>\n",
       "      <td>316.851613</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.473000e+01</td>\n",
       "      <td>9.631000e+01</td>\n",
       "      <td>2.318600e+02</td>\n",
       "      <td>8.362360e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>offnet_mou_7</th>\n",
       "      <td>96140.0</td>\n",
       "      <td>1.970451e+02</td>\n",
       "      <td>325.862803</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.219000e+01</td>\n",
       "      <td>9.173500e+01</td>\n",
       "      <td>2.268150e+02</td>\n",
       "      <td>9.667130e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>offnet_mou_8</th>\n",
       "      <td>94621.0</td>\n",
       "      <td>1.965748e+02</td>\n",
       "      <td>327.170662</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.163000e+01</td>\n",
       "      <td>9.214000e+01</td>\n",
       "      <td>2.282600e+02</td>\n",
       "      <td>1.400734e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>offnet_mou_9</th>\n",
       "      <td>92254.0</td>\n",
       "      <td>1.903372e+02</td>\n",
       "      <td>319.396092</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.713000e+01</td>\n",
       "      <td>8.729000e+01</td>\n",
       "      <td>2.205050e+02</td>\n",
       "      <td>1.031076e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>roam_ic_mou_6</th>\n",
       "      <td>96062.0</td>\n",
       "      <td>9.950013e+00</td>\n",
       "      <td>72.825411</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.372438e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>roam_ic_mou_7</th>\n",
       "      <td>96140.0</td>\n",
       "      <td>7.149898e+00</td>\n",
       "      <td>73.447948</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.537104e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>roam_ic_mou_8</th>\n",
       "      <td>94621.0</td>\n",
       "      <td>7.292981e+00</td>\n",
       "      <td>68.402466</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.309536e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>roam_ic_mou_9</th>\n",
       "      <td>92254.0</td>\n",
       "      <td>6.343841e+00</td>\n",
       "      <td>57.137537</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>8.464030e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>roam_og_mou_6</th>\n",
       "      <td>96062.0</td>\n",
       "      <td>1.391134e+01</td>\n",
       "      <td>71.443196</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.775110e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>roam_og_mou_7</th>\n",
       "      <td>96140.0</td>\n",
       "      <td>9.818732e+00</td>\n",
       "      <td>58.455762</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.812040e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>roam_og_mou_8</th>\n",
       "      <td>94621.0</td>\n",
       "      <td>9.971890e+00</td>\n",
       "      <td>64.713221</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>5.337040e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>roam_og_mou_9</th>\n",
       "      <td>92254.0</td>\n",
       "      <td>8.555519e+00</td>\n",
       "      <td>58.438186</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>4.428460e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loc_og_t2t_mou_6</th>\n",
       "      <td>96062.0</td>\n",
       "      <td>4.710076e+01</td>\n",
       "      <td>150.856393</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.660000e+00</td>\n",
       "      <td>1.191000e+01</td>\n",
       "      <td>4.096000e+01</td>\n",
       "      <td>6.431330e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loc_og_t2t_mou_7</th>\n",
       "      <td>96140.0</td>\n",
       "      <td>4.647301e+01</td>\n",
       "      <td>155.318705</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.630000e+00</td>\n",
       "      <td>1.161000e+01</td>\n",
       "      <td>3.991000e+01</td>\n",
       "      <td>7.400660e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loc_og_t2t_mou_8</th>\n",
       "      <td>94621.0</td>\n",
       "      <td>4.588781e+01</td>\n",
       "      <td>151.184830</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.600000e+00</td>\n",
       "      <td>1.173000e+01</td>\n",
       "      <td>4.011000e+01</td>\n",
       "      <td>1.075256e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loc_og_t2t_mou_9</th>\n",
       "      <td>92254.0</td>\n",
       "      <td>4.458445e+01</td>\n",
       "      <td>147.995390</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.360000e+00</td>\n",
       "      <td>1.126000e+01</td>\n",
       "      <td>3.928000e+01</td>\n",
       "      <td>1.038924e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loc_og_t2m_mou_6</th>\n",
       "      <td>96062.0</td>\n",
       "      <td>9.334209e+01</td>\n",
       "      <td>162.780544</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>9.880000e+00</td>\n",
       "      <td>4.103000e+01</td>\n",
       "      <td>1.103900e+02</td>\n",
       "      <td>4.729740e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loc_og_t2m_mou_7</th>\n",
       "      <td>96140.0</td>\n",
       "      <td>9.139713e+01</td>\n",
       "      <td>157.492308</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.002500e+01</td>\n",
       "      <td>4.043000e+01</td>\n",
       "      <td>1.075600e+02</td>\n",
       "      <td>4.557140e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loc_og_t2m_mou_8</th>\n",
       "      <td>94621.0</td>\n",
       "      <td>9.175513e+01</td>\n",
       "      <td>156.537048</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>9.810000e+00</td>\n",
       "      <td>4.036000e+01</td>\n",
       "      <td>1.090900e+02</td>\n",
       "      <td>4.961330e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loc_og_t2m_mou_9</th>\n",
       "      <td>92254.0</td>\n",
       "      <td>9.046319e+01</td>\n",
       "      <td>158.681454</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>8.810000e+00</td>\n",
       "      <td>3.912000e+01</td>\n",
       "      <td>1.068100e+02</td>\n",
       "      <td>4.429880e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loc_og_t2f_mou_6</th>\n",
       "      <td>96062.0</td>\n",
       "      <td>3.751013e+00</td>\n",
       "      <td>14.230438</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.080000e+00</td>\n",
       "      <td>1.466030e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loc_og_t2f_mou_7</th>\n",
       "      <td>96140.0</td>\n",
       "      <td>3.792985e+00</td>\n",
       "      <td>14.264986</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.090000e+00</td>\n",
       "      <td>1.196430e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loc_og_t2f_mou_8</th>\n",
       "      <td>94621.0</td>\n",
       "      <td>3.677991e+00</td>\n",
       "      <td>13.270996</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.040000e+00</td>\n",
       "      <td>9.284900e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loc_og_t2f_mou_9</th>\n",
       "      <td>92254.0</td>\n",
       "      <td>3.655123e+00</td>\n",
       "      <td>13.457549</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.940000e+00</td>\n",
       "      <td>9.274100e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loc_og_t2c_mou_6</th>\n",
       "      <td>96062.0</td>\n",
       "      <td>1.123056e+00</td>\n",
       "      <td>5.448946</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.428600e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loc_og_t2c_mou_7</th>\n",
       "      <td>96140.0</td>\n",
       "      <td>1.368500e+00</td>\n",
       "      <td>7.533445</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>9.162400e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loc_og_t2c_mou_8</th>\n",
       "      <td>94621.0</td>\n",
       "      <td>1.433821e+00</td>\n",
       "      <td>6.783335</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>5.020900e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loc_og_t2c_mou_9</th>\n",
       "      <td>92254.0</td>\n",
       "      <td>1.232726e+00</td>\n",
       "      <td>5.619021</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.398400e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loc_og_mou_6</th>\n",
       "      <td>96062.0</td>\n",
       "      <td>1.442012e+02</td>\n",
       "      <td>251.751489</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.711000e+01</td>\n",
       "      <td>6.511000e+01</td>\n",
       "      <td>1.682700e+02</td>\n",
       "      <td>1.064338e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loc_og_mou_7</th>\n",
       "      <td>96140.0</td>\n",
       "      <td>1.416705e+02</td>\n",
       "      <td>248.731086</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.748000e+01</td>\n",
       "      <td>6.368500e+01</td>\n",
       "      <td>1.643825e+02</td>\n",
       "      <td>7.674780e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loc_og_mou_8</th>\n",
       "      <td>94621.0</td>\n",
       "      <td>1.413282e+02</td>\n",
       "      <td>245.914311</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.711000e+01</td>\n",
       "      <td>6.373000e+01</td>\n",
       "      <td>1.661100e+02</td>\n",
       "      <td>1.103991e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loc_og_mou_9</th>\n",
       "      <td>92254.0</td>\n",
       "      <td>1.387100e+02</td>\n",
       "      <td>245.934517</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.556000e+01</td>\n",
       "      <td>6.184000e+01</td>\n",
       "      <td>1.622250e+02</td>\n",
       "      <td>1.109926e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_og_t2t_mou_6</th>\n",
       "      <td>96062.0</td>\n",
       "      <td>7.982987e+01</td>\n",
       "      <td>252.476533</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.080750e+01</td>\n",
       "      <td>7.366580e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_og_t2t_mou_7</th>\n",
       "      <td>96140.0</td>\n",
       "      <td>8.329960e+01</td>\n",
       "      <td>263.631042</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.113250e+01</td>\n",
       "      <td>8.133660e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_og_t2t_mou_8</th>\n",
       "      <td>94621.0</td>\n",
       "      <td>8.328267e+01</td>\n",
       "      <td>265.486090</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.058000e+01</td>\n",
       "      <td>8.014430e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_og_t2t_mou_9</th>\n",
       "      <td>92254.0</td>\n",
       "      <td>8.234292e+01</td>\n",
       "      <td>267.184991</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.823000e+01</td>\n",
       "      <td>9.382580e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_og_t2m_mou_6</th>\n",
       "      <td>96062.0</td>\n",
       "      <td>8.729962e+01</td>\n",
       "      <td>255.617850</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.950000e+00</td>\n",
       "      <td>5.329000e+01</td>\n",
       "      <td>8.314760e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_og_t2m_mou_7</th>\n",
       "      <td>96140.0</td>\n",
       "      <td>9.080414e+01</td>\n",
       "      <td>269.347911</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.635000e+00</td>\n",
       "      <td>5.404000e+01</td>\n",
       "      <td>9.284740e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_og_t2m_mou_8</th>\n",
       "      <td>94621.0</td>\n",
       "      <td>8.983839e+01</td>\n",
       "      <td>271.757783</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.310000e+00</td>\n",
       "      <td>5.249000e+01</td>\n",
       "      <td>1.395004e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_og_t2m_mou_9</th>\n",
       "      <td>92254.0</td>\n",
       "      <td>8.627662e+01</td>\n",
       "      <td>261.407396</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.500000e+00</td>\n",
       "      <td>4.856000e+01</td>\n",
       "      <td>1.022343e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_og_t2f_mou_6</th>\n",
       "      <td>96062.0</td>\n",
       "      <td>1.129011e+00</td>\n",
       "      <td>7.984970</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>6.285600e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_og_t2f_mou_7</th>\n",
       "      <td>96140.0</td>\n",
       "      <td>1.115010e+00</td>\n",
       "      <td>8.599406</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>5.446300e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_og_t2f_mou_8</th>\n",
       "      <td>94621.0</td>\n",
       "      <td>1.067792e+00</td>\n",
       "      <td>7.905971</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>5.169100e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_og_t2f_mou_9</th>\n",
       "      <td>92254.0</td>\n",
       "      <td>1.042362e+00</td>\n",
       "      <td>8.261770</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>8.084900e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_og_t2c_mou_6</th>\n",
       "      <td>96062.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_og_t2c_mou_7</th>\n",
       "      <td>96140.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_og_t2c_mou_8</th>\n",
       "      <td>94621.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_og_t2c_mou_9</th>\n",
       "      <td>92254.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_og_mou_6</th>\n",
       "      <td>96062.0</td>\n",
       "      <td>1.682612e+02</td>\n",
       "      <td>389.948499</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.164000e+01</td>\n",
       "      <td>1.448375e+02</td>\n",
       "      <td>8.432990e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_og_mou_7</th>\n",
       "      <td>96140.0</td>\n",
       "      <td>1.752214e+02</td>\n",
       "      <td>408.922934</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.109000e+01</td>\n",
       "      <td>1.506150e+02</td>\n",
       "      <td>1.093673e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_og_mou_8</th>\n",
       "      <td>94621.0</td>\n",
       "      <td>1.741915e+02</td>\n",
       "      <td>411.633049</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.041000e+01</td>\n",
       "      <td>1.479400e+02</td>\n",
       "      <td>1.398006e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_og_mou_9</th>\n",
       "      <td>92254.0</td>\n",
       "      <td>1.696645e+02</td>\n",
       "      <td>405.138658</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>8.410000e+00</td>\n",
       "      <td>1.421050e+02</td>\n",
       "      <td>1.149531e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>isd_og_mou_6</th>\n",
       "      <td>96062.0</td>\n",
       "      <td>7.982775e-01</td>\n",
       "      <td>25.765248</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>5.900660e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>isd_og_mou_7</th>\n",
       "      <td>96140.0</td>\n",
       "      <td>7.765721e-01</td>\n",
       "      <td>25.603052</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>5.490280e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>isd_og_mou_8</th>\n",
       "      <td>94621.0</td>\n",
       "      <td>7.912471e-01</td>\n",
       "      <td>25.544471</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>5.681540e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>isd_og_mou_9</th>\n",
       "      <td>92254.0</td>\n",
       "      <td>7.238921e-01</td>\n",
       "      <td>21.310751</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>4.244530e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spl_og_mou_6</th>\n",
       "      <td>96062.0</td>\n",
       "      <td>3.916811e+00</td>\n",
       "      <td>14.936449</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.430000e+00</td>\n",
       "      <td>1.023210e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spl_og_mou_7</th>\n",
       "      <td>96140.0</td>\n",
       "      <td>4.978279e+00</td>\n",
       "      <td>20.661570</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.710000e+00</td>\n",
       "      <td>2.372510e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spl_og_mou_8</th>\n",
       "      <td>94621.0</td>\n",
       "      <td>5.053769e+00</td>\n",
       "      <td>17.855111</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.990000e+00</td>\n",
       "      <td>1.390880e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spl_og_mou_9</th>\n",
       "      <td>92254.0</td>\n",
       "      <td>4.412767e+00</td>\n",
       "      <td>16.328227</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.230000e+00</td>\n",
       "      <td>1.635710e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>og_others_6</th>\n",
       "      <td>96062.0</td>\n",
       "      <td>4.541571e-01</td>\n",
       "      <td>4.125911</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>8.008900e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>og_others_7</th>\n",
       "      <td>96140.0</td>\n",
       "      <td>3.023539e-02</td>\n",
       "      <td>2.161717</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.701300e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>og_others_8</th>\n",
       "      <td>94621.0</td>\n",
       "      <td>3.337198e-02</td>\n",
       "      <td>2.323464</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.949300e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>og_others_9</th>\n",
       "      <td>92254.0</td>\n",
       "      <td>4.745572e-02</td>\n",
       "      <td>3.635466</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>7.877900e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_og_mou_6</th>\n",
       "      <td>99999.0</td>\n",
       "      <td>3.051334e+02</td>\n",
       "      <td>463.419481</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>4.474000e+01</td>\n",
       "      <td>1.451400e+02</td>\n",
       "      <td>3.728600e+02</td>\n",
       "      <td>1.067403e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_og_mou_7</th>\n",
       "      <td>99999.0</td>\n",
       "      <td>3.102312e+02</td>\n",
       "      <td>480.031178</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>4.301000e+01</td>\n",
       "      <td>1.415300e+02</td>\n",
       "      <td>3.785700e+02</td>\n",
       "      <td>1.136531e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_og_mou_8</th>\n",
       "      <td>99999.0</td>\n",
       "      <td>3.041195e+02</td>\n",
       "      <td>478.150031</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.858000e+01</td>\n",
       "      <td>1.386100e+02</td>\n",
       "      <td>3.699000e+02</td>\n",
       "      <td>1.404306e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_og_mou_9</th>\n",
       "      <td>99999.0</td>\n",
       "      <td>2.892792e+02</td>\n",
       "      <td>468.980002</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.551000e+01</td>\n",
       "      <td>1.254600e+02</td>\n",
       "      <td>3.534800e+02</td>\n",
       "      <td>1.151773e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loc_ic_t2t_mou_6</th>\n",
       "      <td>96062.0</td>\n",
       "      <td>4.792237e+01</td>\n",
       "      <td>140.258485</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.990000e+00</td>\n",
       "      <td>1.569000e+01</td>\n",
       "      <td>4.684000e+01</td>\n",
       "      <td>6.626930e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loc_ic_t2t_mou_7</th>\n",
       "      <td>96140.0</td>\n",
       "      <td>4.799052e+01</td>\n",
       "      <td>145.795055</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.230000e+00</td>\n",
       "      <td>1.574000e+01</td>\n",
       "      <td>4.581000e+01</td>\n",
       "      <td>9.324660e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loc_ic_t2t_mou_8</th>\n",
       "      <td>94621.0</td>\n",
       "      <td>4.721136e+01</td>\n",
       "      <td>137.239552</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.280000e+00</td>\n",
       "      <td>1.603000e+01</td>\n",
       "      <td>4.629000e+01</td>\n",
       "      <td>1.069623e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loc_ic_t2t_mou_9</th>\n",
       "      <td>92254.0</td>\n",
       "      <td>4.628179e+01</td>\n",
       "      <td>140.130610</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.290000e+00</td>\n",
       "      <td>1.566000e+01</td>\n",
       "      <td>4.518000e+01</td>\n",
       "      <td>1.059883e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loc_ic_t2m_mou_6</th>\n",
       "      <td>96062.0</td>\n",
       "      <td>1.074757e+02</td>\n",
       "      <td>171.713903</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.729000e+01</td>\n",
       "      <td>5.649000e+01</td>\n",
       "      <td>1.323875e+02</td>\n",
       "      <td>4.693860e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loc_ic_t2m_mou_7</th>\n",
       "      <td>96140.0</td>\n",
       "      <td>1.071205e+02</td>\n",
       "      <td>169.423620</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.859000e+01</td>\n",
       "      <td>5.708000e+01</td>\n",
       "      <td>1.309600e+02</td>\n",
       "      <td>4.455830e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loc_ic_t2m_mou_8</th>\n",
       "      <td>94621.0</td>\n",
       "      <td>1.084605e+02</td>\n",
       "      <td>169.723759</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.893000e+01</td>\n",
       "      <td>5.824000e+01</td>\n",
       "      <td>1.339300e+02</td>\n",
       "      <td>6.274190e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loc_ic_t2m_mou_9</th>\n",
       "      <td>92254.0</td>\n",
       "      <td>1.061555e+02</td>\n",
       "      <td>165.492803</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.856000e+01</td>\n",
       "      <td>5.661000e+01</td>\n",
       "      <td>1.304900e+02</td>\n",
       "      <td>5.463780e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loc_ic_t2f_mou_6</th>\n",
       "      <td>96062.0</td>\n",
       "      <td>1.208430e+01</td>\n",
       "      <td>40.140895</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>8.800000e-01</td>\n",
       "      <td>8.140000e+00</td>\n",
       "      <td>1.872340e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loc_ic_t2f_mou_7</th>\n",
       "      <td>96140.0</td>\n",
       "      <td>1.259970e+01</td>\n",
       "      <td>42.977442</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>9.300000e-01</td>\n",
       "      <td>8.282500e+00</td>\n",
       "      <td>1.983010e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loc_ic_t2f_mou_8</th>\n",
       "      <td>94621.0</td>\n",
       "      <td>1.175183e+01</td>\n",
       "      <td>39.125379</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>9.300000e-01</td>\n",
       "      <td>8.110000e+00</td>\n",
       "      <td>2.433060e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loc_ic_t2f_mou_9</th>\n",
       "      <td>92254.0</td>\n",
       "      <td>1.217310e+01</td>\n",
       "      <td>43.840776</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>9.600000e-01</td>\n",
       "      <td>8.140000e+00</td>\n",
       "      <td>4.318280e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loc_ic_mou_6</th>\n",
       "      <td>96062.0</td>\n",
       "      <td>1.674911e+02</td>\n",
       "      <td>254.124029</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.039000e+01</td>\n",
       "      <td>9.216000e+01</td>\n",
       "      <td>2.080750e+02</td>\n",
       "      <td>7.454630e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loc_ic_mou_7</th>\n",
       "      <td>96140.0</td>\n",
       "      <td>1.677195e+02</td>\n",
       "      <td>256.242707</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.246000e+01</td>\n",
       "      <td>9.255000e+01</td>\n",
       "      <td>2.058375e+02</td>\n",
       "      <td>9.669910e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loc_ic_mou_8</th>\n",
       "      <td>94621.0</td>\n",
       "      <td>1.674326e+02</td>\n",
       "      <td>250.025523</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.274000e+01</td>\n",
       "      <td>9.383000e+01</td>\n",
       "      <td>2.072800e+02</td>\n",
       "      <td>1.083016e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loc_ic_mou_9</th>\n",
       "      <td>92254.0</td>\n",
       "      <td>1.646193e+02</td>\n",
       "      <td>249.845070</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.229000e+01</td>\n",
       "      <td>9.164000e+01</td>\n",
       "      <td>2.027375e+02</td>\n",
       "      <td>1.079629e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_ic_t2t_mou_6</th>\n",
       "      <td>96062.0</td>\n",
       "      <td>9.575993e+00</td>\n",
       "      <td>54.330607</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>4.060000e+00</td>\n",
       "      <td>5.459560e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_ic_t2t_mou_7</th>\n",
       "      <td>96140.0</td>\n",
       "      <td>1.001190e+01</td>\n",
       "      <td>57.411971</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>4.230000e+00</td>\n",
       "      <td>5.800930e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_ic_t2t_mou_8</th>\n",
       "      <td>94621.0</td>\n",
       "      <td>9.883921e+00</td>\n",
       "      <td>55.073186</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>4.080000e+00</td>\n",
       "      <td>4.309290e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_ic_t2t_mou_9</th>\n",
       "      <td>92254.0</td>\n",
       "      <td>9.432479e+00</td>\n",
       "      <td>53.376273</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.510000e+00</td>\n",
       "      <td>3.819830e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_ic_t2m_mou_6</th>\n",
       "      <td>96062.0</td>\n",
       "      <td>2.072224e+01</td>\n",
       "      <td>80.793414</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.030000e+00</td>\n",
       "      <td>1.503000e+01</td>\n",
       "      <td>5.647160e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_ic_t2m_mou_7</th>\n",
       "      <td>96140.0</td>\n",
       "      <td>2.165641e+01</td>\n",
       "      <td>86.521393</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.040000e+00</td>\n",
       "      <td>1.574000e+01</td>\n",
       "      <td>6.141880e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_ic_t2m_mou_8</th>\n",
       "      <td>94621.0</td>\n",
       "      <td>2.118321e+01</td>\n",
       "      <td>83.683565</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.030000e+00</td>\n",
       "      <td>1.536000e+01</td>\n",
       "      <td>5.645860e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_ic_t2m_mou_9</th>\n",
       "      <td>92254.0</td>\n",
       "      <td>1.962091e+01</td>\n",
       "      <td>74.913050</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.740000e+00</td>\n",
       "      <td>1.426000e+01</td>\n",
       "      <td>5.689760e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_ic_t2f_mou_6</th>\n",
       "      <td>96062.0</td>\n",
       "      <td>2.156397e+00</td>\n",
       "      <td>16.495594</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.351110e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_ic_t2f_mou_7</th>\n",
       "      <td>96140.0</td>\n",
       "      <td>2.216923e+00</td>\n",
       "      <td>16.454061</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.136080e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_ic_t2f_mou_8</th>\n",
       "      <td>94621.0</td>\n",
       "      <td>2.085004e+00</td>\n",
       "      <td>15.812580</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.394890e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_ic_t2f_mou_9</th>\n",
       "      <td>92254.0</td>\n",
       "      <td>2.173419e+00</td>\n",
       "      <td>15.978601</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.431960e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_ic_t2o_mou_6</th>\n",
       "      <td>96062.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_ic_t2o_mou_7</th>\n",
       "      <td>96140.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_ic_t2o_mou_8</th>\n",
       "      <td>94621.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_ic_t2o_mou_9</th>\n",
       "      <td>92254.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_ic_mou_6</th>\n",
       "      <td>96062.0</td>\n",
       "      <td>3.245718e+01</td>\n",
       "      <td>106.283386</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>5.890000e+00</td>\n",
       "      <td>2.693000e+01</td>\n",
       "      <td>5.712110e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_ic_mou_7</th>\n",
       "      <td>96140.0</td>\n",
       "      <td>3.388783e+01</td>\n",
       "      <td>113.720168</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>5.960000e+00</td>\n",
       "      <td>2.831000e+01</td>\n",
       "      <td>6.745760e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_ic_mou_8</th>\n",
       "      <td>94621.0</td>\n",
       "      <td>3.315474e+01</td>\n",
       "      <td>110.127008</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e-02</td>\n",
       "      <td>5.880000e+00</td>\n",
       "      <td>2.771000e+01</td>\n",
       "      <td>5.957140e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_ic_mou_9</th>\n",
       "      <td>92254.0</td>\n",
       "      <td>3.122934e+01</td>\n",
       "      <td>101.982303</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>5.380000e+00</td>\n",
       "      <td>2.569000e+01</td>\n",
       "      <td>5.956660e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_ic_mou_6</th>\n",
       "      <td>99999.0</td>\n",
       "      <td>2.001300e+02</td>\n",
       "      <td>291.651671</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.853000e+01</td>\n",
       "      <td>1.147400e+02</td>\n",
       "      <td>2.516700e+02</td>\n",
       "      <td>7.716140e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_ic_mou_7</th>\n",
       "      <td>99999.0</td>\n",
       "      <td>2.028531e+02</td>\n",
       "      <td>298.124954</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>4.119000e+01</td>\n",
       "      <td>1.163400e+02</td>\n",
       "      <td>2.506600e+02</td>\n",
       "      <td>9.699010e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_ic_mou_8</th>\n",
       "      <td>99999.0</td>\n",
       "      <td>1.987508e+02</td>\n",
       "      <td>289.321094</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.829000e+01</td>\n",
       "      <td>1.146600e+02</td>\n",
       "      <td>2.489900e+02</td>\n",
       "      <td>1.083038e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_ic_mou_9</th>\n",
       "      <td>99999.0</td>\n",
       "      <td>1.892143e+02</td>\n",
       "      <td>284.823024</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.237000e+01</td>\n",
       "      <td>1.058900e+02</td>\n",
       "      <td>2.363200e+02</td>\n",
       "      <td>1.079659e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spl_ic_mou_6</th>\n",
       "      <td>96062.0</td>\n",
       "      <td>6.155660e-02</td>\n",
       "      <td>0.160920</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.976000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spl_ic_mou_7</th>\n",
       "      <td>96140.0</td>\n",
       "      <td>3.358477e-02</td>\n",
       "      <td>0.155725</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.133000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spl_ic_mou_8</th>\n",
       "      <td>94621.0</td>\n",
       "      <td>4.036134e-02</td>\n",
       "      <td>0.146147</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.686000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spl_ic_mou_9</th>\n",
       "      <td>92254.0</td>\n",
       "      <td>1.631370e-01</td>\n",
       "      <td>0.527860</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>6.000000e-02</td>\n",
       "      <td>6.238000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>isd_ic_mou_6</th>\n",
       "      <td>96062.0</td>\n",
       "      <td>7.460608e+00</td>\n",
       "      <td>59.722948</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>6.789410e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>isd_ic_mou_7</th>\n",
       "      <td>96140.0</td>\n",
       "      <td>8.334936e+00</td>\n",
       "      <td>65.219829</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>5.289540e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>isd_ic_mou_8</th>\n",
       "      <td>94621.0</td>\n",
       "      <td>8.442001e+00</td>\n",
       "      <td>63.813098</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>4.127010e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>isd_ic_mou_9</th>\n",
       "      <td>92254.0</td>\n",
       "      <td>8.063003e+00</td>\n",
       "      <td>63.505379</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>5.057740e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ic_others_6</th>\n",
       "      <td>96062.0</td>\n",
       "      <td>8.546555e-01</td>\n",
       "      <td>11.955164</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.362940e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ic_others_7</th>\n",
       "      <td>96140.0</td>\n",
       "      <td>1.012960e+00</td>\n",
       "      <td>12.673099</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.495940e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ic_others_8</th>\n",
       "      <td>94621.0</td>\n",
       "      <td>9.708005e-01</td>\n",
       "      <td>13.284348</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.327510e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ic_others_9</th>\n",
       "      <td>92254.0</td>\n",
       "      <td>1.017162e+00</td>\n",
       "      <td>12.381172</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.005230e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_rech_num_6</th>\n",
       "      <td>99999.0</td>\n",
       "      <td>7.558806e+00</td>\n",
       "      <td>7.078405</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>6.000000e+00</td>\n",
       "      <td>9.000000e+00</td>\n",
       "      <td>3.070000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_rech_num_7</th>\n",
       "      <td>99999.0</td>\n",
       "      <td>7.700367e+00</td>\n",
       "      <td>7.070422</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>6.000000e+00</td>\n",
       "      <td>1.000000e+01</td>\n",
       "      <td>1.380000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_rech_num_8</th>\n",
       "      <td>99999.0</td>\n",
       "      <td>7.212912e+00</td>\n",
       "      <td>7.203753</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>5.000000e+00</td>\n",
       "      <td>9.000000e+00</td>\n",
       "      <td>1.960000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_rech_num_9</th>\n",
       "      <td>99999.0</td>\n",
       "      <td>6.893019e+00</td>\n",
       "      <td>7.096261</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>5.000000e+00</td>\n",
       "      <td>9.000000e+00</td>\n",
       "      <td>1.310000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_rech_amt_6</th>\n",
       "      <td>99999.0</td>\n",
       "      <td>3.275146e+02</td>\n",
       "      <td>398.019701</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.090000e+02</td>\n",
       "      <td>2.300000e+02</td>\n",
       "      <td>4.375000e+02</td>\n",
       "      <td>3.519000e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_rech_amt_7</th>\n",
       "      <td>99999.0</td>\n",
       "      <td>3.229630e+02</td>\n",
       "      <td>408.114237</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+02</td>\n",
       "      <td>2.200000e+02</td>\n",
       "      <td>4.280000e+02</td>\n",
       "      <td>4.033500e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_rech_amt_8</th>\n",
       "      <td>99999.0</td>\n",
       "      <td>3.241571e+02</td>\n",
       "      <td>416.540455</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>9.000000e+01</td>\n",
       "      <td>2.250000e+02</td>\n",
       "      <td>4.345000e+02</td>\n",
       "      <td>4.532000e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_rech_amt_9</th>\n",
       "      <td>99999.0</td>\n",
       "      <td>3.033457e+02</td>\n",
       "      <td>404.588583</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>5.200000e+01</td>\n",
       "      <td>2.000000e+02</td>\n",
       "      <td>4.150000e+02</td>\n",
       "      <td>3.723500e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max_rech_amt_6</th>\n",
       "      <td>99999.0</td>\n",
       "      <td>1.046375e+02</td>\n",
       "      <td>120.614894</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.000000e+01</td>\n",
       "      <td>1.100000e+02</td>\n",
       "      <td>1.200000e+02</td>\n",
       "      <td>4.010000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max_rech_amt_7</th>\n",
       "      <td>99999.0</td>\n",
       "      <td>1.047524e+02</td>\n",
       "      <td>124.523970</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.000000e+01</td>\n",
       "      <td>1.100000e+02</td>\n",
       "      <td>1.280000e+02</td>\n",
       "      <td>4.010000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max_rech_amt_8</th>\n",
       "      <td>99999.0</td>\n",
       "      <td>1.077282e+02</td>\n",
       "      <td>126.902505</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.000000e+01</td>\n",
       "      <td>9.800000e+01</td>\n",
       "      <td>1.440000e+02</td>\n",
       "      <td>4.449000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max_rech_amt_9</th>\n",
       "      <td>99999.0</td>\n",
       "      <td>1.019439e+02</td>\n",
       "      <td>125.375109</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.800000e+01</td>\n",
       "      <td>6.100000e+01</td>\n",
       "      <td>1.440000e+02</td>\n",
       "      <td>3.399000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>last_day_rch_amt_6</th>\n",
       "      <td>99999.0</td>\n",
       "      <td>6.315625e+01</td>\n",
       "      <td>97.356649</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.000000e+01</td>\n",
       "      <td>1.100000e+02</td>\n",
       "      <td>4.010000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>last_day_rch_amt_7</th>\n",
       "      <td>99999.0</td>\n",
       "      <td>5.938580e+01</td>\n",
       "      <td>95.915385</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.000000e+01</td>\n",
       "      <td>1.100000e+02</td>\n",
       "      <td>4.010000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>last_day_rch_amt_8</th>\n",
       "      <td>99999.0</td>\n",
       "      <td>6.264172e+01</td>\n",
       "      <td>104.431816</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.000000e+01</td>\n",
       "      <td>1.300000e+02</td>\n",
       "      <td>4.449000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>last_day_rch_amt_9</th>\n",
       "      <td>99999.0</td>\n",
       "      <td>4.390125e+01</td>\n",
       "      <td>90.809712</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>5.000000e+01</td>\n",
       "      <td>3.399000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_rech_data_6</th>\n",
       "      <td>25153.0</td>\n",
       "      <td>2.463802e+00</td>\n",
       "      <td>2.789128</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>6.100000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_rech_data_7</th>\n",
       "      <td>25571.0</td>\n",
       "      <td>2.666419e+00</td>\n",
       "      <td>3.031593</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>5.400000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_rech_data_8</th>\n",
       "      <td>26339.0</td>\n",
       "      <td>2.651999e+00</td>\n",
       "      <td>3.074987</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>6.000000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_rech_data_9</th>\n",
       "      <td>25922.0</td>\n",
       "      <td>2.441170e+00</td>\n",
       "      <td>2.516339</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>8.400000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max_rech_data_6</th>\n",
       "      <td>25153.0</td>\n",
       "      <td>1.263934e+02</td>\n",
       "      <td>108.477235</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.500000e+01</td>\n",
       "      <td>1.450000e+02</td>\n",
       "      <td>1.770000e+02</td>\n",
       "      <td>1.555000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max_rech_data_7</th>\n",
       "      <td>25571.0</td>\n",
       "      <td>1.267295e+02</td>\n",
       "      <td>109.765267</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.500000e+01</td>\n",
       "      <td>1.450000e+02</td>\n",
       "      <td>1.770000e+02</td>\n",
       "      <td>1.555000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max_rech_data_8</th>\n",
       "      <td>26339.0</td>\n",
       "      <td>1.257173e+02</td>\n",
       "      <td>109.437851</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.500000e+01</td>\n",
       "      <td>1.450000e+02</td>\n",
       "      <td>1.790000e+02</td>\n",
       "      <td>1.555000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max_rech_data_9</th>\n",
       "      <td>25922.0</td>\n",
       "      <td>1.249414e+02</td>\n",
       "      <td>111.363760</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.500000e+01</td>\n",
       "      <td>1.450000e+02</td>\n",
       "      <td>1.790000e+02</td>\n",
       "      <td>1.555000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>count_rech_2g_6</th>\n",
       "      <td>25153.0</td>\n",
       "      <td>1.864668e+00</td>\n",
       "      <td>2.570254</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>4.200000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>count_rech_2g_7</th>\n",
       "      <td>25571.0</td>\n",
       "      <td>2.044699e+00</td>\n",
       "      <td>2.768332</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>4.800000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>count_rech_2g_8</th>\n",
       "      <td>26339.0</td>\n",
       "      <td>2.016288e+00</td>\n",
       "      <td>2.720132</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>4.400000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>count_rech_2g_9</th>\n",
       "      <td>25922.0</td>\n",
       "      <td>1.781807e+00</td>\n",
       "      <td>2.214701</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>4.000000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>count_rech_3g_6</th>\n",
       "      <td>25153.0</td>\n",
       "      <td>5.991333e-01</td>\n",
       "      <td>1.274428</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.900000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>count_rech_3g_7</th>\n",
       "      <td>25571.0</td>\n",
       "      <td>6.217199e-01</td>\n",
       "      <td>1.394524</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>3.500000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>count_rech_3g_8</th>\n",
       "      <td>26339.0</td>\n",
       "      <td>6.357113e-01</td>\n",
       "      <td>1.422827</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>4.500000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>count_rech_3g_9</th>\n",
       "      <td>25922.0</td>\n",
       "      <td>6.593627e-01</td>\n",
       "      <td>1.411513</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>4.900000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>av_rech_amt_data_6</th>\n",
       "      <td>25153.0</td>\n",
       "      <td>1.926010e+02</td>\n",
       "      <td>192.646318</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>8.200000e+01</td>\n",
       "      <td>1.540000e+02</td>\n",
       "      <td>2.520000e+02</td>\n",
       "      <td>7.546000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>av_rech_amt_data_7</th>\n",
       "      <td>25571.0</td>\n",
       "      <td>2.009813e+02</td>\n",
       "      <td>196.791224</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>9.200000e+01</td>\n",
       "      <td>1.540000e+02</td>\n",
       "      <td>2.520000e+02</td>\n",
       "      <td>4.365000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>av_rech_amt_data_8</th>\n",
       "      <td>26339.0</td>\n",
       "      <td>1.975265e+02</td>\n",
       "      <td>191.301305</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>8.700000e+01</td>\n",
       "      <td>1.540000e+02</td>\n",
       "      <td>2.520000e+02</td>\n",
       "      <td>4.076000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>av_rech_amt_data_9</th>\n",
       "      <td>25922.0</td>\n",
       "      <td>1.927343e+02</td>\n",
       "      <td>188.400286</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>6.900000e+01</td>\n",
       "      <td>1.640000e+02</td>\n",
       "      <td>2.520000e+02</td>\n",
       "      <td>4.061000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vol_2g_mb_6</th>\n",
       "      <td>99999.0</td>\n",
       "      <td>5.190496e+01</td>\n",
       "      <td>213.356445</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.028590e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vol_2g_mb_7</th>\n",
       "      <td>99999.0</td>\n",
       "      <td>5.122994e+01</td>\n",
       "      <td>212.302217</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>7.873550e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vol_2g_mb_8</th>\n",
       "      <td>99999.0</td>\n",
       "      <td>5.017015e+01</td>\n",
       "      <td>212.347892</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.111761e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vol_2g_mb_9</th>\n",
       "      <td>99999.0</td>\n",
       "      <td>4.471970e+01</td>\n",
       "      <td>198.653570</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>8.993950e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vol_3g_mb_6</th>\n",
       "      <td>99999.0</td>\n",
       "      <td>1.213962e+02</td>\n",
       "      <td>544.247227</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>4.573540e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vol_3g_mb_7</th>\n",
       "      <td>99999.0</td>\n",
       "      <td>1.289958e+02</td>\n",
       "      <td>541.494013</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.814412e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vol_3g_mb_8</th>\n",
       "      <td>99999.0</td>\n",
       "      <td>1.354107e+02</td>\n",
       "      <td>558.775335</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.003606e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vol_3g_mb_9</th>\n",
       "      <td>99999.0</td>\n",
       "      <td>1.360566e+02</td>\n",
       "      <td>577.394194</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.922127e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>arpu_3g_6</th>\n",
       "      <td>25153.0</td>\n",
       "      <td>8.955506e+01</td>\n",
       "      <td>193.124653</td>\n",
       "      <td>-3.082000e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>4.800000e-01</td>\n",
       "      <td>1.220700e+02</td>\n",
       "      <td>6.362280e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>arpu_3g_7</th>\n",
       "      <td>25571.0</td>\n",
       "      <td>8.938412e+01</td>\n",
       "      <td>195.893924</td>\n",
       "      <td>-2.604000e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>4.200000e-01</td>\n",
       "      <td>1.195600e+02</td>\n",
       "      <td>4.980900e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>arpu_3g_8</th>\n",
       "      <td>26339.0</td>\n",
       "      <td>9.117385e+01</td>\n",
       "      <td>188.180936</td>\n",
       "      <td>-2.449000e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>8.800000e-01</td>\n",
       "      <td>1.220700e+02</td>\n",
       "      <td>3.716900e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>arpu_3g_9</th>\n",
       "      <td>25922.0</td>\n",
       "      <td>1.002641e+02</td>\n",
       "      <td>216.291992</td>\n",
       "      <td>-7.109000e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.605000e+00</td>\n",
       "      <td>1.400100e+02</td>\n",
       "      <td>1.388431e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>arpu_2g_6</th>\n",
       "      <td>25153.0</td>\n",
       "      <td>8.639800e+01</td>\n",
       "      <td>172.767523</td>\n",
       "      <td>-3.583000e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.083000e+01</td>\n",
       "      <td>1.220700e+02</td>\n",
       "      <td>6.433760e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>arpu_2g_7</th>\n",
       "      <td>25571.0</td>\n",
       "      <td>8.591445e+01</td>\n",
       "      <td>176.379871</td>\n",
       "      <td>-1.548000e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>8.810000e+00</td>\n",
       "      <td>1.220700e+02</td>\n",
       "      <td>4.809360e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>arpu_2g_8</th>\n",
       "      <td>26339.0</td>\n",
       "      <td>8.659948e+01</td>\n",
       "      <td>168.247852</td>\n",
       "      <td>-5.583000e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>9.270000e+00</td>\n",
       "      <td>1.220700e+02</td>\n",
       "      <td>3.483170e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>arpu_2g_9</th>\n",
       "      <td>25922.0</td>\n",
       "      <td>9.371203e+01</td>\n",
       "      <td>171.384224</td>\n",
       "      <td>-4.574000e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.480000e+01</td>\n",
       "      <td>1.400100e+02</td>\n",
       "      <td>3.467170e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>night_pck_user_6</th>\n",
       "      <td>25153.0</td>\n",
       "      <td>2.508647e-02</td>\n",
       "      <td>0.156391</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>night_pck_user_7</th>\n",
       "      <td>25571.0</td>\n",
       "      <td>2.303391e-02</td>\n",
       "      <td>0.150014</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>night_pck_user_8</th>\n",
       "      <td>26339.0</td>\n",
       "      <td>2.084362e-02</td>\n",
       "      <td>0.142863</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>night_pck_user_9</th>\n",
       "      <td>25922.0</td>\n",
       "      <td>1.597099e-02</td>\n",
       "      <td>0.125366</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>monthly_2g_6</th>\n",
       "      <td>99999.0</td>\n",
       "      <td>7.964080e-02</td>\n",
       "      <td>0.295058</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>4.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>monthly_2g_7</th>\n",
       "      <td>99999.0</td>\n",
       "      <td>8.322083e-02</td>\n",
       "      <td>0.304395</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>5.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>monthly_2g_8</th>\n",
       "      <td>99999.0</td>\n",
       "      <td>8.100081e-02</td>\n",
       "      <td>0.299568</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>5.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>monthly_2g_9</th>\n",
       "      <td>99999.0</td>\n",
       "      <td>6.878069e-02</td>\n",
       "      <td>0.278120</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>4.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sachet_2g_6</th>\n",
       "      <td>99999.0</td>\n",
       "      <td>3.893839e-01</td>\n",
       "      <td>1.497320</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>4.200000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sachet_2g_7</th>\n",
       "      <td>99999.0</td>\n",
       "      <td>4.396344e-01</td>\n",
       "      <td>1.636230</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>4.800000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sachet_2g_8</th>\n",
       "      <td>99999.0</td>\n",
       "      <td>4.500745e-01</td>\n",
       "      <td>1.630263</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>4.400000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sachet_2g_9</th>\n",
       "      <td>99999.0</td>\n",
       "      <td>3.931039e-01</td>\n",
       "      <td>1.347140</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>4.000000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>monthly_3g_6</th>\n",
       "      <td>99999.0</td>\n",
       "      <td>7.592076e-02</td>\n",
       "      <td>0.363371</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.400000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>monthly_3g_7</th>\n",
       "      <td>99999.0</td>\n",
       "      <td>7.858079e-02</td>\n",
       "      <td>0.387231</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.600000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>monthly_3g_8</th>\n",
       "      <td>99999.0</td>\n",
       "      <td>8.294083e-02</td>\n",
       "      <td>0.384947</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.600000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>monthly_3g_9</th>\n",
       "      <td>99999.0</td>\n",
       "      <td>8.634086e-02</td>\n",
       "      <td>0.384978</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.100000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sachet_3g_6</th>\n",
       "      <td>99999.0</td>\n",
       "      <td>7.478075e-02</td>\n",
       "      <td>0.568344</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.900000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sachet_3g_7</th>\n",
       "      <td>99999.0</td>\n",
       "      <td>8.040080e-02</td>\n",
       "      <td>0.628334</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.500000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sachet_3g_8</th>\n",
       "      <td>99999.0</td>\n",
       "      <td>8.450085e-02</td>\n",
       "      <td>0.660234</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>4.100000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sachet_3g_9</th>\n",
       "      <td>99999.0</td>\n",
       "      <td>8.458085e-02</td>\n",
       "      <td>0.650457</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>4.900000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fb_user_6</th>\n",
       "      <td>25153.0</td>\n",
       "      <td>9.144038e-01</td>\n",
       "      <td>0.279772</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fb_user_7</th>\n",
       "      <td>25571.0</td>\n",
       "      <td>9.087638e-01</td>\n",
       "      <td>0.287950</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fb_user_8</th>\n",
       "      <td>26339.0</td>\n",
       "      <td>8.908083e-01</td>\n",
       "      <td>0.311885</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fb_user_9</th>\n",
       "      <td>25922.0</td>\n",
       "      <td>8.609675e-01</td>\n",
       "      <td>0.345987</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aon</th>\n",
       "      <td>99999.0</td>\n",
       "      <td>1.219855e+03</td>\n",
       "      <td>954.733842</td>\n",
       "      <td>1.800000e+02</td>\n",
       "      <td>4.670000e+02</td>\n",
       "      <td>8.630000e+02</td>\n",
       "      <td>1.807500e+03</td>\n",
       "      <td>4.337000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aug_vbc_3g</th>\n",
       "      <td>99999.0</td>\n",
       "      <td>6.817025e+01</td>\n",
       "      <td>267.580450</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.291622e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jul_vbc_3g</th>\n",
       "      <td>99999.0</td>\n",
       "      <td>6.683906e+01</td>\n",
       "      <td>271.201856</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>9.165600e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jun_vbc_3g</th>\n",
       "      <td>99999.0</td>\n",
       "      <td>6.002120e+01</td>\n",
       "      <td>253.938223</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.116621e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sep_vbc_3g</th>\n",
       "      <td>99999.0</td>\n",
       "      <td>3.299373e+00</td>\n",
       "      <td>32.408353</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.618570e+03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      count          mean            std           min  \\\n",
       "mobile_number       99999.0  7.001207e+09  695669.386290  7.000000e+09   \n",
       "circle_id           99999.0  1.090000e+02       0.000000  1.090000e+02   \n",
       "loc_og_t2o_mou      98981.0  0.000000e+00       0.000000  0.000000e+00   \n",
       "std_og_t2o_mou      98981.0  0.000000e+00       0.000000  0.000000e+00   \n",
       "loc_ic_t2o_mou      98981.0  0.000000e+00       0.000000  0.000000e+00   \n",
       "arpu_6              99999.0  2.829874e+02     328.439770 -2.258709e+03   \n",
       "arpu_7              99999.0  2.785366e+02     338.156291 -2.014045e+03   \n",
       "arpu_8              99999.0  2.791547e+02     344.474791 -9.458080e+02   \n",
       "arpu_9              99999.0  2.616451e+02     341.998630 -1.899505e+03   \n",
       "onnet_mou_6         96062.0  1.323959e+02     297.207406  0.000000e+00   \n",
       "onnet_mou_7         96140.0  1.336708e+02     308.794148  0.000000e+00   \n",
       "onnet_mou_8         94621.0  1.330181e+02     308.951589  0.000000e+00   \n",
       "onnet_mou_9         92254.0  1.303023e+02     308.477668  0.000000e+00   \n",
       "offnet_mou_6        96062.0  1.979356e+02     316.851613  0.000000e+00   \n",
       "offnet_mou_7        96140.0  1.970451e+02     325.862803  0.000000e+00   \n",
       "offnet_mou_8        94621.0  1.965748e+02     327.170662  0.000000e+00   \n",
       "offnet_mou_9        92254.0  1.903372e+02     319.396092  0.000000e+00   \n",
       "roam_ic_mou_6       96062.0  9.950013e+00      72.825411  0.000000e+00   \n",
       "roam_ic_mou_7       96140.0  7.149898e+00      73.447948  0.000000e+00   \n",
       "roam_ic_mou_8       94621.0  7.292981e+00      68.402466  0.000000e+00   \n",
       "roam_ic_mou_9       92254.0  6.343841e+00      57.137537  0.000000e+00   \n",
       "roam_og_mou_6       96062.0  1.391134e+01      71.443196  0.000000e+00   \n",
       "roam_og_mou_7       96140.0  9.818732e+00      58.455762  0.000000e+00   \n",
       "roam_og_mou_8       94621.0  9.971890e+00      64.713221  0.000000e+00   \n",
       "roam_og_mou_9       92254.0  8.555519e+00      58.438186  0.000000e+00   \n",
       "loc_og_t2t_mou_6    96062.0  4.710076e+01     150.856393  0.000000e+00   \n",
       "loc_og_t2t_mou_7    96140.0  4.647301e+01     155.318705  0.000000e+00   \n",
       "loc_og_t2t_mou_8    94621.0  4.588781e+01     151.184830  0.000000e+00   \n",
       "loc_og_t2t_mou_9    92254.0  4.458445e+01     147.995390  0.000000e+00   \n",
       "loc_og_t2m_mou_6    96062.0  9.334209e+01     162.780544  0.000000e+00   \n",
       "loc_og_t2m_mou_7    96140.0  9.139713e+01     157.492308  0.000000e+00   \n",
       "loc_og_t2m_mou_8    94621.0  9.175513e+01     156.537048  0.000000e+00   \n",
       "loc_og_t2m_mou_9    92254.0  9.046319e+01     158.681454  0.000000e+00   \n",
       "loc_og_t2f_mou_6    96062.0  3.751013e+00      14.230438  0.000000e+00   \n",
       "loc_og_t2f_mou_7    96140.0  3.792985e+00      14.264986  0.000000e+00   \n",
       "loc_og_t2f_mou_8    94621.0  3.677991e+00      13.270996  0.000000e+00   \n",
       "loc_og_t2f_mou_9    92254.0  3.655123e+00      13.457549  0.000000e+00   \n",
       "loc_og_t2c_mou_6    96062.0  1.123056e+00       5.448946  0.000000e+00   \n",
       "loc_og_t2c_mou_7    96140.0  1.368500e+00       7.533445  0.000000e+00   \n",
       "loc_og_t2c_mou_8    94621.0  1.433821e+00       6.783335  0.000000e+00   \n",
       "loc_og_t2c_mou_9    92254.0  1.232726e+00       5.619021  0.000000e+00   \n",
       "loc_og_mou_6        96062.0  1.442012e+02     251.751489  0.000000e+00   \n",
       "loc_og_mou_7        96140.0  1.416705e+02     248.731086  0.000000e+00   \n",
       "loc_og_mou_8        94621.0  1.413282e+02     245.914311  0.000000e+00   \n",
       "loc_og_mou_9        92254.0  1.387100e+02     245.934517  0.000000e+00   \n",
       "std_og_t2t_mou_6    96062.0  7.982987e+01     252.476533  0.000000e+00   \n",
       "std_og_t2t_mou_7    96140.0  8.329960e+01     263.631042  0.000000e+00   \n",
       "std_og_t2t_mou_8    94621.0  8.328267e+01     265.486090  0.000000e+00   \n",
       "std_og_t2t_mou_9    92254.0  8.234292e+01     267.184991  0.000000e+00   \n",
       "std_og_t2m_mou_6    96062.0  8.729962e+01     255.617850  0.000000e+00   \n",
       "std_og_t2m_mou_7    96140.0  9.080414e+01     269.347911  0.000000e+00   \n",
       "std_og_t2m_mou_8    94621.0  8.983839e+01     271.757783  0.000000e+00   \n",
       "std_og_t2m_mou_9    92254.0  8.627662e+01     261.407396  0.000000e+00   \n",
       "std_og_t2f_mou_6    96062.0  1.129011e+00       7.984970  0.000000e+00   \n",
       "std_og_t2f_mou_7    96140.0  1.115010e+00       8.599406  0.000000e+00   \n",
       "std_og_t2f_mou_8    94621.0  1.067792e+00       7.905971  0.000000e+00   \n",
       "std_og_t2f_mou_9    92254.0  1.042362e+00       8.261770  0.000000e+00   \n",
       "std_og_t2c_mou_6    96062.0  0.000000e+00       0.000000  0.000000e+00   \n",
       "std_og_t2c_mou_7    96140.0  0.000000e+00       0.000000  0.000000e+00   \n",
       "std_og_t2c_mou_8    94621.0  0.000000e+00       0.000000  0.000000e+00   \n",
       "std_og_t2c_mou_9    92254.0  0.000000e+00       0.000000  0.000000e+00   \n",
       "std_og_mou_6        96062.0  1.682612e+02     389.948499  0.000000e+00   \n",
       "std_og_mou_7        96140.0  1.752214e+02     408.922934  0.000000e+00   \n",
       "std_og_mou_8        94621.0  1.741915e+02     411.633049  0.000000e+00   \n",
       "std_og_mou_9        92254.0  1.696645e+02     405.138658  0.000000e+00   \n",
       "isd_og_mou_6        96062.0  7.982775e-01      25.765248  0.000000e+00   \n",
       "isd_og_mou_7        96140.0  7.765721e-01      25.603052  0.000000e+00   \n",
       "isd_og_mou_8        94621.0  7.912471e-01      25.544471  0.000000e+00   \n",
       "isd_og_mou_9        92254.0  7.238921e-01      21.310751  0.000000e+00   \n",
       "spl_og_mou_6        96062.0  3.916811e+00      14.936449  0.000000e+00   \n",
       "spl_og_mou_7        96140.0  4.978279e+00      20.661570  0.000000e+00   \n",
       "spl_og_mou_8        94621.0  5.053769e+00      17.855111  0.000000e+00   \n",
       "spl_og_mou_9        92254.0  4.412767e+00      16.328227  0.000000e+00   \n",
       "og_others_6         96062.0  4.541571e-01       4.125911  0.000000e+00   \n",
       "og_others_7         96140.0  3.023539e-02       2.161717  0.000000e+00   \n",
       "og_others_8         94621.0  3.337198e-02       2.323464  0.000000e+00   \n",
       "og_others_9         92254.0  4.745572e-02       3.635466  0.000000e+00   \n",
       "total_og_mou_6      99999.0  3.051334e+02     463.419481  0.000000e+00   \n",
       "total_og_mou_7      99999.0  3.102312e+02     480.031178  0.000000e+00   \n",
       "total_og_mou_8      99999.0  3.041195e+02     478.150031  0.000000e+00   \n",
       "total_og_mou_9      99999.0  2.892792e+02     468.980002  0.000000e+00   \n",
       "loc_ic_t2t_mou_6    96062.0  4.792237e+01     140.258485  0.000000e+00   \n",
       "loc_ic_t2t_mou_7    96140.0  4.799052e+01     145.795055  0.000000e+00   \n",
       "loc_ic_t2t_mou_8    94621.0  4.721136e+01     137.239552  0.000000e+00   \n",
       "loc_ic_t2t_mou_9    92254.0  4.628179e+01     140.130610  0.000000e+00   \n",
       "loc_ic_t2m_mou_6    96062.0  1.074757e+02     171.713903  0.000000e+00   \n",
       "loc_ic_t2m_mou_7    96140.0  1.071205e+02     169.423620  0.000000e+00   \n",
       "loc_ic_t2m_mou_8    94621.0  1.084605e+02     169.723759  0.000000e+00   \n",
       "loc_ic_t2m_mou_9    92254.0  1.061555e+02     165.492803  0.000000e+00   \n",
       "loc_ic_t2f_mou_6    96062.0  1.208430e+01      40.140895  0.000000e+00   \n",
       "loc_ic_t2f_mou_7    96140.0  1.259970e+01      42.977442  0.000000e+00   \n",
       "loc_ic_t2f_mou_8    94621.0  1.175183e+01      39.125379  0.000000e+00   \n",
       "loc_ic_t2f_mou_9    92254.0  1.217310e+01      43.840776  0.000000e+00   \n",
       "loc_ic_mou_6        96062.0  1.674911e+02     254.124029  0.000000e+00   \n",
       "loc_ic_mou_7        96140.0  1.677195e+02     256.242707  0.000000e+00   \n",
       "loc_ic_mou_8        94621.0  1.674326e+02     250.025523  0.000000e+00   \n",
       "loc_ic_mou_9        92254.0  1.646193e+02     249.845070  0.000000e+00   \n",
       "std_ic_t2t_mou_6    96062.0  9.575993e+00      54.330607  0.000000e+00   \n",
       "std_ic_t2t_mou_7    96140.0  1.001190e+01      57.411971  0.000000e+00   \n",
       "std_ic_t2t_mou_8    94621.0  9.883921e+00      55.073186  0.000000e+00   \n",
       "std_ic_t2t_mou_9    92254.0  9.432479e+00      53.376273  0.000000e+00   \n",
       "std_ic_t2m_mou_6    96062.0  2.072224e+01      80.793414  0.000000e+00   \n",
       "std_ic_t2m_mou_7    96140.0  2.165641e+01      86.521393  0.000000e+00   \n",
       "std_ic_t2m_mou_8    94621.0  2.118321e+01      83.683565  0.000000e+00   \n",
       "std_ic_t2m_mou_9    92254.0  1.962091e+01      74.913050  0.000000e+00   \n",
       "std_ic_t2f_mou_6    96062.0  2.156397e+00      16.495594  0.000000e+00   \n",
       "std_ic_t2f_mou_7    96140.0  2.216923e+00      16.454061  0.000000e+00   \n",
       "std_ic_t2f_mou_8    94621.0  2.085004e+00      15.812580  0.000000e+00   \n",
       "std_ic_t2f_mou_9    92254.0  2.173419e+00      15.978601  0.000000e+00   \n",
       "std_ic_t2o_mou_6    96062.0  0.000000e+00       0.000000  0.000000e+00   \n",
       "std_ic_t2o_mou_7    96140.0  0.000000e+00       0.000000  0.000000e+00   \n",
       "std_ic_t2o_mou_8    94621.0  0.000000e+00       0.000000  0.000000e+00   \n",
       "std_ic_t2o_mou_9    92254.0  0.000000e+00       0.000000  0.000000e+00   \n",
       "std_ic_mou_6        96062.0  3.245718e+01     106.283386  0.000000e+00   \n",
       "std_ic_mou_7        96140.0  3.388783e+01     113.720168  0.000000e+00   \n",
       "std_ic_mou_8        94621.0  3.315474e+01     110.127008  0.000000e+00   \n",
       "std_ic_mou_9        92254.0  3.122934e+01     101.982303  0.000000e+00   \n",
       "total_ic_mou_6      99999.0  2.001300e+02     291.651671  0.000000e+00   \n",
       "total_ic_mou_7      99999.0  2.028531e+02     298.124954  0.000000e+00   \n",
       "total_ic_mou_8      99999.0  1.987508e+02     289.321094  0.000000e+00   \n",
       "total_ic_mou_9      99999.0  1.892143e+02     284.823024  0.000000e+00   \n",
       "spl_ic_mou_6        96062.0  6.155660e-02       0.160920  0.000000e+00   \n",
       "spl_ic_mou_7        96140.0  3.358477e-02       0.155725  0.000000e+00   \n",
       "spl_ic_mou_8        94621.0  4.036134e-02       0.146147  0.000000e+00   \n",
       "spl_ic_mou_9        92254.0  1.631370e-01       0.527860  0.000000e+00   \n",
       "isd_ic_mou_6        96062.0  7.460608e+00      59.722948  0.000000e+00   \n",
       "isd_ic_mou_7        96140.0  8.334936e+00      65.219829  0.000000e+00   \n",
       "isd_ic_mou_8        94621.0  8.442001e+00      63.813098  0.000000e+00   \n",
       "isd_ic_mou_9        92254.0  8.063003e+00      63.505379  0.000000e+00   \n",
       "ic_others_6         96062.0  8.546555e-01      11.955164  0.000000e+00   \n",
       "ic_others_7         96140.0  1.012960e+00      12.673099  0.000000e+00   \n",
       "ic_others_8         94621.0  9.708005e-01      13.284348  0.000000e+00   \n",
       "ic_others_9         92254.0  1.017162e+00      12.381172  0.000000e+00   \n",
       "total_rech_num_6    99999.0  7.558806e+00       7.078405  0.000000e+00   \n",
       "total_rech_num_7    99999.0  7.700367e+00       7.070422  0.000000e+00   \n",
       "total_rech_num_8    99999.0  7.212912e+00       7.203753  0.000000e+00   \n",
       "total_rech_num_9    99999.0  6.893019e+00       7.096261  0.000000e+00   \n",
       "total_rech_amt_6    99999.0  3.275146e+02     398.019701  0.000000e+00   \n",
       "total_rech_amt_7    99999.0  3.229630e+02     408.114237  0.000000e+00   \n",
       "total_rech_amt_8    99999.0  3.241571e+02     416.540455  0.000000e+00   \n",
       "total_rech_amt_9    99999.0  3.033457e+02     404.588583  0.000000e+00   \n",
       "max_rech_amt_6      99999.0  1.046375e+02     120.614894  0.000000e+00   \n",
       "max_rech_amt_7      99999.0  1.047524e+02     124.523970  0.000000e+00   \n",
       "max_rech_amt_8      99999.0  1.077282e+02     126.902505  0.000000e+00   \n",
       "max_rech_amt_9      99999.0  1.019439e+02     125.375109  0.000000e+00   \n",
       "last_day_rch_amt_6  99999.0  6.315625e+01      97.356649  0.000000e+00   \n",
       "last_day_rch_amt_7  99999.0  5.938580e+01      95.915385  0.000000e+00   \n",
       "last_day_rch_amt_8  99999.0  6.264172e+01     104.431816  0.000000e+00   \n",
       "last_day_rch_amt_9  99999.0  4.390125e+01      90.809712  0.000000e+00   \n",
       "total_rech_data_6   25153.0  2.463802e+00       2.789128  1.000000e+00   \n",
       "total_rech_data_7   25571.0  2.666419e+00       3.031593  1.000000e+00   \n",
       "total_rech_data_8   26339.0  2.651999e+00       3.074987  1.000000e+00   \n",
       "total_rech_data_9   25922.0  2.441170e+00       2.516339  1.000000e+00   \n",
       "max_rech_data_6     25153.0  1.263934e+02     108.477235  1.000000e+00   \n",
       "max_rech_data_7     25571.0  1.267295e+02     109.765267  1.000000e+00   \n",
       "max_rech_data_8     26339.0  1.257173e+02     109.437851  1.000000e+00   \n",
       "max_rech_data_9     25922.0  1.249414e+02     111.363760  1.000000e+00   \n",
       "count_rech_2g_6     25153.0  1.864668e+00       2.570254  0.000000e+00   \n",
       "count_rech_2g_7     25571.0  2.044699e+00       2.768332  0.000000e+00   \n",
       "count_rech_2g_8     26339.0  2.016288e+00       2.720132  0.000000e+00   \n",
       "count_rech_2g_9     25922.0  1.781807e+00       2.214701  0.000000e+00   \n",
       "count_rech_3g_6     25153.0  5.991333e-01       1.274428  0.000000e+00   \n",
       "count_rech_3g_7     25571.0  6.217199e-01       1.394524  0.000000e+00   \n",
       "count_rech_3g_8     26339.0  6.357113e-01       1.422827  0.000000e+00   \n",
       "count_rech_3g_9     25922.0  6.593627e-01       1.411513  0.000000e+00   \n",
       "av_rech_amt_data_6  25153.0  1.926010e+02     192.646318  1.000000e+00   \n",
       "av_rech_amt_data_7  25571.0  2.009813e+02     196.791224  5.000000e-01   \n",
       "av_rech_amt_data_8  26339.0  1.975265e+02     191.301305  5.000000e-01   \n",
       "av_rech_amt_data_9  25922.0  1.927343e+02     188.400286  1.000000e+00   \n",
       "vol_2g_mb_6         99999.0  5.190496e+01     213.356445  0.000000e+00   \n",
       "vol_2g_mb_7         99999.0  5.122994e+01     212.302217  0.000000e+00   \n",
       "vol_2g_mb_8         99999.0  5.017015e+01     212.347892  0.000000e+00   \n",
       "vol_2g_mb_9         99999.0  4.471970e+01     198.653570  0.000000e+00   \n",
       "vol_3g_mb_6         99999.0  1.213962e+02     544.247227  0.000000e+00   \n",
       "vol_3g_mb_7         99999.0  1.289958e+02     541.494013  0.000000e+00   \n",
       "vol_3g_mb_8         99999.0  1.354107e+02     558.775335  0.000000e+00   \n",
       "vol_3g_mb_9         99999.0  1.360566e+02     577.394194  0.000000e+00   \n",
       "arpu_3g_6           25153.0  8.955506e+01     193.124653 -3.082000e+01   \n",
       "arpu_3g_7           25571.0  8.938412e+01     195.893924 -2.604000e+01   \n",
       "arpu_3g_8           26339.0  9.117385e+01     188.180936 -2.449000e+01   \n",
       "arpu_3g_9           25922.0  1.002641e+02     216.291992 -7.109000e+01   \n",
       "arpu_2g_6           25153.0  8.639800e+01     172.767523 -3.583000e+01   \n",
       "arpu_2g_7           25571.0  8.591445e+01     176.379871 -1.548000e+01   \n",
       "arpu_2g_8           26339.0  8.659948e+01     168.247852 -5.583000e+01   \n",
       "arpu_2g_9           25922.0  9.371203e+01     171.384224 -4.574000e+01   \n",
       "night_pck_user_6    25153.0  2.508647e-02       0.156391  0.000000e+00   \n",
       "night_pck_user_7    25571.0  2.303391e-02       0.150014  0.000000e+00   \n",
       "night_pck_user_8    26339.0  2.084362e-02       0.142863  0.000000e+00   \n",
       "night_pck_user_9    25922.0  1.597099e-02       0.125366  0.000000e+00   \n",
       "monthly_2g_6        99999.0  7.964080e-02       0.295058  0.000000e+00   \n",
       "monthly_2g_7        99999.0  8.322083e-02       0.304395  0.000000e+00   \n",
       "monthly_2g_8        99999.0  8.100081e-02       0.299568  0.000000e+00   \n",
       "monthly_2g_9        99999.0  6.878069e-02       0.278120  0.000000e+00   \n",
       "sachet_2g_6         99999.0  3.893839e-01       1.497320  0.000000e+00   \n",
       "sachet_2g_7         99999.0  4.396344e-01       1.636230  0.000000e+00   \n",
       "sachet_2g_8         99999.0  4.500745e-01       1.630263  0.000000e+00   \n",
       "sachet_2g_9         99999.0  3.931039e-01       1.347140  0.000000e+00   \n",
       "monthly_3g_6        99999.0  7.592076e-02       0.363371  0.000000e+00   \n",
       "monthly_3g_7        99999.0  7.858079e-02       0.387231  0.000000e+00   \n",
       "monthly_3g_8        99999.0  8.294083e-02       0.384947  0.000000e+00   \n",
       "monthly_3g_9        99999.0  8.634086e-02       0.384978  0.000000e+00   \n",
       "sachet_3g_6         99999.0  7.478075e-02       0.568344  0.000000e+00   \n",
       "sachet_3g_7         99999.0  8.040080e-02       0.628334  0.000000e+00   \n",
       "sachet_3g_8         99999.0  8.450085e-02       0.660234  0.000000e+00   \n",
       "sachet_3g_9         99999.0  8.458085e-02       0.650457  0.000000e+00   \n",
       "fb_user_6           25153.0  9.144038e-01       0.279772  0.000000e+00   \n",
       "fb_user_7           25571.0  9.087638e-01       0.287950  0.000000e+00   \n",
       "fb_user_8           26339.0  8.908083e-01       0.311885  0.000000e+00   \n",
       "fb_user_9           25922.0  8.609675e-01       0.345987  0.000000e+00   \n",
       "aon                 99999.0  1.219855e+03     954.733842  1.800000e+02   \n",
       "aug_vbc_3g          99999.0  6.817025e+01     267.580450  0.000000e+00   \n",
       "jul_vbc_3g          99999.0  6.683906e+01     271.201856  0.000000e+00   \n",
       "jun_vbc_3g          99999.0  6.002120e+01     253.938223  0.000000e+00   \n",
       "sep_vbc_3g          99999.0  3.299373e+00      32.408353  0.000000e+00   \n",
       "\n",
       "                             25%           50%           75%           max  \n",
       "mobile_number       7.000606e+09  7.001205e+09  7.001812e+09  7.002411e+09  \n",
       "circle_id           1.090000e+02  1.090000e+02  1.090000e+02  1.090000e+02  \n",
       "loc_og_t2o_mou      0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00  \n",
       "std_og_t2o_mou      0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00  \n",
       "loc_ic_t2o_mou      0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00  \n",
       "arpu_6              9.341150e+01  1.977040e+02  3.710600e+02  2.773109e+04  \n",
       "arpu_7              8.698050e+01  1.916400e+02  3.653445e+02  3.514583e+04  \n",
       "arpu_8              8.412600e+01  1.920800e+02  3.693705e+02  3.354362e+04  \n",
       "arpu_9              6.268500e+01  1.768490e+02  3.534665e+02  3.880562e+04  \n",
       "onnet_mou_6         7.380000e+00  3.431000e+01  1.187400e+02  7.376710e+03  \n",
       "onnet_mou_7         6.660000e+00  3.233000e+01  1.155950e+02  8.157780e+03  \n",
       "onnet_mou_8         6.460000e+00  3.236000e+01  1.158600e+02  1.075256e+04  \n",
       "onnet_mou_9         5.330000e+00  2.984000e+01  1.121300e+02  1.042746e+04  \n",
       "offnet_mou_6        3.473000e+01  9.631000e+01  2.318600e+02  8.362360e+03  \n",
       "offnet_mou_7        3.219000e+01  9.173500e+01  2.268150e+02  9.667130e+03  \n",
       "offnet_mou_8        3.163000e+01  9.214000e+01  2.282600e+02  1.400734e+04  \n",
       "offnet_mou_9        2.713000e+01  8.729000e+01  2.205050e+02  1.031076e+04  \n",
       "roam_ic_mou_6       0.000000e+00  0.000000e+00  0.000000e+00  1.372438e+04  \n",
       "roam_ic_mou_7       0.000000e+00  0.000000e+00  0.000000e+00  1.537104e+04  \n",
       "roam_ic_mou_8       0.000000e+00  0.000000e+00  0.000000e+00  1.309536e+04  \n",
       "roam_ic_mou_9       0.000000e+00  0.000000e+00  0.000000e+00  8.464030e+03  \n",
       "roam_og_mou_6       0.000000e+00  0.000000e+00  0.000000e+00  3.775110e+03  \n",
       "roam_og_mou_7       0.000000e+00  0.000000e+00  0.000000e+00  2.812040e+03  \n",
       "roam_og_mou_8       0.000000e+00  0.000000e+00  0.000000e+00  5.337040e+03  \n",
       "roam_og_mou_9       0.000000e+00  0.000000e+00  0.000000e+00  4.428460e+03  \n",
       "loc_og_t2t_mou_6    1.660000e+00  1.191000e+01  4.096000e+01  6.431330e+03  \n",
       "loc_og_t2t_mou_7    1.630000e+00  1.161000e+01  3.991000e+01  7.400660e+03  \n",
       "loc_og_t2t_mou_8    1.600000e+00  1.173000e+01  4.011000e+01  1.075256e+04  \n",
       "loc_og_t2t_mou_9    1.360000e+00  1.126000e+01  3.928000e+01  1.038924e+04  \n",
       "loc_og_t2m_mou_6    9.880000e+00  4.103000e+01  1.103900e+02  4.729740e+03  \n",
       "loc_og_t2m_mou_7    1.002500e+01  4.043000e+01  1.075600e+02  4.557140e+03  \n",
       "loc_og_t2m_mou_8    9.810000e+00  4.036000e+01  1.090900e+02  4.961330e+03  \n",
       "loc_og_t2m_mou_9    8.810000e+00  3.912000e+01  1.068100e+02  4.429880e+03  \n",
       "loc_og_t2f_mou_6    0.000000e+00  0.000000e+00  2.080000e+00  1.466030e+03  \n",
       "loc_og_t2f_mou_7    0.000000e+00  0.000000e+00  2.090000e+00  1.196430e+03  \n",
       "loc_og_t2f_mou_8    0.000000e+00  0.000000e+00  2.040000e+00  9.284900e+02  \n",
       "loc_og_t2f_mou_9    0.000000e+00  0.000000e+00  1.940000e+00  9.274100e+02  \n",
       "loc_og_t2c_mou_6    0.000000e+00  0.000000e+00  0.000000e+00  3.428600e+02  \n",
       "loc_og_t2c_mou_7    0.000000e+00  0.000000e+00  0.000000e+00  9.162400e+02  \n",
       "loc_og_t2c_mou_8    0.000000e+00  0.000000e+00  0.000000e+00  5.020900e+02  \n",
       "loc_og_t2c_mou_9    0.000000e+00  0.000000e+00  0.000000e+00  3.398400e+02  \n",
       "loc_og_mou_6        1.711000e+01  6.511000e+01  1.682700e+02  1.064338e+04  \n",
       "loc_og_mou_7        1.748000e+01  6.368500e+01  1.643825e+02  7.674780e+03  \n",
       "loc_og_mou_8        1.711000e+01  6.373000e+01  1.661100e+02  1.103991e+04  \n",
       "loc_og_mou_9        1.556000e+01  6.184000e+01  1.622250e+02  1.109926e+04  \n",
       "std_og_t2t_mou_6    0.000000e+00  0.000000e+00  3.080750e+01  7.366580e+03  \n",
       "std_og_t2t_mou_7    0.000000e+00  0.000000e+00  3.113250e+01  8.133660e+03  \n",
       "std_og_t2t_mou_8    0.000000e+00  0.000000e+00  3.058000e+01  8.014430e+03  \n",
       "std_og_t2t_mou_9    0.000000e+00  0.000000e+00  2.823000e+01  9.382580e+03  \n",
       "std_og_t2m_mou_6    0.000000e+00  3.950000e+00  5.329000e+01  8.314760e+03  \n",
       "std_og_t2m_mou_7    0.000000e+00  3.635000e+00  5.404000e+01  9.284740e+03  \n",
       "std_og_t2m_mou_8    0.000000e+00  3.310000e+00  5.249000e+01  1.395004e+04  \n",
       "std_og_t2m_mou_9    0.000000e+00  2.500000e+00  4.856000e+01  1.022343e+04  \n",
       "std_og_t2f_mou_6    0.000000e+00  0.000000e+00  0.000000e+00  6.285600e+02  \n",
       "std_og_t2f_mou_7    0.000000e+00  0.000000e+00  0.000000e+00  5.446300e+02  \n",
       "std_og_t2f_mou_8    0.000000e+00  0.000000e+00  0.000000e+00  5.169100e+02  \n",
       "std_og_t2f_mou_9    0.000000e+00  0.000000e+00  0.000000e+00  8.084900e+02  \n",
       "std_og_t2c_mou_6    0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00  \n",
       "std_og_t2c_mou_7    0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00  \n",
       "std_og_t2c_mou_8    0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00  \n",
       "std_og_t2c_mou_9    0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00  \n",
       "std_og_mou_6        0.000000e+00  1.164000e+01  1.448375e+02  8.432990e+03  \n",
       "std_og_mou_7        0.000000e+00  1.109000e+01  1.506150e+02  1.093673e+04  \n",
       "std_og_mou_8        0.000000e+00  1.041000e+01  1.479400e+02  1.398006e+04  \n",
       "std_og_mou_9        0.000000e+00  8.410000e+00  1.421050e+02  1.149531e+04  \n",
       "isd_og_mou_6        0.000000e+00  0.000000e+00  0.000000e+00  5.900660e+03  \n",
       "isd_og_mou_7        0.000000e+00  0.000000e+00  0.000000e+00  5.490280e+03  \n",
       "isd_og_mou_8        0.000000e+00  0.000000e+00  0.000000e+00  5.681540e+03  \n",
       "isd_og_mou_9        0.000000e+00  0.000000e+00  0.000000e+00  4.244530e+03  \n",
       "spl_og_mou_6        0.000000e+00  0.000000e+00  2.430000e+00  1.023210e+03  \n",
       "spl_og_mou_7        0.000000e+00  0.000000e+00  3.710000e+00  2.372510e+03  \n",
       "spl_og_mou_8        0.000000e+00  0.000000e+00  3.990000e+00  1.390880e+03  \n",
       "spl_og_mou_9        0.000000e+00  0.000000e+00  3.230000e+00  1.635710e+03  \n",
       "og_others_6         0.000000e+00  0.000000e+00  0.000000e+00  8.008900e+02  \n",
       "og_others_7         0.000000e+00  0.000000e+00  0.000000e+00  3.701300e+02  \n",
       "og_others_8         0.000000e+00  0.000000e+00  0.000000e+00  3.949300e+02  \n",
       "og_others_9         0.000000e+00  0.000000e+00  0.000000e+00  7.877900e+02  \n",
       "total_og_mou_6      4.474000e+01  1.451400e+02  3.728600e+02  1.067403e+04  \n",
       "total_og_mou_7      4.301000e+01  1.415300e+02  3.785700e+02  1.136531e+04  \n",
       "total_og_mou_8      3.858000e+01  1.386100e+02  3.699000e+02  1.404306e+04  \n",
       "total_og_mou_9      2.551000e+01  1.254600e+02  3.534800e+02  1.151773e+04  \n",
       "loc_ic_t2t_mou_6    2.990000e+00  1.569000e+01  4.684000e+01  6.626930e+03  \n",
       "loc_ic_t2t_mou_7    3.230000e+00  1.574000e+01  4.581000e+01  9.324660e+03  \n",
       "loc_ic_t2t_mou_8    3.280000e+00  1.603000e+01  4.629000e+01  1.069623e+04  \n",
       "loc_ic_t2t_mou_9    3.290000e+00  1.566000e+01  4.518000e+01  1.059883e+04  \n",
       "loc_ic_t2m_mou_6    1.729000e+01  5.649000e+01  1.323875e+02  4.693860e+03  \n",
       "loc_ic_t2m_mou_7    1.859000e+01  5.708000e+01  1.309600e+02  4.455830e+03  \n",
       "loc_ic_t2m_mou_8    1.893000e+01  5.824000e+01  1.339300e+02  6.274190e+03  \n",
       "loc_ic_t2m_mou_9    1.856000e+01  5.661000e+01  1.304900e+02  5.463780e+03  \n",
       "loc_ic_t2f_mou_6    0.000000e+00  8.800000e-01  8.140000e+00  1.872340e+03  \n",
       "loc_ic_t2f_mou_7    0.000000e+00  9.300000e-01  8.282500e+00  1.983010e+03  \n",
       "loc_ic_t2f_mou_8    0.000000e+00  9.300000e-01  8.110000e+00  2.433060e+03  \n",
       "loc_ic_t2f_mou_9    0.000000e+00  9.600000e-01  8.140000e+00  4.318280e+03  \n",
       "loc_ic_mou_6        3.039000e+01  9.216000e+01  2.080750e+02  7.454630e+03  \n",
       "loc_ic_mou_7        3.246000e+01  9.255000e+01  2.058375e+02  9.669910e+03  \n",
       "loc_ic_mou_8        3.274000e+01  9.383000e+01  2.072800e+02  1.083016e+04  \n",
       "loc_ic_mou_9        3.229000e+01  9.164000e+01  2.027375e+02  1.079629e+04  \n",
       "std_ic_t2t_mou_6    0.000000e+00  0.000000e+00  4.060000e+00  5.459560e+03  \n",
       "std_ic_t2t_mou_7    0.000000e+00  0.000000e+00  4.230000e+00  5.800930e+03  \n",
       "std_ic_t2t_mou_8    0.000000e+00  0.000000e+00  4.080000e+00  4.309290e+03  \n",
       "std_ic_t2t_mou_9    0.000000e+00  0.000000e+00  3.510000e+00  3.819830e+03  \n",
       "std_ic_t2m_mou_6    0.000000e+00  2.030000e+00  1.503000e+01  5.647160e+03  \n",
       "std_ic_t2m_mou_7    0.000000e+00  2.040000e+00  1.574000e+01  6.141880e+03  \n",
       "std_ic_t2m_mou_8    0.000000e+00  2.030000e+00  1.536000e+01  5.645860e+03  \n",
       "std_ic_t2m_mou_9    0.000000e+00  1.740000e+00  1.426000e+01  5.689760e+03  \n",
       "std_ic_t2f_mou_6    0.000000e+00  0.000000e+00  0.000000e+00  1.351110e+03  \n",
       "std_ic_t2f_mou_7    0.000000e+00  0.000000e+00  0.000000e+00  1.136080e+03  \n",
       "std_ic_t2f_mou_8    0.000000e+00  0.000000e+00  0.000000e+00  1.394890e+03  \n",
       "std_ic_t2f_mou_9    0.000000e+00  0.000000e+00  0.000000e+00  1.431960e+03  \n",
       "std_ic_t2o_mou_6    0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00  \n",
       "std_ic_t2o_mou_7    0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00  \n",
       "std_ic_t2o_mou_8    0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00  \n",
       "std_ic_t2o_mou_9    0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00  \n",
       "std_ic_mou_6        0.000000e+00  5.890000e+00  2.693000e+01  5.712110e+03  \n",
       "std_ic_mou_7        0.000000e+00  5.960000e+00  2.831000e+01  6.745760e+03  \n",
       "std_ic_mou_8        1.000000e-02  5.880000e+00  2.771000e+01  5.957140e+03  \n",
       "std_ic_mou_9        0.000000e+00  5.380000e+00  2.569000e+01  5.956660e+03  \n",
       "total_ic_mou_6      3.853000e+01  1.147400e+02  2.516700e+02  7.716140e+03  \n",
       "total_ic_mou_7      4.119000e+01  1.163400e+02  2.506600e+02  9.699010e+03  \n",
       "total_ic_mou_8      3.829000e+01  1.146600e+02  2.489900e+02  1.083038e+04  \n",
       "total_ic_mou_9      3.237000e+01  1.058900e+02  2.363200e+02  1.079659e+04  \n",
       "spl_ic_mou_6        0.000000e+00  0.000000e+00  0.000000e+00  1.976000e+01  \n",
       "spl_ic_mou_7        0.000000e+00  0.000000e+00  0.000000e+00  2.133000e+01  \n",
       "spl_ic_mou_8        0.000000e+00  0.000000e+00  0.000000e+00  1.686000e+01  \n",
       "spl_ic_mou_9        0.000000e+00  0.000000e+00  6.000000e-02  6.238000e+01  \n",
       "isd_ic_mou_6        0.000000e+00  0.000000e+00  0.000000e+00  6.789410e+03  \n",
       "isd_ic_mou_7        0.000000e+00  0.000000e+00  0.000000e+00  5.289540e+03  \n",
       "isd_ic_mou_8        0.000000e+00  0.000000e+00  0.000000e+00  4.127010e+03  \n",
       "isd_ic_mou_9        0.000000e+00  0.000000e+00  0.000000e+00  5.057740e+03  \n",
       "ic_others_6         0.000000e+00  0.000000e+00  0.000000e+00  1.362940e+03  \n",
       "ic_others_7         0.000000e+00  0.000000e+00  0.000000e+00  1.495940e+03  \n",
       "ic_others_8         0.000000e+00  0.000000e+00  0.000000e+00  2.327510e+03  \n",
       "ic_others_9         0.000000e+00  0.000000e+00  0.000000e+00  1.005230e+03  \n",
       "total_rech_num_6    3.000000e+00  6.000000e+00  9.000000e+00  3.070000e+02  \n",
       "total_rech_num_7    3.000000e+00  6.000000e+00  1.000000e+01  1.380000e+02  \n",
       "total_rech_num_8    3.000000e+00  5.000000e+00  9.000000e+00  1.960000e+02  \n",
       "total_rech_num_9    3.000000e+00  5.000000e+00  9.000000e+00  1.310000e+02  \n",
       "total_rech_amt_6    1.090000e+02  2.300000e+02  4.375000e+02  3.519000e+04  \n",
       "total_rech_amt_7    1.000000e+02  2.200000e+02  4.280000e+02  4.033500e+04  \n",
       "total_rech_amt_8    9.000000e+01  2.250000e+02  4.345000e+02  4.532000e+04  \n",
       "total_rech_amt_9    5.200000e+01  2.000000e+02  4.150000e+02  3.723500e+04  \n",
       "max_rech_amt_6      3.000000e+01  1.100000e+02  1.200000e+02  4.010000e+03  \n",
       "max_rech_amt_7      3.000000e+01  1.100000e+02  1.280000e+02  4.010000e+03  \n",
       "max_rech_amt_8      3.000000e+01  9.800000e+01  1.440000e+02  4.449000e+03  \n",
       "max_rech_amt_9      2.800000e+01  6.100000e+01  1.440000e+02  3.399000e+03  \n",
       "last_day_rch_amt_6  0.000000e+00  3.000000e+01  1.100000e+02  4.010000e+03  \n",
       "last_day_rch_amt_7  0.000000e+00  3.000000e+01  1.100000e+02  4.010000e+03  \n",
       "last_day_rch_amt_8  0.000000e+00  3.000000e+01  1.300000e+02  4.449000e+03  \n",
       "last_day_rch_amt_9  0.000000e+00  0.000000e+00  5.000000e+01  3.399000e+03  \n",
       "total_rech_data_6   1.000000e+00  1.000000e+00  3.000000e+00  6.100000e+01  \n",
       "total_rech_data_7   1.000000e+00  1.000000e+00  3.000000e+00  5.400000e+01  \n",
       "total_rech_data_8   1.000000e+00  1.000000e+00  3.000000e+00  6.000000e+01  \n",
       "total_rech_data_9   1.000000e+00  2.000000e+00  3.000000e+00  8.400000e+01  \n",
       "max_rech_data_6     2.500000e+01  1.450000e+02  1.770000e+02  1.555000e+03  \n",
       "max_rech_data_7     2.500000e+01  1.450000e+02  1.770000e+02  1.555000e+03  \n",
       "max_rech_data_8     2.500000e+01  1.450000e+02  1.790000e+02  1.555000e+03  \n",
       "max_rech_data_9     2.500000e+01  1.450000e+02  1.790000e+02  1.555000e+03  \n",
       "count_rech_2g_6     1.000000e+00  1.000000e+00  2.000000e+00  4.200000e+01  \n",
       "count_rech_2g_7     1.000000e+00  1.000000e+00  2.000000e+00  4.800000e+01  \n",
       "count_rech_2g_8     1.000000e+00  1.000000e+00  2.000000e+00  4.400000e+01  \n",
       "count_rech_2g_9     1.000000e+00  1.000000e+00  2.000000e+00  4.000000e+01  \n",
       "count_rech_3g_6     0.000000e+00  0.000000e+00  1.000000e+00  2.900000e+01  \n",
       "count_rech_3g_7     0.000000e+00  0.000000e+00  1.000000e+00  3.500000e+01  \n",
       "count_rech_3g_8     0.000000e+00  0.000000e+00  1.000000e+00  4.500000e+01  \n",
       "count_rech_3g_9     0.000000e+00  0.000000e+00  1.000000e+00  4.900000e+01  \n",
       "av_rech_amt_data_6  8.200000e+01  1.540000e+02  2.520000e+02  7.546000e+03  \n",
       "av_rech_amt_data_7  9.200000e+01  1.540000e+02  2.520000e+02  4.365000e+03  \n",
       "av_rech_amt_data_8  8.700000e+01  1.540000e+02  2.520000e+02  4.076000e+03  \n",
       "av_rech_amt_data_9  6.900000e+01  1.640000e+02  2.520000e+02  4.061000e+03  \n",
       "vol_2g_mb_6         0.000000e+00  0.000000e+00  0.000000e+00  1.028590e+04  \n",
       "vol_2g_mb_7         0.000000e+00  0.000000e+00  0.000000e+00  7.873550e+03  \n",
       "vol_2g_mb_8         0.000000e+00  0.000000e+00  0.000000e+00  1.111761e+04  \n",
       "vol_2g_mb_9         0.000000e+00  0.000000e+00  0.000000e+00  8.993950e+03  \n",
       "vol_3g_mb_6         0.000000e+00  0.000000e+00  0.000000e+00  4.573540e+04  \n",
       "vol_3g_mb_7         0.000000e+00  0.000000e+00  0.000000e+00  2.814412e+04  \n",
       "vol_3g_mb_8         0.000000e+00  0.000000e+00  0.000000e+00  3.003606e+04  \n",
       "vol_3g_mb_9         0.000000e+00  0.000000e+00  0.000000e+00  3.922127e+04  \n",
       "arpu_3g_6           0.000000e+00  4.800000e-01  1.220700e+02  6.362280e+03  \n",
       "arpu_3g_7           0.000000e+00  4.200000e-01  1.195600e+02  4.980900e+03  \n",
       "arpu_3g_8           0.000000e+00  8.800000e-01  1.220700e+02  3.716900e+03  \n",
       "arpu_3g_9           0.000000e+00  2.605000e+00  1.400100e+02  1.388431e+04  \n",
       "arpu_2g_6           0.000000e+00  1.083000e+01  1.220700e+02  6.433760e+03  \n",
       "arpu_2g_7           0.000000e+00  8.810000e+00  1.220700e+02  4.809360e+03  \n",
       "arpu_2g_8           0.000000e+00  9.270000e+00  1.220700e+02  3.483170e+03  \n",
       "arpu_2g_9           0.000000e+00  1.480000e+01  1.400100e+02  3.467170e+03  \n",
       "night_pck_user_6    0.000000e+00  0.000000e+00  0.000000e+00  1.000000e+00  \n",
       "night_pck_user_7    0.000000e+00  0.000000e+00  0.000000e+00  1.000000e+00  \n",
       "night_pck_user_8    0.000000e+00  0.000000e+00  0.000000e+00  1.000000e+00  \n",
       "night_pck_user_9    0.000000e+00  0.000000e+00  0.000000e+00  1.000000e+00  \n",
       "monthly_2g_6        0.000000e+00  0.000000e+00  0.000000e+00  4.000000e+00  \n",
       "monthly_2g_7        0.000000e+00  0.000000e+00  0.000000e+00  5.000000e+00  \n",
       "monthly_2g_8        0.000000e+00  0.000000e+00  0.000000e+00  5.000000e+00  \n",
       "monthly_2g_9        0.000000e+00  0.000000e+00  0.000000e+00  4.000000e+00  \n",
       "sachet_2g_6         0.000000e+00  0.000000e+00  0.000000e+00  4.200000e+01  \n",
       "sachet_2g_7         0.000000e+00  0.000000e+00  0.000000e+00  4.800000e+01  \n",
       "sachet_2g_8         0.000000e+00  0.000000e+00  0.000000e+00  4.400000e+01  \n",
       "sachet_2g_9         0.000000e+00  0.000000e+00  0.000000e+00  4.000000e+01  \n",
       "monthly_3g_6        0.000000e+00  0.000000e+00  0.000000e+00  1.400000e+01  \n",
       "monthly_3g_7        0.000000e+00  0.000000e+00  0.000000e+00  1.600000e+01  \n",
       "monthly_3g_8        0.000000e+00  0.000000e+00  0.000000e+00  1.600000e+01  \n",
       "monthly_3g_9        0.000000e+00  0.000000e+00  0.000000e+00  1.100000e+01  \n",
       "sachet_3g_6         0.000000e+00  0.000000e+00  0.000000e+00  2.900000e+01  \n",
       "sachet_3g_7         0.000000e+00  0.000000e+00  0.000000e+00  3.500000e+01  \n",
       "sachet_3g_8         0.000000e+00  0.000000e+00  0.000000e+00  4.100000e+01  \n",
       "sachet_3g_9         0.000000e+00  0.000000e+00  0.000000e+00  4.900000e+01  \n",
       "fb_user_6           1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  \n",
       "fb_user_7           1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  \n",
       "fb_user_8           1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  \n",
       "fb_user_9           1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  \n",
       "aon                 4.670000e+02  8.630000e+02  1.807500e+03  4.337000e+03  \n",
       "aug_vbc_3g          0.000000e+00  0.000000e+00  0.000000e+00  1.291622e+04  \n",
       "jul_vbc_3g          0.000000e+00  0.000000e+00  0.000000e+00  9.165600e+03  \n",
       "jun_vbc_3g          0.000000e+00  0.000000e+00  0.000000e+00  1.116621e+04  \n",
       "sep_vbc_3g          0.000000e+00  0.000000e+00  0.000000e+00  2.618570e+03  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "churn_df.describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "00A5vEB7ceUh"
   },
   "source": [
    "After describing and taking a look at the columns, we observe that there exist some columns with missing value and zeroes. We will clean this data before proceeding with model building."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hEZZvIiySVK7"
   },
   "source": [
    "## **1. Data Cleaning**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "L3iTx5foSdK1"
   },
   "source": [
    "### **1.1 Check for Null Values**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "TUMvOy21Sc1P",
    "outputId": "d2ebae6d-8a68-4294-9fe3-188b039fab94"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count_rech_2g_6             74.85\n",
       "date_of_last_rech_data_6    74.85\n",
       "count_rech_3g_6             74.85\n",
       "av_rech_amt_data_6          74.85\n",
       "max_rech_data_6             74.85\n",
       "total_rech_data_6           74.85\n",
       "arpu_3g_6                   74.85\n",
       "arpu_2g_6                   74.85\n",
       "night_pck_user_6            74.85\n",
       "fb_user_6                   74.85\n",
       "arpu_3g_7                   74.43\n",
       "count_rech_2g_7             74.43\n",
       "fb_user_7                   74.43\n",
       "count_rech_3g_7             74.43\n",
       "arpu_2g_7                   74.43\n",
       "av_rech_amt_data_7          74.43\n",
       "max_rech_data_7             74.43\n",
       "night_pck_user_7            74.43\n",
       "total_rech_data_7           74.43\n",
       "date_of_last_rech_data_7    74.43\n",
       "night_pck_user_9            74.08\n",
       "date_of_last_rech_data_9    74.08\n",
       "fb_user_9                   74.08\n",
       "arpu_2g_9                   74.08\n",
       "max_rech_data_9             74.08\n",
       "arpu_3g_9                   74.08\n",
       "total_rech_data_9           74.08\n",
       "av_rech_amt_data_9          74.08\n",
       "count_rech_3g_9             74.08\n",
       "count_rech_2g_9             74.08\n",
       "fb_user_8                   73.66\n",
       "av_rech_amt_data_8          73.66\n",
       "count_rech_3g_8             73.66\n",
       "count_rech_2g_8             73.66\n",
       "date_of_last_rech_data_8    73.66\n",
       "total_rech_data_8           73.66\n",
       "max_rech_data_8             73.66\n",
       "arpu_3g_8                   73.66\n",
       "arpu_2g_8                   73.66\n",
       "night_pck_user_8            73.66\n",
       "std_ic_t2m_mou_9             7.75\n",
       "spl_ic_mou_9                 7.75\n",
       "loc_ic_mou_9                 7.75\n",
       "isd_ic_mou_9                 7.75\n",
       "std_ic_t2o_mou_9             7.75\n",
       "loc_ic_t2f_mou_9             7.75\n",
       "ic_others_9                  7.75\n",
       "loc_og_t2f_mou_9             7.75\n",
       "loc_ic_t2m_mou_9             7.75\n",
       "loc_og_t2c_mou_9             7.75\n",
       "loc_og_t2m_mou_9             7.75\n",
       "loc_ic_t2t_mou_9             7.75\n",
       "loc_og_t2t_mou_9             7.75\n",
       "std_ic_mou_9                 7.75\n",
       "std_og_t2t_mou_9             7.75\n",
       "roam_og_mou_9                7.75\n",
       "std_ic_t2t_mou_9             7.75\n",
       "std_ic_t2f_mou_9             7.75\n",
       "loc_og_mou_9                 7.75\n",
       "offnet_mou_9                 7.75\n",
       "std_og_t2m_mou_9             7.75\n",
       "std_og_t2f_mou_9             7.75\n",
       "std_og_t2c_mou_9             7.75\n",
       "isd_og_mou_9                 7.75\n",
       "onnet_mou_9                  7.75\n",
       "std_og_mou_9                 7.75\n",
       "spl_og_mou_9                 7.75\n",
       "og_others_9                  7.75\n",
       "roam_ic_mou_9                7.75\n",
       "std_ic_t2t_mou_8             5.38\n",
       "offnet_mou_8                 5.38\n",
       "ic_others_8                  5.38\n",
       "isd_ic_mou_8                 5.38\n",
       "loc_ic_mou_8                 5.38\n",
       "std_og_t2c_mou_8             5.38\n",
       "spl_ic_mou_8                 5.38\n",
       "og_others_8                  5.38\n",
       "loc_ic_t2f_mou_8             5.38\n",
       "std_og_t2f_mou_8             5.38\n",
       "loc_og_mou_8                 5.38\n",
       "std_ic_mou_8                 5.38\n",
       "std_ic_t2m_mou_8             5.38\n",
       "std_og_t2m_mou_8             5.38\n",
       "std_ic_t2o_mou_8             5.38\n",
       "loc_og_t2c_mou_8             5.38\n",
       "std_og_mou_8                 5.38\n",
       "std_ic_t2f_mou_8             5.38\n",
       "roam_ic_mou_8                5.38\n",
       "loc_og_t2t_mou_8             5.38\n",
       "loc_ic_t2t_mou_8             5.38\n",
       "roam_og_mou_8                5.38\n",
       "onnet_mou_8                  5.38\n",
       "std_og_t2t_mou_8             5.38\n",
       "loc_og_t2m_mou_8             5.38\n",
       "isd_og_mou_8                 5.38\n",
       "spl_og_mou_8                 5.38\n",
       "loc_ic_t2m_mou_8             5.38\n",
       "loc_og_t2f_mou_8             5.38\n",
       "date_of_last_rech_9          4.76\n",
       "og_others_6                  3.94\n",
       "spl_og_mou_6                 3.94\n",
       "std_ic_t2f_mou_6             3.94\n",
       "std_og_t2f_mou_6             3.94\n",
       "loc_ic_t2f_mou_6             3.94\n",
       "std_ic_t2m_mou_6             3.94\n",
       "std_og_t2t_mou_6             3.94\n",
       "loc_ic_t2t_mou_6             3.94\n",
       "std_og_t2c_mou_6             3.94\n",
       "loc_ic_t2m_mou_6             3.94\n",
       "loc_ic_mou_6                 3.94\n",
       "std_og_mou_6                 3.94\n",
       "isd_og_mou_6                 3.94\n",
       "std_ic_t2t_mou_6             3.94\n",
       "loc_og_t2c_mou_6             3.94\n",
       "std_ic_t2o_mou_6             3.94\n",
       "onnet_mou_6                  3.94\n",
       "std_og_t2m_mou_6             3.94\n",
       "ic_others_6                  3.94\n",
       "offnet_mou_6                 3.94\n",
       "isd_ic_mou_6                 3.94\n",
       "loc_og_t2f_mou_6             3.94\n",
       "spl_ic_mou_6                 3.94\n",
       "loc_og_mou_6                 3.94\n",
       "roam_og_mou_6                3.94\n",
       "loc_og_t2m_mou_6             3.94\n",
       "std_ic_mou_6                 3.94\n",
       "loc_og_t2t_mou_6             3.94\n",
       "roam_ic_mou_6                3.94\n",
       "roam_ic_mou_7                3.86\n",
       "roam_og_mou_7                3.86\n",
       "og_others_7                  3.86\n",
       "spl_og_mou_7                 3.86\n",
       "offnet_mou_7                 3.86\n",
       "std_og_t2t_mou_7             3.86\n",
       "onnet_mou_7                  3.86\n",
       "isd_og_mou_7                 3.86\n",
       "std_og_mou_7                 3.86\n",
       "std_og_t2c_mou_7             3.86\n",
       "std_og_t2f_mou_7             3.86\n",
       "std_ic_t2f_mou_7             3.86\n",
       "loc_og_t2t_mou_7             3.86\n",
       "std_og_t2m_mou_7             3.86\n",
       "std_ic_t2m_mou_7             3.86\n",
       "loc_ic_t2t_mou_7             3.86\n",
       "std_ic_t2o_mou_7             3.86\n",
       "loc_og_t2m_mou_7             3.86\n",
       "loc_ic_t2m_mou_7             3.86\n",
       "loc_og_t2f_mou_7             3.86\n",
       "loc_ic_t2f_mou_7             3.86\n",
       "loc_og_t2c_mou_7             3.86\n",
       "ic_others_7                  3.86\n",
       "loc_ic_mou_7                 3.86\n",
       "isd_ic_mou_7                 3.86\n",
       "spl_ic_mou_7                 3.86\n",
       "std_ic_t2t_mou_7             3.86\n",
       "loc_og_mou_7                 3.86\n",
       "std_ic_mou_7                 3.86\n",
       "date_of_last_rech_8          3.62\n",
       "date_of_last_rech_7          1.77\n",
       "last_date_of_month_9         1.66\n",
       "date_of_last_rech_6          1.61\n",
       "last_date_of_month_8         1.10\n",
       "loc_ic_t2o_mou               1.02\n",
       "std_og_t2o_mou               1.02\n",
       "loc_og_t2o_mou               1.02\n",
       "last_date_of_month_7         0.60\n",
       "dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check for percentage of null values\n",
    "null_count_col = round(churn_df.isnull().sum() * 100/len(churn_df.index),2).sort_values(ascending=False)\n",
    "null_count_col[null_count_col>0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "S23-mu4NyiDR"
   },
   "source": [
    "We can observe some of the features are repeated for each month. These are also the features with null values. We will analyse these columns and find an appropriate value to impute with. We will segregate these columns into Date columns and Categorical and numerical columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OqmY2XRkDV10"
   },
   "source": [
    "### **1.2 Segregating columns into Date, Categorical and Numerical columns**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "w2QGhh3lDjIQ"
   },
   "source": [
    "#### a. Date columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "MxBWStPV0fYS"
   },
   "outputs": [],
   "source": [
    "# Function to drop columns\n",
    "def drop_columns(drop_col_list):\n",
    "    churn_df.drop(drop_col_list, axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "bQQuzRGM_GWi",
    "outputId": "c4446145-b1e3-40ff-8728-ce35b8f1880d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['last_date_of_month_6',\n",
       " 'last_date_of_month_7',\n",
       " 'last_date_of_month_8',\n",
       " 'last_date_of_month_9',\n",
       " 'date_of_last_rech_6',\n",
       " 'date_of_last_rech_7',\n",
       " 'date_of_last_rech_8',\n",
       " 'date_of_last_rech_9',\n",
       " 'date_of_last_rech_data_6',\n",
       " 'date_of_last_rech_data_7',\n",
       " 'date_of_last_rech_data_8',\n",
       " 'date_of_last_rech_data_9']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get all data columns\n",
    "date_cols = [feature for feature in churn_df.select_dtypes(['object']).columns]\n",
    "date_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "zycoAyAlIL4m",
    "outputId": "067f9c50-0758-4e41-aa60-a24cfc3a5b34"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date_of_last_rech_data_6    74.85\n",
       "date_of_last_rech_data_7    74.43\n",
       "date_of_last_rech_data_9    74.08\n",
       "date_of_last_rech_data_8    73.66\n",
       "date_of_last_rech_9          4.76\n",
       "date_of_last_rech_8          3.62\n",
       "date_of_last_rech_7          1.77\n",
       "last_date_of_month_9         1.66\n",
       "date_of_last_rech_6          1.61\n",
       "last_date_of_month_8         1.10\n",
       "last_date_of_month_7         0.60\n",
       "last_date_of_month_6         0.00\n",
       "dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check for percentage of null values\n",
    "date_cols = round(churn_df[date_cols].isnull().sum() * 100/len(churn_df[date_cols].index),2).sort_values(ascending=False)\n",
    "date_cols"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pXLy1U7dhDCb"
   },
   "source": [
    "We observe that date of recharge data column has large number of missing values. We will drop these columns later."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Zf1ni5F0DpWv"
   },
   "source": [
    "#### b. Categorical columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "88FIcvFKcuth"
   },
   "source": [
    "We observe that some of the columns are boolean columns. However they already have two values to denote True and False (1 and 0). We will check if there are any missing values in these columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "Twbw0hHw92wv"
   },
   "outputs": [],
   "source": [
    "cat_cols = [\n",
    "        'night_pck_user_6','fb_user_6', #6th month\n",
    "        'night_pck_user_7','fb_user_7', #7th month\n",
    "        'night_pck_user_8','fb_user_8', #8th month\n",
    "        'night_pck_user_9','fb_user_9', #9th month\n",
    "    ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "e5J3ApZoJT-9",
    "outputId": "c1082938-4e95-4173-fd99-fb8e8a62c203"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fb_user_6           74.85\n",
       "night_pck_user_6    74.85\n",
       "fb_user_7           74.43\n",
       "night_pck_user_7    74.43\n",
       "fb_user_9           74.08\n",
       "night_pck_user_9    74.08\n",
       "fb_user_8           73.66\n",
       "night_pck_user_8    73.66\n",
       "dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check for percentage of null values\n",
    "cat_cols = round(churn_df[cat_cols].isnull().sum() * 100/len(churn_df[cat_cols].index),2).sort_values(ascending=False)\n",
    "cat_cols"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FJEA3ZFi-KDj"
   },
   "source": [
    "Since `Night pack user` and `Facebook user` columns are catgorical columns, If the user is not a night pack user or not a facebook user we'll impute such columns with `-1` to denote a seperate category."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "cr01_Ky1E0O7"
   },
   "outputs": [],
   "source": [
    "# impute night pack user and facebook user columns with -1 to indicate seperate category.\n",
    "churn_df[cat_cols.keys()] = churn_df[cat_cols.keys()].fillna(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "UoTN8kj7SI6_",
    "outputId": "81339500-dc4c-4478-bf6c-df6376f346b7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 99999 entries, 0 to 99998\n",
      "Data columns (total 8 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   fb_user_6         99999 non-null  float64\n",
      " 1   night_pck_user_6  99999 non-null  float64\n",
      " 2   fb_user_7         99999 non-null  float64\n",
      " 3   night_pck_user_7  99999 non-null  float64\n",
      " 4   fb_user_9         99999 non-null  float64\n",
      " 5   night_pck_user_9  99999 non-null  float64\n",
      " 6   fb_user_8         99999 non-null  float64\n",
      " 7   night_pck_user_8  99999 non-null  float64\n",
      "dtypes: float64(8)\n",
      "memory usage: 6.1 MB\n"
     ]
    }
   ],
   "source": [
    "churn_df[cat_cols.keys()].info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2cRaxi2CDtlw"
   },
   "source": [
    "#### c. Numerical columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "GlVCB95dDwqn",
    "outputId": "74a660b9-04f7-4a75-c276-78b432e75ee4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['loc_og_t2o_mou',\n",
       " 'std_og_t2o_mou',\n",
       " 'loc_ic_t2o_mou',\n",
       " 'arpu_6',\n",
       " 'arpu_7',\n",
       " 'arpu_8',\n",
       " 'arpu_9',\n",
       " 'onnet_mou_6',\n",
       " 'onnet_mou_7',\n",
       " 'onnet_mou_8',\n",
       " 'onnet_mou_9',\n",
       " 'offnet_mou_6',\n",
       " 'offnet_mou_7',\n",
       " 'offnet_mou_8',\n",
       " 'offnet_mou_9',\n",
       " 'roam_ic_mou_6',\n",
       " 'roam_ic_mou_7',\n",
       " 'roam_ic_mou_8',\n",
       " 'roam_ic_mou_9',\n",
       " 'roam_og_mou_6',\n",
       " 'roam_og_mou_7',\n",
       " 'roam_og_mou_8',\n",
       " 'roam_og_mou_9',\n",
       " 'loc_og_t2t_mou_6',\n",
       " 'loc_og_t2t_mou_7',\n",
       " 'loc_og_t2t_mou_8',\n",
       " 'loc_og_t2t_mou_9',\n",
       " 'loc_og_t2m_mou_6',\n",
       " 'loc_og_t2m_mou_7',\n",
       " 'loc_og_t2m_mou_8',\n",
       " 'loc_og_t2m_mou_9',\n",
       " 'loc_og_t2f_mou_6',\n",
       " 'loc_og_t2f_mou_7',\n",
       " 'loc_og_t2f_mou_8',\n",
       " 'loc_og_t2f_mou_9',\n",
       " 'loc_og_t2c_mou_6',\n",
       " 'loc_og_t2c_mou_7',\n",
       " 'loc_og_t2c_mou_8',\n",
       " 'loc_og_t2c_mou_9',\n",
       " 'loc_og_mou_6',\n",
       " 'loc_og_mou_7',\n",
       " 'loc_og_mou_8',\n",
       " 'loc_og_mou_9',\n",
       " 'std_og_t2t_mou_6',\n",
       " 'std_og_t2t_mou_7',\n",
       " 'std_og_t2t_mou_8',\n",
       " 'std_og_t2t_mou_9',\n",
       " 'std_og_t2m_mou_6',\n",
       " 'std_og_t2m_mou_7',\n",
       " 'std_og_t2m_mou_8',\n",
       " 'std_og_t2m_mou_9',\n",
       " 'std_og_t2f_mou_6',\n",
       " 'std_og_t2f_mou_7',\n",
       " 'std_og_t2f_mou_8',\n",
       " 'std_og_t2f_mou_9',\n",
       " 'std_og_t2c_mou_6',\n",
       " 'std_og_t2c_mou_7',\n",
       " 'std_og_t2c_mou_8',\n",
       " 'std_og_t2c_mou_9',\n",
       " 'std_og_mou_6',\n",
       " 'std_og_mou_7',\n",
       " 'std_og_mou_8',\n",
       " 'std_og_mou_9',\n",
       " 'isd_og_mou_6',\n",
       " 'isd_og_mou_7',\n",
       " 'isd_og_mou_8',\n",
       " 'isd_og_mou_9',\n",
       " 'spl_og_mou_6',\n",
       " 'spl_og_mou_7',\n",
       " 'spl_og_mou_8',\n",
       " 'spl_og_mou_9',\n",
       " 'og_others_6',\n",
       " 'og_others_7',\n",
       " 'og_others_8',\n",
       " 'og_others_9',\n",
       " 'total_og_mou_6',\n",
       " 'total_og_mou_7',\n",
       " 'total_og_mou_8',\n",
       " 'total_og_mou_9',\n",
       " 'loc_ic_t2t_mou_6',\n",
       " 'loc_ic_t2t_mou_7',\n",
       " 'loc_ic_t2t_mou_8',\n",
       " 'loc_ic_t2t_mou_9',\n",
       " 'loc_ic_t2m_mou_6',\n",
       " 'loc_ic_t2m_mou_7',\n",
       " 'loc_ic_t2m_mou_8',\n",
       " 'loc_ic_t2m_mou_9',\n",
       " 'loc_ic_t2f_mou_6',\n",
       " 'loc_ic_t2f_mou_7',\n",
       " 'loc_ic_t2f_mou_8',\n",
       " 'loc_ic_t2f_mou_9',\n",
       " 'loc_ic_mou_6',\n",
       " 'loc_ic_mou_7',\n",
       " 'loc_ic_mou_8',\n",
       " 'loc_ic_mou_9',\n",
       " 'std_ic_t2t_mou_6',\n",
       " 'std_ic_t2t_mou_7',\n",
       " 'std_ic_t2t_mou_8',\n",
       " 'std_ic_t2t_mou_9',\n",
       " 'std_ic_t2m_mou_6',\n",
       " 'std_ic_t2m_mou_7',\n",
       " 'std_ic_t2m_mou_8',\n",
       " 'std_ic_t2m_mou_9',\n",
       " 'std_ic_t2f_mou_6',\n",
       " 'std_ic_t2f_mou_7',\n",
       " 'std_ic_t2f_mou_8',\n",
       " 'std_ic_t2f_mou_9',\n",
       " 'std_ic_t2o_mou_6',\n",
       " 'std_ic_t2o_mou_7',\n",
       " 'std_ic_t2o_mou_8',\n",
       " 'std_ic_t2o_mou_9',\n",
       " 'std_ic_mou_6',\n",
       " 'std_ic_mou_7',\n",
       " 'std_ic_mou_8',\n",
       " 'std_ic_mou_9',\n",
       " 'total_ic_mou_6',\n",
       " 'total_ic_mou_7',\n",
       " 'total_ic_mou_8',\n",
       " 'total_ic_mou_9',\n",
       " 'spl_ic_mou_6',\n",
       " 'spl_ic_mou_7',\n",
       " 'spl_ic_mou_8',\n",
       " 'spl_ic_mou_9',\n",
       " 'isd_ic_mou_6',\n",
       " 'isd_ic_mou_7',\n",
       " 'isd_ic_mou_8',\n",
       " 'isd_ic_mou_9',\n",
       " 'ic_others_6',\n",
       " 'ic_others_7',\n",
       " 'ic_others_8',\n",
       " 'ic_others_9',\n",
       " 'total_rech_data_6',\n",
       " 'total_rech_data_7',\n",
       " 'total_rech_data_8',\n",
       " 'total_rech_data_9',\n",
       " 'max_rech_data_6',\n",
       " 'max_rech_data_7',\n",
       " 'max_rech_data_8',\n",
       " 'max_rech_data_9',\n",
       " 'count_rech_2g_6',\n",
       " 'count_rech_2g_7',\n",
       " 'count_rech_2g_8',\n",
       " 'count_rech_2g_9',\n",
       " 'count_rech_3g_6',\n",
       " 'count_rech_3g_7',\n",
       " 'count_rech_3g_8',\n",
       " 'count_rech_3g_9',\n",
       " 'av_rech_amt_data_6',\n",
       " 'av_rech_amt_data_7',\n",
       " 'av_rech_amt_data_8',\n",
       " 'av_rech_amt_data_9',\n",
       " 'vol_2g_mb_6',\n",
       " 'vol_2g_mb_7',\n",
       " 'vol_2g_mb_8',\n",
       " 'vol_2g_mb_9',\n",
       " 'vol_3g_mb_6',\n",
       " 'vol_3g_mb_7',\n",
       " 'vol_3g_mb_8',\n",
       " 'vol_3g_mb_9',\n",
       " 'arpu_3g_6',\n",
       " 'arpu_3g_7',\n",
       " 'arpu_3g_8',\n",
       " 'arpu_3g_9',\n",
       " 'arpu_2g_6',\n",
       " 'arpu_2g_7',\n",
       " 'arpu_2g_8',\n",
       " 'arpu_2g_9',\n",
       " 'aug_vbc_3g',\n",
       " 'jul_vbc_3g',\n",
       " 'jun_vbc_3g',\n",
       " 'sep_vbc_3g']"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get all categorical Basement columns\n",
    "numerical_cols = [feature for feature in churn_df.select_dtypes(['int', 'float']).columns if feature not in cat_cols]\n",
    "numerical_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "63HTSGWHMZvM",
    "outputId": "3fac9f3f-e565-4ac9-b205-cf1f3f003a6d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['total_rech_data_6',\n",
       " 'total_rech_data_7',\n",
       " 'total_rech_data_8',\n",
       " 'total_rech_data_9',\n",
       " 'max_rech_data_6',\n",
       " 'max_rech_data_7',\n",
       " 'max_rech_data_8',\n",
       " 'max_rech_data_9',\n",
       " 'count_rech_2g_6',\n",
       " 'count_rech_2g_7',\n",
       " 'count_rech_2g_8',\n",
       " 'count_rech_2g_9',\n",
       " 'count_rech_3g_6',\n",
       " 'count_rech_3g_7',\n",
       " 'count_rech_3g_8',\n",
       " 'count_rech_3g_9',\n",
       " 'av_rech_amt_data_6',\n",
       " 'av_rech_amt_data_7',\n",
       " 'av_rech_amt_data_8',\n",
       " 'av_rech_amt_data_9']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get all numerical recharge columns\n",
    "recharge_cols = [feature for feature in churn_df[numerical_cols] if 'rech' in feature]\n",
    "recharge_cols"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "H5WTpQeC9JNY"
   },
   "source": [
    "### **1.3 Drop columns**\n",
    "\n",
    "- Drop all the Id columns - `mobile_number, circle_id`. \n",
    "- Drop all date columns since months June, July, August and September are encoded as 6, 7, 8 and 9, respectively.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ai3vMFIdR1qU"
   },
   "source": [
    "#### a. Drop Id and Date columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "YtXUvm6Cr5Wi",
    "outputId": "c9001c2d-3f9e-463c-ba19-fd1423d596f4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mobile_number               99999\n",
       "arpu_6                      85681\n",
       "arpu_7                      85308\n",
       "arpu_8                      83615\n",
       "arpu_9                      79937\n",
       "total_og_mou_7              40477\n",
       "total_og_mou_6              40327\n",
       "total_og_mou_8              40074\n",
       "total_og_mou_9              39160\n",
       "total_ic_mou_6              32247\n",
       "total_ic_mou_7              32242\n",
       "total_ic_mou_8              32128\n",
       "total_ic_mou_9              31260\n",
       "offnet_mou_6                31140\n",
       "offnet_mou_7                31023\n",
       "offnet_mou_8                30908\n",
       "offnet_mou_9                30077\n",
       "loc_ic_mou_6                28569\n",
       "loc_ic_mou_7                28390\n",
       "loc_ic_mou_8                28200\n",
       "std_og_mou_7                27951\n",
       "loc_ic_mou_9                27697\n",
       "std_og_mou_6                27502\n",
       "std_og_mou_8                27491\n",
       "std_og_mou_9                26553\n",
       "loc_og_mou_6                26372\n",
       "loc_og_mou_7                26091\n",
       "loc_og_mou_8                25990\n",
       "loc_og_mou_9                25376\n",
       "onnet_mou_7                 24336\n",
       "onnet_mou_6                 24313\n",
       "onnet_mou_8                 24089\n",
       "onnet_mou_9                 23565\n",
       "loc_ic_t2m_mou_6            22065\n",
       "loc_ic_t2m_mou_7            21918\n",
       "loc_ic_t2m_mou_8            21886\n",
       "loc_ic_t2m_mou_9            21484\n",
       "loc_og_t2m_mou_6            20905\n",
       "loc_og_t2m_mou_7            20637\n",
       "loc_og_t2m_mou_8            20544\n",
       "loc_og_t2m_mou_9            20141\n",
       "std_og_t2m_mou_7            20018\n",
       "std_og_t2m_mou_8            19786\n",
       "std_og_t2m_mou_6            19734\n",
       "std_og_t2m_mou_9            19052\n",
       "std_og_t2t_mou_7            18567\n",
       "std_og_t2t_mou_8            18291\n",
       "std_og_t2t_mou_6            18244\n",
       "std_og_t2t_mou_9            17934\n",
       "vol_2g_mb_6                 15201\n",
       "vol_2g_mb_7                 15114\n",
       "vol_2g_mb_8                 14994\n",
       "vol_3g_mb_8                 14960\n",
       "aug_vbc_3g                  14676\n",
       "vol_3g_mb_7                 14519\n",
       "vol_3g_mb_9                 14472\n",
       "jul_vbc_3g                  14162\n",
       "vol_2g_mb_9                 13919\n",
       "vol_3g_mb_6                 13773\n",
       "loc_ic_t2t_mou_6            13540\n",
       "loc_og_t2t_mou_6            13539\n",
       "loc_ic_t2t_mou_7            13511\n",
       "loc_og_t2t_mou_7            13411\n",
       "loc_ic_t2t_mou_8            13346\n",
       "loc_og_t2t_mou_8            13336\n",
       "jun_vbc_3g                  13312\n",
       "loc_ic_t2t_mou_9            12993\n",
       "loc_og_t2t_mou_9            12949\n",
       "std_ic_mou_7                11889\n",
       "std_ic_mou_8                11662\n",
       "std_ic_mou_6                11646\n",
       "std_ic_mou_9                11266\n",
       "std_ic_t2m_mou_7             9464\n",
       "std_ic_t2m_mou_6             9308\n",
       "std_ic_t2m_mou_8             9304\n",
       "std_ic_t2m_mou_9             8933\n",
       "arpu_3g_9                    8063\n",
       "roam_og_mou_6                8038\n",
       "arpu_3g_8                    7787\n",
       "arpu_3g_6                    7418\n",
       "loc_ic_t2f_mou_7             7395\n",
       "loc_ic_t2f_mou_6             7250\n",
       "arpu_3g_7                    7246\n",
       "loc_ic_t2f_mou_8             7097\n",
       "loc_ic_t2f_mou_9             7091\n",
       "arpu_2g_6                    6990\n",
       "arpu_2g_9                    6795\n",
       "arpu_2g_8                    6652\n",
       "roam_og_mou_7                6639\n",
       "arpu_2g_7                    6586\n",
       "roam_ic_mou_6                6512\n",
       "roam_og_mou_8                6504\n",
       "std_ic_t2t_mou_7             6481\n",
       "std_ic_t2t_mou_8             6352\n",
       "std_ic_t2t_mou_6             6279\n",
       "std_ic_t2t_mou_9             6157\n",
       "roam_og_mou_9                5882\n",
       "isd_ic_mou_8                 5844\n",
       "isd_ic_mou_7                 5789\n",
       "isd_ic_mou_9                 5557\n",
       "isd_ic_mou_6                 5521\n",
       "roam_ic_mou_8                5315\n",
       "roam_ic_mou_7                5230\n",
       "roam_ic_mou_9                4827\n",
       "spl_og_mou_7                 4396\n",
       "spl_og_mou_8                 4390\n",
       "spl_og_mou_9                 4095\n",
       "spl_og_mou_6                 3965\n",
       "loc_og_t2f_mou_7             3863\n",
       "loc_og_t2f_mou_6             3860\n",
       "loc_og_t2f_mou_8             3807\n",
       "loc_og_t2f_mou_9             3758\n",
       "sep_vbc_3g                   3720\n",
       "aon                          3489\n",
       "std_ic_t2f_mou_7             3209\n",
       "std_ic_t2f_mou_6             3125\n",
       "std_ic_t2f_mou_9             3090\n",
       "std_ic_t2f_mou_8             3051\n",
       "loc_og_t2c_mou_8             2516\n",
       "std_og_t2f_mou_6             2450\n",
       "loc_og_t2c_mou_7             2426\n",
       "std_og_t2f_mou_7             2391\n",
       "total_rech_amt_8             2347\n",
       "std_og_t2f_mou_8             2333\n",
       "loc_og_t2c_mou_9             2332\n",
       "total_rech_amt_7             2329\n",
       "total_rech_amt_6             2305\n",
       "total_rech_amt_9             2304\n",
       "std_og_t2f_mou_9             2295\n",
       "loc_og_t2c_mou_6             2235\n",
       "ic_others_7                  2002\n",
       "ic_others_9                  1923\n",
       "ic_others_8                  1896\n",
       "ic_others_6                  1817\n",
       "isd_og_mou_6                 1381\n",
       "isd_og_mou_7                 1380\n",
       "isd_og_mou_8                 1276\n",
       "isd_og_mou_9                 1255\n",
       "og_others_6                  1018\n",
       "av_rech_amt_data_8            973\n",
       "av_rech_amt_data_7            961\n",
       "av_rech_amt_data_9            945\n",
       "av_rech_amt_data_6            887\n",
       "spl_ic_mou_9                  384\n",
       "og_others_9                   235\n",
       "og_others_8                   216\n",
       "max_rech_amt_8                213\n",
       "max_rech_amt_6                202\n",
       "max_rech_amt_9                201\n",
       "last_day_rch_amt_8            199\n",
       "og_others_7                   187\n",
       "last_day_rch_amt_6            186\n",
       "last_day_rch_amt_9            185\n",
       "max_rech_amt_7                183\n",
       "last_day_rch_amt_7            173\n",
       "spl_ic_mou_7                  107\n",
       "total_rech_num_6              102\n",
       "spl_ic_mou_8                  102\n",
       "total_rech_num_7              101\n",
       "total_rech_num_9               97\n",
       "total_rech_num_8               96\n",
       "spl_ic_mou_6                   84\n",
       "max_rech_data_8                50\n",
       "max_rech_data_9                50\n",
       "max_rech_data_7                48\n",
       "max_rech_data_6                48\n",
       "total_rech_data_8              46\n",
       "total_rech_data_7              42\n",
       "total_rech_data_9              37\n",
       "total_rech_data_6              37\n",
       "count_rech_2g_7                36\n",
       "sachet_2g_7                    35\n",
       "count_rech_2g_8                34\n",
       "sachet_2g_8                    34\n",
       "sachet_2g_9                    32\n",
       "sachet_2g_6                    32\n",
       "count_rech_2g_9                32\n",
       "date_of_last_rech_8            31\n",
       "date_of_last_rech_data_7       31\n",
       "count_rech_2g_6                31\n",
       "date_of_last_rech_data_8       31\n",
       "date_of_last_rech_7            31\n",
       "date_of_last_rech_6            30\n",
       "date_of_last_rech_data_6       30\n",
       "date_of_last_rech_9            30\n",
       "date_of_last_rech_data_9       30\n",
       "sachet_3g_8                    29\n",
       "count_rech_3g_8                29\n",
       "count_rech_3g_7                28\n",
       "sachet_3g_7                    27\n",
       "sachet_3g_9                    27\n",
       "count_rech_3g_9                27\n",
       "count_rech_3g_6                25\n",
       "sachet_3g_6                    25\n",
       "monthly_3g_7                   15\n",
       "monthly_3g_8                   12\n",
       "monthly_3g_6                   12\n",
       "monthly_3g_9                   11\n",
       "monthly_2g_8                    6\n",
       "monthly_2g_7                    6\n",
       "monthly_2g_6                    5\n",
       "monthly_2g_9                    5\n",
       "fb_user_6                       3\n",
       "fb_user_7                       3\n",
       "fb_user_8                       3\n",
       "fb_user_9                       3\n",
       "night_pck_user_6                3\n",
       "night_pck_user_9                3\n",
       "night_pck_user_8                3\n",
       "night_pck_user_7                3\n",
       "last_date_of_month_6            1\n",
       "last_date_of_month_8            1\n",
       "loc_ic_t2o_mou                  1\n",
       "std_og_t2o_mou                  1\n",
       "loc_og_t2o_mou                  1\n",
       "circle_id                       1\n",
       "last_date_of_month_7            1\n",
       "std_og_t2c_mou_6                1\n",
       "last_date_of_month_9            1\n",
       "std_ic_t2o_mou_7                1\n",
       "std_og_t2c_mou_7                1\n",
       "std_ic_t2o_mou_8                1\n",
       "std_ic_t2o_mou_9                1\n",
       "std_og_t2c_mou_9                1\n",
       "std_og_t2c_mou_8                1\n",
       "std_ic_t2o_mou_6                1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check for count of unique values\n",
    "churn_df.nunique().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "EhaEkpzb9fKf",
    "outputId": "a11e4bd4-52b3-41e9-e918-2c3f53da334a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset size before column drop: (99999, 226)\n"
     ]
    }
   ],
   "source": [
    "print(f'Dataset size before column drop: {churn_df.shape}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "I79OO4Sv0fIG"
   },
   "outputs": [],
   "source": [
    "id_col = ['mobile_number', 'circle_id']\n",
    "drop_columns(id_col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ot_7_mpE9e8v",
    "outputId": "49c67ddd-ba73-4faa-a2d0-c756efacec53"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset size after column drop: (99999, 224)\n"
     ]
    }
   ],
   "source": [
    "print(f'Dataset size after column drop: {churn_df.shape}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HNuh7adlg7Bj"
   },
   "source": [
    "#### b. Drop Date column"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7fpx4cxySNrc"
   },
   "source": [
    "Drop `date_of_last_rech_data` as they are last date of mobile data recharge with more than 70% missing data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "KiRuHnyDR6ek"
   },
   "outputs": [],
   "source": [
    "date_col = [\n",
    "    'last_date_of_month_6', 'last_date_of_month_7', 'last_date_of_month_8', 'last_date_of_month_9',    # last date of month\n",
    "    'date_of_last_rech_data_6', 'date_of_last_rech_data_7', 'date_of_last_rech_data_9', 'date_of_last_rech_data_8'   # date of last data rech \n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "pI3GCgnTh4VQ",
    "outputId": "53f5a0a3-96ed-4030-edb1-a903b95f3845"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset size before column drop: (99999, 224)\n"
     ]
    }
   ],
   "source": [
    "print(f'Dataset size before column drop: {churn_df.shape}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "vKNiv7BQh4Ve"
   },
   "outputs": [],
   "source": [
    "drop_columns(date_col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "upxVyXIEh4Vf",
    "outputId": "b146dc34-dd35-452b-dc32-9b482cdb8099"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset size after column drop: (99999, 216)\n"
     ]
    }
   ],
   "source": [
    "print(f'Dataset size after column drop: {churn_df.shape}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "v1zlVnvvlb-x"
   },
   "source": [
    "#### c. Drop columns with no variance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "evhVz7uBvOYm"
   },
   "source": [
    "We have successfully treated all the null columns in our dataset. Now let's look at the columns with low variance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "GDtX1gFXvNgt",
    "outputId": "313d3b45-b652-4683-d47e-9d518143d61a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "loc_ic_t2o_mou      1\n",
       "std_og_t2o_mou      1\n",
       "std_ic_t2o_mou_6    1\n",
       "std_og_t2c_mou_6    1\n",
       "std_og_t2c_mou_7    1\n",
       "std_og_t2c_mou_8    1\n",
       "std_og_t2c_mou_9    1\n",
       "std_ic_t2o_mou_7    1\n",
       "std_ic_t2o_mou_8    1\n",
       "std_ic_t2o_mou_9    1\n",
       "loc_og_t2o_mou      1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique_count = churn_df.nunique().sort_values(ascending=False)\n",
    "one_unique_cols = unique_count[unique_count==1]\n",
    "one_unique_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 317
    },
    "id": "ZUDITvHlv2NN",
    "outputId": "6baadf75-da12-48a5-a939-d75b550923b4"
   },
   "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>loc_ic_t2o_mou</th>\n",
       "      <th>std_og_t2o_mou</th>\n",
       "      <th>std_ic_t2o_mou_6</th>\n",
       "      <th>std_og_t2c_mou_6</th>\n",
       "      <th>std_og_t2c_mou_7</th>\n",
       "      <th>std_og_t2c_mou_8</th>\n",
       "      <th>std_og_t2c_mou_9</th>\n",
       "      <th>std_ic_t2o_mou_7</th>\n",
       "      <th>std_ic_t2o_mou_8</th>\n",
       "      <th>std_ic_t2o_mou_9</th>\n",
       "      <th>loc_og_t2o_mou</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>98981.0</td>\n",
       "      <td>98981.0</td>\n",
       "      <td>96062.0</td>\n",
       "      <td>96062.0</td>\n",
       "      <td>96140.0</td>\n",
       "      <td>94621.0</td>\n",
       "      <td>92254.0</td>\n",
       "      <td>96140.0</td>\n",
       "      <td>94621.0</td>\n",
       "      <td>92254.0</td>\n",
       "      <td>98981.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       loc_ic_t2o_mou  std_og_t2o_mou  std_ic_t2o_mou_6  std_og_t2c_mou_6  \\\n",
       "count         98981.0         98981.0           96062.0           96062.0   \n",
       "mean              0.0             0.0               0.0               0.0   \n",
       "std               0.0             0.0               0.0               0.0   \n",
       "min               0.0             0.0               0.0               0.0   \n",
       "25%               0.0             0.0               0.0               0.0   \n",
       "50%               0.0             0.0               0.0               0.0   \n",
       "75%               0.0             0.0               0.0               0.0   \n",
       "max               0.0             0.0               0.0               0.0   \n",
       "\n",
       "       std_og_t2c_mou_7  std_og_t2c_mou_8  std_og_t2c_mou_9  std_ic_t2o_mou_7  \\\n",
       "count           96140.0           94621.0           92254.0           96140.0   \n",
       "mean                0.0               0.0               0.0               0.0   \n",
       "std                 0.0               0.0               0.0               0.0   \n",
       "min                 0.0               0.0               0.0               0.0   \n",
       "25%                 0.0               0.0               0.0               0.0   \n",
       "50%                 0.0               0.0               0.0               0.0   \n",
       "75%                 0.0               0.0               0.0               0.0   \n",
       "max                 0.0               0.0               0.0               0.0   \n",
       "\n",
       "       std_ic_t2o_mou_8  std_ic_t2o_mou_9  loc_og_t2o_mou  \n",
       "count           94621.0           92254.0         98981.0  \n",
       "mean                0.0               0.0             0.0  \n",
       "std                 0.0               0.0             0.0  \n",
       "min                 0.0               0.0             0.0  \n",
       "25%                 0.0               0.0             0.0  \n",
       "50%                 0.0               0.0             0.0  \n",
       "75%                 0.0               0.0             0.0  \n",
       "max                 0.0               0.0             0.0  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lets describe these columns for more information\n",
    "churn_df[one_unique_cols.keys()].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "BQIoOpZQy2Bd",
    "outputId": "e364205b-99b3-43bc-bdec-4c8f623436f8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of columns to drop 11\n"
     ]
    }
   ],
   "source": [
    "print(f'Number of columns to drop {len(one_unique_cols.keys())}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "XUnBzxRXyHUt",
    "outputId": "2077bafa-9b1b-4085-f490-2cd0f14ba58c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset size after column drop: (99999, 205)\n"
     ]
    }
   ],
   "source": [
    "# We observe that these columns have either 0 or null values. There is no variance in these columns. Hence we drop them\n",
    "drop_columns(one_unique_cols.keys())\n",
    "print(f'Dataset size after column drop: {churn_df.shape}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KCad7xO7aetV"
   },
   "source": [
    "#### d. Drop count_rech_2g and count_rech_3g columns\n",
    "\n",
    "Let us look at the total recharge column. We observe that it is the addition of 2g and 3g recharge."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "id": "jTpPNwoH0bT6",
    "outputId": "f875cea4-a0b2-4b64-c852-15d59a1dc291"
   },
   "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>total_rech_data_6</th>\n",
       "      <th>count_rech_2g_6</th>\n",
       "      <th>count_rech_3g_6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9463</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17672</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57996</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98495</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11815</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       total_rech_data_6  count_rech_2g_6  count_rech_3g_6\n",
       "9463                 NaN              NaN              NaN\n",
       "17672                NaN              NaN              NaN\n",
       "57996                2.0              2.0              0.0\n",
       "98495                3.0              0.0              3.0\n",
       "11815                NaN              NaN              NaN"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "churn_df[['total_rech_data_6','count_rech_2g_6', 'count_rech_3g_6']].sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "X9pMcIZE9e8F"
   },
   "source": [
    "- `total_rech_data` column give total count of mobile internet recharge.\n",
    "\n",
    "  `total_rech_data = count_rech_2g + count_rech_3g` \n",
    "  \n",
    "  So we will drop `count_rech_2g and count_rech_3g` column for all months"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "id": "3-65HmNk9aZP"
   },
   "outputs": [],
   "source": [
    "# dropping 2g and 3g data recharge columns\n",
    "count_rech = [\n",
    "     'count_rech_2g_6', 'count_rech_3g_6',   # 6th month\n",
    "     'count_rech_2g_7', 'count_rech_3g_7',   # 7th month\n",
    "     'count_rech_2g_8', 'count_rech_3g_8',   # 8th month\n",
    "     'count_rech_2g_9', 'count_rech_3g_9'    # 9th month\n",
    " ]\n",
    "drop_columns(count_rech)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "HcRt-vgHiM_P",
    "outputId": "876544bf-0b71-4ee1-dc1b-91c0a06928f3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset size after column drop: (99999, 197)\n"
     ]
    }
   ],
   "source": [
    "print(f'Dataset size after column drop: {churn_df.shape}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AzMu6Bu0DlL-"
   },
   "source": [
    "### **1.4 Impute columns**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Fu5Omkx9qVm3"
   },
   "source": [
    "#### a. Impute Data and Recharge columns\n",
    "count_rech_2g_6 and count_rech_3g_6 is Count of 2G and 3g recharges done for month of July. Since the value is missing, we assume that the customer has done 0 recharges.  Similarly we have columns for other months also. Thus we will impute the missing values with 0. \n",
    "`max_rech_data_6, total_rech_data_6` is the amount of data and `av_rech_amt_data_6','total_rech_amt_6` is the cost of recharge. Both these values can be imputed as 0 as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "dQQzyLUqfAdA",
    "outputId": "9cb4dc5a-2a18-4b0c-c6de-1742c29e31f3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['total_rech_data_6',\n",
       " 'total_rech_data_7',\n",
       " 'total_rech_data_8',\n",
       " 'total_rech_data_9',\n",
       " 'max_rech_data_6',\n",
       " 'max_rech_data_7',\n",
       " 'max_rech_data_8',\n",
       " 'max_rech_data_9',\n",
       " 'av_rech_amt_data_6',\n",
       " 'av_rech_amt_data_7',\n",
       " 'av_rech_amt_data_8',\n",
       " 'av_rech_amt_data_9']"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get all numerical recharge columns\n",
    "numerical_cols = [feature for feature in churn_df.select_dtypes(['int', 'float']).columns if feature not in cat_cols]\n",
    "recharge_cols = [feature for feature in churn_df[numerical_cols] if 'rech' in feature]\n",
    "recharge_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "id": "KPKqwpc-1hMm"
   },
   "outputs": [],
   "source": [
    "# imputing recharge columns with 0\n",
    "churn_df[recharge_cols] = churn_df[recharge_cols].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "89ZsVIETigUp",
    "outputId": "1e124522-6acc-4a1c-f059-e5546fb190f5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset size after column drop: (99999, 197)\n"
     ]
    }
   ],
   "source": [
    "print(f'Dataset size after column drop: {churn_df.shape}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SAMu6myJ60d6"
   },
   "source": [
    "#### b. Impute ARPU columns\n",
    "ARPU is average revenue per user. Missing values mean there was no revenue generated. Hence we impute such columns with 0 as well - `arpu_2g_6, arpu_3g_6`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "3EBa1FNbtVSX",
    "outputId": "2a9258c1-c1cf-49b9-f7fb-4e6ae0a20a6e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "List of columns with 'arpu' attribute:\n",
      "\n",
      " Index(['arpu_6', 'arpu_7', 'arpu_8', 'arpu_9', 'arpu_3g_6', 'arpu_3g_7',\n",
      "       'arpu_3g_8', 'arpu_3g_9', 'arpu_2g_6', 'arpu_2g_7', 'arpu_2g_8',\n",
      "       'arpu_2g_9'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# List of columns with \"arpu\" attribute\n",
    "arpu_col = churn_df.filter(regex='arpu', axis=1).columns\n",
    "print(\"List of columns with 'arpu' attribute:\\n\\n\", arpu_col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "id": "QOcXj0_K95Pb"
   },
   "outputs": [],
   "source": [
    "churn_df[arpu_col] = churn_df[arpu_col].fillna(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9BeBd6iH7mI4"
   },
   "source": [
    "#### c. Imputing MOU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7_c5w3mD7gZm",
    "outputId": "03f3005d-6fdb-4db7-99e4-2c5e56390eee"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "spl_ic_mou_9           7.75\n",
       "loc_ic_t2t_mou_9       7.75\n",
       "loc_og_mou_9           7.75\n",
       "std_og_t2m_mou_9       7.75\n",
       "std_og_t2f_mou_9       7.75\n",
       "loc_og_t2c_mou_9       7.75\n",
       "std_og_mou_9           7.75\n",
       "isd_og_mou_9           7.75\n",
       "loc_og_t2f_mou_9       7.75\n",
       "spl_og_mou_9           7.75\n",
       "loc_og_t2m_mou_9       7.75\n",
       "og_others_9            7.75\n",
       "ic_others_9            7.75\n",
       "loc_og_t2t_mou_9       7.75\n",
       "isd_ic_mou_9           7.75\n",
       "roam_og_mou_9          7.75\n",
       "std_og_t2t_mou_9       7.75\n",
       "roam_ic_mou_9          7.75\n",
       "offnet_mou_9           7.75\n",
       "loc_ic_t2m_mou_9       7.75\n",
       "loc_ic_t2f_mou_9       7.75\n",
       "loc_ic_mou_9           7.75\n",
       "onnet_mou_9            7.75\n",
       "std_ic_t2m_mou_9       7.75\n",
       "std_ic_t2t_mou_9       7.75\n",
       "std_ic_t2f_mou_9       7.75\n",
       "std_ic_mou_9           7.75\n",
       "ic_others_8            5.38\n",
       "std_og_t2m_mou_8       5.38\n",
       "spl_ic_mou_8           5.38\n",
       "loc_ic_t2f_mou_8       5.38\n",
       "std_og_t2f_mou_8       5.38\n",
       "loc_ic_mou_8           5.38\n",
       "std_og_mou_8           5.38\n",
       "std_og_t2t_mou_8       5.38\n",
       "std_ic_t2t_mou_8       5.38\n",
       "isd_ic_mou_8           5.38\n",
       "spl_og_mou_8           5.38\n",
       "loc_ic_t2t_mou_8       5.38\n",
       "og_others_8            5.38\n",
       "std_ic_t2m_mou_8       5.38\n",
       "std_ic_mou_8           5.38\n",
       "std_ic_t2f_mou_8       5.38\n",
       "isd_og_mou_8           5.38\n",
       "loc_og_t2t_mou_8       5.38\n",
       "loc_og_t2c_mou_8       5.38\n",
       "onnet_mou_8            5.38\n",
       "offnet_mou_8           5.38\n",
       "roam_ic_mou_8          5.38\n",
       "roam_og_mou_8          5.38\n",
       "loc_og_t2m_mou_8       5.38\n",
       "loc_og_t2f_mou_8       5.38\n",
       "loc_ic_t2m_mou_8       5.38\n",
       "loc_og_mou_8           5.38\n",
       "date_of_last_rech_9    4.76\n",
       "std_og_mou_6           3.94\n",
       "roam_ic_mou_6          3.94\n",
       "std_og_t2m_mou_6       3.94\n",
       "isd_ic_mou_6           3.94\n",
       "loc_og_t2t_mou_6       3.94\n",
       "spl_ic_mou_6           3.94\n",
       "loc_ic_t2t_mou_6       3.94\n",
       "roam_og_mou_6          3.94\n",
       "std_ic_mou_6           3.94\n",
       "loc_ic_t2m_mou_6       3.94\n",
       "loc_og_t2m_mou_6       3.94\n",
       "std_ic_t2f_mou_6       3.94\n",
       "std_og_t2t_mou_6       3.94\n",
       "std_ic_t2m_mou_6       3.94\n",
       "offnet_mou_6           3.94\n",
       "std_ic_t2t_mou_6       3.94\n",
       "onnet_mou_6            3.94\n",
       "loc_ic_mou_6           3.94\n",
       "loc_ic_t2f_mou_6       3.94\n",
       "ic_others_6            3.94\n",
       "std_og_t2f_mou_6       3.94\n",
       "spl_og_mou_6           3.94\n",
       "isd_og_mou_6           3.94\n",
       "loc_og_t2c_mou_6       3.94\n",
       "og_others_6            3.94\n",
       "loc_og_t2f_mou_6       3.94\n",
       "loc_og_mou_6           3.94\n",
       "loc_ic_mou_7           3.86\n",
       "loc_og_t2c_mou_7       3.86\n",
       "onnet_mou_7            3.86\n",
       "isd_og_mou_7           3.86\n",
       "spl_og_mou_7           3.86\n",
       "std_ic_t2t_mou_7       3.86\n",
       "std_og_t2m_mou_7       3.86\n",
       "loc_ic_t2m_mou_7       3.86\n",
       "loc_ic_t2f_mou_7       3.86\n",
       "std_ic_t2m_mou_7       3.86\n",
       "offnet_mou_7           3.86\n",
       "std_ic_t2f_mou_7       3.86\n",
       "loc_og_t2m_mou_7       3.86\n",
       "std_og_t2t_mou_7       3.86\n",
       "std_ic_mou_7           3.86\n",
       "og_others_7            3.86\n",
       "ic_others_7            3.86\n",
       "loc_og_t2t_mou_7       3.86\n",
       "isd_ic_mou_7           3.86\n",
       "std_og_t2f_mou_7       3.86\n",
       "roam_ic_mou_7          3.86\n",
       "spl_ic_mou_7           3.86\n",
       "std_og_mou_7           3.86\n",
       "loc_ic_t2t_mou_7       3.86\n",
       "roam_og_mou_7          3.86\n",
       "loc_og_t2f_mou_7       3.86\n",
       "loc_og_mou_7           3.86\n",
       "date_of_last_rech_8    3.62\n",
       "date_of_last_rech_7    1.77\n",
       "date_of_last_rech_6    1.61\n",
       "dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check for percentage of null values.\n",
    "null_count_cols = round(churn_df.isnull().sum() * 100/len(churn_df.index),2).sort_values(ascending=False)\n",
    "null_count_cols = null_count_cols[null_count_cols>0]\n",
    "null_count_cols"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "r004tJfY1KMu"
   },
   "source": [
    "Most of the columns are mou columns. MOU stands for minutes of usage. We assume that these columns had no usage at all. Thus, we impute all these columns with 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "et_4ItIud6UZ",
    "outputId": "e1ab4c3c-882d-468d-8b73-e7c5cd09f94a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "List of columns with 'mou' attribute: \n",
      "\n",
      " Index(['onnet_mou_6', 'onnet_mou_7', 'onnet_mou_8', 'onnet_mou_9',\n",
      "       'offnet_mou_6', 'offnet_mou_7', 'offnet_mou_8', 'offnet_mou_9',\n",
      "       'roam_ic_mou_6', 'roam_ic_mou_7',\n",
      "       ...\n",
      "       'total_ic_mou_8', 'total_ic_mou_9', 'spl_ic_mou_6', 'spl_ic_mou_7',\n",
      "       'spl_ic_mou_8', 'spl_ic_mou_9', 'isd_ic_mou_6', 'isd_ic_mou_7',\n",
      "       'isd_ic_mou_8', 'isd_ic_mou_9'],\n",
      "      dtype='object', length=108)\n"
     ]
    }
   ],
   "source": [
    "# List of columns with \"mou\" attribute\n",
    "mou_col = churn_df.filter(regex='mou', axis=1).columns\n",
    "print(\"List of columns with 'mou' attribute: \\n\\n\", mou_col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "id": "A408ZiLgecyR"
   },
   "outputs": [],
   "source": [
    "churn_df[mou_col] = churn_df[mou_col].fillna(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SEjCobS94IGI"
   },
   "source": [
    "#### d. Imputing Incoming and Outgoing others"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "yMKsqz1Yenwr",
    "outputId": "fb74f8be-7e00-4833-c6fb-ddd5c1fdfe34"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ic_others_9            7.75\n",
       "og_others_9            7.75\n",
       "ic_others_8            5.38\n",
       "og_others_8            5.38\n",
       "date_of_last_rech_9    4.76\n",
       "ic_others_6            3.94\n",
       "og_others_6            3.94\n",
       "ic_others_7            3.86\n",
       "og_others_7            3.86\n",
       "date_of_last_rech_8    3.62\n",
       "date_of_last_rech_7    1.77\n",
       "date_of_last_rech_6    1.61\n",
       "dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check for percentage of null values greater than 0 percent.\n",
    "null_count_cols = round(churn_df.isnull().sum() * 100/len(churn_df.index),2).sort_values(ascending=False)\n",
    "null_count_cols = null_count_cols[null_count_cols>0]\n",
    "null_count_cols"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "L-3kEy1fksc1"
   },
   "source": [
    "`ic` and `og` are incoming and outgoing. These columns will also be measured in munutes. We assume that there were 0 minutes spent for missing values of these fields."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "id": "xN2bT6iWLjS1"
   },
   "outputs": [],
   "source": [
    "# List of columns to impute with zero\n",
    "impute_cols = [\n",
    "    'og_others_6', 'og_others_7', 'og_others_8', 'og_others_9',\n",
    "    'ic_others_6', 'ic_others_7', 'ic_others_8', 'ic_others_9'\n",
    "]\n",
    "\n",
    "churn_df[impute_cols] = churn_df[impute_cols].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "gMnTcrCMlOq_",
    "outputId": "ffdb386a-4f83-49cf-9ede-3f80ec4ac317"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date_of_last_rech_9    4.76\n",
       "date_of_last_rech_8    3.62\n",
       "date_of_last_rech_7    1.77\n",
       "date_of_last_rech_6    1.61\n",
       "dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check for percentage of null values greater than 0 percent.\n",
    "null_count_cols = round(churn_df.isnull().sum() * 100/len(churn_df.index),2).sort_values(ascending=False)\n",
    "null_count_cols = null_count_cols[null_count_cols>0]\n",
    "null_count_cols"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Dpa_BL3jjC91"
   },
   "source": [
    "We will use `date of recharge` columns to derieve the gap between last recharges and then drop the columns."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QIwBo7TQ4UIB"
   },
   "source": [
    "Now we will proceed to derieve more information from the existing columns."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MvihmY7F9bZN"
   },
   "source": [
    "## **2. Derive new features**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FkL1Bb8imhh_"
   },
   "source": [
    "The dataset contains customer-level information for a span of four consecutive months - June, July, August and September. The months are encoded as 6, 7, 8 and 9, respectively. \n",
    "\n",
    "The business objective is to predict the churn in the last (i.e. the ninth) month using the data (features) from the first three months. To do this task well, understanding the typical customer behaviour during churn will be helpful.\n",
    "\n",
    "Since we are working over a four-month window, the first two months are the ‘good’ phase, the third month is the ‘action’ phase, while the fourth month is the ‘churn’ phase."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6fI-Nhuefy3J"
   },
   "source": [
    "### 2.1 Calculate total recharge amount  in June and July.\n",
    "Total amount is calculated as follow:\n",
    "- call recharge amount (`total_rech_amt`) + data recharge amount (`total_rech_data_6 * av_rech_amt_data`)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "B5zqyKaFf_Od"
   },
   "source": [
    "#### a. Calculate Total amount for HVC and LVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "id": "-kcJPb_Ff85l"
   },
   "outputs": [],
   "source": [
    "# High-value customers from month 6 (June) and 7 (July)\n",
    "churn_df['total_rech_amt_call_data_6'] = (\n",
    "        (churn_df['total_rech_data_6'] * churn_df['av_rech_amt_data_6']) +  churn_df['total_rech_amt_6']\n",
    ")\n",
    "\n",
    "churn_df['total_rech_amt_call_data_7'] = (\n",
    "        (churn_df['total_rech_data_7'] * churn_df['av_rech_amt_data_7']) +  churn_df['total_rech_amt_7']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "id": "qUwAPEaFgBGp"
   },
   "outputs": [],
   "source": [
    "# Low-value customers from month 8 (August)\n",
    "churn_df['total_rech_amt_call_data_8'] = (\n",
    "        (churn_df['total_rech_data_8'] * churn_df['av_rech_amt_data_8']) +  churn_df['total_rech_amt_8']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "id": "rJHlnGNGgCKi",
    "outputId": "07ef523f-1f66-43d9-80de-7b5abbb7efac"
   },
   "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>total_rech_amt_call_data_6</th>\n",
       "      <th>total_rech_amt_call_data_7</th>\n",
       "      <th>total_rech_amt_call_data_8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>614.0</td>\n",
       "      <td>504.0</td>\n",
       "      <td>504.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>74.0</td>\n",
       "      <td>538.0</td>\n",
       "      <td>383.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>168.0</td>\n",
       "      <td>315.0</td>\n",
       "      <td>116.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>230.0</td>\n",
       "      <td>310.0</td>\n",
       "      <td>601.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>252.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>287.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_rech_amt_call_data_6  total_rech_amt_call_data_7  \\\n",
       "0                       614.0                       504.0   \n",
       "1                        74.0                       538.0   \n",
       "2                       168.0                       315.0   \n",
       "3                       230.0                       310.0   \n",
       "4                       252.0                       350.0   \n",
       "\n",
       "   total_rech_amt_call_data_8  \n",
       "0                       504.0  \n",
       "1                       383.0  \n",
       "2                       116.0  \n",
       "3                       601.0  \n",
       "4                       287.0  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "churn_df[['total_rech_amt_call_data_6', 'total_rech_amt_call_data_7', 'total_rech_amt_call_data_8']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3AeGbP-lgFea"
   },
   "source": [
    "#### b. Average recharge done by HVC in June and July"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "id": "alfRrRrQgF5f"
   },
   "outputs": [],
   "source": [
    "# calculate average of June and July\n",
    "churn_df['av_rech_amt_call_data_6_7'] = np.round((churn_df['total_rech_amt_call_data_6'] + churn_df['total_rech_amt_call_data_7']),2)/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ZfdqvV37ktbF",
    "outputId": "c270f1ac-8921-44b0-fdc5-54d9ee1164e2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset size after column drop: (99999, 201)\n"
     ]
    }
   ],
   "source": [
    "print(f'Dataset size after column drop: {churn_df.shape}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2ah-dloZ3msC"
   },
   "source": [
    "#### c. Average recharge done by LVC in August"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "id": "qgsheshn3nXw"
   },
   "outputs": [],
   "source": [
    "churn_df['av_rech_amt_call_data_8'] = churn_df['total_rech_amt_call_data_8']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "id": "Z5cKmklR31C-",
    "outputId": "750fbf89-3377-4d9f-9cf7-687fff1b9c5c"
   },
   "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>av_rech_amt_call_data_6_7</th>\n",
       "      <th>av_rech_amt_call_data_8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>559.0</td>\n",
       "      <td>504.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>306.0</td>\n",
       "      <td>383.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>241.5</td>\n",
       "      <td>116.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>270.0</td>\n",
       "      <td>601.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>301.0</td>\n",
       "      <td>287.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   av_rech_amt_call_data_6_7  av_rech_amt_call_data_8\n",
       "0                      559.0                    504.0\n",
       "1                      306.0                    383.0\n",
       "2                      241.5                    116.0\n",
       "3                      270.0                    601.0\n",
       "4                      301.0                    287.0"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "churn_df[['av_rech_amt_call_data_6_7', 'av_rech_amt_call_data_8']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "id": "6W6wQ8v6kt3q"
   },
   "outputs": [],
   "source": [
    "drop_rech_data_col = [\n",
    "    'total_rech_data_6','av_rech_amt_data_6','total_rech_amt_6',   # 6th month\n",
    "    'total_rech_data_7','av_rech_amt_data_7','total_rech_amt_7',   # 7th month\n",
    "    'total_rech_data_8','av_rech_amt_data_8','total_rech_amt_8',   # 8th month\n",
    "    'total_rech_amt_call_data_6', 'total_rech_amt_call_data_7', 'total_rech_amt_call_data_8'   #6,7,8th month\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "op0sIlR5j-pf"
   },
   "source": [
    "Since we have derieved Average of recharge amount data for 6th 7th month and 8th, we will drop these columns. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "nl5upOmZugOt",
    "outputId": "a27f3083-8c33-4075-d935-36b42c51fbfe"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset size after column drop: (99999, 190)\n"
     ]
    }
   ],
   "source": [
    "drop_columns(drop_rech_data_col)\n",
    "print(f'Dataset size after column drop: {churn_df.shape}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vrOXGmJ4_CiC"
   },
   "source": [
    "### 2.2 Convert `AON` in months\n",
    "As per data dictionary `AON is Age on network - number of days the customer is using the operator T network`. To find the number of months, we will divide by 30."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "id": "-TnC7otj_Dkj"
   },
   "outputs": [],
   "source": [
    "churn_df['aon_month'] = churn_df['aon'] / 30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "id": "JB8Y2rGJ_Dq3"
   },
   "outputs": [],
   "source": [
    "# Drop AON since we have Age on network in months\n",
    "drop_columns(['aon'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "siHrgVjQiEBf"
   },
   "source": [
    "### 2.3 Derive last recharge gap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "id": "lgTg_VQaiErK"
   },
   "outputs": [],
   "source": [
    "# Convert to datetime format\n",
    "churn_df['date_of_last_rech_6'] = pd.to_datetime(churn_df['date_of_last_rech_6'], format='%m/%d/%Y')\n",
    "churn_df['date_of_last_rech_7'] = pd.to_datetime(churn_df['date_of_last_rech_7'], format='%m/%d/%Y')\n",
    "churn_df['date_of_last_rech_8'] = pd.to_datetime(churn_df['date_of_last_rech_8'], format='%m/%d/%Y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "id": "hKkOweEFiErK"
   },
   "outputs": [],
   "source": [
    "# function to calculate last recharge difference\n",
    "def rech_age_gap(date_col_list):\n",
    "    for i in range(1, len(date_col_list)):\n",
    "        rech_gap = np.where(\n",
    "            churn_df[date_col_list[i]].notna() & churn_df[date_col_list[i-1]].notna(), \n",
    "            ( churn_df[date_col_list[i]] - churn_df[date_col_list[i-1]] ).dt.days, 0\n",
    "        )\n",
    "\n",
    "        churn_df[f'last_rech_age_gap_{i}'] = rech_gap\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "id": "oyRClCnXi8TB"
   },
   "outputs": [],
   "source": [
    "date_col_list = ['date_of_last_rech_6', 'date_of_last_rech_7', 'date_of_last_rech_8']\n",
    "\n",
    "# call function\n",
    "rech_age_gap(date_col_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "id": "VyR0e1xEiErL",
    "outputId": "9787c64a-0744-4714-e364-cc7b3d3bce66"
   },
   "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>last_rech_age_gap_1</th>\n",
       "      <th>last_rech_age_gap_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25.0</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>32.0</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37.0</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33.0</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   last_rech_age_gap_1  last_rech_age_gap_2\n",
       "0                 25.0                 23.0\n",
       "1                 32.0                 28.0\n",
       "2                 37.0                 21.0\n",
       "3                 33.0                 31.0\n",
       "4                 32.0                 12.0"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "churn_df[churn_df.filter(regex='last_rech_age_gap_').columns].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2D92eTkTk_dz"
   },
   "source": [
    "Since we've drieved new columns, we will drop the previous columns which had missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "NFP9JNEalN8J",
    "outputId": "1191c21f-9154-4890-e412-e31f378d013f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset size after column drop: (99999, 189)\n"
     ]
    }
   ],
   "source": [
    "# drop date columns\n",
    "drop_columns(date_col_list)\n",
    "print(f'Dataset size after column drop: {churn_df.shape}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mnnktpFmC44D"
   },
   "source": [
    "### 2.4 Derive features from good phase months"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0mP7CUYkC5v9"
   },
   "source": [
    "First two months are the `good` phase, the third month is the `action` phase, while the fourth month is the `churn` phase.\n",
    "\n",
    "- The `good` phase: In this phase, the customer is happy with the service and behaves as usual.\n",
    "- The `action` phase: In this phase, the customer usually shows different behaviour than the ‘good’ months\n",
    "- The `churn` phase: In this phase, the customer is said to have churned."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9cV7KHaEC50N",
    "outputId": "d6781d55-280c-4ded-8c7d-c91d606d57f3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['arpu',\n",
       " 'onnet_mou',\n",
       " 'offnet_mou',\n",
       " 'roam_ic_mou',\n",
       " 'roam_og_mou',\n",
       " 'loc_og_t2t_mou',\n",
       " 'loc_og_t2m_mou',\n",
       " 'loc_og_t2f_mou',\n",
       " 'loc_og_t2c_mou',\n",
       " 'loc_og_mou',\n",
       " 'std_og_t2t_mou',\n",
       " 'std_og_t2m_mou',\n",
       " 'std_og_t2f_mou',\n",
       " 'std_og_mou',\n",
       " 'isd_og_mou',\n",
       " 'spl_og_mou',\n",
       " 'og_others',\n",
       " 'total_og_mou',\n",
       " 'loc_ic_t2t_mou',\n",
       " 'loc_ic_t2m_mou',\n",
       " 'loc_ic_t2f_mou',\n",
       " 'loc_ic_mou',\n",
       " 'std_ic_t2t_mou',\n",
       " 'std_ic_t2m_mou',\n",
       " 'std_ic_t2f_mou',\n",
       " 'std_ic_mou',\n",
       " 'total_ic_mou',\n",
       " 'spl_ic_mou',\n",
       " 'isd_ic_mou',\n",
       " 'ic_others',\n",
       " 'total_rech_num',\n",
       " 'max_rech_amt',\n",
       " 'last_day_rch_amt',\n",
       " 'max_rech_data',\n",
       " 'vol_2g_mb',\n",
       " 'vol_3g_mb',\n",
       " 'arpu_3g',\n",
       " 'arpu_2g',\n",
       " 'night_pck_user',\n",
       " 'monthly_2g',\n",
       " 'sachet_2g',\n",
       " 'monthly_3g',\n",
       " 'sachet_3g',\n",
       " 'fb_user',\n",
       " 'av_rech_amt_call_data_6']"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_list = churn_df.filter(regex='_6').columns.str[:-2]\n",
    "col_list = list(col_list)\n",
    "col_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "id": "jXVVtH5STu_i"
   },
   "outputs": [],
   "source": [
    "# since we have already taken average of 6th and 7th month in the derieved column, \n",
    "# we will remove this column from the list\n",
    "col_list.remove('av_rech_amt_call_data_6')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "JmSTAOsYNLa4",
    "outputId": "b6b08b62-89b0-4cbb-f42f-1b752ba6421c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of dataset before deriving new features (99999, 189)\n"
     ]
    }
   ],
   "source": [
    "print(\"Shape of dataset before deriving new features\", churn_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "id": "ZgtKaLaeZn1K"
   },
   "outputs": [],
   "source": [
    "for col in col_list:\n",
    "    churn_df[f'{col}_diff'] = churn_df[f'{col}_8'] - ((churn_df[f'{col}_6'] + churn_df[f'{col}_7']) / 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Pfu2vg4yC58p",
    "outputId": "b44d2c22-0534-4c45-cd9d-350239665d6f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of dataset after deriving new features (99999, 233)\n"
     ]
    }
   ],
   "source": [
    "print(\"Shape of dataset after deriving new features\", churn_df.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ftnyPKokgM73"
   },
   "source": [
    "## **3. Filter high-value customers**\n",
    "\n",
    "Customers who have recharged with an amount more than or equal to X, where X is the 70th percentile of the average recharge amount in the first two months "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "EZ9L8P3sgJw7",
    "outputId": "97d072ed-f79c-4404-f712-fb02f115682d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "478.0"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 70th percentile of the average recharge amount\n",
    "churn_df['av_rech_amt_call_data_6_7'].quantile(0.70)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "id": "L4HEItpQgPZE"
   },
   "outputs": [],
   "source": [
    "churn_df = churn_df[\n",
    "    churn_df['av_rech_amt_call_data_6_7'] >= churn_df['av_rech_amt_call_data_6_7'].quantile(0.70)\n",
    "].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Z26UodragQsO",
    "outputId": "a1ef7a5b-b25b-4d88-b824-d1878a9ded53"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30001, 233)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "churn_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "stB7gzF-gULk"
   },
   "source": [
    "## **4. Tag churners and remove attributes of the churn phase**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "loL9ayhWgWoO"
   },
   "source": [
    "### 4.1 Tag churners "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Suk8lM6bHbuk"
   },
   "source": [
    "Now tag the churned customers (churn=1, else 0) based on the fourth month (9 - September) as follows: \n",
    "\n",
    "Those who have not made any calls (either incoming or outgoing) AND have not used mobile internet even once in the churn phase. The attributes you need to use to tag churners are:\n",
    "\n",
    "- total_ic_mou_9\n",
    "- total_og_mou_9\n",
    "- vol_2g_mb_9\n",
    "- vol_3g_mb_9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "id": "WEmPAxgbgScN"
   },
   "outputs": [],
   "source": [
    "churn_df['target'] = (\n",
    "    churn_df[['total_ic_mou_9','total_og_mou_9', 'vol_2g_mb_9','vol_3g_mb_9']].sum(axis=1) == 0\n",
    ").astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tbh7AI2_Hmsq"
   },
   "source": [
    "### 4.2 Remove attributes of churner"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "srfVUy7Igbne"
   },
   "source": [
    "After tagging churners, remove all the attributes corresponding to the churn phase (all attributes having ‘ _9’, etc. in their names)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "FM8F_MFKgdKl",
    "outputId": "dbc2b28d-0e2b-4dd1-9c08-fd35f3befad5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['arpu_9', 'onnet_mou_9', 'offnet_mou_9', 'roam_ic_mou_9',\n",
      "       'roam_og_mou_9', 'loc_og_t2t_mou_9', 'loc_og_t2m_mou_9',\n",
      "       'loc_og_t2f_mou_9', 'loc_og_t2c_mou_9', 'loc_og_mou_9',\n",
      "       'std_og_t2t_mou_9', 'std_og_t2m_mou_9', 'std_og_t2f_mou_9',\n",
      "       'std_og_mou_9', 'isd_og_mou_9', 'spl_og_mou_9', 'og_others_9',\n",
      "       'total_og_mou_9', 'loc_ic_t2t_mou_9', 'loc_ic_t2m_mou_9',\n",
      "       'loc_ic_t2f_mou_9', 'loc_ic_mou_9', 'std_ic_t2t_mou_9',\n",
      "       'std_ic_t2m_mou_9', 'std_ic_t2f_mou_9', 'std_ic_mou_9',\n",
      "       'total_ic_mou_9', 'spl_ic_mou_9', 'isd_ic_mou_9', 'ic_others_9',\n",
      "       'total_rech_num_9', 'total_rech_amt_9', 'max_rech_amt_9',\n",
      "       'date_of_last_rech_9', 'last_day_rch_amt_9', 'total_rech_data_9',\n",
      "       'max_rech_data_9', 'av_rech_amt_data_9', 'vol_2g_mb_9', 'vol_3g_mb_9',\n",
      "       'arpu_3g_9', 'arpu_2g_9', 'night_pck_user_9', 'monthly_2g_9',\n",
      "       'sachet_2g_9', 'monthly_3g_9', 'sachet_3g_9', 'fb_user_9'],\n",
      "      dtype='object')\n",
      "------------------------------------------------------------\n",
      "Columns count 48\n"
     ]
    }
   ],
   "source": [
    "# get list of columns having attributes _9\n",
    "columns_9 = []\n",
    "columns_9 = churn_df.filter(regex='_9').columns\n",
    "print(columns_9)\n",
    "print(\"---\"*20)\n",
    "print(\"Columns count\", len(columns_9))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ykO9L398gejQ",
    "outputId": "ae1928f9-386b-464c-b560-21a9ec3bd3b8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset size before column drop: (30001, 234)\n"
     ]
    }
   ],
   "source": [
    "print(f'Dataset size before column drop: {churn_df.shape}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yhx_LAzvsJMH"
   },
   "source": [
    "`sep_vbc_3g` this column gives information about `vbc` from 9th month (September)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "id": "6ZTTS2KGr1ve"
   },
   "outputs": [],
   "source": [
    "drop_columns(['sep_vbc_3g'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "GWWK8tfNhfJ4",
    "outputId": "b4b03bc5-5d16-4a6b-e6be-29c90a2a9e2f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset size after column drop: (30001, 185)\n"
     ]
    }
   ],
   "source": [
    "drop_columns(columns_9)\n",
    "print(f'Dataset size after column drop: {churn_df.shape}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "MDrE4fP2eC8a",
    "outputId": "b61ce906-260d-4948-b154-e8b338d750c1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30001, 185)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "churn_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 321
    },
    "id": "BJV1RgFimWed",
    "outputId": "fc61b86e-a25d-4b8e-b5ff-9f7da051a910"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='target'>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(churn_df['target'].value_counts(normalize=True)*100).plot(kind='pie', figsize=(5, 5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8qIW7ffq8FcA",
    "outputId": "c78caec3-7856-4a67-8b28-76c47163ba9d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Churn Rate: 8.14%\n"
     ]
    }
   ],
   "source": [
    "churn = (np.sum(churn_df['target'])/len(churn_df['target'].index))*100\n",
    "print(f'Churn Rate: {np.round(churn,2)}%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fQVVSTmertEa"
   },
   "source": [
    "## **5. EDA**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "id": "Otx5ZRV_rtBj"
   },
   "outputs": [],
   "source": [
    "# function to plot bar chart\n",
    "def plot_mean_bar_chart(col_list):\n",
    "    mean_data = churn_df.groupby('target')[col_list].mean().T\n",
    "    mean_data.plot.bar(figsize=(10,5))\n",
    "    plt.xticks(rotation=0)\n",
    "    plt.legend([\"Non Churn\", \"Churn\"])\n",
    "    plt.show()\n",
    "    \n",
    "    return mean_data.T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iB1nlcVWrs-F"
   },
   "source": [
    "#### **1. Average Revenue Per User (ARPU)**\n",
    "- As per data dictionary `ARPU is Average revenue per user`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 446
    },
    "id": "nSdAaFhVrs8u",
    "outputId": "f659271f-09ef-4c2b-e185-2633eaf9f920"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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>arpu_6</th>\n",
       "      <th>arpu_7</th>\n",
       "      <th>arpu_8</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>549.220390</td>\n",
       "      <td>562.608191</td>\n",
       "      <td>532.635710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>663.158246</td>\n",
       "      <td>540.165968</td>\n",
       "      <td>237.200793</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            arpu_6      arpu_7      arpu_8\n",
       "target                                    \n",
       "0       549.220390  562.608191  532.635710\n",
       "1       663.158246  540.165968  237.200793"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arpu_cols = churn_df.filter(regex='arpu').columns\n",
    "arpu_cols = ['arpu_6', 'arpu_7', 'arpu_8']\n",
    "plot_mean_bar_chart(arpu_cols)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_h2RPz9ors6E"
   },
   "source": [
    "- We can see that there is drop for ARPU in 8th month for churn users"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Fnrr5Kfjrs4n"
   },
   "source": [
    "#### **2. Monthly service (MONTHLY)**\n",
    "- As per data dictionary `MONTHLY is Service schemes with validity equivalent to a month`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 446
    },
    "id": "7am-jgQors1n",
    "outputId": "12f8b10a-2f0f-4e86-ab2e-ca8cb42cc741"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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>monthly_2g_6</th>\n",
       "      <th>monthly_2g_7</th>\n",
       "      <th>monthly_2g_8</th>\n",
       "      <th>monthly_3g_6</th>\n",
       "      <th>monthly_3g_7</th>\n",
       "      <th>monthly_3g_8</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.181676</td>\n",
       "      <td>0.193215</td>\n",
       "      <td>0.165203</td>\n",
       "      <td>0.219557</td>\n",
       "      <td>0.236575</td>\n",
       "      <td>0.224673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.093404</td>\n",
       "      <td>0.070873</td>\n",
       "      <td>0.022941</td>\n",
       "      <td>0.197460</td>\n",
       "      <td>0.143794</td>\n",
       "      <td>0.056944</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        monthly_2g_6  monthly_2g_7  monthly_2g_8  monthly_3g_6  monthly_3g_7  \\\n",
       "target                                                                         \n",
       "0           0.181676      0.193215      0.165203      0.219557      0.236575   \n",
       "1           0.093404      0.070873      0.022941      0.197460      0.143794   \n",
       "\n",
       "        monthly_3g_8  \n",
       "target                \n",
       "0           0.224673  \n",
       "1           0.056944  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_cols = ['monthly_2g_6', 'monthly_2g_7', 'monthly_2g_8',\n",
    "                'monthly_3g_6', 'monthly_3g_7', 'monthly_3g_8']\n",
    "plot_mean_bar_chart(monthly_cols)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LFDGtOVjrs0F"
   },
   "source": [
    "- We can see that there is drop for 2g and 3g MONTHLY subscription in 8th month for churn users"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "b5uJg8wpr5by"
   },
   "source": [
    "#### **3.VBC** \n",
    "\n",
    "- As per data dictionary `VBC is Volume based cost - when no specific scheme is not purchased and paid as per usage`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 446
    },
    "id": "RiFX8Jncr5bz",
    "outputId": "07462532-31a1-4a40-9680-b4a8d9b22d99"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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>aug_vbc_3g</th>\n",
       "      <th>jul_vbc_3g</th>\n",
       "      <th>jun_vbc_3g</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>180.501280</td>\n",
       "      <td>186.206078</td>\n",
       "      <td>162.370343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40.851975</td>\n",
       "      <td>96.076317</td>\n",
       "      <td>115.096575</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        aug_vbc_3g  jul_vbc_3g  jun_vbc_3g\n",
       "target                                    \n",
       "0       180.501280  186.206078  162.370343\n",
       "1        40.851975   96.076317  115.096575"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vbc_cols = churn_df.filter(regex='vbc').columns\n",
    "plot_mean_bar_chart(vbc_cols)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OKumd0Xyr5b0"
   },
   "source": [
    "- We can see that there is drop for VBC in 8th month for churn users"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IFTKegvHrsw2"
   },
   "source": [
    "#### **4. Mobile internet usage volume (in MB)**\n",
    "\n",
    "\n",
    "- As per data dictionary `VOL is Mobile internet usage volume (in MB)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 448
    },
    "id": "yZcezyvvrsr7",
    "outputId": "7d87f0ba-a4c1-4776-8786-19b5b308276a"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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>vol_2g_mb_6</th>\n",
       "      <th>vol_2g_mb_7</th>\n",
       "      <th>vol_2g_mb_8</th>\n",
       "      <th>vol_3g_mb_6</th>\n",
       "      <th>vol_3g_mb_7</th>\n",
       "      <th>vol_3g_mb_8</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>127.830679</td>\n",
       "      <td>128.229264</td>\n",
       "      <td>113.243187</td>\n",
       "      <td>349.825292</td>\n",
       "      <td>383.548331</td>\n",
       "      <td>374.884935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>111.748697</td>\n",
       "      <td>89.736321</td>\n",
       "      <td>23.152745</td>\n",
       "      <td>281.407722</td>\n",
       "      <td>219.450729</td>\n",
       "      <td>87.399685</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        vol_2g_mb_6  vol_2g_mb_7  vol_2g_mb_8  vol_3g_mb_6  vol_3g_mb_7  \\\n",
       "target                                                                    \n",
       "0        127.830679   128.229264   113.243187   349.825292   383.548331   \n",
       "1        111.748697    89.736321    23.152745   281.407722   219.450729   \n",
       "\n",
       "        vol_3g_mb_8  \n",
       "target               \n",
       "0        374.884935  \n",
       "1         87.399685  "
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vol_cols = ['vol_2g_mb_6', 'vol_2g_mb_7', 'vol_2g_mb_8', \n",
    "            'vol_3g_mb_6', 'vol_3g_mb_7', 'vol_3g_mb_8']\n",
    "plot_mean_bar_chart(vol_cols)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XI2ZD2hdrsqd"
   },
   "source": [
    "- We can see that there is drop for 2g and 3g mobile internet usage(MB) in 8th month for churn users"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TIzy7IsVrsnj"
   },
   "source": [
    "### **5. Average recharge amount**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 446
    },
    "id": "Qhua5ar2rsmA",
    "outputId": "6b271b5f-3200-4614-8675-4b48c673f544"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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       "      <th></th>\n",
       "      <th>av_rech_amt_call_data_6_7</th>\n",
       "      <th>av_rech_amt_call_data_8</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1079.260088</td>\n",
       "      <td>336.676182</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        av_rech_amt_call_data_6_7  av_rech_amt_call_data_8\n",
       "target                                                    \n",
       "0                     1159.118200              1110.586829\n",
       "1                     1079.260088               336.676182"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg_rech_call_amt = ['av_rech_amt_call_data_6_7', 'av_rech_amt_call_data_8']\n",
    "plot_mean_bar_chart(avg_rech_call_amt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "a3D4eMS-rsiy"
   },
   "source": [
    "- We can see that there is drop for average recharge amount (call + internet) in 8th month for churn users"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "clcOcK9TJZUd"
   },
   "source": [
    "### **6. Total Incoming MOU**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 446
    },
    "id": "rWvr0sgkJZ5F",
    "outputId": "190c0ddd-ea46-47cc-d47f-5bcbc0a31b87"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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>total_ic_mou_6</th>\n",
       "      <th>total_ic_mou_7</th>\n",
       "      <th>total_ic_mou_8</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>295.131789</td>\n",
       "      <td>306.840677</td>\n",
       "      <td>299.096525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>231.451344</td>\n",
       "      <td>182.907534</td>\n",
       "      <td>63.444342</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        total_ic_mou_6  total_ic_mou_7  total_ic_mou_8\n",
       "target                                                \n",
       "0           295.131789      306.840677      299.096525\n",
       "1           231.451344      182.907534       63.444342"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_ic = ['total_ic_mou_6', 'total_ic_mou_7', 'total_ic_mou_8']\n",
    "plot_mean_bar_chart(total_ic)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "E1k0AOf-JaS2"
   },
   "source": [
    "- We can see that there is drop for total incoming mou in 8th month for churn users"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "D1oL2JgoJabB"
   },
   "source": [
    "### **7. Total Outgoing MOU**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 446
    },
    "id": "MUuhX28yJgAQ",
    "outputId": "92bf2937-48fc-44a5-c9a0-cbeda9101dab"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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>total_og_mou_6</th>\n",
       "      <th>total_og_mou_7</th>\n",
       "      <th>total_og_mou_8</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>593.763075</td>\n",
       "      <td>628.442026</td>\n",
       "      <td>581.998003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>791.678435</td>\n",
       "      <td>627.508218</td>\n",
       "      <td>209.361368</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        total_og_mou_6  total_og_mou_7  total_og_mou_8\n",
       "target                                                \n",
       "0           593.763075      628.442026      581.998003\n",
       "1           791.678435      627.508218      209.361368"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_og = ['total_og_mou_6', 'total_og_mou_7', 'total_og_mou_8']\n",
    "plot_mean_bar_chart(total_og)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ReVYEElQJfkB"
   },
   "source": [
    "- We can see that there is drop for total outgoing mou in 8th month for churn users"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BMpqkUu5GbOE"
   },
   "source": [
    "#### **Observations:**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-mu0hI6zGxGr"
   },
   "source": [
    "- Total incoming and outgoing MOU is reduced in the month of august\n",
    "- Average recharge amount reduced in the month of august\n",
    "- Total data recharge reduced in the month of august\n",
    "- This Reduction is not just normal but significant.\n",
    "- Those who churned were customers who used services for less days as compared to customers who didn't churn.\n",
    "- Overall most of the services that customer was using, reduced to low in action phase 8th month."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lDjjfpy6_UJB"
   },
   "source": [
    "## **6. Data Preparation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 317
    },
    "id": "42CsYh9lA--W",
    "outputId": "cd9e965f-d14e-44aa-cc83-f6ab6fac7f94"
   },
   "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>arpu_6</th>\n",
       "      <th>arpu_7</th>\n",
       "      <th>arpu_8</th>\n",
       "      <th>onnet_mou_6</th>\n",
       "      <th>onnet_mou_7</th>\n",
       "      <th>onnet_mou_8</th>\n",
       "      <th>offnet_mou_6</th>\n",
       "      <th>offnet_mou_7</th>\n",
       "      <th>offnet_mou_8</th>\n",
       "      <th>roam_ic_mou_6</th>\n",
       "      <th>roam_ic_mou_7</th>\n",
       "      <th>roam_ic_mou_8</th>\n",
       "      <th>roam_og_mou_6</th>\n",
       "      <th>roam_og_mou_7</th>\n",
       "      <th>roam_og_mou_8</th>\n",
       "      <th>loc_og_t2t_mou_6</th>\n",
       "      <th>loc_og_t2t_mou_7</th>\n",
       "      <th>loc_og_t2t_mou_8</th>\n",
       "      <th>loc_og_t2m_mou_6</th>\n",
       "      <th>loc_og_t2m_mou_7</th>\n",
       "      <th>loc_og_t2m_mou_8</th>\n",
       "      <th>loc_og_t2f_mou_6</th>\n",
       "      <th>loc_og_t2f_mou_7</th>\n",
       "      <th>loc_og_t2f_mou_8</th>\n",
       "      <th>loc_og_t2c_mou_6</th>\n",
       "      <th>loc_og_t2c_mou_7</th>\n",
       "      <th>loc_og_t2c_mou_8</th>\n",
       "      <th>loc_og_mou_6</th>\n",
       "      <th>loc_og_mou_7</th>\n",
       "      <th>loc_og_mou_8</th>\n",
       "      <th>std_og_t2t_mou_6</th>\n",
       "      <th>std_og_t2t_mou_7</th>\n",
       "      <th>std_og_t2t_mou_8</th>\n",
       "      <th>std_og_t2m_mou_6</th>\n",
       "      <th>std_og_t2m_mou_7</th>\n",
       "      <th>std_og_t2m_mou_8</th>\n",
       "      <th>std_og_t2f_mou_6</th>\n",
       "      <th>std_og_t2f_mou_7</th>\n",
       "      <th>std_og_t2f_mou_8</th>\n",
       "      <th>std_og_mou_6</th>\n",
       "      <th>std_og_mou_7</th>\n",
       "      <th>std_og_mou_8</th>\n",
       "      <th>isd_og_mou_6</th>\n",
       "      <th>isd_og_mou_7</th>\n",
       "      <th>isd_og_mou_8</th>\n",
       "      <th>spl_og_mou_6</th>\n",
       "      <th>spl_og_mou_7</th>\n",
       "      <th>spl_og_mou_8</th>\n",
       "      <th>og_others_6</th>\n",
       "      <th>og_others_7</th>\n",
       "      <th>og_others_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>loc_ic_t2t_mou_6</th>\n",
       "      <th>loc_ic_t2t_mou_7</th>\n",
       "      <th>loc_ic_t2t_mou_8</th>\n",
       "      <th>loc_ic_t2m_mou_6</th>\n",
       "      <th>loc_ic_t2m_mou_7</th>\n",
       "      <th>loc_ic_t2m_mou_8</th>\n",
       "      <th>loc_ic_t2f_mou_6</th>\n",
       "      <th>loc_ic_t2f_mou_7</th>\n",
       "      <th>loc_ic_t2f_mou_8</th>\n",
       "      <th>loc_ic_mou_6</th>\n",
       "      <th>loc_ic_mou_7</th>\n",
       "      <th>loc_ic_mou_8</th>\n",
       "      <th>std_ic_t2t_mou_6</th>\n",
       "      <th>std_ic_t2t_mou_7</th>\n",
       "      <th>std_ic_t2t_mou_8</th>\n",
       "      <th>std_ic_t2m_mou_6</th>\n",
       "      <th>std_ic_t2m_mou_7</th>\n",
       "      <th>std_ic_t2m_mou_8</th>\n",
       "      <th>std_ic_t2f_mou_6</th>\n",
       "      <th>std_ic_t2f_mou_7</th>\n",
       "      <th>std_ic_t2f_mou_8</th>\n",
       "      <th>std_ic_mou_6</th>\n",
       "      <th>std_ic_mou_7</th>\n",
       "      <th>std_ic_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>spl_ic_mou_6</th>\n",
       "      <th>spl_ic_mou_7</th>\n",
       "      <th>spl_ic_mou_8</th>\n",
       "      <th>isd_ic_mou_6</th>\n",
       "      <th>isd_ic_mou_7</th>\n",
       "      <th>isd_ic_mou_8</th>\n",
       "      <th>ic_others_6</th>\n",
       "      <th>ic_others_7</th>\n",
       "      <th>ic_others_8</th>\n",
       "      <th>total_rech_num_6</th>\n",
       "      <th>total_rech_num_7</th>\n",
       "      <th>total_rech_num_8</th>\n",
       "      <th>max_rech_amt_6</th>\n",
       "      <th>max_rech_amt_7</th>\n",
       "      <th>max_rech_amt_8</th>\n",
       "      <th>last_day_rch_amt_6</th>\n",
       "      <th>last_day_rch_amt_7</th>\n",
       "      <th>last_day_rch_amt_8</th>\n",
       "      <th>max_rech_data_6</th>\n",
       "      <th>max_rech_data_7</th>\n",
       "      <th>max_rech_data_8</th>\n",
       "      <th>vol_2g_mb_6</th>\n",
       "      <th>vol_2g_mb_7</th>\n",
       "      <th>vol_2g_mb_8</th>\n",
       "      <th>vol_3g_mb_6</th>\n",
       "      <th>vol_3g_mb_7</th>\n",
       "      <th>vol_3g_mb_8</th>\n",
       "      <th>arpu_3g_6</th>\n",
       "      <th>arpu_3g_7</th>\n",
       "      <th>arpu_3g_8</th>\n",
       "      <th>arpu_2g_6</th>\n",
       "      <th>arpu_2g_7</th>\n",
       "      <th>arpu_2g_8</th>\n",
       "      <th>night_pck_user_6</th>\n",
       "      <th>night_pck_user_7</th>\n",
       "      <th>night_pck_user_8</th>\n",
       "      <th>monthly_2g_6</th>\n",
       "      <th>monthly_2g_7</th>\n",
       "      <th>monthly_2g_8</th>\n",
       "      <th>sachet_2g_6</th>\n",
       "      <th>sachet_2g_7</th>\n",
       "      <th>sachet_2g_8</th>\n",
       "      <th>monthly_3g_6</th>\n",
       "      <th>monthly_3g_7</th>\n",
       "      <th>monthly_3g_8</th>\n",
       "      <th>sachet_3g_6</th>\n",
       "      <th>sachet_3g_7</th>\n",
       "      <th>sachet_3g_8</th>\n",
       "      <th>fb_user_6</th>\n",
       "      <th>fb_user_7</th>\n",
       "      <th>fb_user_8</th>\n",
       "      <th>aug_vbc_3g</th>\n",
       "      <th>jul_vbc_3g</th>\n",
       "      <th>jun_vbc_3g</th>\n",
       "      <th>av_rech_amt_call_data_6_7</th>\n",
       "      <th>av_rech_amt_call_data_8</th>\n",
       "      <th>aon_month</th>\n",
       "      <th>last_rech_age_gap_1</th>\n",
       "      <th>last_rech_age_gap_2</th>\n",
       "      <th>arpu_diff</th>\n",
       "      <th>onnet_mou_diff</th>\n",
       "      <th>offnet_mou_diff</th>\n",
       "      <th>roam_ic_mou_diff</th>\n",
       "      <th>roam_og_mou_diff</th>\n",
       "      <th>loc_og_t2t_mou_diff</th>\n",
       "      <th>loc_og_t2m_mou_diff</th>\n",
       "      <th>loc_og_t2f_mou_diff</th>\n",
       "      <th>loc_og_t2c_mou_diff</th>\n",
       "      <th>loc_og_mou_diff</th>\n",
       "      <th>std_og_t2t_mou_diff</th>\n",
       "      <th>std_og_t2m_mou_diff</th>\n",
       "      <th>std_og_t2f_mou_diff</th>\n",
       "      <th>std_og_mou_diff</th>\n",
       "      <th>isd_og_mou_diff</th>\n",
       "      <th>spl_og_mou_diff</th>\n",
       "      <th>og_others_diff</th>\n",
       "      <th>total_og_mou_diff</th>\n",
       "      <th>loc_ic_t2t_mou_diff</th>\n",
       "      <th>loc_ic_t2m_mou_diff</th>\n",
       "      <th>loc_ic_t2f_mou_diff</th>\n",
       "      <th>loc_ic_mou_diff</th>\n",
       "      <th>std_ic_t2t_mou_diff</th>\n",
       "      <th>std_ic_t2m_mou_diff</th>\n",
       "      <th>std_ic_t2f_mou_diff</th>\n",
       "      <th>std_ic_mou_diff</th>\n",
       "      <th>total_ic_mou_diff</th>\n",
       "      <th>spl_ic_mou_diff</th>\n",
       "      <th>isd_ic_mou_diff</th>\n",
       "      <th>ic_others_diff</th>\n",
       "      <th>total_rech_num_diff</th>\n",
       "      <th>max_rech_amt_diff</th>\n",
       "      <th>last_day_rch_amt_diff</th>\n",
       "      <th>max_rech_data_diff</th>\n",
       "      <th>vol_2g_mb_diff</th>\n",
       "      <th>vol_3g_mb_diff</th>\n",
       "      <th>arpu_3g_diff</th>\n",
       "      <th>arpu_2g_diff</th>\n",
       "      <th>night_pck_user_diff</th>\n",
       "      <th>monthly_2g_diff</th>\n",
       "      <th>sachet_2g_diff</th>\n",
       "      <th>monthly_3g_diff</th>\n",
       "      <th>sachet_3g_diff</th>\n",
       "      <th>fb_user_diff</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.00000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "      <td>30001.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>558.490824</td>\n",
       "      <td>560.782203</td>\n",
       "      <td>508.597957</td>\n",
       "      <td>260.793024</td>\n",
       "      <td>267.819295</td>\n",
       "      <td>234.112539</td>\n",
       "      <td>373.693961</td>\n",
       "      <td>378.103169</td>\n",
       "      <td>335.077044</td>\n",
       "      <td>16.110355</td>\n",
       "      <td>12.642504</td>\n",
       "      <td>12.500551</td>\n",
       "      <td>26.571547</td>\n",
       "      <td>20.152086</td>\n",
       "      <td>19.865615</td>\n",
       "      <td>84.484753</td>\n",
       "      <td>85.674287</td>\n",
       "      <td>78.077112</td>\n",
       "      <td>163.243297</td>\n",
       "      <td>163.009963</td>\n",
       "      <td>151.756812</td>\n",
       "      <td>6.344115</td>\n",
       "      <td>6.423683</td>\n",
       "      <td>5.886385</td>\n",
       "      <td>1.575104</td>\n",
       "      <td>1.889875</td>\n",
       "      <td>1.707746</td>\n",
       "      <td>254.080388</td>\n",
       "      <td>255.116292</td>\n",
       "      <td>235.728174</td>\n",
       "      <td>165.885296</td>\n",
       "      <td>174.233972</td>\n",
       "      <td>148.441175</td>\n",
       "      <td>179.433389</td>\n",
       "      <td>187.920768</td>\n",
       "      <td>157.350068</td>\n",
       "      <td>1.803548</td>\n",
       "      <td>1.826051</td>\n",
       "      <td>1.584734</td>\n",
       "      <td>347.125955</td>\n",
       "      <td>363.984508</td>\n",
       "      <td>307.379315</td>\n",
       "      <td>2.171525</td>\n",
       "      <td>2.105743</td>\n",
       "      <td>1.948970</td>\n",
       "      <td>5.818270</td>\n",
       "      <td>7.108646</td>\n",
       "      <td>6.557204</td>\n",
       "      <td>0.661092</td>\n",
       "      <td>0.042905</td>\n",
       "      <td>0.057607</td>\n",
       "      <td>609.866251</td>\n",
       "      <td>628.366047</td>\n",
       "      <td>551.678812</td>\n",
       "      <td>65.453285</td>\n",
       "      <td>66.636931</td>\n",
       "      <td>62.619432</td>\n",
       "      <td>150.412393</td>\n",
       "      <td>151.62521</td>\n",
       "      <td>146.232731</td>\n",
       "      <td>15.278467</td>\n",
       "      <td>16.035566</td>\n",
       "      <td>14.403175</td>\n",
       "      <td>231.153776</td>\n",
       "      <td>234.307412</td>\n",
       "      <td>223.264679</td>\n",
       "      <td>14.894779</td>\n",
       "      <td>15.421504</td>\n",
       "      <td>13.913705</td>\n",
       "      <td>29.195215</td>\n",
       "      <td>30.822517</td>\n",
       "      <td>27.922597</td>\n",
       "      <td>2.695855</td>\n",
       "      <td>2.798443</td>\n",
       "      <td>2.561970</td>\n",
       "      <td>46.789434</td>\n",
       "      <td>49.046114</td>\n",
       "      <td>44.401653</td>\n",
       "      <td>289.950496</td>\n",
       "      <td>296.756987</td>\n",
       "      <td>279.922931</td>\n",
       "      <td>0.061035</td>\n",
       "      <td>0.019765</td>\n",
       "      <td>0.026567</td>\n",
       "      <td>10.784689</td>\n",
       "      <td>11.875796</td>\n",
       "      <td>10.998257</td>\n",
       "      <td>1.153449</td>\n",
       "      <td>1.500338</td>\n",
       "      <td>1.224455</td>\n",
       "      <td>11.851005</td>\n",
       "      <td>11.718843</td>\n",
       "      <td>9.972868</td>\n",
       "      <td>169.284824</td>\n",
       "      <td>172.201727</td>\n",
       "      <td>160.144462</td>\n",
       "      <td>100.893604</td>\n",
       "      <td>100.356021</td>\n",
       "      <td>92.393654</td>\n",
       "      <td>78.079564</td>\n",
       "      <td>79.841805</td>\n",
       "      <td>72.492917</td>\n",
       "      <td>126.522185</td>\n",
       "      <td>125.097326</td>\n",
       "      <td>105.913072</td>\n",
       "      <td>344.258568</td>\n",
       "      <td>370.196701</td>\n",
       "      <td>351.493998</td>\n",
       "      <td>67.007633</td>\n",
       "      <td>68.621327</td>\n",
       "      <td>62.773480</td>\n",
       "      <td>63.439374</td>\n",
       "      <td>64.701428</td>\n",
       "      <td>58.395301</td>\n",
       "      <td>-0.427919</td>\n",
       "      <td>-0.418953</td>\n",
       "      <td>-0.456885</td>\n",
       "      <td>0.174494</td>\n",
       "      <td>0.183261</td>\n",
       "      <td>0.153628</td>\n",
       "      <td>1.066064</td>\n",
       "      <td>1.214026</td>\n",
       "      <td>1.054865</td>\n",
       "      <td>0.217759</td>\n",
       "      <td>0.229026</td>\n",
       "      <td>0.211026</td>\n",
       "      <td>0.209393</td>\n",
       "      <td>0.227492</td>\n",
       "      <td>0.202393</td>\n",
       "      <td>0.075997</td>\n",
       "      <td>0.092597</td>\n",
       "      <td>0.008833</td>\n",
       "      <td>169.138860</td>\n",
       "      <td>178.872765</td>\n",
       "      <td>158.523962</td>\n",
       "      <td>1152.620628</td>\n",
       "      <td>1047.618398</td>\n",
       "      <td>40.312292</td>\n",
       "      <td>30.617479</td>\n",
       "      <td>29.685777</td>\n",
       "      <td>-51.038557</td>\n",
       "      <td>-30.193620</td>\n",
       "      <td>-40.821520</td>\n",
       "      <td>-1.875878</td>\n",
       "      <td>-3.496201</td>\n",
       "      <td>-7.002408</td>\n",
       "      <td>-11.369818</td>\n",
       "      <td>-0.497514</td>\n",
       "      <td>-0.024744</td>\n",
       "      <td>-18.870166</td>\n",
       "      <td>-21.618459</td>\n",
       "      <td>-26.327010</td>\n",
       "      <td>-0.230066</td>\n",
       "      <td>-48.175917</td>\n",
       "      <td>-0.189665</td>\n",
       "      <td>0.093746</td>\n",
       "      <td>-0.294391</td>\n",
       "      <td>-67.437337</td>\n",
       "      <td>-3.425675</td>\n",
       "      <td>-4.786070</td>\n",
       "      <td>-1.253842</td>\n",
       "      <td>-9.465915</td>\n",
       "      <td>-1.244436</td>\n",
       "      <td>-2.086269</td>\n",
       "      <td>-0.185179</td>\n",
       "      <td>-3.516120</td>\n",
       "      <td>-13.430810</td>\n",
       "      <td>-0.013833</td>\n",
       "      <td>-0.331985</td>\n",
       "      <td>-0.102438</td>\n",
       "      <td>-1.812056</td>\n",
       "      <td>-10.598813</td>\n",
       "      <td>-8.231159</td>\n",
       "      <td>-6.467768</td>\n",
       "      <td>-19.896683</td>\n",
       "      <td>-5.733637</td>\n",
       "      <td>-5.041000</td>\n",
       "      <td>-5.675100</td>\n",
       "      <td>-0.033449</td>\n",
       "      <td>-0.025249</td>\n",
       "      <td>-0.085180</td>\n",
       "      <td>-0.012366</td>\n",
       "      <td>-0.016049</td>\n",
       "      <td>-0.075464</td>\n",
       "      <td>0.081364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>460.640461</td>\n",
       "      <td>479.776947</td>\n",
       "      <td>501.961981</td>\n",
       "      <td>459.644368</td>\n",
       "      <td>479.993989</td>\n",
       "      <td>458.448598</td>\n",
       "      <td>482.523558</td>\n",
       "      <td>498.923555</td>\n",
       "      <td>482.062509</td>\n",
       "      <td>76.302156</td>\n",
       "      <td>75.785903</td>\n",
       "      <td>74.125281</td>\n",
       "      <td>116.205525</td>\n",
       "      <td>96.100428</td>\n",
       "      <td>104.719009</td>\n",
       "      <td>228.794004</td>\n",
       "      <td>240.525999</td>\n",
       "      <td>227.373609</td>\n",
       "      <td>250.116700</td>\n",
       "      <td>241.576705</td>\n",
       "      <td>233.660905</td>\n",
       "      <td>22.123561</td>\n",
       "      <td>21.944183</td>\n",
       "      <td>19.502281</td>\n",
       "      <td>6.303385</td>\n",
       "      <td>9.158984</td>\n",
       "      <td>7.208138</td>\n",
       "      <td>378.339061</td>\n",
       "      <td>375.418979</td>\n",
       "      <td>363.370172</td>\n",
       "      <td>403.831965</td>\n",
       "      <td>421.190177</td>\n",
       "      <td>397.564547</td>\n",
       "      <td>409.025814</td>\n",
       "      <td>433.632143</td>\n",
       "      <td>409.100284</td>\n",
       "      <td>11.979429</td>\n",
       "      <td>12.980269</td>\n",
       "      <td>10.931804</td>\n",
       "      <td>608.629048</td>\n",
       "      <td>639.799336</td>\n",
       "      <td>608.976504</td>\n",
       "      <td>45.852190</td>\n",
       "      <td>45.545227</td>\n",
       "      <td>44.712024</td>\n",
       "      <td>17.567237</td>\n",
       "      <td>21.788304</td>\n",
       "      <td>20.311857</td>\n",
       "      <td>4.107960</td>\n",
       "      <td>2.676794</td>\n",
       "      <td>3.315125</td>\n",
       "      <td>690.873430</td>\n",
       "      <td>717.219171</td>\n",
       "      <td>700.478180</td>\n",
       "      <td>163.738880</td>\n",
       "      <td>167.403827</td>\n",
       "      <td>158.912293</td>\n",
       "      <td>219.198969</td>\n",
       "      <td>218.24326</td>\n",
       "      <td>214.882624</td>\n",
       "      <td>45.751937</td>\n",
       "      <td>49.165972</td>\n",
       "      <td>43.305191</td>\n",
       "      <td>314.821196</td>\n",
       "      <td>315.995071</td>\n",
       "      <td>308.315153</td>\n",
       "      <td>77.695681</td>\n",
       "      <td>83.042730</td>\n",
       "      <td>71.338522</td>\n",
       "      <td>98.210477</td>\n",
       "      <td>105.664499</td>\n",
       "      <td>103.341828</td>\n",
       "      <td>19.099927</td>\n",
       "      <td>19.514504</td>\n",
       "      <td>19.896938</td>\n",
       "      <td>137.501469</td>\n",
       "      <td>148.161116</td>\n",
       "      <td>138.813305</td>\n",
       "      <td>365.523475</td>\n",
       "      <td>373.747232</td>\n",
       "      <td>361.175175</td>\n",
       "      <td>0.188058</td>\n",
       "      <td>0.181859</td>\n",
       "      <td>0.110697</td>\n",
       "      <td>76.729297</td>\n",
       "      <td>80.179312</td>\n",
       "      <td>70.600636</td>\n",
       "      <td>13.985934</td>\n",
       "      <td>15.997307</td>\n",
       "      <td>12.783238</td>\n",
       "      <td>9.429132</td>\n",
       "      <td>9.432418</td>\n",
       "      <td>9.274370</td>\n",
       "      <td>175.315958</td>\n",
       "      <td>181.390742</td>\n",
       "      <td>173.207799</td>\n",
       "      <td>143.737572</td>\n",
       "      <td>140.925669</td>\n",
       "      <td>145.182324</td>\n",
       "      <td>112.731468</td>\n",
       "      <td>114.273245</td>\n",
       "      <td>110.567519</td>\n",
       "      <td>330.530793</td>\n",
       "      <td>331.548010</td>\n",
       "      <td>313.384838</td>\n",
       "      <td>913.747778</td>\n",
       "      <td>915.525911</td>\n",
       "      <td>919.021056</td>\n",
       "      <td>179.161038</td>\n",
       "      <td>183.133851</td>\n",
       "      <td>170.173104</td>\n",
       "      <td>161.008235</td>\n",
       "      <td>165.284232</td>\n",
       "      <td>152.625125</td>\n",
       "      <td>0.521484</td>\n",
       "      <td>0.518111</td>\n",
       "      <td>0.520464</td>\n",
       "      <td>0.434953</td>\n",
       "      <td>0.450423</td>\n",
       "      <td>0.406486</td>\n",
       "      <td>2.507197</td>\n",
       "      <td>2.736158</td>\n",
       "      <td>2.535940</td>\n",
       "      <td>0.613199</td>\n",
       "      <td>0.659125</td>\n",
       "      <td>0.617549</td>\n",
       "      <td>0.985674</td>\n",
       "      <td>1.089929</td>\n",
       "      <td>1.100107</td>\n",
       "      <td>0.976332</td>\n",
       "      <td>0.973118</td>\n",
       "      <td>0.972260</td>\n",
       "      <td>420.974354</td>\n",
       "      <td>443.544138</td>\n",
       "      <td>416.734860</td>\n",
       "      <td>1358.713982</td>\n",
       "      <td>1804.608844</td>\n",
       "      <td>31.922600</td>\n",
       "      <td>6.043344</td>\n",
       "      <td>7.200803</td>\n",
       "      <td>334.872321</td>\n",
       "      <td>308.732579</td>\n",
       "      <td>349.434892</td>\n",
       "      <td>65.972062</td>\n",
       "      <td>94.165541</td>\n",
       "      <td>138.240028</td>\n",
       "      <td>148.307664</td>\n",
       "      <td>14.046863</td>\n",
       "      <td>6.974352</td>\n",
       "      <td>213.766985</td>\n",
       "      <td>273.056056</td>\n",
       "      <td>303.760044</td>\n",
       "      <td>7.536745</td>\n",
       "      <td>427.952693</td>\n",
       "      <td>14.001271</td>\n",
       "      <td>18.088160</td>\n",
       "      <td>3.318381</td>\n",
       "      <td>502.630069</td>\n",
       "      <td>102.050822</td>\n",
       "      <td>134.931249</td>\n",
       "      <td>29.499749</td>\n",
       "      <td>180.801296</td>\n",
       "      <td>58.644175</td>\n",
       "      <td>74.416033</td>\n",
       "      <td>13.707181</td>\n",
       "      <td>98.256037</td>\n",
       "      <td>221.899902</td>\n",
       "      <td>0.174520</td>\n",
       "      <td>56.736330</td>\n",
       "      <td>9.582714</td>\n",
       "      <td>6.755899</td>\n",
       "      <td>136.893018</td>\n",
       "      <td>145.251806</td>\n",
       "      <td>78.343307</td>\n",
       "      <td>256.114926</td>\n",
       "      <td>624.877555</td>\n",
       "      <td>130.961477</td>\n",
       "      <td>116.362092</td>\n",
       "      <td>0.421236</td>\n",
       "      <td>0.353771</td>\n",
       "      <td>1.867673</td>\n",
       "      <td>0.474128</td>\n",
       "      <td>0.871149</td>\n",
       "      <td>0.732055</td>\n",
       "      <td>0.273398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-2258.709000</td>\n",
       "      <td>-2014.045000</td>\n",
       "      <td>-945.808000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>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>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>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>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>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>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>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>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>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>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>0.000000</td>\n",
       "      <td>0.00000</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>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>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>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>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>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>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>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>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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-30.280000</td>\n",
       "      <td>-26.040000</td>\n",
       "      <td>-24.490000</td>\n",
       "      <td>-15.320000</td>\n",
       "      <td>-15.480000</td>\n",
       "      <td>-24.430000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</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>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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>478.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-5811.052500</td>\n",
       "      <td>-5295.315000</td>\n",
       "      <td>-7094.390000</td>\n",
       "      <td>-1296.985000</td>\n",
       "      <td>-2410.835000</td>\n",
       "      <td>-3875.375000</td>\n",
       "      <td>-2863.535000</td>\n",
       "      <td>-1319.850000</td>\n",
       "      <td>-384.385000</td>\n",
       "      <td>-3867.830000</td>\n",
       "      <td>-5391.210000</td>\n",
       "      <td>-7037.535000</td>\n",
       "      <td>-314.280000</td>\n",
       "      <td>-7127.425000</td>\n",
       "      <td>-619.650000</td>\n",
       "      <td>-1081.910000</td>\n",
       "      <td>-304.905000</td>\n",
       "      <td>-7213.410000</td>\n",
       "      <td>-3021.870000</td>\n",
       "      <td>-2387.110000</td>\n",
       "      <td>-928.210000</td>\n",
       "      <td>-3841.055000</td>\n",
       "      <td>-2897.680000</td>\n",
       "      <td>-2624.905000</td>\n",
       "      <td>-636.500000</td>\n",
       "      <td>-2777.350000</td>\n",
       "      <td>-5710.150000</td>\n",
       "      <td>-16.280000</td>\n",
       "      <td>-5534.595000</td>\n",
       "      <td>-408.240000</td>\n",
       "      <td>-147.500000</td>\n",
       "      <td>-3010.000000</td>\n",
       "      <td>-3065.000000</td>\n",
       "      <td>-1303.000000</td>\n",
       "      <td>-4740.925000</td>\n",
       "      <td>-12385.505000</td>\n",
       "      <td>-2944.290000</td>\n",
       "      <td>-2980.040000</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>-3.000000</td>\n",
       "      <td>-26.000000</td>\n",
       "      <td>-7.500000</td>\n",
       "      <td>-21.500000</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>309.865000</td>\n",
       "      <td>309.826000</td>\n",
       "      <td>231.473000</td>\n",
       "      <td>17.080000</td>\n",
       "      <td>16.030000</td>\n",
       "      <td>10.390000</td>\n",
       "      <td>71.610000</td>\n",
       "      <td>69.910000</td>\n",
       "      <td>46.740000</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>0.000000</td>\n",
       "      <td>4.380000</td>\n",
       "      <td>4.610000</td>\n",
       "      <td>2.530000</td>\n",
       "      <td>19.210000</td>\n",
       "      <td>20.460000</td>\n",
       "      <td>13.090000</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>0.000000</td>\n",
       "      <td>32.260000</td>\n",
       "      <td>34.480000</td>\n",
       "      <td>22.460000</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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.380000</td>\n",
       "      <td>0.150000</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>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>116.580000</td>\n",
       "      <td>116.490000</td>\n",
       "      <td>72.360000</td>\n",
       "      <td>6.090000</td>\n",
       "      <td>6.780000</td>\n",
       "      <td>4.880000</td>\n",
       "      <td>28.080000</td>\n",
       "      <td>30.78000</td>\n",
       "      <td>24.210000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>47.880000</td>\n",
       "      <td>52.510000</td>\n",
       "      <td>40.530000</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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.030000</td>\n",
       "      <td>1.050000</td>\n",
       "      <td>0.410000</td>\n",
       "      <td>72.880000</td>\n",
       "      <td>78.990000</td>\n",
       "      <td>61.360000</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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>20.000000</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>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>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>0.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</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>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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>603.000000</td>\n",
       "      <td>396.000000</td>\n",
       "      <td>15.333333</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>-180.179500</td>\n",
       "      <td>-52.345000</td>\n",
       "      <td>-100.900000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-16.615000</td>\n",
       "      <td>-38.820000</td>\n",
       "      <td>-1.210000</td>\n",
       "      <td>-0.365000</td>\n",
       "      <td>-58.880000</td>\n",
       "      <td>-12.160000</td>\n",
       "      <td>-25.980000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-54.595000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-2.750000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-168.025000</td>\n",
       "      <td>-15.170000</td>\n",
       "      <td>-34.405000</td>\n",
       "      <td>-2.995000</td>\n",
       "      <td>-49.805000</td>\n",
       "      <td>-2.830000</td>\n",
       "      <td>-7.410000</td>\n",
       "      <td>-0.115000</td>\n",
       "      <td>-11.905000</td>\n",
       "      <td>-67.230000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.025000</td>\n",
       "      <td>-4.000000</td>\n",
       "      <td>-33.000000</td>\n",
       "      <td>-52.000000</td>\n",
       "      <td>-7.000000</td>\n",
       "      <td>-21.120000</td>\n",
       "      <td>-2.270000</td>\n",
       "      <td>-4.080000</td>\n",
       "      <td>-10.545000</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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>481.694000</td>\n",
       "      <td>480.943000</td>\n",
       "      <td>427.585000</td>\n",
       "      <td>84.580000</td>\n",
       "      <td>82.810000</td>\n",
       "      <td>65.610000</td>\n",
       "      <td>222.540000</td>\n",
       "      <td>220.030000</td>\n",
       "      <td>182.790000</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>0.000000</td>\n",
       "      <td>24.330000</td>\n",
       "      <td>24.680000</td>\n",
       "      <td>20.730000</td>\n",
       "      <td>77.180000</td>\n",
       "      <td>78.880000</td>\n",
       "      <td>67.760000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>124.540000</td>\n",
       "      <td>127.880000</td>\n",
       "      <td>110.810000</td>\n",
       "      <td>2.710000</td>\n",
       "      <td>2.680000</td>\n",
       "      <td>0.630000</td>\n",
       "      <td>16.360000</td>\n",
       "      <td>16.040000</td>\n",
       "      <td>9.460000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>45.130000</td>\n",
       "      <td>43.980000</td>\n",
       "      <td>25.480000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.210000</td>\n",
       "      <td>0.710000</td>\n",
       "      <td>0.430000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>410.630000</td>\n",
       "      <td>425.590000</td>\n",
       "      <td>333.540000</td>\n",
       "      <td>25.080000</td>\n",
       "      <td>25.780000</td>\n",
       "      <td>22.610000</td>\n",
       "      <td>84.060000</td>\n",
       "      <td>85.66000</td>\n",
       "      <td>80.040000</td>\n",
       "      <td>1.840000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.680000</td>\n",
       "      <td>134.490000</td>\n",
       "      <td>137.590000</td>\n",
       "      <td>128.730000</td>\n",
       "      <td>0.380000</td>\n",
       "      <td>0.450000</td>\n",
       "      <td>0.110000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.180000</td>\n",
       "      <td>3.710000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.410000</td>\n",
       "      <td>12.090000</td>\n",
       "      <td>9.290000</td>\n",
       "      <td>183.730000</td>\n",
       "      <td>187.580000</td>\n",
       "      <td>173.110000</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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.050000</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>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>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>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>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>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>800.000000</td>\n",
       "      <td>688.000000</td>\n",
       "      <td>28.200000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>-40.754500</td>\n",
       "      <td>-3.210000</td>\n",
       "      <td>-11.025000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.030000</td>\n",
       "      <td>-3.090000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-4.955000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.030000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.405000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-14.625000</td>\n",
       "      <td>-1.045000</td>\n",
       "      <td>-2.160000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-3.940000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.215000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.640000</td>\n",
       "      <td>-5.610000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.500000</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>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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>699.943000</td>\n",
       "      <td>698.315000</td>\n",
       "      <td>661.491000</td>\n",
       "      <td>290.440000</td>\n",
       "      <td>290.240000</td>\n",
       "      <td>239.960000</td>\n",
       "      <td>487.940000</td>\n",
       "      <td>494.010000</td>\n",
       "      <td>438.890000</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>0.000000</td>\n",
       "      <td>77.980000</td>\n",
       "      <td>78.340000</td>\n",
       "      <td>72.040000</td>\n",
       "      <td>208.930000</td>\n",
       "      <td>208.410000</td>\n",
       "      <td>196.960000</td>\n",
       "      <td>4.230000</td>\n",
       "      <td>4.380000</td>\n",
       "      <td>3.930000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>332.090000</td>\n",
       "      <td>333.390000</td>\n",
       "      <td>310.610000</td>\n",
       "      <td>109.610000</td>\n",
       "      <td>116.610000</td>\n",
       "      <td>78.190000</td>\n",
       "      <td>154.540000</td>\n",
       "      <td>160.280000</td>\n",
       "      <td>117.780000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>465.390000</td>\n",
       "      <td>499.290000</td>\n",
       "      <td>357.230000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.210000</td>\n",
       "      <td>6.940000</td>\n",
       "      <td>6.150000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>858.760000</td>\n",
       "      <td>891.440000</td>\n",
       "      <td>774.780000</td>\n",
       "      <td>67.430000</td>\n",
       "      <td>68.640000</td>\n",
       "      <td>65.080000</td>\n",
       "      <td>188.710000</td>\n",
       "      <td>190.66000</td>\n",
       "      <td>185.730000</td>\n",
       "      <td>11.980000</td>\n",
       "      <td>12.240000</td>\n",
       "      <td>11.160000</td>\n",
       "      <td>297.140000</td>\n",
       "      <td>298.710000</td>\n",
       "      <td>285.440000</td>\n",
       "      <td>8.200000</td>\n",
       "      <td>8.590000</td>\n",
       "      <td>7.040000</td>\n",
       "      <td>23.600000</td>\n",
       "      <td>24.940000</td>\n",
       "      <td>21.810000</td>\n",
       "      <td>0.110000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>41.490000</td>\n",
       "      <td>43.810000</td>\n",
       "      <td>38.380000</td>\n",
       "      <td>371.990000</td>\n",
       "      <td>377.310000</td>\n",
       "      <td>361.730000</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>0.000000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>198.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>154.000000</td>\n",
       "      <td>154.000000</td>\n",
       "      <td>154.000000</td>\n",
       "      <td>83.450000</td>\n",
       "      <td>77.770000</td>\n",
       "      <td>51.650000</td>\n",
       "      <td>358.740000</td>\n",
       "      <td>410.700000</td>\n",
       "      <td>356.190000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>35.690000</td>\n",
       "      <td>20.920000</td>\n",
       "      <td>50.670000</td>\n",
       "      <td>47.220000</td>\n",
       "      <td>26.900000</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>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</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>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>128.800000</td>\n",
       "      <td>137.290000</td>\n",
       "      <td>98.440000</td>\n",
       "      <td>1208.000000</td>\n",
       "      <td>1152.000000</td>\n",
       "      <td>58.533333</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>83.239000</td>\n",
       "      <td>18.410000</td>\n",
       "      <td>41.040000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.245000</td>\n",
       "      <td>21.405000</td>\n",
       "      <td>0.230000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>30.125000</td>\n",
       "      <td>0.495000</td>\n",
       "      <td>4.160000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.105000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.310000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>67.915000</td>\n",
       "      <td>9.205000</td>\n",
       "      <td>25.180000</td>\n",
       "      <td>1.325000</td>\n",
       "      <td>34.405000</td>\n",
       "      <td>0.330000</td>\n",
       "      <td>2.790000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.995000</td>\n",
       "      <td>44.280000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>25.000000</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>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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>27731.088000</td>\n",
       "      <td>35145.834000</td>\n",
       "      <td>33543.624000</td>\n",
       "      <td>7376.710000</td>\n",
       "      <td>8157.780000</td>\n",
       "      <td>10752.560000</td>\n",
       "      <td>8362.360000</td>\n",
       "      <td>9667.130000</td>\n",
       "      <td>14007.340000</td>\n",
       "      <td>2613.310000</td>\n",
       "      <td>3813.290000</td>\n",
       "      <td>4169.810000</td>\n",
       "      <td>3775.110000</td>\n",
       "      <td>2812.040000</td>\n",
       "      <td>5337.040000</td>\n",
       "      <td>6431.330000</td>\n",
       "      <td>7400.660000</td>\n",
       "      <td>10752.560000</td>\n",
       "      <td>4729.740000</td>\n",
       "      <td>4557.140000</td>\n",
       "      <td>4961.330000</td>\n",
       "      <td>1466.030000</td>\n",
       "      <td>1196.430000</td>\n",
       "      <td>928.490000</td>\n",
       "      <td>271.440000</td>\n",
       "      <td>569.710000</td>\n",
       "      <td>351.830000</td>\n",
       "      <td>10643.380000</td>\n",
       "      <td>7674.780000</td>\n",
       "      <td>11039.910000</td>\n",
       "      <td>7366.580000</td>\n",
       "      <td>8133.660000</td>\n",
       "      <td>8014.430000</td>\n",
       "      <td>8314.760000</td>\n",
       "      <td>9284.740000</td>\n",
       "      <td>13950.040000</td>\n",
       "      <td>628.560000</td>\n",
       "      <td>544.630000</td>\n",
       "      <td>516.910000</td>\n",
       "      <td>8432.990000</td>\n",
       "      <td>10936.730000</td>\n",
       "      <td>13980.060000</td>\n",
       "      <td>5900.660000</td>\n",
       "      <td>5490.280000</td>\n",
       "      <td>5681.540000</td>\n",
       "      <td>1023.210000</td>\n",
       "      <td>1265.790000</td>\n",
       "      <td>954.510000</td>\n",
       "      <td>609.810000</td>\n",
       "      <td>370.130000</td>\n",
       "      <td>394.930000</td>\n",
       "      <td>10674.030000</td>\n",
       "      <td>11365.310000</td>\n",
       "      <td>14043.060000</td>\n",
       "      <td>6351.440000</td>\n",
       "      <td>5709.590000</td>\n",
       "      <td>4003.210000</td>\n",
       "      <td>4693.860000</td>\n",
       "      <td>4171.51000</td>\n",
       "      <td>4643.490000</td>\n",
       "      <td>1678.410000</td>\n",
       "      <td>1983.010000</td>\n",
       "      <td>1588.530000</td>\n",
       "      <td>7454.630000</td>\n",
       "      <td>6466.740000</td>\n",
       "      <td>5388.740000</td>\n",
       "      <td>5459.560000</td>\n",
       "      <td>5800.930000</td>\n",
       "      <td>4309.290000</td>\n",
       "      <td>4630.230000</td>\n",
       "      <td>3470.380000</td>\n",
       "      <td>5645.860000</td>\n",
       "      <td>1351.110000</td>\n",
       "      <td>1136.080000</td>\n",
       "      <td>1394.890000</td>\n",
       "      <td>5459.630000</td>\n",
       "      <td>6745.760000</td>\n",
       "      <td>5957.140000</td>\n",
       "      <td>7716.140000</td>\n",
       "      <td>7442.860000</td>\n",
       "      <td>6066.630000</td>\n",
       "      <td>19.760000</td>\n",
       "      <td>21.330000</td>\n",
       "      <td>6.230000</td>\n",
       "      <td>6789.410000</td>\n",
       "      <td>4747.910000</td>\n",
       "      <td>3432.880000</td>\n",
       "      <td>1344.140000</td>\n",
       "      <td>1495.940000</td>\n",
       "      <td>1209.860000</td>\n",
       "      <td>307.000000</td>\n",
       "      <td>138.000000</td>\n",
       "      <td>196.000000</td>\n",
       "      <td>4010.000000</td>\n",
       "      <td>4010.000000</td>\n",
       "      <td>4449.000000</td>\n",
       "      <td>4010.000000</td>\n",
       "      <td>4010.000000</td>\n",
       "      <td>4449.000000</td>\n",
       "      <td>1555.000000</td>\n",
       "      <td>1555.000000</td>\n",
       "      <td>1555.000000</td>\n",
       "      <td>10285.900000</td>\n",
       "      <td>7873.550000</td>\n",
       "      <td>11117.610000</td>\n",
       "      <td>45735.400000</td>\n",
       "      <td>28144.120000</td>\n",
       "      <td>30036.060000</td>\n",
       "      <td>6362.280000</td>\n",
       "      <td>4980.900000</td>\n",
       "      <td>3716.900000</td>\n",
       "      <td>6433.760000</td>\n",
       "      <td>4809.360000</td>\n",
       "      <td>3467.170000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12916.220000</td>\n",
       "      <td>9165.600000</td>\n",
       "      <td>11166.210000</td>\n",
       "      <td>61236.000000</td>\n",
       "      <td>95884.500000</td>\n",
       "      <td>144.033333</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>12808.617000</td>\n",
       "      <td>6359.920000</td>\n",
       "      <td>12604.240000</td>\n",
       "      <td>2330.200000</td>\n",
       "      <td>3057.150000</td>\n",
       "      <td>6412.690000</td>\n",
       "      <td>3158.700000</td>\n",
       "      <td>452.720000</td>\n",
       "      <td>193.355000</td>\n",
       "      <td>4619.975000</td>\n",
       "      <td>4806.465000</td>\n",
       "      <td>12685.460000</td>\n",
       "      <td>209.935000</td>\n",
       "      <td>12715.480000</td>\n",
       "      <td>544.780000</td>\n",
       "      <td>747.620000</td>\n",
       "      <td>366.590000</td>\n",
       "      <td>12768.705000</td>\n",
       "      <td>3743.340000</td>\n",
       "      <td>3159.400000</td>\n",
       "      <td>1039.310000</td>\n",
       "      <td>3485.370000</td>\n",
       "      <td>3313.875000</td>\n",
       "      <td>2978.665000</td>\n",
       "      <td>1023.320000</td>\n",
       "      <td>3321.395000</td>\n",
       "      <td>3485.370000</td>\n",
       "      <td>4.450000</td>\n",
       "      <td>3163.690000</td>\n",
       "      <td>459.280000</td>\n",
       "      <td>130.500000</td>\n",
       "      <td>3949.000000</td>\n",
       "      <td>3949.000000</td>\n",
       "      <td>1352.000000</td>\n",
       "      <td>8062.295000</td>\n",
       "      <td>15646.390000</td>\n",
       "      <td>3716.900000</td>\n",
       "      <td>3027.375000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>26.500000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             arpu_6        arpu_7        arpu_8   onnet_mou_6   onnet_mou_7  \\\n",
       "count  30001.000000  30001.000000  30001.000000  30001.000000  30001.000000   \n",
       "mean     558.490824    560.782203    508.597957    260.793024    267.819295   \n",
       "std      460.640461    479.776947    501.961981    459.644368    479.993989   \n",
       "min    -2258.709000  -2014.045000   -945.808000      0.000000      0.000000   \n",
       "25%      309.865000    309.826000    231.473000     17.080000     16.030000   \n",
       "50%      481.694000    480.943000    427.585000     84.580000     82.810000   \n",
       "75%      699.943000    698.315000    661.491000    290.440000    290.240000   \n",
       "max    27731.088000  35145.834000  33543.624000   7376.710000   8157.780000   \n",
       "\n",
       "        onnet_mou_8  offnet_mou_6  offnet_mou_7  offnet_mou_8  roam_ic_mou_6  \\\n",
       "count  30001.000000  30001.000000  30001.000000  30001.000000   30001.000000   \n",
       "mean     234.112539    373.693961    378.103169    335.077044      16.110355   \n",
       "std      458.448598    482.523558    498.923555    482.062509      76.302156   \n",
       "min        0.000000      0.000000      0.000000      0.000000       0.000000   \n",
       "25%       10.390000     71.610000     69.910000     46.740000       0.000000   \n",
       "50%       65.610000    222.540000    220.030000    182.790000       0.000000   \n",
       "75%      239.960000    487.940000    494.010000    438.890000       0.000000   \n",
       "max    10752.560000   8362.360000   9667.130000  14007.340000    2613.310000   \n",
       "\n",
       "       roam_ic_mou_7  roam_ic_mou_8  roam_og_mou_6  roam_og_mou_7  \\\n",
       "count   30001.000000   30001.000000   30001.000000   30001.000000   \n",
       "mean       12.642504      12.500551      26.571547      20.152086   \n",
       "std        75.785903      74.125281     116.205525      96.100428   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.000000       0.000000       0.000000       0.000000   \n",
       "50%         0.000000       0.000000       0.000000       0.000000   \n",
       "75%         0.000000       0.000000       0.000000       0.000000   \n",
       "max      3813.290000    4169.810000    3775.110000    2812.040000   \n",
       "\n",
       "       roam_og_mou_8  loc_og_t2t_mou_6  loc_og_t2t_mou_7  loc_og_t2t_mou_8  \\\n",
       "count   30001.000000      30001.000000      30001.000000      30001.000000   \n",
       "mean       19.865615         84.484753         85.674287         78.077112   \n",
       "std       104.719009        228.794004        240.525999        227.373609   \n",
       "min         0.000000          0.000000          0.000000          0.000000   \n",
       "25%         0.000000          4.380000          4.610000          2.530000   \n",
       "50%         0.000000         24.330000         24.680000         20.730000   \n",
       "75%         0.000000         77.980000         78.340000         72.040000   \n",
       "max      5337.040000       6431.330000       7400.660000      10752.560000   \n",
       "\n",
       "       loc_og_t2m_mou_6  loc_og_t2m_mou_7  loc_og_t2m_mou_8  loc_og_t2f_mou_6  \\\n",
       "count      30001.000000      30001.000000      30001.000000      30001.000000   \n",
       "mean         163.243297        163.009963        151.756812          6.344115   \n",
       "std          250.116700        241.576705        233.660905         22.123561   \n",
       "min            0.000000          0.000000          0.000000          0.000000   \n",
       "25%           19.210000         20.460000         13.090000          0.000000   \n",
       "50%           77.180000         78.880000         67.760000          0.000000   \n",
       "75%          208.930000        208.410000        196.960000          4.230000   \n",
       "max         4729.740000       4557.140000       4961.330000       1466.030000   \n",
       "\n",
       "       loc_og_t2f_mou_7  loc_og_t2f_mou_8  loc_og_t2c_mou_6  loc_og_t2c_mou_7  \\\n",
       "count      30001.000000      30001.000000      30001.000000      30001.000000   \n",
       "mean           6.423683          5.886385          1.575104          1.889875   \n",
       "std           21.944183         19.502281          6.303385          9.158984   \n",
       "min            0.000000          0.000000          0.000000          0.000000   \n",
       "25%            0.000000          0.000000          0.000000          0.000000   \n",
       "50%            0.100000          0.000000          0.000000          0.000000   \n",
       "75%            4.380000          3.930000          0.000000          0.100000   \n",
       "max         1196.430000        928.490000        271.440000        569.710000   \n",
       "\n",
       "       loc_og_t2c_mou_8  loc_og_mou_6  loc_og_mou_7  loc_og_mou_8  \\\n",
       "count      30001.000000  30001.000000  30001.000000  30001.000000   \n",
       "mean           1.707746    254.080388    255.116292    235.728174   \n",
       "std            7.208138    378.339061    375.418979    363.370172   \n",
       "min            0.000000      0.000000      0.000000      0.000000   \n",
       "25%            0.000000     32.260000     34.480000     22.460000   \n",
       "50%            0.000000    124.540000    127.880000    110.810000   \n",
       "75%            0.010000    332.090000    333.390000    310.610000   \n",
       "max          351.830000  10643.380000   7674.780000  11039.910000   \n",
       "\n",
       "       std_og_t2t_mou_6  std_og_t2t_mou_7  std_og_t2t_mou_8  std_og_t2m_mou_6  \\\n",
       "count      30001.000000      30001.000000      30001.000000      30001.000000   \n",
       "mean         165.885296        174.233972        148.441175        179.433389   \n",
       "std          403.831965        421.190177        397.564547        409.025814   \n",
       "min            0.000000          0.000000          0.000000          0.000000   \n",
       "25%            0.000000          0.000000          0.000000          0.000000   \n",
       "50%            2.710000          2.680000          0.630000         16.360000   \n",
       "75%          109.610000        116.610000         78.190000        154.540000   \n",
       "max         7366.580000       8133.660000       8014.430000       8314.760000   \n",
       "\n",
       "       std_og_t2m_mou_7  std_og_t2m_mou_8  std_og_t2f_mou_6  std_og_t2f_mou_7  \\\n",
       "count      30001.000000      30001.000000      30001.000000      30001.000000   \n",
       "mean         187.920768        157.350068          1.803548          1.826051   \n",
       "std          433.632143        409.100284         11.979429         12.980269   \n",
       "min            0.000000          0.000000          0.000000          0.000000   \n",
       "25%            0.000000          0.000000          0.000000          0.000000   \n",
       "50%           16.040000          9.460000          0.000000          0.000000   \n",
       "75%          160.280000        117.780000          0.000000          0.000000   \n",
       "max         9284.740000      13950.040000        628.560000        544.630000   \n",
       "\n",
       "       std_og_t2f_mou_8  std_og_mou_6  std_og_mou_7  std_og_mou_8  \\\n",
       "count      30001.000000  30001.000000  30001.000000  30001.000000   \n",
       "mean           1.584734    347.125955    363.984508    307.379315   \n",
       "std           10.931804    608.629048    639.799336    608.976504   \n",
       "min            0.000000      0.000000      0.000000      0.000000   \n",
       "25%            0.000000      0.380000      0.150000      0.000000   \n",
       "50%            0.000000     45.130000     43.980000     25.480000   \n",
       "75%            0.000000    465.390000    499.290000    357.230000   \n",
       "max          516.910000   8432.990000  10936.730000  13980.060000   \n",
       "\n",
       "       isd_og_mou_6  isd_og_mou_7  isd_og_mou_8  spl_og_mou_6  spl_og_mou_7  \\\n",
       "count  30001.000000  30001.000000  30001.000000  30001.000000  30001.000000   \n",
       "mean       2.171525      2.105743      1.948970      5.818270      7.108646   \n",
       "std       45.852190     45.545227     44.712024     17.567237     21.788304   \n",
       "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "50%        0.000000      0.000000      0.000000      0.210000      0.710000   \n",
       "75%        0.000000      0.000000      0.000000      5.210000      6.940000   \n",
       "max     5900.660000   5490.280000   5681.540000   1023.210000   1265.790000   \n",
       "\n",
       "       spl_og_mou_8   og_others_6   og_others_7   og_others_8  total_og_mou_6  \\\n",
       "count  30001.000000  30001.000000  30001.000000  30001.000000    30001.000000   \n",
       "mean       6.557204      0.661092      0.042905      0.057607      609.866251   \n",
       "std       20.311857      4.107960      2.676794      3.315125      690.873430   \n",
       "min        0.000000      0.000000      0.000000      0.000000        0.000000   \n",
       "25%        0.000000      0.000000      0.000000      0.000000      116.580000   \n",
       "50%        0.430000      0.000000      0.000000      0.000000      410.630000   \n",
       "75%        6.150000      0.000000      0.000000      0.000000      858.760000   \n",
       "max      954.510000    609.810000    370.130000    394.930000    10674.030000   \n",
       "\n",
       "       total_og_mou_7  total_og_mou_8  loc_ic_t2t_mou_6  loc_ic_t2t_mou_7  \\\n",
       "count    30001.000000    30001.000000      30001.000000      30001.000000   \n",
       "mean       628.366047      551.678812         65.453285         66.636931   \n",
       "std        717.219171      700.478180        163.738880        167.403827   \n",
       "min          0.000000        0.000000          0.000000          0.000000   \n",
       "25%        116.490000       72.360000          6.090000          6.780000   \n",
       "50%        425.590000      333.540000         25.080000         25.780000   \n",
       "75%        891.440000      774.780000         67.430000         68.640000   \n",
       "max      11365.310000    14043.060000       6351.440000       5709.590000   \n",
       "\n",
       "       loc_ic_t2t_mou_8  loc_ic_t2m_mou_6  loc_ic_t2m_mou_7  loc_ic_t2m_mou_8  \\\n",
       "count      30001.000000      30001.000000       30001.00000      30001.000000   \n",
       "mean          62.619432        150.412393         151.62521        146.232731   \n",
       "std          158.912293        219.198969         218.24326        214.882624   \n",
       "min            0.000000          0.000000           0.00000          0.000000   \n",
       "25%            4.880000         28.080000          30.78000         24.210000   \n",
       "50%           22.610000         84.060000          85.66000         80.040000   \n",
       "75%           65.080000        188.710000         190.66000        185.730000   \n",
       "max         4003.210000       4693.860000        4171.51000       4643.490000   \n",
       "\n",
       "       loc_ic_t2f_mou_6  loc_ic_t2f_mou_7  loc_ic_t2f_mou_8  loc_ic_mou_6  \\\n",
       "count      30001.000000      30001.000000      30001.000000  30001.000000   \n",
       "mean          15.278467         16.035566         14.403175    231.153776   \n",
       "std           45.751937         49.165972         43.305191    314.821196   \n",
       "min            0.000000          0.000000          0.000000      0.000000   \n",
       "25%            0.000000          0.000000          0.000000     47.880000   \n",
       "50%            1.840000          2.000000          1.680000    134.490000   \n",
       "75%           11.980000         12.240000         11.160000    297.140000   \n",
       "max         1678.410000       1983.010000       1588.530000   7454.630000   \n",
       "\n",
       "       loc_ic_mou_7  loc_ic_mou_8  std_ic_t2t_mou_6  std_ic_t2t_mou_7  \\\n",
       "count  30001.000000  30001.000000      30001.000000      30001.000000   \n",
       "mean     234.307412    223.264679         14.894779         15.421504   \n",
       "std      315.995071    308.315153         77.695681         83.042730   \n",
       "min        0.000000      0.000000          0.000000          0.000000   \n",
       "25%       52.510000     40.530000          0.000000          0.000000   \n",
       "50%      137.590000    128.730000          0.380000          0.450000   \n",
       "75%      298.710000    285.440000          8.200000          8.590000   \n",
       "max     6466.740000   5388.740000       5459.560000       5800.930000   \n",
       "\n",
       "       std_ic_t2t_mou_8  std_ic_t2m_mou_6  std_ic_t2m_mou_7  std_ic_t2m_mou_8  \\\n",
       "count      30001.000000      30001.000000      30001.000000      30001.000000   \n",
       "mean          13.913705         29.195215         30.822517         27.922597   \n",
       "std           71.338522         98.210477        105.664499        103.341828   \n",
       "min            0.000000          0.000000          0.000000          0.000000   \n",
       "25%            0.000000          0.000000          0.000000          0.000000   \n",
       "50%            0.110000          5.000000          5.180000          3.710000   \n",
       "75%            7.040000         23.600000         24.940000         21.810000   \n",
       "max         4309.290000       4630.230000       3470.380000       5645.860000   \n",
       "\n",
       "       std_ic_t2f_mou_6  std_ic_t2f_mou_7  std_ic_t2f_mou_8  std_ic_mou_6  \\\n",
       "count      30001.000000      30001.000000      30001.000000  30001.000000   \n",
       "mean           2.695855          2.798443          2.561970     46.789434   \n",
       "std           19.099927         19.514504         19.896938    137.501469   \n",
       "min            0.000000          0.000000          0.000000      0.000000   \n",
       "25%            0.000000          0.000000          0.000000      1.030000   \n",
       "50%            0.000000          0.000000          0.000000     11.410000   \n",
       "75%            0.110000          0.200000          0.080000     41.490000   \n",
       "max         1351.110000       1136.080000       1394.890000   5459.630000   \n",
       "\n",
       "       std_ic_mou_7  std_ic_mou_8  total_ic_mou_6  total_ic_mou_7  \\\n",
       "count  30001.000000  30001.000000    30001.000000    30001.000000   \n",
       "mean      49.046114     44.401653      289.950496      296.756987   \n",
       "std      148.161116    138.813305      365.523475      373.747232   \n",
       "min        0.000000      0.000000        0.000000        0.000000   \n",
       "25%        1.050000      0.410000       72.880000       78.990000   \n",
       "50%       12.090000      9.290000      183.730000      187.580000   \n",
       "75%       43.810000     38.380000      371.990000      377.310000   \n",
       "max     6745.760000   5957.140000     7716.140000     7442.860000   \n",
       "\n",
       "       total_ic_mou_8  spl_ic_mou_6  spl_ic_mou_7  spl_ic_mou_8  isd_ic_mou_6  \\\n",
       "count    30001.000000  30001.000000  30001.000000  30001.000000  30001.000000   \n",
       "mean       279.922931      0.061035      0.019765      0.026567     10.784689   \n",
       "std        361.175175      0.188058      0.181859      0.110697     76.729297   \n",
       "min          0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%         61.360000      0.000000      0.000000      0.000000      0.000000   \n",
       "50%        173.110000      0.000000      0.000000      0.000000      0.000000   \n",
       "75%        361.730000      0.000000      0.000000      0.000000      0.000000   \n",
       "max       6066.630000     19.760000     21.330000      6.230000   6789.410000   \n",
       "\n",
       "       isd_ic_mou_7  isd_ic_mou_8   ic_others_6   ic_others_7   ic_others_8  \\\n",
       "count  30001.000000  30001.000000  30001.000000  30001.000000  30001.000000   \n",
       "mean      11.875796     10.998257      1.153449      1.500338      1.224455   \n",
       "std       80.179312     70.600636     13.985934     15.997307     12.783238   \n",
       "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "50%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "75%        0.000000      0.000000      0.050000      0.000000      0.000000   \n",
       "max     4747.910000   3432.880000   1344.140000   1495.940000   1209.860000   \n",
       "\n",
       "       total_rech_num_6  total_rech_num_7  total_rech_num_8  max_rech_amt_6  \\\n",
       "count      30001.000000      30001.000000      30001.000000    30001.000000   \n",
       "mean          11.851005         11.718843          9.972868      169.284824   \n",
       "std            9.429132          9.432418          9.274370      175.315958   \n",
       "min            0.000000          0.000000          0.000000        0.000000   \n",
       "25%            6.000000          6.000000          4.000000      110.000000   \n",
       "50%            9.000000          9.000000          8.000000      120.000000   \n",
       "75%           15.000000         15.000000         13.000000      200.000000   \n",
       "max          307.000000        138.000000        196.000000     4010.000000   \n",
       "\n",
       "       max_rech_amt_7  max_rech_amt_8  last_day_rch_amt_6  last_day_rch_amt_7  \\\n",
       "count    30001.000000    30001.000000        30001.000000        30001.000000   \n",
       "mean       172.201727      160.144462          100.893604          100.356021   \n",
       "std        181.390742      173.207799          143.737572          140.925669   \n",
       "min          0.000000        0.000000            0.000000            0.000000   \n",
       "25%        110.000000       50.000000           25.000000           20.000000   \n",
       "50%        128.000000      130.000000           67.000000           50.000000   \n",
       "75%        200.000000      198.000000          120.000000          130.000000   \n",
       "max       4010.000000     4449.000000         4010.000000         4010.000000   \n",
       "\n",
       "       last_day_rch_amt_8  max_rech_data_6  max_rech_data_7  max_rech_data_8  \\\n",
       "count        30001.000000     30001.000000     30001.000000     30001.000000   \n",
       "mean            92.393654        78.079564        79.841805        72.492917   \n",
       "std            145.182324       112.731468       114.273245       110.567519   \n",
       "min              0.000000         0.000000         0.000000         0.000000   \n",
       "25%              0.000000         0.000000         0.000000         0.000000   \n",
       "50%             50.000000        25.000000        25.000000        23.000000   \n",
       "75%            130.000000       154.000000       154.000000       154.000000   \n",
       "max           4449.000000      1555.000000      1555.000000      1555.000000   \n",
       "\n",
       "        vol_2g_mb_6   vol_2g_mb_7   vol_2g_mb_8   vol_3g_mb_6   vol_3g_mb_7  \\\n",
       "count  30001.000000  30001.000000  30001.000000  30001.000000  30001.000000   \n",
       "mean     126.522185    125.097326    105.913072    344.258568    370.196701   \n",
       "std      330.530793    331.548010    313.384838    913.747778    915.525911   \n",
       "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "50%        0.000000      0.050000      0.000000      0.000000      0.000000   \n",
       "75%       83.450000     77.770000     51.650000    358.740000    410.700000   \n",
       "max    10285.900000   7873.550000  11117.610000  45735.400000  28144.120000   \n",
       "\n",
       "        vol_3g_mb_8     arpu_3g_6     arpu_3g_7     arpu_3g_8     arpu_2g_6  \\\n",
       "count  30001.000000  30001.000000  30001.000000  30001.000000  30001.000000   \n",
       "mean     351.493998     67.007633     68.621327     62.773480     63.439374   \n",
       "std      919.021056    179.161038    183.133851    170.173104    161.008235   \n",
       "min        0.000000    -30.280000    -26.040000    -24.490000    -15.320000   \n",
       "25%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "50%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "75%      356.190000     35.000000     35.690000     20.920000     50.670000   \n",
       "max    30036.060000   6362.280000   4980.900000   3716.900000   6433.760000   \n",
       "\n",
       "          arpu_2g_7     arpu_2g_8  night_pck_user_6  night_pck_user_7  \\\n",
       "count  30001.000000  30001.000000      30001.000000      30001.000000   \n",
       "mean      64.701428     58.395301         -0.427919         -0.418953   \n",
       "std      165.284232    152.625125          0.521484          0.518111   \n",
       "min      -15.480000    -24.430000         -1.000000         -1.000000   \n",
       "25%        0.000000      0.000000         -1.000000         -1.000000   \n",
       "50%        0.000000      0.000000          0.000000          0.000000   \n",
       "75%       47.220000     26.900000          0.000000          0.000000   \n",
       "max     4809.360000   3467.170000          1.000000          1.000000   \n",
       "\n",
       "       night_pck_user_8  monthly_2g_6  monthly_2g_7  monthly_2g_8  \\\n",
       "count      30001.000000  30001.000000  30001.000000  30001.000000   \n",
       "mean          -0.456885      0.174494      0.183261      0.153628   \n",
       "std            0.520464      0.434953      0.450423      0.406486   \n",
       "min           -1.000000      0.000000      0.000000      0.000000   \n",
       "25%           -1.000000      0.000000      0.000000      0.000000   \n",
       "50%            0.000000      0.000000      0.000000      0.000000   \n",
       "75%            0.000000      0.000000      0.000000      0.000000   \n",
       "max            1.000000      4.000000      5.000000      5.000000   \n",
       "\n",
       "        sachet_2g_6   sachet_2g_7   sachet_2g_8  monthly_3g_6  monthly_3g_7  \\\n",
       "count  30001.000000  30001.000000  30001.000000  30001.000000  30001.000000   \n",
       "mean       1.066064      1.214026      1.054865      0.217759      0.229026   \n",
       "std        2.507197      2.736158      2.535940      0.613199      0.659125   \n",
       "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "50%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "75%        1.000000      1.000000      1.000000      0.000000      0.000000   \n",
       "max       42.000000     48.000000     44.000000     14.000000     16.000000   \n",
       "\n",
       "       monthly_3g_8   sachet_3g_6   sachet_3g_7   sachet_3g_8     fb_user_6  \\\n",
       "count  30001.000000  30001.000000  30001.000000  30001.000000  30001.000000   \n",
       "mean       0.211026      0.209393      0.227492      0.202393      0.075997   \n",
       "std        0.617549      0.985674      1.089929      1.100107      0.976332   \n",
       "min        0.000000      0.000000      0.000000      0.000000     -1.000000   \n",
       "25%        0.000000      0.000000      0.000000      0.000000     -1.000000   \n",
       "50%        0.000000      0.000000      0.000000      0.000000      1.000000   \n",
       "75%        0.000000      0.000000      0.000000      0.000000      1.000000   \n",
       "max       16.000000     29.000000     35.000000     41.000000      1.000000   \n",
       "\n",
       "          fb_user_7     fb_user_8    aug_vbc_3g    jul_vbc_3g    jun_vbc_3g  \\\n",
       "count  30001.000000  30001.000000  30001.000000  30001.000000  30001.000000   \n",
       "mean       0.092597      0.008833    169.138860    178.872765    158.523962   \n",
       "std        0.973118      0.972260    420.974354    443.544138    416.734860   \n",
       "min       -1.000000     -1.000000      0.000000      0.000000      0.000000   \n",
       "25%       -1.000000     -1.000000      0.000000      0.000000      0.000000   \n",
       "50%        1.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "75%        1.000000      1.000000    128.800000    137.290000     98.440000   \n",
       "max        1.000000      1.000000  12916.220000   9165.600000  11166.210000   \n",
       "\n",
       "       av_rech_amt_call_data_6_7  av_rech_amt_call_data_8     aon_month  \\\n",
       "count               30001.000000             30001.000000  30001.000000   \n",
       "mean                 1152.620628              1047.618398     40.312292   \n",
       "std                  1358.713982              1804.608844     31.922600   \n",
       "min                   478.000000                 0.000000      6.000000   \n",
       "25%                   603.000000               396.000000     15.333333   \n",
       "50%                   800.000000               688.000000     28.200000   \n",
       "75%                  1208.000000              1152.000000     58.533333   \n",
       "max                 61236.000000             95884.500000    144.033333   \n",
       "\n",
       "       last_rech_age_gap_1  last_rech_age_gap_2     arpu_diff  onnet_mou_diff  \\\n",
       "count         30001.000000         30001.000000  30001.000000    30001.000000   \n",
       "mean             30.617479            29.685777    -51.038557      -30.193620   \n",
       "std               6.043344             7.200803    334.872321      308.732579   \n",
       "min               0.000000             0.000000  -5811.052500    -5295.315000   \n",
       "25%              28.000000            28.000000   -180.179500      -52.345000   \n",
       "50%              31.000000            31.000000    -40.754500       -3.210000   \n",
       "75%              33.000000            33.000000     83.239000       18.410000   \n",
       "max              60.000000            61.000000  12808.617000     6359.920000   \n",
       "\n",
       "       offnet_mou_diff  roam_ic_mou_diff  roam_og_mou_diff  \\\n",
       "count     30001.000000      30001.000000      30001.000000   \n",
       "mean        -40.821520         -1.875878         -3.496201   \n",
       "std         349.434892         65.972062         94.165541   \n",
       "min       -7094.390000      -1296.985000      -2410.835000   \n",
       "25%        -100.900000          0.000000          0.000000   \n",
       "50%         -11.025000          0.000000          0.000000   \n",
       "75%          41.040000          0.000000          0.000000   \n",
       "max       12604.240000       2330.200000       3057.150000   \n",
       "\n",
       "       loc_og_t2t_mou_diff  loc_og_t2m_mou_diff  loc_og_t2f_mou_diff  \\\n",
       "count         30001.000000         30001.000000         30001.000000   \n",
       "mean             -7.002408           -11.369818            -0.497514   \n",
       "std             138.240028           148.307664            14.046863   \n",
       "min           -3875.375000         -2863.535000         -1319.850000   \n",
       "25%             -16.615000           -38.820000            -1.210000   \n",
       "50%              -1.030000            -3.090000             0.000000   \n",
       "75%               8.245000            21.405000             0.230000   \n",
       "max            6412.690000          3158.700000           452.720000   \n",
       "\n",
       "       loc_og_t2c_mou_diff  loc_og_mou_diff  std_og_t2t_mou_diff  \\\n",
       "count         30001.000000     30001.000000         30001.000000   \n",
       "mean             -0.024744       -18.870166           -21.618459   \n",
       "std               6.974352       213.766985           273.056056   \n",
       "min            -384.385000     -3867.830000         -5391.210000   \n",
       "25%              -0.365000       -58.880000           -12.160000   \n",
       "50%               0.000000        -4.955000             0.000000   \n",
       "75%               0.000000        30.125000             0.495000   \n",
       "max             193.355000      4619.975000          4806.465000   \n",
       "\n",
       "       std_og_t2m_mou_diff  std_og_t2f_mou_diff  std_og_mou_diff  \\\n",
       "count         30001.000000         30001.000000     30001.000000   \n",
       "mean            -26.327010            -0.230066       -48.175917   \n",
       "std             303.760044             7.536745       427.952693   \n",
       "min           -7037.535000          -314.280000     -7127.425000   \n",
       "25%             -25.980000             0.000000       -54.595000   \n",
       "50%              -0.030000             0.000000        -0.405000   \n",
       "75%               4.160000             0.000000         8.105000   \n",
       "max           12685.460000           209.935000     12715.480000   \n",
       "\n",
       "       isd_og_mou_diff  spl_og_mou_diff  og_others_diff  total_og_mou_diff  \\\n",
       "count     30001.000000     30001.000000    30001.000000       30001.000000   \n",
       "mean         -0.189665         0.093746       -0.294391         -67.437337   \n",
       "std          14.001271        18.088160        3.318381         502.630069   \n",
       "min        -619.650000     -1081.910000     -304.905000       -7213.410000   \n",
       "25%           0.000000        -2.750000        0.000000        -168.025000   \n",
       "50%           0.000000         0.000000        0.000000         -14.625000   \n",
       "75%           0.000000         1.310000        0.000000          67.915000   \n",
       "max         544.780000       747.620000      366.590000       12768.705000   \n",
       "\n",
       "       loc_ic_t2t_mou_diff  loc_ic_t2m_mou_diff  loc_ic_t2f_mou_diff  \\\n",
       "count         30001.000000         30001.000000         30001.000000   \n",
       "mean             -3.425675            -4.786070            -1.253842   \n",
       "std             102.050822           134.931249            29.499749   \n",
       "min           -3021.870000         -2387.110000          -928.210000   \n",
       "25%             -15.170000           -34.405000            -2.995000   \n",
       "50%              -1.045000            -2.160000             0.000000   \n",
       "75%               9.205000            25.180000             1.325000   \n",
       "max            3743.340000          3159.400000          1039.310000   \n",
       "\n",
       "       loc_ic_mou_diff  std_ic_t2t_mou_diff  std_ic_t2m_mou_diff  \\\n",
       "count     30001.000000         30001.000000         30001.000000   \n",
       "mean         -9.465915            -1.244436            -2.086269   \n",
       "std         180.801296            58.644175            74.416033   \n",
       "min       -3841.055000         -2897.680000         -2624.905000   \n",
       "25%         -49.805000            -2.830000            -7.410000   \n",
       "50%          -3.940000             0.000000            -0.215000   \n",
       "75%          34.405000             0.330000             2.790000   \n",
       "max        3485.370000          3313.875000          2978.665000   \n",
       "\n",
       "       std_ic_t2f_mou_diff  std_ic_mou_diff  total_ic_mou_diff  \\\n",
       "count         30001.000000     30001.000000       30001.000000   \n",
       "mean             -0.185179        -3.516120         -13.430810   \n",
       "std              13.707181        98.256037         221.899902   \n",
       "min            -636.500000     -2777.350000       -5710.150000   \n",
       "25%              -0.115000       -11.905000         -67.230000   \n",
       "50%               0.000000        -0.640000          -5.610000   \n",
       "75%               0.000000         4.995000          44.280000   \n",
       "max            1023.320000      3321.395000        3485.370000   \n",
       "\n",
       "       spl_ic_mou_diff  isd_ic_mou_diff  ic_others_diff  total_rech_num_diff  \\\n",
       "count     30001.000000     30001.000000    30001.000000         30001.000000   \n",
       "mean         -0.013833        -0.331985       -0.102438            -1.812056   \n",
       "std           0.174520        56.736330        9.582714             6.755899   \n",
       "min         -16.280000     -5534.595000     -408.240000          -147.500000   \n",
       "25%           0.000000         0.000000       -0.025000            -4.000000   \n",
       "50%           0.000000         0.000000        0.000000            -1.500000   \n",
       "75%           0.000000         0.000000        0.000000             1.000000   \n",
       "max           4.450000      3163.690000      459.280000           130.500000   \n",
       "\n",
       "       max_rech_amt_diff  last_day_rch_amt_diff  max_rech_data_diff  \\\n",
       "count       30001.000000           30001.000000        30001.000000   \n",
       "mean          -10.598813              -8.231159           -6.467768   \n",
       "std           136.893018             145.251806           78.343307   \n",
       "min         -3010.000000           -3065.000000        -1303.000000   \n",
       "25%           -33.000000             -52.000000           -7.000000   \n",
       "50%             0.000000               0.000000            0.000000   \n",
       "75%            25.000000              25.000000            0.000000   \n",
       "max          3949.000000            3949.000000         1352.000000   \n",
       "\n",
       "       vol_2g_mb_diff  vol_3g_mb_diff  arpu_3g_diff  arpu_2g_diff  \\\n",
       "count    30001.000000    30001.000000  30001.000000  30001.000000   \n",
       "mean       -19.896683       -5.733637     -5.041000     -5.675100   \n",
       "std        256.114926      624.877555    130.961477    116.362092   \n",
       "min      -4740.925000   -12385.505000  -2944.290000  -2980.040000   \n",
       "25%        -21.120000       -2.270000     -4.080000    -10.545000   \n",
       "50%          0.000000        0.000000      0.000000      0.000000   \n",
       "75%          0.000000        0.000000      0.000000      0.000000   \n",
       "max       8062.295000    15646.390000   3716.900000   3027.375000   \n",
       "\n",
       "       night_pck_user_diff  monthly_2g_diff  sachet_2g_diff  monthly_3g_diff  \\\n",
       "count         30001.000000     30001.000000    30001.000000     30001.000000   \n",
       "mean             -0.033449        -0.025249       -0.085180        -0.012366   \n",
       "std               0.421236         0.353771        1.867673         0.474128   \n",
       "min              -2.000000        -3.000000      -26.000000        -7.500000   \n",
       "25%               0.000000         0.000000        0.000000         0.000000   \n",
       "50%               0.000000         0.000000        0.000000         0.000000   \n",
       "75%               0.000000         0.000000        0.000000         0.000000   \n",
       "max               2.000000         3.500000       26.500000         7.000000   \n",
       "\n",
       "       sachet_3g_diff  fb_user_diff        target  \n",
       "count    30001.000000  30001.000000  30001.000000  \n",
       "mean        -0.016049     -0.075464      0.081364  \n",
       "std          0.871149      0.732055      0.273398  \n",
       "min        -21.500000     -2.000000      0.000000  \n",
       "25%          0.000000      0.000000      0.000000  \n",
       "50%          0.000000      0.000000      0.000000  \n",
       "75%          0.000000      0.000000      0.000000  \n",
       "max         36.000000      2.000000      1.000000  "
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " churn_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 224
    },
    "id": "tgZiVHwnGOkp",
    "outputId": "8dcee284-3349-4a7e-eb8d-dc49eab0eda6"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>arpu_6</th>\n",
       "      <th>arpu_7</th>\n",
       "      <th>arpu_8</th>\n",
       "      <th>onnet_mou_6</th>\n",
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       "      <th>offnet_mou_6</th>\n",
       "      <th>offnet_mou_7</th>\n",
       "      <th>offnet_mou_8</th>\n",
       "      <th>roam_ic_mou_6</th>\n",
       "      <th>roam_ic_mou_7</th>\n",
       "      <th>roam_ic_mou_8</th>\n",
       "      <th>roam_og_mou_6</th>\n",
       "      <th>roam_og_mou_7</th>\n",
       "      <th>roam_og_mou_8</th>\n",
       "      <th>loc_og_t2t_mou_6</th>\n",
       "      <th>loc_og_t2t_mou_7</th>\n",
       "      <th>loc_og_t2t_mou_8</th>\n",
       "      <th>loc_og_t2m_mou_6</th>\n",
       "      <th>loc_og_t2m_mou_7</th>\n",
       "      <th>loc_og_t2m_mou_8</th>\n",
       "      <th>loc_og_t2f_mou_6</th>\n",
       "      <th>loc_og_t2f_mou_7</th>\n",
       "      <th>loc_og_t2f_mou_8</th>\n",
       "      <th>loc_og_t2c_mou_6</th>\n",
       "      <th>loc_og_t2c_mou_7</th>\n",
       "      <th>loc_og_t2c_mou_8</th>\n",
       "      <th>loc_og_mou_6</th>\n",
       "      <th>loc_og_mou_7</th>\n",
       "      <th>loc_og_mou_8</th>\n",
       "      <th>std_og_t2t_mou_6</th>\n",
       "      <th>std_og_t2t_mou_7</th>\n",
       "      <th>std_og_t2t_mou_8</th>\n",
       "      <th>std_og_t2m_mou_6</th>\n",
       "      <th>std_og_t2m_mou_7</th>\n",
       "      <th>std_og_t2m_mou_8</th>\n",
       "      <th>std_og_t2f_mou_6</th>\n",
       "      <th>std_og_t2f_mou_7</th>\n",
       "      <th>std_og_t2f_mou_8</th>\n",
       "      <th>std_og_mou_6</th>\n",
       "      <th>std_og_mou_7</th>\n",
       "      <th>std_og_mou_8</th>\n",
       "      <th>isd_og_mou_6</th>\n",
       "      <th>isd_og_mou_7</th>\n",
       "      <th>isd_og_mou_8</th>\n",
       "      <th>spl_og_mou_6</th>\n",
       "      <th>spl_og_mou_7</th>\n",
       "      <th>spl_og_mou_8</th>\n",
       "      <th>og_others_6</th>\n",
       "      <th>og_others_7</th>\n",
       "      <th>og_others_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>loc_ic_t2t_mou_6</th>\n",
       "      <th>loc_ic_t2t_mou_7</th>\n",
       "      <th>loc_ic_t2t_mou_8</th>\n",
       "      <th>loc_ic_t2m_mou_6</th>\n",
       "      <th>loc_ic_t2m_mou_7</th>\n",
       "      <th>loc_ic_t2m_mou_8</th>\n",
       "      <th>loc_ic_t2f_mou_6</th>\n",
       "      <th>loc_ic_t2f_mou_7</th>\n",
       "      <th>loc_ic_t2f_mou_8</th>\n",
       "      <th>loc_ic_mou_6</th>\n",
       "      <th>loc_ic_mou_7</th>\n",
       "      <th>loc_ic_mou_8</th>\n",
       "      <th>std_ic_t2t_mou_6</th>\n",
       "      <th>std_ic_t2t_mou_7</th>\n",
       "      <th>std_ic_t2t_mou_8</th>\n",
       "      <th>std_ic_t2m_mou_6</th>\n",
       "      <th>std_ic_t2m_mou_7</th>\n",
       "      <th>std_ic_t2m_mou_8</th>\n",
       "      <th>std_ic_t2f_mou_6</th>\n",
       "      <th>std_ic_t2f_mou_7</th>\n",
       "      <th>std_ic_t2f_mou_8</th>\n",
       "      <th>std_ic_mou_6</th>\n",
       "      <th>std_ic_mou_7</th>\n",
       "      <th>std_ic_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>spl_ic_mou_6</th>\n",
       "      <th>spl_ic_mou_7</th>\n",
       "      <th>spl_ic_mou_8</th>\n",
       "      <th>isd_ic_mou_6</th>\n",
       "      <th>isd_ic_mou_7</th>\n",
       "      <th>isd_ic_mou_8</th>\n",
       "      <th>ic_others_6</th>\n",
       "      <th>ic_others_7</th>\n",
       "      <th>ic_others_8</th>\n",
       "      <th>total_rech_num_6</th>\n",
       "      <th>total_rech_num_7</th>\n",
       "      <th>total_rech_num_8</th>\n",
       "      <th>max_rech_amt_6</th>\n",
       "      <th>max_rech_amt_7</th>\n",
       "      <th>max_rech_amt_8</th>\n",
       "      <th>last_day_rch_amt_6</th>\n",
       "      <th>last_day_rch_amt_7</th>\n",
       "      <th>last_day_rch_amt_8</th>\n",
       "      <th>max_rech_data_6</th>\n",
       "      <th>max_rech_data_7</th>\n",
       "      <th>max_rech_data_8</th>\n",
       "      <th>vol_2g_mb_6</th>\n",
       "      <th>vol_2g_mb_7</th>\n",
       "      <th>vol_2g_mb_8</th>\n",
       "      <th>vol_3g_mb_6</th>\n",
       "      <th>vol_3g_mb_7</th>\n",
       "      <th>vol_3g_mb_8</th>\n",
       "      <th>arpu_3g_6</th>\n",
       "      <th>arpu_3g_7</th>\n",
       "      <th>arpu_3g_8</th>\n",
       "      <th>arpu_2g_6</th>\n",
       "      <th>arpu_2g_7</th>\n",
       "      <th>arpu_2g_8</th>\n",
       "      <th>night_pck_user_6</th>\n",
       "      <th>night_pck_user_7</th>\n",
       "      <th>night_pck_user_8</th>\n",
       "      <th>monthly_2g_6</th>\n",
       "      <th>monthly_2g_7</th>\n",
       "      <th>monthly_2g_8</th>\n",
       "      <th>sachet_2g_6</th>\n",
       "      <th>sachet_2g_7</th>\n",
       "      <th>sachet_2g_8</th>\n",
       "      <th>monthly_3g_6</th>\n",
       "      <th>monthly_3g_7</th>\n",
       "      <th>monthly_3g_8</th>\n",
       "      <th>sachet_3g_6</th>\n",
       "      <th>sachet_3g_7</th>\n",
       "      <th>sachet_3g_8</th>\n",
       "      <th>fb_user_6</th>\n",
       "      <th>fb_user_7</th>\n",
       "      <th>fb_user_8</th>\n",
       "      <th>aug_vbc_3g</th>\n",
       "      <th>jul_vbc_3g</th>\n",
       "      <th>jun_vbc_3g</th>\n",
       "      <th>av_rech_amt_call_data_6_7</th>\n",
       "      <th>av_rech_amt_call_data_8</th>\n",
       "      <th>aon_month</th>\n",
       "      <th>last_rech_age_gap_1</th>\n",
       "      <th>last_rech_age_gap_2</th>\n",
       "      <th>arpu_diff</th>\n",
       "      <th>onnet_mou_diff</th>\n",
       "      <th>offnet_mou_diff</th>\n",
       "      <th>roam_ic_mou_diff</th>\n",
       "      <th>roam_og_mou_diff</th>\n",
       "      <th>loc_og_t2t_mou_diff</th>\n",
       "      <th>loc_og_t2m_mou_diff</th>\n",
       "      <th>loc_og_t2f_mou_diff</th>\n",
       "      <th>loc_og_t2c_mou_diff</th>\n",
       "      <th>loc_og_mou_diff</th>\n",
       "      <th>std_og_t2t_mou_diff</th>\n",
       "      <th>std_og_t2m_mou_diff</th>\n",
       "      <th>std_og_t2f_mou_diff</th>\n",
       "      <th>std_og_mou_diff</th>\n",
       "      <th>isd_og_mou_diff</th>\n",
       "      <th>spl_og_mou_diff</th>\n",
       "      <th>og_others_diff</th>\n",
       "      <th>total_og_mou_diff</th>\n",
       "      <th>loc_ic_t2t_mou_diff</th>\n",
       "      <th>loc_ic_t2m_mou_diff</th>\n",
       "      <th>loc_ic_t2f_mou_diff</th>\n",
       "      <th>loc_ic_mou_diff</th>\n",
       "      <th>std_ic_t2t_mou_diff</th>\n",
       "      <th>std_ic_t2m_mou_diff</th>\n",
       "      <th>std_ic_t2f_mou_diff</th>\n",
       "      <th>std_ic_mou_diff</th>\n",
       "      <th>total_ic_mou_diff</th>\n",
       "      <th>spl_ic_mou_diff</th>\n",
       "      <th>isd_ic_mou_diff</th>\n",
       "      <th>ic_others_diff</th>\n",
       "      <th>total_rech_num_diff</th>\n",
       "      <th>max_rech_amt_diff</th>\n",
       "      <th>last_day_rch_amt_diff</th>\n",
       "      <th>max_rech_data_diff</th>\n",
       "      <th>vol_2g_mb_diff</th>\n",
       "      <th>vol_3g_mb_diff</th>\n",
       "      <th>arpu_3g_diff</th>\n",
       "      <th>arpu_2g_diff</th>\n",
       "      <th>night_pck_user_diff</th>\n",
       "      <th>monthly_2g_diff</th>\n",
       "      <th>sachet_2g_diff</th>\n",
       "      <th>monthly_3g_diff</th>\n",
       "      <th>sachet_3g_diff</th>\n",
       "      <th>fb_user_diff</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>197.385</td>\n",
       "      <td>214.816</td>\n",
       "      <td>213.803</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</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>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</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>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.13</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.15</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.44</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.44</td>\n",
       "      <td>0.00</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>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>252</td>\n",
       "      <td>252</td>\n",
       "      <td>252</td>\n",
       "      <td>252</td>\n",
       "      <td>252</td>\n",
       "      <td>252</td>\n",
       "      <td>252.0</td>\n",
       "      <td>252.0</td>\n",
       "      <td>252.0</td>\n",
       "      <td>30.13</td>\n",
       "      <td>1.32</td>\n",
       "      <td>5.75</td>\n",
       "      <td>83.57</td>\n",
       "      <td>150.76</td>\n",
       "      <td>109.61</td>\n",
       "      <td>212.17</td>\n",
       "      <td>212.17</td>\n",
       "      <td>212.17</td>\n",
       "      <td>212.17</td>\n",
       "      <td>212.17</td>\n",
       "      <td>212.17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>30.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>101.20</td>\n",
       "      <td>559.0</td>\n",
       "      <td>504.0</td>\n",
       "      <td>32.266667</td>\n",
       "      <td>25.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>7.7025</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.160</td>\n",
       "      <td>4.130</td>\n",
       "      <td>1.150</td>\n",
       "      <td>5.440</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>5.440</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-1.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-9.975</td>\n",
       "      <td>-7.555</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1069.180</td>\n",
       "      <td>1349.850</td>\n",
       "      <td>3171.480</td>\n",
       "      <td>57.84</td>\n",
       "      <td>54.68</td>\n",
       "      <td>52.29</td>\n",
       "      <td>453.43</td>\n",
       "      <td>567.16</td>\n",
       "      <td>325.91</td>\n",
       "      <td>16.23</td>\n",
       "      <td>33.49</td>\n",
       "      <td>31.64</td>\n",
       "      <td>23.74</td>\n",
       "      <td>12.59</td>\n",
       "      <td>38.06</td>\n",
       "      <td>51.39</td>\n",
       "      <td>31.38</td>\n",
       "      <td>40.28</td>\n",
       "      <td>308.63</td>\n",
       "      <td>447.38</td>\n",
       "      <td>162.28</td>\n",
       "      <td>62.13</td>\n",
       "      <td>55.14</td>\n",
       "      <td>53.23</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>422.16</td>\n",
       "      <td>533.91</td>\n",
       "      <td>255.79</td>\n",
       "      <td>4.30</td>\n",
       "      <td>23.29</td>\n",
       "      <td>12.01</td>\n",
       "      <td>49.89</td>\n",
       "      <td>31.76</td>\n",
       "      <td>49.14</td>\n",
       "      <td>6.66</td>\n",
       "      <td>20.08</td>\n",
       "      <td>16.68</td>\n",
       "      <td>60.86</td>\n",
       "      <td>75.14</td>\n",
       "      <td>77.84</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.18</td>\n",
       "      <td>10.01</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.50</td>\n",
       "      <td>0.0</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>58.14</td>\n",
       "      <td>32.26</td>\n",
       "      <td>27.31</td>\n",
       "      <td>217.56</td>\n",
       "      <td>221.49</td>\n",
       "      <td>121.19</td>\n",
       "      <td>152.16</td>\n",
       "      <td>101.46</td>\n",
       "      <td>39.53</td>\n",
       "      <td>427.88</td>\n",
       "      <td>355.23</td>\n",
       "      <td>188.04</td>\n",
       "      <td>36.89</td>\n",
       "      <td>11.83</td>\n",
       "      <td>30.39</td>\n",
       "      <td>91.44</td>\n",
       "      <td>126.99</td>\n",
       "      <td>141.33</td>\n",
       "      <td>52.19</td>\n",
       "      <td>34.24</td>\n",
       "      <td>22.21</td>\n",
       "      <td>180.54</td>\n",
       "      <td>173.08</td>\n",
       "      <td>193.94</td>\n",
       "      <td>626.46</td>\n",
       "      <td>558.04</td>\n",
       "      <td>428.74</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.06</td>\n",
       "      <td>14.53</td>\n",
       "      <td>31.59</td>\n",
       "      <td>15.74</td>\n",
       "      <td>15.19</td>\n",
       "      <td>15.14</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>1580</td>\n",
       "      <td>790</td>\n",
       "      <td>1580</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>779</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>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>57.74</td>\n",
       "      <td>19.38</td>\n",
       "      <td>18.74</td>\n",
       "      <td>1185.0</td>\n",
       "      <td>3638.0</td>\n",
       "      <td>26.733333</td>\n",
       "      <td>28.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1961.9650</td>\n",
       "      <td>-3.97</td>\n",
       "      <td>-184.385</td>\n",
       "      <td>6.78</td>\n",
       "      <td>19.895</td>\n",
       "      <td>-1.105</td>\n",
       "      <td>-215.725</td>\n",
       "      <td>-5.405</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-222.245</td>\n",
       "      <td>-1.785</td>\n",
       "      <td>8.315</td>\n",
       "      <td>3.31</td>\n",
       "      <td>9.840</td>\n",
       "      <td>9.920</td>\n",
       "      <td>4.25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-198.225</td>\n",
       "      <td>-17.890</td>\n",
       "      <td>-98.335</td>\n",
       "      <td>-87.280</td>\n",
       "      <td>-203.515</td>\n",
       "      <td>6.030</td>\n",
       "      <td>32.115</td>\n",
       "      <td>-21.005</td>\n",
       "      <td>17.130</td>\n",
       "      <td>-163.510</td>\n",
       "      <td>-0.105</td>\n",
       "      <td>23.295</td>\n",
       "      <td>-0.325</td>\n",
       "      <td>2.0</td>\n",
       "      <td>395.0</td>\n",
       "      <td>779.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>378.721</td>\n",
       "      <td>492.223</td>\n",
       "      <td>137.362</td>\n",
       "      <td>413.69</td>\n",
       "      <td>351.03</td>\n",
       "      <td>35.08</td>\n",
       "      <td>94.66</td>\n",
       "      <td>80.63</td>\n",
       "      <td>136.48</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>297.13</td>\n",
       "      <td>217.59</td>\n",
       "      <td>12.49</td>\n",
       "      <td>80.96</td>\n",
       "      <td>70.58</td>\n",
       "      <td>50.54</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>7.15</td>\n",
       "      <td>378.09</td>\n",
       "      <td>288.18</td>\n",
       "      <td>63.04</td>\n",
       "      <td>116.56</td>\n",
       "      <td>133.43</td>\n",
       "      <td>22.58</td>\n",
       "      <td>13.69</td>\n",
       "      <td>10.04</td>\n",
       "      <td>75.69</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>130.26</td>\n",
       "      <td>143.48</td>\n",
       "      <td>98.28</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.23</td>\n",
       "      <td>0.0</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>23.84</td>\n",
       "      <td>9.84</td>\n",
       "      <td>0.31</td>\n",
       "      <td>57.58</td>\n",
       "      <td>13.98</td>\n",
       "      <td>15.48</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>81.43</td>\n",
       "      <td>23.83</td>\n",
       "      <td>15.79</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.10</td>\n",
       "      <td>22.43</td>\n",
       "      <td>4.08</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>22.43</td>\n",
       "      <td>4.66</td>\n",
       "      <td>0.75</td>\n",
       "      <td>103.86</td>\n",
       "      <td>28.49</td>\n",
       "      <td>16.54</td>\n",
       "      <td>0.00</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>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>19</td>\n",
       "      <td>21</td>\n",
       "      <td>14</td>\n",
       "      <td>90</td>\n",
       "      <td>154</td>\n",
       "      <td>30</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>356.00</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.00</td>\n",
       "      <td>750.95</td>\n",
       "      <td>11.94</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>19.83</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21.03</td>\n",
       "      <td>910.65</td>\n",
       "      <td>122.16</td>\n",
       "      <td>696.0</td>\n",
       "      <td>327.0</td>\n",
       "      <td>10.500000</td>\n",
       "      <td>36.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>-298.1100</td>\n",
       "      <td>-347.28</td>\n",
       "      <td>48.835</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-244.870</td>\n",
       "      <td>-25.230</td>\n",
       "      <td>0.000</td>\n",
       "      <td>7.15</td>\n",
       "      <td>-270.095</td>\n",
       "      <td>-102.415</td>\n",
       "      <td>63.825</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-38.590</td>\n",
       "      <td>0.000</td>\n",
       "      <td>10.23</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-298.450</td>\n",
       "      <td>-16.530</td>\n",
       "      <td>-20.300</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-36.840</td>\n",
       "      <td>-0.190</td>\n",
       "      <td>-12.605</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-12.795</td>\n",
       "      <td>-49.635</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>-92.0</td>\n",
       "      <td>-15.0</td>\n",
       "      <td>-54.0</td>\n",
       "      <td>-177.970</td>\n",
       "      <td>-363.535</td>\n",
       "      <td>19.830</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.5</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>514.453</td>\n",
       "      <td>597.753</td>\n",
       "      <td>637.760</td>\n",
       "      <td>102.41</td>\n",
       "      <td>132.11</td>\n",
       "      <td>85.14</td>\n",
       "      <td>757.93</td>\n",
       "      <td>896.68</td>\n",
       "      <td>983.39</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.48</td>\n",
       "      <td>6.16</td>\n",
       "      <td>23.34</td>\n",
       "      <td>91.81</td>\n",
       "      <td>87.93</td>\n",
       "      <td>104.81</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.58</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>97.04</td>\n",
       "      <td>94.09</td>\n",
       "      <td>129.74</td>\n",
       "      <td>97.93</td>\n",
       "      <td>125.94</td>\n",
       "      <td>61.79</td>\n",
       "      <td>665.36</td>\n",
       "      <td>808.74</td>\n",
       "      <td>876.99</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>763.29</td>\n",
       "      <td>934.69</td>\n",
       "      <td>938.79</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>860.34</td>\n",
       "      <td>1028.79</td>\n",
       "      <td>1068.54</td>\n",
       "      <td>2.48</td>\n",
       "      <td>10.19</td>\n",
       "      <td>19.54</td>\n",
       "      <td>118.23</td>\n",
       "      <td>74.63</td>\n",
       "      <td>129.16</td>\n",
       "      <td>4.61</td>\n",
       "      <td>2.84</td>\n",
       "      <td>10.39</td>\n",
       "      <td>125.33</td>\n",
       "      <td>87.68</td>\n",
       "      <td>159.11</td>\n",
       "      <td>14.06</td>\n",
       "      <td>5.98</td>\n",
       "      <td>0.18</td>\n",
       "      <td>67.69</td>\n",
       "      <td>38.23</td>\n",
       "      <td>101.74</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>81.76</td>\n",
       "      <td>44.21</td>\n",
       "      <td>101.93</td>\n",
       "      <td>207.09</td>\n",
       "      <td>131.89</td>\n",
       "      <td>261.04</td>\n",
       "      <td>0.00</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>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>22</td>\n",
       "      <td>26</td>\n",
       "      <td>27</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>30</td>\n",
       "      <td>20</td>\n",
       "      <td>50</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>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>640.0</td>\n",
       "      <td>718.0</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>31.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>81.6570</td>\n",
       "      <td>-32.12</td>\n",
       "      <td>156.085</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>18.020</td>\n",
       "      <td>14.940</td>\n",
       "      <td>1.205</td>\n",
       "      <td>0.00</td>\n",
       "      <td>34.175</td>\n",
       "      <td>-50.145</td>\n",
       "      <td>139.940</td>\n",
       "      <td>0.00</td>\n",
       "      <td>89.800</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>123.975</td>\n",
       "      <td>13.205</td>\n",
       "      <td>32.730</td>\n",
       "      <td>6.665</td>\n",
       "      <td>52.605</td>\n",
       "      <td>-9.840</td>\n",
       "      <td>48.780</td>\n",
       "      <td>0.000</td>\n",
       "      <td>38.945</td>\n",
       "      <td>91.550</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>74.350</td>\n",
       "      <td>193.897</td>\n",
       "      <td>366.966</td>\n",
       "      <td>48.96</td>\n",
       "      <td>50.66</td>\n",
       "      <td>33.58</td>\n",
       "      <td>85.41</td>\n",
       "      <td>89.36</td>\n",
       "      <td>205.89</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>48.96</td>\n",
       "      <td>50.66</td>\n",
       "      <td>33.58</td>\n",
       "      <td>82.94</td>\n",
       "      <td>83.01</td>\n",
       "      <td>148.56</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>17.71</td>\n",
       "      <td>131.91</td>\n",
       "      <td>133.68</td>\n",
       "      <td>182.14</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.36</td>\n",
       "      <td>6.35</td>\n",
       "      <td>39.61</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.36</td>\n",
       "      <td>6.35</td>\n",
       "      <td>39.61</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.71</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>134.38</td>\n",
       "      <td>140.04</td>\n",
       "      <td>239.48</td>\n",
       "      <td>20.71</td>\n",
       "      <td>61.04</td>\n",
       "      <td>76.64</td>\n",
       "      <td>95.91</td>\n",
       "      <td>113.36</td>\n",
       "      <td>146.84</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.71</td>\n",
       "      <td>116.63</td>\n",
       "      <td>174.41</td>\n",
       "      <td>224.21</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.00</td>\n",
       "      <td>13.38</td>\n",
       "      <td>2.43</td>\n",
       "      <td>14.89</td>\n",
       "      <td>43.91</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.94</td>\n",
       "      <td>14.89</td>\n",
       "      <td>57.29</td>\n",
       "      <td>119.58</td>\n",
       "      <td>222.89</td>\n",
       "      <td>298.33</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>28.23</td>\n",
       "      <td>3.74</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.35</td>\n",
       "      <td>13.06</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>179</td>\n",
       "      <td>179</td>\n",
       "      <td>0</td>\n",
       "      <td>179</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>179.0</td>\n",
       "      <td>179.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>599.09</td>\n",
       "      <td>1009.92</td>\n",
       "      <td>0.00</td>\n",
       "      <td>306.57</td>\n",
       "      <td>197.27</td>\n",
       "      <td>0.00</td>\n",
       "      <td>301.78</td>\n",
       "      <td>150.67</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>40.45</td>\n",
       "      <td>51.86</td>\n",
       "      <td>0.00</td>\n",
       "      <td>583.0</td>\n",
       "      <td>979.0</td>\n",
       "      <td>20.133333</td>\n",
       "      <td>19.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>232.8425</td>\n",
       "      <td>-16.23</td>\n",
       "      <td>118.505</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-16.230</td>\n",
       "      <td>65.585</td>\n",
       "      <td>0.000</td>\n",
       "      <td>17.71</td>\n",
       "      <td>49.345</td>\n",
       "      <td>0.000</td>\n",
       "      <td>35.255</td>\n",
       "      <td>0.00</td>\n",
       "      <td>35.255</td>\n",
       "      <td>-0.005</td>\n",
       "      <td>17.66</td>\n",
       "      <td>0.0</td>\n",
       "      <td>102.270</td>\n",
       "      <td>35.765</td>\n",
       "      <td>42.205</td>\n",
       "      <td>0.710</td>\n",
       "      <td>78.690</td>\n",
       "      <td>13.125</td>\n",
       "      <td>35.250</td>\n",
       "      <td>0.000</td>\n",
       "      <td>48.375</td>\n",
       "      <td>127.095</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-10.375</td>\n",
       "      <td>10.385</td>\n",
       "      <td>0.0</td>\n",
       "      <td>89.5</td>\n",
       "      <td>-89.5</td>\n",
       "      <td>89.5</td>\n",
       "      <td>-0.230</td>\n",
       "      <td>710.375</td>\n",
       "      <td>43.985</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     arpu_6    arpu_7    arpu_8  onnet_mou_6  onnet_mou_7  onnet_mou_8  \\\n",
       "0   197.385   214.816   213.803         0.00         0.00         0.00   \n",
       "1  1069.180  1349.850  3171.480        57.84        54.68        52.29   \n",
       "2   378.721   492.223   137.362       413.69       351.03        35.08   \n",
       "3   514.453   597.753   637.760       102.41       132.11        85.14   \n",
       "4    74.350   193.897   366.966        48.96        50.66        33.58   \n",
       "\n",
       "   offnet_mou_6  offnet_mou_7  offnet_mou_8  roam_ic_mou_6  roam_ic_mou_7  \\\n",
       "0          0.00          0.00          0.00           0.00           0.00   \n",
       "1        453.43        567.16        325.91          16.23          33.49   \n",
       "2         94.66         80.63        136.48           0.00           0.00   \n",
       "3        757.93        896.68        983.39           0.00           0.00   \n",
       "4         85.41         89.36        205.89           0.00           0.00   \n",
       "\n",
       "   roam_ic_mou_8  roam_og_mou_6  roam_og_mou_7  roam_og_mou_8  \\\n",
       "0           0.00           0.00           0.00           0.00   \n",
       "1          31.64          23.74          12.59          38.06   \n",
       "2           0.00           0.00           0.00           0.00   \n",
       "3           0.00           0.00           0.00           0.00   \n",
       "4           0.00           0.00           0.00           0.00   \n",
       "\n",
       "   loc_og_t2t_mou_6  loc_og_t2t_mou_7  loc_og_t2t_mou_8  loc_og_t2m_mou_6  \\\n",
       "0              0.00              0.00              0.00              0.00   \n",
       "1             51.39             31.38             40.28            308.63   \n",
       "2            297.13            217.59             12.49             80.96   \n",
       "3              4.48              6.16             23.34             91.81   \n",
       "4             48.96             50.66             33.58             82.94   \n",
       "\n",
       "   loc_og_t2m_mou_7  loc_og_t2m_mou_8  loc_og_t2f_mou_6  loc_og_t2f_mou_7  \\\n",
       "0              0.00              0.00              0.00              0.00   \n",
       "1            447.38            162.28             62.13             55.14   \n",
       "2             70.58             50.54              0.00              0.00   \n",
       "3             87.93            104.81              0.75              0.00   \n",
       "4             83.01            148.56              0.00              0.00   \n",
       "\n",
       "   loc_og_t2f_mou_8  loc_og_t2c_mou_6  loc_og_t2c_mou_7  loc_og_t2c_mou_8  \\\n",
       "0              0.00               0.0               0.0              0.00   \n",
       "1             53.23               0.0               0.0              0.00   \n",
       "2              0.00               0.0               0.0              7.15   \n",
       "3              1.58               0.0               0.0              0.00   \n",
       "4              0.00               0.0               0.0             17.71   \n",
       "\n",
       "   loc_og_mou_6  loc_og_mou_7  loc_og_mou_8  std_og_t2t_mou_6  \\\n",
       "0          0.00          0.00          0.00              0.00   \n",
       "1        422.16        533.91        255.79              4.30   \n",
       "2        378.09        288.18         63.04            116.56   \n",
       "3         97.04         94.09        129.74             97.93   \n",
       "4        131.91        133.68        182.14              0.00   \n",
       "\n",
       "   std_og_t2t_mou_7  std_og_t2t_mou_8  std_og_t2m_mou_6  std_og_t2m_mou_7  \\\n",
       "0              0.00              0.00              0.00              0.00   \n",
       "1             23.29             12.01             49.89             31.76   \n",
       "2            133.43             22.58             13.69             10.04   \n",
       "3            125.94             61.79            665.36            808.74   \n",
       "4              0.00              0.00              2.36              6.35   \n",
       "\n",
       "   std_og_t2m_mou_8  std_og_t2f_mou_6  std_og_t2f_mou_7  std_og_t2f_mou_8  \\\n",
       "0              0.00              0.00              0.00              0.00   \n",
       "1             49.14              6.66             20.08             16.68   \n",
       "2             75.69              0.00              0.00              0.00   \n",
       "3            876.99              0.00              0.00              0.00   \n",
       "4             39.61              0.00              0.00              0.00   \n",
       "\n",
       "   std_og_mou_6  std_og_mou_7  std_og_mou_8  isd_og_mou_6  isd_og_mou_7  \\\n",
       "0          0.00          0.00          0.00           0.0          0.00   \n",
       "1         60.86         75.14         77.84           0.0          0.18   \n",
       "2        130.26        143.48         98.28           0.0          0.00   \n",
       "3        763.29        934.69        938.79           0.0          0.00   \n",
       "4          2.36          6.35         39.61           0.0          0.01   \n",
       "\n",
       "   isd_og_mou_8  spl_og_mou_6  spl_og_mou_7  spl_og_mou_8  og_others_6  \\\n",
       "0          0.00           0.0           0.0          0.00          0.0   \n",
       "1         10.01           4.5           0.0          6.50          0.0   \n",
       "2          0.00           0.0           0.0         10.23          0.0   \n",
       "3          0.00           0.0           0.0          0.00          0.0   \n",
       "4          0.00           0.1           0.0         17.71          0.0   \n",
       "\n",
       "   og_others_7  og_others_8  total_og_mou_6  total_og_mou_7  total_og_mou_8  \\\n",
       "0          0.0          0.0            0.00            0.00            0.00   \n",
       "1          0.0          0.0          487.53          609.24          350.16   \n",
       "2          0.0          0.0          508.36          431.66          171.56   \n",
       "3          0.0          0.0          860.34         1028.79         1068.54   \n",
       "4          0.0          0.0          134.38          140.04          239.48   \n",
       "\n",
       "   loc_ic_t2t_mou_6  loc_ic_t2t_mou_7  loc_ic_t2t_mou_8  loc_ic_t2m_mou_6  \\\n",
       "0              0.00              0.00              0.16              0.00   \n",
       "1             58.14             32.26             27.31            217.56   \n",
       "2             23.84              9.84              0.31             57.58   \n",
       "3              2.48             10.19             19.54            118.23   \n",
       "4             20.71             61.04             76.64             95.91   \n",
       "\n",
       "   loc_ic_t2m_mou_7  loc_ic_t2m_mou_8  loc_ic_t2f_mou_6  loc_ic_t2f_mou_7  \\\n",
       "0              0.00              4.13              0.00              0.00   \n",
       "1            221.49            121.19            152.16            101.46   \n",
       "2             13.98             15.48              0.00              0.00   \n",
       "3             74.63            129.16              4.61              2.84   \n",
       "4            113.36            146.84              0.00              0.00   \n",
       "\n",
       "   loc_ic_t2f_mou_8  loc_ic_mou_6  loc_ic_mou_7  loc_ic_mou_8  \\\n",
       "0              1.15          0.00          0.00          5.44   \n",
       "1             39.53        427.88        355.23        188.04   \n",
       "2              0.00         81.43         23.83         15.79   \n",
       "3             10.39        125.33         87.68        159.11   \n",
       "4              0.71        116.63        174.41        224.21   \n",
       "\n",
       "   std_ic_t2t_mou_6  std_ic_t2t_mou_7  std_ic_t2t_mou_8  std_ic_t2m_mou_6  \\\n",
       "0              0.00              0.00              0.00              0.00   \n",
       "1             36.89             11.83             30.39             91.44   \n",
       "2              0.00              0.58              0.10             22.43   \n",
       "3             14.06              5.98              0.18             67.69   \n",
       "4              0.51              0.00             13.38              2.43   \n",
       "\n",
       "   std_ic_t2m_mou_7  std_ic_t2m_mou_8  std_ic_t2f_mou_6  std_ic_t2f_mou_7  \\\n",
       "0              0.00              0.00              0.00              0.00   \n",
       "1            126.99            141.33             52.19             34.24   \n",
       "2              4.08              0.65              0.00              0.00   \n",
       "3             38.23            101.74              0.00              0.00   \n",
       "4             14.89             43.91              0.00              0.00   \n",
       "\n",
       "   std_ic_t2f_mou_8  std_ic_mou_6  std_ic_mou_7  std_ic_mou_8  total_ic_mou_6  \\\n",
       "0              0.00          0.00          0.00          0.00            0.00   \n",
       "1             22.21        180.54        173.08        193.94          626.46   \n",
       "2              0.00         22.43          4.66          0.75          103.86   \n",
       "3              0.00         81.76         44.21        101.93          207.09   \n",
       "4              0.00          2.94         14.89         57.29          119.58   \n",
       "\n",
       "   total_ic_mou_7  total_ic_mou_8  spl_ic_mou_6  spl_ic_mou_7  spl_ic_mou_8  \\\n",
       "0            0.00            5.44          0.00           0.0           0.0   \n",
       "1          558.04          428.74          0.21           0.0           0.0   \n",
       "2           28.49           16.54          0.00           0.0           0.0   \n",
       "3          131.89          261.04          0.00           0.0           0.0   \n",
       "4          222.89          298.33          0.00           0.0           0.0   \n",
       "\n",
       "   isd_ic_mou_6  isd_ic_mou_7  isd_ic_mou_8  ic_others_6  ic_others_7  \\\n",
       "0          0.00          0.00          0.00         0.00         0.00   \n",
       "1          2.06         14.53         31.59        15.74        15.19   \n",
       "2          0.00          0.00          0.00         0.00         0.00   \n",
       "3          0.00          0.00          0.00         0.00         0.00   \n",
       "4          0.00         28.23          3.74         0.00         5.35   \n",
       "\n",
       "   ic_others_8  total_rech_num_6  total_rech_num_7  total_rech_num_8  \\\n",
       "0         0.00                 4                 3                 2   \n",
       "1        15.14                 5                 5                 7   \n",
       "2         0.00                19                21                14   \n",
       "3         0.00                22                26                27   \n",
       "4        13.06                 3                 5                 4   \n",
       "\n",
       "   max_rech_amt_6  max_rech_amt_7  max_rech_amt_8  last_day_rch_amt_6  \\\n",
       "0             252             252             252                 252   \n",
       "1            1580             790            1580                   0   \n",
       "2              90             154              30                  50   \n",
       "3              50              50              50                  30   \n",
       "4               0             179             179                   0   \n",
       "\n",
       "   last_day_rch_amt_7  last_day_rch_amt_8  max_rech_data_6  max_rech_data_7  \\\n",
       "0                 252                 252            252.0            252.0   \n",
       "1                   0                 779              0.0              0.0   \n",
       "2                   0                  10              0.0            154.0   \n",
       "3                  20                  50              0.0              0.0   \n",
       "4                 179                   0              0.0            179.0   \n",
       "\n",
       "   max_rech_data_8  vol_2g_mb_6  vol_2g_mb_7  vol_2g_mb_8  vol_3g_mb_6  \\\n",
       "0            252.0        30.13         1.32         5.75        83.57   \n",
       "1              0.0         0.00         0.00         0.00         0.00   \n",
       "2             23.0         0.00       356.00         0.03         0.00   \n",
       "3              0.0         0.00         0.00         0.00         0.00   \n",
       "4            179.0         0.00         0.48         0.01         0.00   \n",
       "\n",
       "   vol_3g_mb_7  vol_3g_mb_8  arpu_3g_6  arpu_3g_7  arpu_3g_8  arpu_2g_6  \\\n",
       "0       150.76       109.61     212.17     212.17     212.17     212.17   \n",
       "1         0.00         0.00       0.00       0.00       0.00       0.00   \n",
       "2       750.95        11.94       0.00       0.00      19.83       0.00   \n",
       "3         0.00         0.00       0.00       0.00       0.00       0.00   \n",
       "4       599.09      1009.92       0.00     306.57     197.27       0.00   \n",
       "\n",
       "   arpu_2g_7  arpu_2g_8  night_pck_user_6  night_pck_user_7  night_pck_user_8  \\\n",
       "0     212.17     212.17               0.0               0.0               0.0   \n",
       "1       0.00       0.00              -1.0              -1.0              -1.0   \n",
       "2       0.00       0.00              -1.0               0.0               0.0   \n",
       "3       0.00       0.00              -1.0              -1.0              -1.0   \n",
       "4     301.78     150.67              -1.0               0.0               0.0   \n",
       "\n",
       "   monthly_2g_6  monthly_2g_7  monthly_2g_8  sachet_2g_6  sachet_2g_7  \\\n",
       "0             0             0             0            0            0   \n",
       "1             0             0             0            0            0   \n",
       "2             0             1             0            0            1   \n",
       "3             0             0             0            0            0   \n",
       "4             0             0             0            0            0   \n",
       "\n",
       "   sachet_2g_8  monthly_3g_6  monthly_3g_7  monthly_3g_8  sachet_3g_6  \\\n",
       "0            0             1             1             1            0   \n",
       "1            0             0             0             0            0   \n",
       "2            3             0             0             0            0   \n",
       "3            0             0             0             0            0   \n",
       "4            0             0             2             1            0   \n",
       "\n",
       "   sachet_3g_7  sachet_3g_8  fb_user_6  fb_user_7  fb_user_8  aug_vbc_3g  \\\n",
       "0            0            0        1.0        1.0        1.0       30.40   \n",
       "1            0            0       -1.0       -1.0       -1.0       57.74   \n",
       "2            0            0       -1.0        1.0        1.0       21.03   \n",
       "3            0            0       -1.0       -1.0       -1.0        0.00   \n",
       "4            0            1       -1.0        1.0        1.0       40.45   \n",
       "\n",
       "   jul_vbc_3g  jun_vbc_3g  av_rech_amt_call_data_6_7  av_rech_amt_call_data_8  \\\n",
       "0        0.00      101.20                      559.0                    504.0   \n",
       "1       19.38       18.74                     1185.0                   3638.0   \n",
       "2      910.65      122.16                      696.0                    327.0   \n",
       "3        0.00        0.00                      640.0                    718.0   \n",
       "4       51.86        0.00                      583.0                    979.0   \n",
       "\n",
       "   aon_month  last_rech_age_gap_1  last_rech_age_gap_2  arpu_diff  \\\n",
       "0  32.266667                 25.0                 23.0     7.7025   \n",
       "1  26.733333                 28.0                 32.0  1961.9650   \n",
       "2  10.500000                 36.0                 30.0  -298.1100   \n",
       "3  24.000000                 31.0                 31.0    81.6570   \n",
       "4  20.133333                 19.0                 48.0   232.8425   \n",
       "\n",
       "   onnet_mou_diff  offnet_mou_diff  roam_ic_mou_diff  roam_og_mou_diff  \\\n",
       "0            0.00            0.000              0.00             0.000   \n",
       "1           -3.97         -184.385              6.78            19.895   \n",
       "2         -347.28           48.835              0.00             0.000   \n",
       "3          -32.12          156.085              0.00             0.000   \n",
       "4          -16.23          118.505              0.00             0.000   \n",
       "\n",
       "   loc_og_t2t_mou_diff  loc_og_t2m_mou_diff  loc_og_t2f_mou_diff  \\\n",
       "0                0.000                0.000                0.000   \n",
       "1               -1.105             -215.725               -5.405   \n",
       "2             -244.870              -25.230                0.000   \n",
       "3               18.020               14.940                1.205   \n",
       "4              -16.230               65.585                0.000   \n",
       "\n",
       "   loc_og_t2c_mou_diff  loc_og_mou_diff  std_og_t2t_mou_diff  \\\n",
       "0                 0.00            0.000                0.000   \n",
       "1                 0.00         -222.245               -1.785   \n",
       "2                 7.15         -270.095             -102.415   \n",
       "3                 0.00           34.175              -50.145   \n",
       "4                17.71           49.345                0.000   \n",
       "\n",
       "   std_og_t2m_mou_diff  std_og_t2f_mou_diff  std_og_mou_diff  isd_og_mou_diff  \\\n",
       "0                0.000                 0.00            0.000            0.000   \n",
       "1                8.315                 3.31            9.840            9.920   \n",
       "2               63.825                 0.00          -38.590            0.000   \n",
       "3              139.940                 0.00           89.800            0.000   \n",
       "4               35.255                 0.00           35.255           -0.005   \n",
       "\n",
       "   spl_og_mou_diff  og_others_diff  total_og_mou_diff  loc_ic_t2t_mou_diff  \\\n",
       "0             0.00             0.0              0.000                0.160   \n",
       "1             4.25             0.0           -198.225              -17.890   \n",
       "2            10.23             0.0           -298.450              -16.530   \n",
       "3             0.00             0.0            123.975               13.205   \n",
       "4            17.66             0.0            102.270               35.765   \n",
       "\n",
       "   loc_ic_t2m_mou_diff  loc_ic_t2f_mou_diff  loc_ic_mou_diff  \\\n",
       "0                4.130                1.150            5.440   \n",
       "1              -98.335              -87.280         -203.515   \n",
       "2              -20.300                0.000          -36.840   \n",
       "3               32.730                6.665           52.605   \n",
       "4               42.205                0.710           78.690   \n",
       "\n",
       "   std_ic_t2t_mou_diff  std_ic_t2m_mou_diff  std_ic_t2f_mou_diff  \\\n",
       "0                0.000                0.000                0.000   \n",
       "1                6.030               32.115              -21.005   \n",
       "2               -0.190              -12.605                0.000   \n",
       "3               -9.840               48.780                0.000   \n",
       "4               13.125               35.250                0.000   \n",
       "\n",
       "   std_ic_mou_diff  total_ic_mou_diff  spl_ic_mou_diff  isd_ic_mou_diff  \\\n",
       "0            0.000              5.440            0.000            0.000   \n",
       "1           17.130           -163.510           -0.105           23.295   \n",
       "2          -12.795            -49.635            0.000            0.000   \n",
       "3           38.945             91.550            0.000            0.000   \n",
       "4           48.375            127.095            0.000          -10.375   \n",
       "\n",
       "   ic_others_diff  total_rech_num_diff  max_rech_amt_diff  \\\n",
       "0           0.000                 -1.5                0.0   \n",
       "1          -0.325                  2.0              395.0   \n",
       "2           0.000                 -6.0              -92.0   \n",
       "3           0.000                  3.0                0.0   \n",
       "4          10.385                  0.0               89.5   \n",
       "\n",
       "   last_day_rch_amt_diff  max_rech_data_diff  vol_2g_mb_diff  vol_3g_mb_diff  \\\n",
       "0                    0.0                 0.0          -9.975          -7.555   \n",
       "1                  779.0                 0.0           0.000           0.000   \n",
       "2                  -15.0               -54.0        -177.970        -363.535   \n",
       "3                   25.0                 0.0           0.000           0.000   \n",
       "4                  -89.5                89.5          -0.230         710.375   \n",
       "\n",
       "   arpu_3g_diff  arpu_2g_diff  night_pck_user_diff  monthly_2g_diff  \\\n",
       "0         0.000          0.00                  0.0              0.0   \n",
       "1         0.000          0.00                  0.0              0.0   \n",
       "2        19.830          0.00                  0.5             -0.5   \n",
       "3         0.000          0.00                  0.0              0.0   \n",
       "4        43.985         -0.22                  0.5              0.0   \n",
       "\n",
       "   sachet_2g_diff  monthly_3g_diff  sachet_3g_diff  fb_user_diff  target  \n",
       "0             0.0              0.0             0.0           0.0       1  \n",
       "1             0.0              0.0             0.0           0.0       1  \n",
       "2             2.5              0.0             0.0           1.0       0  \n",
       "3             0.0              0.0             0.0           0.0       0  \n",
       "4             0.0              0.0             1.0           1.0       0  "
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "churn_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "g64hBGFQ0cdf"
   },
   "source": [
    "### **6.1 Spliting the data into train and test sets**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "id": "f8qqRzFkq-6a"
   },
   "outputs": [],
   "source": [
    "churn_df_copy = churn_df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "id": "fT1C64sQqFuA"
   },
   "outputs": [],
   "source": [
    "y = churn_df_copy.pop(\"target\")\n",
    "X = churn_df_copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "id": "g-Tzh8_mTcyl"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Putting feature variable to X\n",
    "X = churn_df.drop(['target'],axis=1)\n",
    "\n",
    "# Putting response variable to y\n",
    "y = churn_df['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "id": "KkkU3agS1B1A"
   },
   "outputs": [],
   "source": [
    "# since our data has class imbalance for target variable, we will use stratify.\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.7, test_size=0.3, stratify=y, random_state=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "CVz9lgNr1kBT",
    "outputId": "ae2245c9-afa7-4e3a-d5ea-55e1bc007d27"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data size : (21000, 184)\n",
      "Test  data size : (9001, 184)\n"
     ]
    }
   ],
   "source": [
    "print(\"Train data size :\", X_train.shape)\n",
    "print(\"Test  data size :\", X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "id": "9b4sthawDcvl"
   },
   "outputs": [],
   "source": [
    "x_train_cols = X_train.columns\n",
    "x_test_cols = X_test.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "VneaAA55yvFu",
    "outputId": "45efb352-c13b-4e85-f1d9-f8fa32fb2bde"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    91.862\n",
       "1     8.138\n",
       "Name: target, dtype: float64"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking fo data imbalance\n",
    "round((y_train.value_counts()/y_train.size) * 100, 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aNjklBiS3Dd8"
   },
   "source": [
    "### **6.2 Data Scaling**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "id": "-eWKYeVw3W1c"
   },
   "outputs": [],
   "source": [
    "# initialize the scaler\n",
    "scaler = StandardScaler()\n",
    "\n",
    "# transform train data\n",
    "X_train_sc = scaler.fit_transform(X_train)\n",
    "\n",
    "# transform test data\n",
    "X_test_sc = scaler.fit_transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2qYHdezx_DOU"
   },
   "source": [
    "## **7. PCA (High Performance Model)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "id": "flNkPuJGQC1A"
   },
   "outputs": [],
   "source": [
    "def evaluate_model(classifier, X_train, X_test):\n",
    "    print(\"Train ROC AUC   :\", round(metrics.roc_auc_score(y_train, classifier.predict(X_train)), 2))\n",
    "    sensitivity, specificity, _ = sensitivity_specificity_support(y_train, classifier.predict(X_train), average='binary') \n",
    "    print(\"Sensitivity\\t: \", round(sensitivity, 2), \"\\n\", \"Specificity\\t: \", round(specificity, 2), sep='')\n",
    "    accuracy = accuracy_score(y_train, classifier.predict(X_train))\n",
    "    print(\"Train Accuracy  :\", \"{:2.2}\".format(accuracy))\n",
    "\n",
    "    print(\"-\"*50)\n",
    "    print(\"Test ROC AUC    :\", round(metrics.roc_auc_score(y_test, classifier.predict(X_test)), 2))\n",
    "    sensitivity_t, specificity_t, _ = sensitivity_specificity_support(y_test, classifier.predict(X_test), average='binary') \n",
    "    print(\"Sensitivity\\t: \", round(sensitivity_t, 2), \"\\n\", \"Specificity\\t: \", round(specificity_t, 2), sep='')\n",
    "    accuracy = accuracy_score(y_test, classifier.predict(X_test))\n",
    "    print(\"Test Accuracy   :\", \"{:2.2}\".format(accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "id": "l1d4Jp4rkypH"
   },
   "outputs": [],
   "source": [
    "# Function to plot plot_confusion_matrix\n",
    "def plot_confusion_matrix(y_actual, y_pred):\n",
    "    accuracy = accuracy_score(y_actual, y_pred)\n",
    "    print(\"Accuracy\", \"{:2.3}\".format(accuracy))\n",
    "\n",
    "    conf_matrix = confusion_matrix(y_actual, y_pred)\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=(4, 4))\n",
    "    ax.matshow(conf_matrix, cmap=plt.cm.Reds, alpha=0.3)\n",
    "    for i in range(conf_matrix.shape[0]):\n",
    "        for j in range(conf_matrix.shape[1]):\n",
    "            ax.text(x=j, y=i,s=conf_matrix[i, j], va='center', ha='center')\n",
    "\n",
    "    plt.xlabel('Predictions')\n",
    "    plt.ylabel('Actuals')\n",
    "    plt.title('Confusion Matrix', fontsize=12)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "id": "4s3jbvFaDKAZ"
   },
   "outputs": [],
   "source": [
    "# Function to plot top 20 feature_importances\n",
    "def plot_feature_importances(feature_importances, cols):\n",
    "    features = pd.DataFrame(feature_importances, columns=['coef_value']).set_index(cols)\n",
    "    features = features.sort_values(by='coef_value', ascending=False)\n",
    "    top_features = features\n",
    "    features = features.head(20)\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    sns.barplot(x='coef_value', y=features.index, data=features)\n",
    "    plt.show()\n",
    "    return top_features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Z2EyNTKG5XC2"
   },
   "source": [
    "### **7.1 Components from the PCA**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Tw_RzJO1m3ur"
   },
   "source": [
    "We will run PCA for feature reduction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "id": "7Jw_tzBc1od5"
   },
   "outputs": [],
   "source": [
    "# initialize object\n",
    "pca = PCA(random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "wdZEXMTm38F8",
    "outputId": "ef6858d0-6ab5-4e33-fdbc-af8f9c813037"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PCA(random_state=42)"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fit the scaled data\n",
    "pca.fit(X_train_sc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8v_f4hvC38AU",
    "outputId": "3c1711c5-4ce9-4716-bf42-e83d4badd90a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-6.20159774e-02, -5.83848491e-02, -4.30467346e-02, ...,\n",
       "        -1.66805668e-02,  3.35191425e-04, -2.37952437e-02],\n",
       "       [ 1.23208164e-01,  1.39604632e-01,  1.58114341e-01, ...,\n",
       "         2.21580116e-02,  1.25260521e-02,  4.64700354e-02],\n",
       "       [ 8.77251588e-02,  1.30097205e-01,  1.49067798e-01, ...,\n",
       "         1.45196554e-02,  2.15424053e-02,  5.50157326e-02],\n",
       "       ...,\n",
       "       [-0.00000000e+00,  1.42111800e-15, -1.42638006e-15, ...,\n",
       "        -4.66285084e-02, -2.75137096e-02, -1.42863468e-04],\n",
       "       [-0.00000000e+00, -3.43908929e-16,  7.81348922e-16, ...,\n",
       "         2.46066432e-02, -2.02297146e-03, -4.55639461e-03],\n",
       "       [ 0.00000000e+00,  3.86084394e-16, -1.67568924e-16, ...,\n",
       "         2.19165387e-02,  1.53604110e-02, -1.30449321e-02]])"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.components_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Fx1lwkfa6Am8"
   },
   "source": [
    "Looking at the explained variance ratio for each component"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-IjzFca-371u",
    "outputId": "0943fd28-9720-478c-bf5d-3d50f046acd0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([8.35988292e-02, 7.02484606e-02, 5.08569205e-02, 4.55750072e-02,\n",
       "       3.25038006e-02, 3.14967835e-02, 2.95495975e-02, 2.51573593e-02,\n",
       "       2.17252263e-02, 2.03186383e-02, 2.01394239e-02, 1.91066684e-02,\n",
       "       1.82652886e-02, 1.68379275e-02, 1.59364427e-02, 1.54268845e-02,\n",
       "       1.52814838e-02, 1.48717000e-02, 1.45044613e-02, 1.38334259e-02,\n",
       "       1.33844706e-02, 1.29623971e-02, 1.25592893e-02, 1.22784529e-02,\n",
       "       1.19042486e-02, 1.14193882e-02, 1.13180074e-02, 1.05818501e-02,\n",
       "       1.05384771e-02, 1.03784438e-02, 1.00936947e-02, 9.94444808e-03,\n",
       "       9.61844000e-03, 9.59379288e-03, 8.70802792e-03, 8.45741927e-03,\n",
       "       8.20634410e-03, 7.79273729e-03, 7.66528277e-03, 7.47392573e-03,\n",
       "       7.33911728e-03, 7.11696271e-03, 6.99361002e-03, 6.95955021e-03,\n",
       "       6.72133263e-03, 6.57618932e-03, 6.36144584e-03, 6.32068200e-03,\n",
       "       6.25372311e-03, 6.21637932e-03, 5.98829990e-03, 5.88454797e-03,\n",
       "       5.76998745e-03, 5.66574147e-03, 5.59030776e-03, 5.06595337e-03,\n",
       "       5.00412697e-03, 4.86077301e-03, 4.67208923e-03, 4.64679661e-03,\n",
       "       4.56765591e-03, 4.51639309e-03, 4.28380683e-03, 4.22395273e-03,\n",
       "       4.03129944e-03, 3.94192085e-03, 3.86641600e-03, 3.76023715e-03,\n",
       "       3.41127540e-03, 3.20338758e-03, 3.05189160e-03, 2.91162943e-03,\n",
       "       2.86626537e-03, 2.63839145e-03, 2.50831655e-03, 2.41842940e-03,\n",
       "       2.36640363e-03, 2.31087505e-03, 1.94554766e-03, 1.92372076e-03,\n",
       "       1.81127608e-03, 1.74391438e-03, 1.66968127e-03, 1.62456272e-03,\n",
       "       1.60374858e-03, 1.58945017e-03, 1.54600753e-03, 1.45457126e-03,\n",
       "       1.35324770e-03, 1.28636356e-03, 1.26962579e-03, 1.25815643e-03,\n",
       "       1.24545393e-03, 1.22210562e-03, 1.18308897e-03, 1.11133890e-03,\n",
       "       1.10647693e-03, 9.95982192e-04, 9.76677307e-04, 9.29835098e-04,\n",
       "       8.54498146e-04, 8.14604107e-04, 7.37775752e-04, 6.75640195e-04,\n",
       "       6.49326992e-04, 6.11403700e-04, 5.95702170e-04, 5.80911755e-04,\n",
       "       5.20409287e-04, 4.20220334e-04, 3.79635502e-04, 3.52519425e-04,\n",
       "       3.38357688e-04, 2.04038674e-04, 1.74298029e-04, 1.06657765e-04,\n",
       "       7.29272917e-05, 4.58973565e-05, 1.11942972e-05, 1.75183110e-06,\n",
       "       1.39652025e-06, 3.69389234e-07, 1.17871555e-11, 6.99909383e-12,\n",
       "       6.55191416e-12, 3.46168810e-12, 3.09448765e-12, 2.86169284e-12,\n",
       "       2.52967584e-12, 1.24011750e-12, 1.06522853e-12, 9.98039857e-13,\n",
       "       8.52259385e-13, 6.54530095e-13, 5.70595307e-13, 4.32787896e-13,\n",
       "       4.12945658e-13, 2.31495866e-13, 1.49624083e-13, 1.03766308e-13,\n",
       "       7.73941796e-32, 2.21654083e-32, 1.59927977e-32, 1.23562251e-32,\n",
       "       1.15746603e-32, 1.01047445e-32, 9.50189804e-33, 8.92327758e-33,\n",
       "       8.38920380e-33, 8.24954521e-33, 7.82401782e-33, 6.52386946e-33,\n",
       "       6.25996054e-33, 5.63855137e-33, 5.53117390e-33, 5.11704264e-33,\n",
       "       5.05955278e-33, 4.83926352e-33, 4.40758519e-33, 4.06858130e-33,\n",
       "       3.82956667e-33, 3.45741895e-33, 3.38102447e-33, 3.28887023e-33,\n",
       "       2.96703241e-33, 2.96532098e-33, 2.85546540e-33, 2.73542935e-33,\n",
       "       2.68620386e-33, 2.66307119e-33, 2.59399775e-33, 2.55033042e-33,\n",
       "       2.38281123e-33, 2.38156675e-33, 2.32831156e-33, 2.01128532e-33,\n",
       "       1.84656925e-33, 1.57456288e-33, 1.23031787e-33, 1.14041083e-33,\n",
       "       9.99403074e-34, 7.92799659e-34, 6.27109989e-34, 3.50216393e-34])"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "id": "obgS0sae6HD1"
   },
   "outputs": [],
   "source": [
    "# calculating the cumulative sum \n",
    "var_cumu = np.cumsum(pca.explained_variance_ratio_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "AMm8uKq1wTi2",
    "outputId": "8542a604-8f66-4337-bac1-6320e73d913d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 8.0\n",
      "1 15.0\n",
      "2 20.0\n",
      "3 25.0\n",
      "4 28.000000000000004\n",
      "5 31.0\n",
      "6 34.0\n",
      "7 37.0\n",
      "8 39.0\n",
      "9 41.0\n",
      "10 43.0\n",
      "11 45.0\n",
      "12 47.0\n",
      "13 49.0\n",
      "14 50.0\n",
      "15 52.0\n",
      "16 53.0\n",
      "17 55.00000000000001\n",
      "18 56.00000000000001\n",
      "19 57.99999999999999\n",
      "20 59.0\n",
      "21 60.0\n",
      "22 61.0\n",
      "23 63.0\n",
      "24 64.0\n",
      "25 65.0\n",
      "26 66.0\n",
      "27 67.0\n",
      "28 68.0\n",
      "29 69.0\n",
      "30 70.0\n",
      "31 71.0\n",
      "32 72.0\n",
      "33 73.0\n",
      "34 74.0\n",
      "35 75.0\n",
      "36 76.0\n",
      "37 76.0\n",
      "38 77.0\n",
      "39 78.0\n",
      "40 79.0\n",
      "41 79.0\n",
      "42 80.0\n",
      "43 81.0\n",
      "44 82.0\n",
      "45 82.0\n",
      "46 83.0\n",
      "47 83.0\n",
      "48 84.0\n",
      "49 85.0\n",
      "50 85.0\n",
      "51 86.0\n",
      "52 86.0\n",
      "53 87.0\n",
      "54 88.0\n",
      "55 88.0\n",
      "56 89.0\n",
      "57 89.0\n",
      "58 90.0\n",
      "59 90.0\n",
      "60 90.0\n",
      "61 91.0\n",
      "62 91.0\n",
      "63 92.0\n",
      "64 92.0\n",
      "65 93.0\n",
      "66 93.0\n",
      "67 93.0\n",
      "68 94.0\n",
      "69 94.0\n",
      "70 94.0\n",
      "71 95.0\n",
      "72 95.0\n",
      "73 95.0\n",
      "74 95.0\n",
      "75 96.0\n",
      "76 96.0\n",
      "77 96.0\n",
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      "79 96.0\n",
      "80 97.0\n",
      "81 97.0\n",
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      "86 98.0\n",
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      "92 98.0\n",
      "93 99.0\n",
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      "104 100.0\n",
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      "120 100.0\n",
      "121 100.0\n",
      "122 100.0\n",
      "123 100.0\n",
      "124 100.0\n",
      "125 100.0\n",
      "126 100.0\n",
      "127 100.0\n",
      "128 100.0\n",
      "129 100.0\n",
      "130 100.0\n",
      "131 100.0\n",
      "132 100.0\n",
      "133 100.0\n",
      "134 100.0\n",
      "135 100.0\n",
      "136 100.0\n",
      "137 100.0\n",
      "138 100.0\n",
      "139 100.0\n",
      "140 100.0\n",
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      "143 100.0\n",
      "144 100.0\n",
      "145 100.0\n",
      "146 100.0\n",
      "147 100.0\n",
      "148 100.0\n",
      "149 100.0\n",
      "150 100.0\n",
      "151 100.0\n",
      "152 100.0\n",
      "153 100.0\n",
      "154 100.0\n",
      "155 100.0\n",
      "156 100.0\n",
      "157 100.0\n",
      "158 100.0\n",
      "159 100.0\n",
      "160 100.0\n",
      "161 100.0\n",
      "162 100.0\n",
      "163 100.0\n",
      "164 100.0\n",
      "165 100.0\n",
      "166 100.0\n",
      "167 100.0\n",
      "168 100.0\n",
      "169 100.0\n",
      "170 100.0\n",
      "171 100.0\n",
      "172 100.0\n",
      "173 100.0\n",
      "174 100.0\n",
      "175 100.0\n",
      "176 100.0\n",
      "177 100.0\n",
      "178 100.0\n",
      "179 100.0\n",
      "180 100.0\n",
      "181 100.0\n",
      "182 100.0\n",
      "183 100.0\n"
     ]
    }
   ],
   "source": [
    "for index, i in enumerate(var_cumu):\n",
    "  print(str(index)+' '+ str(100*np.round(i,2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XRLYYEGWJZ1m"
   },
   "source": [
    "We require **71 variables** to explain 95% variance of the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "X1OWVcEG6KrZ"
   },
   "source": [
    "### **7.2 Making a scree plot for the explained variance**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "id": "pAIzRIXKRqS6"
   },
   "outputs": [],
   "source": [
    "from imblearn.metrics import sensitivity_specificity_support"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 483
    },
    "id": "z0Ls8G7H6HAw",
    "outputId": "c60bd6e8-4a43-45bf-f8e3-2d0aae420987"
   },
   "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": [
    "fig = plt.figure(figsize=[12,8])\n",
    "plt.vlines(x=71, ymax=1, ymin=0, colors=\"r\", linestyles=\"--\")\n",
    "plt.hlines(y=0.95, xmax=140, xmin=20, colors=\"g\", linestyles=\"--\")\n",
    "plt.plot(var_cumu)\n",
    "plt.ylabel(\"Cumulative variance explained\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7NubtTC66gdi"
   },
   "source": [
    "Perform PCA with 71 components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "id": "1Uh2ckKw6G7S"
   },
   "outputs": [],
   "source": [
    "pca_final = IncrementalPCA(n_components=71)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "n9N2BcJB66gM"
   },
   "source": [
    "Applying the transformation on the train and test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "HtlSKpIQ6G4x",
    "outputId": "3c165a7b-bb71-4451-c353-b7232d1b7f0e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(21000, 71)"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fit the scaled X_train\n",
    "X_train_pca = pca_final.fit_transform(X_train_sc)\n",
    "X_train_pca.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Unb7Y57S6qN8",
    "outputId": "9bd3bbdc-9e8e-417a-a0e8-e4299de6b1c6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9001, 71)"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_pca = pca_final.transform(X_test_sc)\n",
    "X_test_pca.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-gUvgiYf7Bcj"
   },
   "source": [
    "**Applying logistic regression on the data on our Principal components**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "id": "HVCGmij89wrf"
   },
   "outputs": [],
   "source": [
    "model_lr = LogisticRegression(class_weight='balanced')\n",
    "model_lr_pca = model_lr.fit(X_train_pca, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "3oFAtI2Rg6Da",
    "outputId": "da77c919-5cd4-4e5f-e11d-371dadd56ab3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8280952380952381"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_lr_pca.score(X_train_pca, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "id": "xtI4R8_nkTBe"
   },
   "outputs": [],
   "source": [
    "y_pred_train = model_lr_pca.predict_proba(X_train_pca)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "id": "VDZT_9G3lYgr"
   },
   "outputs": [],
   "source": [
    "y_pred_test = model_lr_pca.predict(X_test_pca)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ykKrryAJkhDg",
    "outputId": "2258b85c-88af-411b-bfc2-1dc1e07e6c74"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.82      0.90      8269\n",
      "           1       0.29      0.82      0.43       732\n",
      "\n",
      "    accuracy                           0.82      9001\n",
      "   macro avg       0.64      0.82      0.66      9001\n",
      "weighted avg       0.93      0.82      0.86      9001\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred_pca = model_lr_pca.predict(X_test_pca)  \n",
    "print(metrics.classification_report(y_test, y_pred_pca))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "irAaVnYKzT5n"
   },
   "source": [
    "Model evaluation and confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "gX2CW5iukg5Z",
    "outputId": "3d75e8e5-db54-435a-e0bd-1d636cc1b511"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train ROC AUC   : 0.83\n",
      "Sensitivity\t: 0.84\n",
      "Specificity\t: 0.83\n",
      "Train Accuracy  : 0.83\n",
      "--------------------------------------------------\n",
      "Test ROC AUC    : 0.82\n",
      "Sensitivity\t: 0.82\n",
      "Specificity\t: 0.82\n",
      "Test Accuracy   : 0.82\n"
     ]
    }
   ],
   "source": [
    "evaluate_model(model_lr_pca, X_train_pca, X_test_pca)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 307
    },
    "id": "owL3PHoBJO_k",
    "outputId": "9d2b5155-add3-4789-ca20-7d94a51215da"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy 0.825\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# call function to plot confusion matrix\n",
    "plot_confusion_matrix(y_test, y_pred_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Hgyf7n4z5K1S"
   },
   "source": [
    "## **8. High Interpretable Model**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vIOkybX7MhBr"
   },
   "source": [
    "### 8.1 Applying Random forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "id": "JdOpTEU4BY8k"
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "JP8hLqTHrsJ_",
    "outputId": "c7d058d2-9207-4324-8cfe-e4f37d213f8e",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(class_weight='balanced')"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc = RandomForestClassifier(class_weight='balanced')\n",
    "rfc.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "p7uIOmfzMhBs"
   },
   "source": [
    "Making predictions on the test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "GYQ5Nppfrr2a",
    "outputId": "01e07cd6-831b-492a-c081-66ae9e95c553"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 0, 0, 0])"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc_predictions = rfc.predict(X_test)\n",
    "rfc_predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NcgC1FA-MhBs"
   },
   "source": [
    "Model evaluation and confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "CE7AyV-4ZEL4",
    "outputId": "43123599-a793-44c4-d237-6a3f1699c07a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train ROC AUC   : 1.0\n",
      "Sensitivity\t: 1.0\n",
      "Specificity\t: 1.0\n",
      "Train Accuracy  : 1.0\n",
      "--------------------------------------------------\n",
      "Test ROC AUC    : 0.71\n",
      "Sensitivity\t: 0.44\n",
      "Specificity\t: 0.99\n",
      "Test Accuracy   : 0.94\n"
     ]
    }
   ],
   "source": [
    "evaluate_model(rfc, X_train, X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 307
    },
    "id": "s9DtzleUMhBs",
    "outputId": "d4859f8c-196f-4d8d-a52a-3a5fe05a6cd1",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy 0.942\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQMAAAEWCAYAAABiyvLjAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAV00lEQVR4nO3deXwV1fnH8c9DwiICkSCBKC5QEQUEBIqWVkVRBHHXuiMuSBHRqrjXX1HcgOKCSqXWDUFpXXBDNqsi1gUIiOJWtODCooRdkTU8vz/uSRowy03M3IHk+369eCX3zJkzzyS533tm7tzB3B0RkWpxFyAiOwaFgYgACgMRCRQGIgIoDEQkUBiICKAwqHTMbBcze8XM1pjZs79gnHPNbGpF1hYHM5tkZr3jrmNnoDCIiZmdY2Y5ZvajmS0Nf7S/q4ChTwcaAQ3c/fflHcTdn3L3bhVQzzbMrIuZuZmN3669bWifluQ4t5jZ2NL6uXsPdx9dznKrFIVBDMzsauA+4E4ST9y9gb8CJ1XA8PsA8919SwWMFZVcoLOZNSjU1huYX1EbsAT9fZeFu+tfCv8BGcCPwO9L6FOTRFgsCf/uA2qGZV2ARcBAYBmwFLgwLLsV2ARsDtu4GLgFGFto7H0BB9LD4wuABcAPwELg3ELt/y60XmdgFrAmfO1caNk04DbgnTDOVGD3YvYtv/5RwGWhLS20/RmYVqjvCOBbYC0wGzgstHffbj8/LFTHHaGO9cB+oa1PWP4Q8Fyh8YcCrwMW99/FjvBPyZl6vwFqAS+U0OdPwKFAO6At0Am4udDyxiRCZU8ST/iRZlbf3QeRmG38093ruPujJRViZrsC9wM93L0uiSf83CL6ZQKvhr4NgHuAV7d7ZT8HuBDIAmoA15S0beBJ4Pzw/bHAJySCr7BZJH4GmcDTwLNmVsvdJ2+3n20LrdML6AvUBb7ebryBQBszu8DMDiPxs+vtIRmqOoVB6jUAlnvJ0/hzgcHuvszdc0m84vcqtHxzWL7Z3SeSeHVsUc56tgKtzWwXd1/q7p8U0acn8IW7j3H3Le4+DvgcOKFQn8fdfb67rweeIfEkLpa7vwtkmlkLEqHwZBF9xrr7irDNu0nMmErbzyfc/ZOwzubtxvsJOI9EmI0FLnf3RaWMV2UoDFJvBbC7maWX0GcPtn1V+zq0FYyxXZj8BNQpayHuvg44E+gHLDWzV83sgCTqya9pz0KPvytHPWOAAcCRFDFTMrOBZvZZeGdkNYnZ0O6ljPltSQvdfSaJwyIjEVoSKAxS7z1gA3ByCX2WkDgRmG9vfj6FTtY6oHahx40LL3T3Ke5+DJBN4tX+70nUk1/T4nLWlG8M0B+YGF61C4Rp/PXAGUB9d9+NxPkKyy+9mDFLnPKb2WUkZhhLgOvKXXklpDBIMXdfQ+JE2UgzO9nMaptZdTPrYWbDQrdxwM1m1tDMdg/9S30brRhzgcPNbG8zywBuzF9gZo3M7MRw7mAjicONvCLGmAjsH94OTTezM4GWwIRy1gSAuy8EjiBxjmR7dYEtJN55SDezPwP1Ci3/Hti3LO8YmNn+wO0kDhV6AdeZWbvyVV/5KAxi4O73AFeTOCmYS2JqOwB4MXS5HcgBPgLmAXNCW3m29RrwzzDWbLZ9AlcjcVJtCbCSxBOzfxFjrACOD31XkHhFPd7dl5enpu3G/re7FzXrmQJMIvF249ckZlOFDwHyL6haYWZzSttOOCwbCwx19w/d/QvgJmCMmdX8JftQWZhOpIoIaGYgIoHCQEQAhYGIBAoDEQEUBiISKAwiZGbdzew/Zvalmd0Qdz1SOjN7zMyWmdnHcdeSagqDiJhZGjAS6EHiAp2zzaxlvFVJEp4g8anIKkdhEJ1OwJfuvsDdNwH/oGLuVyARcvfpJC7AqnIUBtHZk22vmFvEth/sEdmhKAyiY0W06XJP2WEpDKKzCNir0OMmlP+ThyKRUxhEZxbQ3MyamlkN4Czg5ZhrEimWwiAi4eYjA0h8+u4z4Jli7iIkOxAzG0finhMtzGyRmV0cd02pok8tigigmYGIBAoDEQEUBiISKAxEBFAYpISZ9Y27Bimbqvg7UxikRpX7w6oEqtzvTGEgIsAOdp1B/YwM3yMrK+4yKtyqNWupn1Gv9I47oV0q6X7l5i6nYcPS/vOmnc+8j+at3bhpU0ZRy0r6L75Sbo+sLMbde3/cZUgZtOlxdNwlSBlkNsxaVtwyHSaICKAwEJFAYSAigMJARAKFgYgACgMRCRQGIgIoDEQkUBiICKAwEJFAYSAigMJARAKFgYgACgMRCRQGIgIoDEQkUBiICKAwEJFAYSAigMJARAKFgYgACgMRCRQGIgIoDEQkUBiICKAwEJFAYSAigMJARAKFgYgACgMRCRQGIgIoDEQkUBiICKAwEJFAYSAigMJARAKFgYgACgMRCRQGIgIoDEQkUBiICKAwEJFAYSAigMJARAKFgYgACgMRCRQGIgIoDEQkUBiICKAwKLcxL77AKf37cepll3L9X4aycdMmpv77bU7p3492J/bkky/mF/Rd/P33dDrtZM64YgBnXDGA20Y+ULDs4huv58R+lxQsW7F6dQx7U7Vc1OcSsrL3pHXbdgVtzz73HK3atKVa9Zrk5Mz+2TrffPMNdTLqM/zue1JYaWqlRzm4mXUHRgBpwCPuPiTK7aXK9yuW8/QrL/PCX0dRq2ZNrh1yJ5Onv8VBLVpw7003b/Nkz9ekcTbP3P9gkePdNfBaWjXfP+qyJbjg/PMZ0L8/5194YUFb61atGP/sM/zh0suKXOeqgdfQo/uxqSoxFpGFgZmlASOBY4BFwCwze9ndP41qm6mUtzWPjZs2kZ6ezvqNG2mY2YBme+0dd1mShMMPP4yvvvpqm7YDDzyw2P4vvvQSzZo2Y9dda0dcWbyinBl0Ar509wUAZvYP4CRgpw+DRg12p/cpp3LsRb2pVaMGvzm4PZ3bty9xncXff8cZfxxAnV1qM6DX+bRv1bpg2Z9H3EtatTS6du5M3zPPxsyi3gVJ0rp16xg6bDivTZlUqQ8RINpzBnsC3xZ6vCi0bcPM+ppZjpnlrFqzNsJyKs7aH3/gzRnvM/GRx3lt9FjWb9jAhDffKLZ/w8xMpjw2mmdGPMg1fS7hhuHD+PGnnwC485pref7Bh3h8yDDmfPJJieNI6g26ZTBXXXkFderUibuUyEUZBkW9vPnPGtwfdveO7t6xfka9CMupOO/PncuejRqTmZFB9fR0unb+LR9+9lmx/WtUr85u9RL71nK/5uzVOJuvFy8CErMMgF1r1+a4I7owb/5/ot8BSdqMmTO57oab2PdXzbnv/ge4c8hQHhz517jLikSUhwmLgL0KPW4CLIlweynTuGFDPvr8c9Zv2ECtmjWZ8eFcWu7XvNj+K9esIaNOHdLS0lj03VK+XrKEJo2z2ZKXxw8//kj9jAw2b9nC9FkzOaRdu9TtiJTq7bfeLPj+llsHU6dOHQZc1j/GiqITZRjMApqbWVNgMXAWcE6E20uZNi0O4Jjf/o6zrryCtLQ0DmjWjNO79+D1995lyN8eYtWaNQwYfAstmjZj1ODbmfPxPEY+NZb0tDSqVavGzZcNIKNuXX7asIFLB/0fW/K2kJe3lUPbteO0bt3j3r1K7+xzz2PaW9NZvnw5TfZpyq2D/kxmZn0u/+NV5Obm0vPEk2jXti1TJr0ad6kpZe4/m7lX3OBmxwH3kXhr8TF3v6Ok/q2aN/dx994fWT1S8dr0ODruEqQMMhtmfbly5aoip7GRXmfg7hOBiVFuQ0Qqhq5AFBFAYSAigcJARACFgYgECgMRARQGIhIoDEQEUBiISKAwEBFAYSAigcJARACFgYgECgMRARQGIhIoDEQEUBiISKAwEBFAYSAigcJARACFgYgECgMRARQGIhIoDEQEUBiISKAwEBFAYSAigcJARACFgYgECgMRARQGIhIoDEQEUBiISFCmMDCz+mbWJqpiRCQ+pYaBmU0zs3pmlgl8CDxuZvdEX5qIpFIyM4MMd18LnAo87u4dgKOjLUtEUi2ZMEg3s2zgDGBCxPWISEySCYPBwBTgS3efZWbNgC+iLUtEUi29tA7u/izwbKHHC4DToixKRFKv2DAwswcAL265u18RSUUiEouSZgY5KatCRGJXbBi4++hUFiIi8Sr1nIGZNQSuB1oCtfLb3f2oCOsSkRRL5t2Ep4DPgKbArcBXwKwIaxKRGCQTBg3c/VFgs7u/5e4XAYdGXJeIpFiphwnA5vB1qZn1BJYATaIrSUTikEwY3G5mGcBA4AGgHnBVpFWJSMolc9FR/iXIa4Ajoy1HROKSzLsJj1PExUfh3IGIVBLJHCYU/nBSLeAUEucNKtwu9epx0LGafOxMfOvWuEuQsij2muLkDhOeL/zYzMYB//rFRYnIDqU8tz1rDuxd0YWISLySOWfwA9tOLr4jcUWiiFQiyRwm1E1FISISr2Tugfh6Mm0isnMr6X4GtYDawO5mVh+wsKgesEcKahORFCrpMOEPwJUknviz+V8YrAVGRluWiKRaSfczGAGMMLPL3f2BFNYkIjFI5q3FrWa2W/6D8B+p9I+uJBGJQzJhcIm7r85/4O6rgEsiq0hEYpFMGFQzs/zzBZhZGlAjupJEJA7JfDZhCvCMmY0icfFRP2BSpFWJSMolEwbXA32BS0m8o/ABkB1lUSKSeqUeJrj7VuB9YAHQEehK4p6IIlKJlHTR0f7AWcDZwArgnwDurs8Yi1RCJR0mfA68DZzg7l8CmJludyZSSZV0mHAaiU8ovmlmfzezrvzvKkQRqWSKDQN3f8HdzwQOAKaRuAlqIzN7yMy6pag+EUmRZE4grnP3p9z9eBK3SJ8L3BB1YSKSWmW605G7r3T3v+m/VhOpfMpz2zMRqYQUBiICKAxEJFAYiAigMBCRQGEgIoDCQEQChYGIAAoDEQkUBiICKAxEJFAYiAigMBCRQGEgIoDCQEQChYGIAAoDEQkUBiICKAxEJFAYiAigMBCRQGEgIoDCQEQChYGIAAoDEQkUBiICKAxEJFAYiAigMBCRQGFQQfLy8mj/60M54eRTAVi5ciXdevRk/5at6dajJ6tWrQJg8+bNXHBRH9oc3JGWB7XjrqF/ibPsKmnDhg0c0vl3tOvwa1q3PZhBtw4G4NobbuTA1m1o274jp55+BqtXr95mvW+++Ya69Rsw/J57Y6g6epGFgZk9ZmbLzOzjqLaxIxnxwIMceECLgsdDhg3nqCO7MP/TjznqyC4MGTYcgGefe56NGzfy0Qc55Mx4l4cfeYSvvvo6nqKrqJo1a/L61MnMnT2LD3JmMmXqa7w/YwbHdD2KeXPn8OGcHJo3b/6zoL76muvoceyxMVUdvShnBk8A3SMcf4exaNEiJk6azMUXXVjQ9vIrE+jd6zwAevc6j5defgUAM2Pdup/YsmUL69evp0b1GtSrVzeWuqsqM6NOnTpAYqa2efNmzIxuxxxDeno6AIce0onFixcVrPPiSy/TtFlTWrY8MJaaUyGyMHD36cDKqMbfkVw18FqG3nUH1ar978f5/bJlZGdnA5Cdnc2y3FwATj/tVHbdtTZ77N2UfX61PwOvvpLMzMxY6q7K8vLyOLhjJxrtuRdHd+3KIZ06bbP88SdG0z3MAtatW8ew4Xcz6OY/xVFqysR+zsDM+ppZjpnl5C7PjbucMpvw6kQaZmXRoX37pPrPnDWLtLQ0Fn+9gAXzP+Oee0ewYMHCiKuU7aWlpfFBzky+XfhfZuXM4uOPPylYdsddQ0hPT+fcc84GYNDg27jyissLZhOVVXrcBbj7w8DDAB07dPCYyymzd959j1cmTGDS5Mls2LCRtWvX0qv3hTTKymLp0qVkZ2ezdOlSsho2BODpfzzDsd26Ub16dbKysujc+TfkzJ5Ns2ZNY96Tqmm33XbjiMMPZ/LUqbRu3YrRT47h1YmT+NeUSZgZADNnzuT58eO5/qabWL16DdWqVaNWrVoM6H9pzNVXrNhnBju7u+64jW8X/peFX/yHcWOf5KgjuzBm9OOccEJPRo8ZC8DoMWM58YTjAdh7rya8OW0a7s66deuYMWMmB7RoUcIWpKLl5uYWvFOwfv16Xn/jDQ5o0YLJU6YybPjdvDT+OWrXrl3Qf/qbb7Dwi/ks/GI+f7x8ADdef12lCwLYAWYGldUN117Dmeecx2NPjGbvvfbimXFPAXDZpf24qE9fDmrXAXfngt69aNPmoJirrVqWLv2OCy7uQ15eHlu3buX3p5/G8T2Po/mBLdm4cSPdevQE4JBDOjFq5IMxV5s65h7NzNzMxgFdgN2B74FB7v5oSet07NDBZ73/TiT1SFQs7gKkDBpkNf5y5apVzYtaFtnMwN3PjmpsEal4OmcgIoDCQEQChYGIAAoDEQkUBiICKAxEJFAYiAigMBCRQGEgIoDCQEQChYGIAAoDEQkUBiICKAxEJFAYiAigMBCRQGEgIoDCQEQChYGIAAoDEQkUBiICKAxEJFAYiAigMBCRQGEgIoDCQEQChYGIAAoDEQkUBiICKAxEJFAYiAigMBCRQGEgIoDCQEQChYGIAAoDEQkUBiICKAxEJFAYiAigMBCRQGEgIoDCQEQChYGIAAoDEQkUBiICKAxEJFAYiAigMBCRQGEgIgCYu8ddQwEzywW+jruOCOwOLI+7CCmTyvo728fdGxa1YIcKg8rKzHLcvWPcdUjyquLvTIcJIgIoDEQkUBikxsNxFyBlVuV+ZzpnUAmZWR4wD0gHPgN6u/tP5RzrCWCCuz9nZo8A97j7p8X07QJscvd3w+N+wE/u/mR5ti2ppZlB5bTe3du5e2tgE9Cv8EIzSyvPoO7ep7ggCLoAnQv1H6Ug2HkoDCq/t4H9zKyLmb1pZk8D88wszcz+YmazzOwjM/sDgCU8aGafmtmrQFb+QGY2zcw6hu+7m9kcM/vQzF43s31JhM5VZjbXzA4zs1vM7JrQv52ZvR+29YKZ1S805lAzm2lm883ssNDeKrTNDes0T+UPrSpKj7sAiY6ZpQM9gMmhqRPQ2t0XmllfYI27/9rMagLvmNlU4GCgBXAQ0Aj4FHhsu3EbAn8HDg9jZbr7SjMbBfzo7sNDv66FVnsSuNzd3zKzwcAg4MqwLN3dO5nZcaH9aBLBMsLdnzKzGkC5ZjOSPIVB5bSLmc0N378NPEpi+j7T3ReG9m5AGzM7PTzOAJoDhwPj3D0PWGJmbxQx/qHA9Pyx3H1lScWYWQawm7u/FZpGA88W6jI+fJ0N7Bu+fw/4k5k1Aca7+xcl77L8UgqDymm9u7cr3GBmAOsKN5F4pZ6yXb/jgNLOKlsSfcpiY/iaR/ibdPenzWwG0BOYYmZ93L2oYJIKonMGVdcU4FIzqw5gZvub2a7AdOCscE4hGziyiHXfA44ws6Zh3czQ/gNQd/vO7r4GWJV/PgDoBby1fb/CzKwZsMDd7wdeBtqUdQelbDQzqLoeITEln2OJaUMucDLwAnAUibcm51PEk9bdc8M5h/FmVg1YBhwDvAI8Z2YnAZdvt1pvYJSZ1QYWABeWUt+ZwHlmthn4Dhhcjn2UMtB1BiIC6DBBRAKFgYgACgMRCRQGIgIoDEQkUBiICKAwEJHg/wHv9SG+u/PzywAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# call function to plot confusion matrix\n",
    "plot_confusion_matrix(y_test, rfc_predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nJb8JG9Xnmg-"
   },
   "source": [
    "Applying Random Forest without any paramater tuning overfits the training data and gives low sensitivity. Hence we will perform parameter tuning to increase the sensitivity."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CTgxT4TRMhBt"
   },
   "source": [
    "### 8.2 Hyperparameter tunning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "id": "JGX99goXoZei"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV, StratifiedKFold, RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "id": "0GoF94ovoZ8r"
   },
   "outputs": [],
   "source": [
    "param_grid = {\n",
    "    'max_depth': [10, 20, 30],\n",
    "    'min_samples_leaf': range(100, 400, 100),\n",
    "    'min_samples_split': range(100, 400, 100),\n",
    "    'n_estimators': [200, 300],\n",
    "}\n",
    "\n",
    "folds = StratifiedKFold(n_splits = 5, shuffle = True, random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "id": "VS5s43yCoaCJ"
   },
   "outputs": [],
   "source": [
    "estimator = RandomForestClassifier(class_weight='balanced', random_state=42, criterion='entropy')\n",
    "\n",
    "random_search = RandomizedSearchCV(estimator, param_distributions=param_grid, scoring='recall', cv=folds, verbose=2,\n",
    "                               random_state=42, return_train_score=True, n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 422
    },
    "id": "-uBrHwizoe1a",
    "outputId": "f3de0827-53d9-40a9-a6b9-b207ebda963d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n",
      "Wall time: 2min 12s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=StratifiedKFold(n_splits=5, random_state=42, shuffle=True),\n",
       "                   estimator=RandomForestClassifier(class_weight='balanced',\n",
       "                                                    criterion='entropy',\n",
       "                                                    random_state=42),\n",
       "                   n_jobs=-1,\n",
       "                   param_distributions={'max_depth': [10, 20, 30],\n",
       "                                        'min_samples_leaf': range(100, 400, 100),\n",
       "                                        'min_samples_split': range(100, 400, 100),\n",
       "                                        'n_estimators': [200, 300]},\n",
       "                   random_state=42, return_train_score=True, scoring='recall',\n",
       "                   verbose=2)"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "random_search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1--jaRB2MhBu"
   },
   "source": [
    "#### Best estimator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0chTspz6oe4f",
    "outputId": "15cf2bf5-44c5-4f15-a4a4-2b2755c11a0c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(class_weight='balanced', criterion='entropy',\n",
       "                       max_depth=10, min_samples_leaf=300,\n",
       "                       min_samples_split=300, n_estimators=300,\n",
       "                       random_state=42)"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# best estimator\n",
    "rf_best = random_search.best_estimator_\n",
    "rf_best"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "id": "8uPCH2phoe7L",
    "outputId": "f762d922-2e81-4147-a1b1-3aaf63005c1a"
   },
   "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>mean_train_score</th>\n",
       "      <th>mean_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.851959</td>\n",
       "      <td>0.793439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.823288</td>\n",
       "      <td>0.801043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.823580</td>\n",
       "      <td>0.799873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.823580</td>\n",
       "      <td>0.799871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.833088</td>\n",
       "      <td>0.796361</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean_train_score  mean_test_score\n",
       "0          0.851959         0.793439\n",
       "1          0.823288         0.801043\n",
       "2          0.823580         0.799873\n",
       "3          0.823580         0.799871\n",
       "4          0.833088         0.796361"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_result_rf = pd.DataFrame(random_search.cv_results_)\n",
    "cv_result_rf[['mean_train_score', 'mean_test_score']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uHFM4O5iJRF9"
   },
   "source": [
    "### 8.3 Final model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "id": "IoxKZOSwgAN8"
   },
   "outputs": [],
   "source": [
    "rfc_final = RandomForestClassifier(\n",
    "    class_weight='balanced', criterion='entropy', max_depth=10, max_features='auto',\n",
    "    min_samples_leaf=300, min_samples_split=300, n_estimators=300,\n",
    "    n_jobs=-1, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "GGZ5LNaWK36V",
    "outputId": "f064def9-fb4b-4702-edc4-c17336112d57"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 2.88 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(class_weight='balanced', criterion='entropy',\n",
       "                       max_depth=10, min_samples_leaf=300,\n",
       "                       min_samples_split=300, n_estimators=300, n_jobs=-1,\n",
       "                       random_state=42)"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "rfc_final.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VodkmWb7K6EU"
   },
   "source": [
    "#### Making predictions on the test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "DDcPAKi1K86t",
    "outputId": "25fcd0f6-f750-42af-a189-77ead2e0dbd7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 0, 0, 0])"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc_final_pred = rfc_final.predict(X_test)\n",
    "rfc_final_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qiCNJMvzMhBu"
   },
   "source": [
    "#### Model evaluation and confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0jYnbyAL1zpG",
    "outputId": "ad10d93f-fa8a-4f24-fdb3-2b6948a4488e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train ROC AUC   : 0.86\n",
      "Sensitivity\t: 0.83\n",
      "Specificity\t: 0.89\n",
      "Train Accuracy  : 0.89\n",
      "--------------------------------------------------\n",
      "Test ROC AUC    : 0.84\n",
      "Sensitivity\t: 0.8\n",
      "Specificity\t: 0.89\n",
      "Test Accuracy   : 0.88\n"
     ]
    }
   ],
   "source": [
    "evaluate_model(rfc_final, X_train, X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 307
    },
    "id": "nWUHBIPb7KPh",
    "outputId": "fac1c305-7e0d-4acb-8a9d-ca1c42d1f961"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy 0.882\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# call function to plot confusion matrix\n",
    "plot_confusion_matrix(y_test, rfc_final_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QVPR3-PqHifh"
   },
   "source": [
    "### 8.4 Feature Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 389
    },
    "id": "MwZkuHhrMhBv",
    "outputId": "a796123d-28eb-4886-b31e-e2feac097154",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# feature importance for top 20 features\n",
    "top_featues = plot_feature_importances(rfc_final.feature_importances_*100, x_train_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 514
    },
    "id": "qKkhO8XgnRyJ",
    "outputId": "0ae7f361-1f2b-4a23-83ed-d8bbce155d76"
   },
   "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>coef_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>loc_ic_mou_8</th>\n",
       "      <td>8.790198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>av_rech_amt_call_data_8</th>\n",
       "      <td>7.265496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_ic_mou_8</th>\n",
       "      <td>6.889105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loc_ic_t2t_mou_8</th>\n",
       "      <td>4.066995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loc_ic_t2m_mou_8</th>\n",
       "      <td>4.011501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loc_og_mou_8</th>\n",
       "      <td>3.869094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_og_mou_8</th>\n",
       "      <td>3.850907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>last_day_rch_amt_8</th>\n",
       "      <td>3.383003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loc_og_t2t_mou_8</th>\n",
       "      <td>3.340630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>arpu_diff</th>\n",
       "      <td>3.151577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>arpu_8</th>\n",
       "      <td>2.732377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loc_og_t2m_mou_8</th>\n",
       "      <td>2.393678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_og_mou_diff</th>\n",
       "      <td>2.234315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>roam_og_mou_8</th>\n",
       "      <td>2.152179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>roam_ic_mou_8</th>\n",
       "      <td>2.043191</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         coef_value\n",
       "loc_ic_mou_8               8.790198\n",
       "av_rech_amt_call_data_8    7.265496\n",
       "total_ic_mou_8             6.889105\n",
       "loc_ic_t2t_mou_8           4.066995\n",
       "loc_ic_t2m_mou_8           4.011501\n",
       "loc_og_mou_8               3.869094\n",
       "total_og_mou_8             3.850907\n",
       "last_day_rch_amt_8         3.383003\n",
       "loc_og_t2t_mou_8           3.340630\n",
       "arpu_diff                  3.151577\n",
       "arpu_8                     2.732377\n",
       "loc_og_t2m_mou_8           2.393678\n",
       "total_og_mou_diff          2.234315\n",
       "roam_og_mou_8              2.152179\n",
       "roam_ic_mou_8              2.043191"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_featues.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "A4UdjjPXMhBv",
    "outputId": "b5a5aa08-514a-4afd-86d9-c38d776d7f34"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['loc_ic_mou_8',\n",
       " 'av_rech_amt_call_data_8',\n",
       " 'total_ic_mou_8',\n",
       " 'loc_ic_t2t_mou_8',\n",
       " 'loc_ic_t2m_mou_8',\n",
       " 'loc_og_mou_8',\n",
       " 'total_og_mou_8',\n",
       " 'last_day_rch_amt_8',\n",
       " 'loc_og_t2t_mou_8',\n",
       " 'arpu_diff',\n",
       " 'arpu_8',\n",
       " 'loc_og_t2m_mou_8',\n",
       " 'total_og_mou_diff',\n",
       " 'roam_og_mou_8',\n",
       " 'roam_ic_mou_8']"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get top 15 features list\n",
    "top_n_featues = top_featues.sort_values(by='coef_value', ascending=False)[:15]\n",
    "top_featues_list = list(top_n_featues.index)\n",
    "top_featues_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "id": "5fsdwNPmDP8o"
   },
   "outputs": [],
   "source": [
    "# X_train for top features\n",
    "X_train_top = X_train[top_featues_list]\n",
    "\n",
    "# X_test for top features\n",
    "X_test_top = X_test[top_featues_list]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IOIO-cY5DQEI"
   },
   "source": [
    "### 8.5 Model on top 15 features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "id": "G8ZnliKN6Kga"
   },
   "outputs": [],
   "source": [
    "top_feature_model = RandomForestClassifier(\n",
    "    class_weight='balanced', criterion='entropy', max_depth=10, max_features='auto',\n",
    "    min_samples_leaf=300, min_samples_split=300, n_estimators=300,\n",
    "    n_jobs=-1, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "zDB-GZTh6Kg0",
    "outputId": "1fbcd0b0-e83a-4ded-a69f-b9852797d558"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 1.31 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(class_weight='balanced', criterion='entropy',\n",
       "                       max_depth=10, min_samples_leaf=300,\n",
       "                       min_samples_split=300, n_estimators=300, n_jobs=-1,\n",
       "                       random_state=42)"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "top_feature_model.fit(X_train_top, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zOZvIlWK6Kg3"
   },
   "source": [
    "Making predictions on the test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "62YfAfPf6Kg3",
    "outputId": "6495b1f2-7102-4515-ada4-e9c8248a2ec3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 0, 0, 0])"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_model_pred = top_feature_model.predict(X_test_top)\n",
    "top_model_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "HyZ2VlHv6Kg4",
    "outputId": "420fcfcb-aed9-4d88-b099-2320ad0d3ae5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train ROC AUC   : 0.85\n",
      "Sensitivity\t: 0.83\n",
      "Specificity\t: 0.87\n",
      "Train Accuracy  : 0.87\n",
      "--------------------------------------------------\n",
      "Test ROC AUC    : 0.84\n",
      "Sensitivity\t: 0.81\n",
      "Specificity\t: 0.87\n",
      "Test Accuracy   : 0.86\n"
     ]
    }
   ],
   "source": [
    "evaluate_model(top_feature_model, X_train_top, X_test_top)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RvqpuiNB6Kg4"
   },
   "source": [
    "Accuracy and confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 307
    },
    "id": "aeT5MueL6Kg5",
    "outputId": "31c7aaa0-3a4f-4182-f01f-b8927fcdcda5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy 0.864\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# call function to plot confusion matrix\n",
    "plot_confusion_matrix(y_test, top_model_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 389
    },
    "id": "ltlW6uvG6xnV",
    "outputId": "c9bade16-e905-4569-b8b7-4190ddd7e3b3"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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H7Y6NNlhngB8pIiIiYvjpcwVY0v7AZGBP2y9LuhkYDVwB/DHwIDCzhMnevNQ4JPCPtv9ft3mOo/qxfE+WdDu/N4cCmwG72H5N0sJSa/cxlje8Xs5q/kroJtdooN7w+SWNBi4EOmz/StLXehq7v/0aHA4cX46/A1y8CvVGREREtJz+rACPAZ4rwW4HYI/SfjXwCeBPqMJwf10PHCVpAwBJW0l6CzAb+OOyoky3LRD9NQZ4qoTfA4C3DvD8F4EN++gzm2r1FEmjJG1E79doIG4BPl3G3AI4oLSvCLFPl2vWuAe6sd5m/XryOLBfOT4QeHgVao6IiIhoOf1Z+ZwFfKFsK/gZ1Y/4sf2cpJ8CO9qe298Jbd8g6d3A7eUercXAn9q+X9LfA3MkLaPaInHEgD4NXA5cK6kTuI9qdXogrgWuKjfP/bntH/fQ53hgmqTPAcuownCP12iAZlIF0S7gIWAOgO3fSfpGaV9ItX95hUuAiyT9HtgT6K1fT/4MOFfSWlR7pwd8415EREREK1LznQsRK203fozP/Ns96y4jImLYOOTIWXWXEBG9kHS37Y6e3hvOz+SNiIiIiBh0q3XzV6Oyd3d2D2+9f4CPA+vPXDsDl3VrXmJ790Gc46tUj01r9B3bzR731p9x13jt3ea7ANi7W/O5tqevifkiIiIihrtBC8Al5E4arPH6mKtrTc9Vgu5qhd1exl3jtXeb79ihmisiIiKiFQxaAI6Rb+Nx78h+t4iIiGh52QMcEREREW0lATgiIiIi2koCcERERES0lewBjn576tmHOe/yD9ZdRkSshuMOvb7uEiIiapcV4IiIiIhoKwnAEREREdFWEoAjIiIioq0kAEdEREREW0kAjoiIiIi20vIBWNLiQRzrdEmTB3jOJEkfaXh9qKT55es2SRNL+1hJXxysWgeDpLMk3S/pAUnnSVLdNUVERESsaS0fgAeT7VNt/2iAp00CPtLw+lFgP9sTgL8DppX2scCwCcCS9gL2BiYAOwG7AvvVWlRERETEEBgxAViVsyUtkNQlaWrDeyeVtnmSzmgyxiWSDinHu5YV3HmS5krasIf+6wCnA1Ml3Sdpqu3bbD9XutwBbF2OzwC2K/3O7mX+/SXNkXSlpIcknVFWlOeW+rcr/d4qaXZZZZ4tadvu9ZfXzVbHDYwG1gHWBdYGFjXpHxERETEijKRfhHEw1WrsRGAccJekW0rbJ4Ddbb8saZO+BirB9gpgqu27JG0E/L57P9uvSjoV6LD9pR6G+hxwXTk+BdjJ9qQ+pp8IvBt4FngEuNj2bpKOB/4c+AvgfOBbti+VdBRwXvmM/Wb7dkk3AU8AAs63/UD3fpKOBo4G2HjT0QOZIiIiImJYGjErwMA+wAzby2wvAuZQ/Vh/MjDd9ssAtp/tx1jvAp6wfVc55wXbSwdSjKQDqALwyQM5D7jL9hO2lwC/AG4o7V3A+HK8J/Cf5fgyqs8+IJK2pwraWwNbAQdK2rd7P9vTbHfY7thgo3UGOk1ERETEsDOSAnBvN3CJ6sf9Ax1roOesPFmaAFwMHGT7mQGevqTheHnD6+X0vmK/otallO9puaGtWWKdAtxhe7HtxVQr1XsMsNaIiIiIljOSAvAtVHtxR0naDNgXmEu1gnqUpPUA+rMFAngQ2FLSruWcDSX1Fj5fBP53f3DZj3s1cJjth3rrt5puAz5djg8FflKOFwK7lOODqPb19uYxYD9Ja0lam+oGuDdsgYiIiIgYaUZSAJ4JzAfmATcCJ9l+0vYs4BqgU9J9wIl9DWT7VWAq8G+S5gE/pLphrCc3ATuuuAkOOBXYFLiwtHWWMZ8Bbi036fV4E9wAHAccKWk+cBhwfGn/BlWonQvsDrzUZIyrqLZYdFFds3m2r13NuiIiIiKGPdmr/JP+aDPbvn2MT/y77JKIaGXHHXp93SVERAwJSXfb7ujpvZG0AhwRERER0aeR9Bi0fpN0AdUvgWh0ru3pTc75IHBmt+ZHbU9Zhfl3pnp6Q6Mltncf6FjDaa6IiIiIVpAtENFvHR0d7uzsrLuMiIiIiD5lC0RERERERJEAHBERERFtJQE4IiIiItpKAnBEREREtJW2fApErJqFv3uYI2d+qO4yIoa96VNm1V1CREQ0kRXgiIiIiGgrCcARERER0VYSgCMiIiKirSQAR0RERERbSQCOiIiIiLaSADyCSbpE0iHl+GJJO5bjT0l6QNJN5fUMSfMlnVBnvRERERFDIY9BGwYkjbK9bE3OYfvzDS8/B3zR9k2S/gDYy/Zb1+T8EREREcNFVoCHgKTvSbpb0v2Sji5tiyWdLulOYE9JCyWdKWlu+dq+9PvfVdwV5zWZR5LOl/RTSf8DvKXhvZsldUg6FdgHuEjS2cANwFsk3SfpfT2MebSkTkmdr7zw6mBdkoiIiIjaJAAPjaNs7wJ0AMdJ2hRYH1hge3fbPyn9XrC9G3A+8K+rMM8U4F3AzsCfAXt172D7dKATONT2XwMfB35he5LtH/fQf5rtDtsdozdaZxVKioiIiBheEoCHxnGS5gF3ANsA7wCWAd/t1m9Gw597rsI8+wIzbC+z/Thw4yrWGxERETFiZQ/wGiZpf2AysKftlyXdDIwGXulh3697OF5K+YuKJAF9LcO6j/cjIiIi2lpWgNe8McBzJfzuAOzRpO/Uhj9vL8cLgV3K8UHA2k3OvwX4tKRRkrYADljlqiMiIiJGqKwAr3mzgC9Img/8jGobRG/WLTfFvQn4k9L2DeC/Jc0FZgMvNTl/JnAg0AU8BMxZzdojIiIiRhzZ+Yn5cCBpIdBh++m6a+nNuO3H+I/OXpWtyRHtZfqUWXWXEBHR9iTdbbujp/eyBSIiIiIi2kq2QAwTtsf3t6+knYHLujUvsb37oBYVERERMQJlC0T0W0dHhzs7O+suIyIiIqJP2QIREREREVEkAEdEREREW0kAjoiIiIi2kpvgot8e/t0TfGTm1+suI6IWP5jyN3WXEBERgyQrwBERERHRVhKAIyIiIqKtJABHRERERFtJAI6IiIiItpIAHBERERFtpeUCsKSxkr7YR5/xkj7Tj7HGS1oweNUNL5KOkHR+Of6CpM+W4x0k3SfpXknbSTpO0gOSLq+34oiIiIg1r+UCMDAWaBqAgfFAnwG4ndi+yPa3ystPAP9t+z22f0F1PT9i+9DaCoyIiIgYIq0YgM8AtisrmGeXrwWSuiRNbejzvtLnhLLS+2NJ95SvvfozkaTRkqaXse+VdEBpX0/SlZLmS7pC0p2Sevxd06X/YklnSrpb0o8k7SbpZkmPSPp4H3P97ypuef19Sfs3metISQ9JmgPs3dD+NUknSvoI8BfA5yXdJOki4O3ANZJO6M91iYiIiGhlrfiLME4BdrI9SdIngS8AE4FxwF2Sbil9TrT9MagCK/AB269IegcwA+g1sDY4FsD2zpJ2AG6Q9E6qFdPnbE+QtBNwXx/jrA/cbPtkSTOBrwMfAHYELgWuaTJXv0naAjgN2AV4HrgJuLexj+0flNC72PY55bwPAQfYfrqHMY8GjgYYvdmYgZQTERERMSy14gpwo32AGbaX2V4EzAF27aHf2sA3JHUB36EKnv0d/zIA2w8CvwTeWdr/q7QvAOb3Mc6rwKxy3AXMsf1aOR7fx1wDsTtV0P6t7VeBKwZ4/hvYnma7w3bHOhutv7rDRURERNSuFVeAG6mf/U4AFlGtFL8JeGU1x+/vvCu8ZtvleDmwBMD2ckkrvge9jbmU1/9FZXQfc7mP9yMiIiLaWiuuAL8IbFiObwGmSholaTNgX2Butz4AY4AnbC8HDgNG9XOuW4BDAcp2hG2BnwE/Af64tO8I7Lw6H6iPuRYCkyS9SdI2wG5NxrgT2F/SppLWBj41CHVFREREjCgttwJs+xlJt5bHl11Htf1gHtXK50m2n5T0DLBU0jzgEuBC4LuSPkW1L/alfk53IXBR2TqxFDjC9hJJFwKXSppPtcd2PtWe29XR21y3Ao9SbZdYANzT2wC2n5D0NeB24InSt79hPyIiIqItaOVP5qO/JI0C1i431W0HzAbeWfbdjlhjtt/Ke599TN1lRNTiB1P+pu4SIiJiACTdbbvHhx603ArwMLEecFPZZiDgmJEefiMiIiJGigRgQNIHgTO7NT9qe0pP/W2/SA+PUZN0J7But+bDbHcNSqE1zRURERExkiQAA7avB64fhHF2H4Ryht1cERERESNJAnD02zvGbpF9kBEREdHyWvExaBERERERqywBOCIiIiLaSgJwRERERLSV7AGOfnv4uaf56HcvrruMiEHzP5/8fN0lREREDbICHBERERFtJQE4IiIiItpKAnBEREREtJUE4IiIiIhoKwnAEREREdFWhlUAlrR4Fc/7C0nrDaD/EZLOX5W5VpWk/SV9f4jmGivpi/3od5ak+yU9IOk8SRqK+iIiIiLqNKwC8Gr4C6DfAXhNkjQcHi03FmgagCXtBewNTAB2AnYF9lvjlUVERETUbFgGYEkbSJot6R5JXZIOKu3rS/ofSfMkLZA0VdJxwJbATZJuajLmkZIekjSHKvitaP8jSXdKulfSjyRtLulNkh6WtFnp8yZJP5c0rpexL5H0z2X+MyVtX8aaVz7DdqXrBpKukvSgpMubrbhKOlXSXeVzTlvRV9LNkv5F0i1l5XZXSVeXer9eTj8D2E7SfZLO7mUKA6OBdYB1gbWBRb3VExERETFSDIfVyp68Akyx/UIJnXdIugb4EPC47Y8CSBpj+3lJfwkcYPvpngaTtAVwGrAL8DxwE3BvefsnwB62LenzwEm2/0rSt4FDgX8FJgPzehu/eCcw2fYySXcCZ9ieKWk01V80tgHeA/wh8DhwK1UQ/0kv451v+/RS/2XAx4Bry3uv2t5X0vHAf5fP9SzwC0n/ApwC7GR7Um/F2r69BPYnAJX5Hujh2h0NHA0wetwmTT5+RERERGsYlivAVIHsHyTNB34EbAVsDnQBkyWdKel9tp/v53i7Azfb/q3tV4ErGt7bGrheUhfw11QBFeCbwGfL8VHA9D7m+E4JvxsCW9meCWD7Fdsvlz5zbf/a9nLgPmB8k/EOKCvTXcCBDXUBXFP+7ALut/2E7SXAI1RBu0+StgfeTfX5twIOlLRv9362p9nusN2xzkYb9mfoiIiIiGFtuAbgQ4HNgF3KKuYiYLTth6hWO7uAf5R06gDGdC/t/0a1+rkz8H+ptgVg+1fAIkkHUgXo6/oY/6XyZ7MbyZY0HC+jlxX4smp8IXBIqesbK+rqNs7ybmMu723MHkwB7rC92PZiqs+3Rz/PjYiIiGhZwzUAjwGesv2apAOAtwJI2hJ42fa3gXOA95b+LwLNlifvBPaXtKmktYFPdZvrN+X48G7nXQx8G7jS9rL+FG77BeDXkj5Ral53IE+oKFaE3aclbQAcMsDz+7oeAI8B+0laq1yT/YA3bIGIiIiIGGmGawC+HOiQ1Em1Gvxgad8ZmCvpPuCrwIqbvqYB1/V2E5ztJ4CvAbdTbam4p+HtrwHfkfRjoPse32uADeh7+0N3hwHHlS0ctwF/MJCTbf+OatW3C/gecNcAz38GuLXcQNfbTXBXAb8oc8yj2uN8bS99IyIiIkYM2b3tDAhJHcC/2H5f3bUMB2O2G+99zvqbusuIGDT/88nP111CRESsIZLutt3R03vD9SkQtZN0CnAM1Qp0RERERIwQIy4Al0eQrdut+TDbXQMZx/YZVM/TbRz7q7x+/zBUT3/4+wEXunLMmcDbujWfbPv6VR2z2/g7A5d1a15ie/fBGD8iIiKi1Yy4ALwmg10JuqscdnsZc8pgjtfD+F3ApDU5R0REREQrGXEBONacd2w8LnsmIyIiouUN16dARERERESsEQnAEREREdFWEoAjIiIioq1kD3D028+f+x1/dNXVdZcR0S/XHnJw3SVERMQwlRXgiIiIiGgrCcARERER0VYSgCMiIiKirSQAR0RERERbSQCOiIiIiLYyYgOwpLGSvthHn/GSPtOPscZLWjB41dVP0p9I6pI0X9IsSePqrikiIiJiKIzYAAyMBZoGYGA80GcAHmkkrQWcCxxgewIwH/hSvVVFREREDI2RHIDPALaTdJ+ks8vXgrLqObWhz/tKnxPKSu+PJd1Tvvbqz0SSRkuaXsa+V9IBpX09SVeWVdYrJN0pqaPJOIslnSnpbkk/krSbpJslPSLp433MdYSk8xvG+r6k/XubqnytL0nARsDj/fmsEREREa1uJP8ijFOAnWxPkvRJ4AvARGAccJekW0qfE21/DKrACnzA9iuS3gHMAHoNrA2OBbC9s6QdgBskvZNqBfo52xMk7QTc18c46wM32z5Z0kzg68AHgB2BS4FrmszVb7Zfk3QM0AW8BDy8YtzuJB0NHA3w5nHZJRERERGtbySvADfaB5hhe5ntRcAcYNce+q0NfENSF/AdquDZ3/EvA7D9IPBL4J2l/b9K+wKqrQbNvArMKsddwBzbr5Xj8X3M1W+S1gaOAd4DbFnq+nJPfW1Ps91hu2OdjcYMZJqIiIiIYaldArD62e8EYBHVSnEHsM5qjt/feVd4zbbL8XJgCYDt5axcre9tzKW8/vs5usk8k8q4vyjzXQn0a7tHRERERKsbyQH4RWDDcnwLMFXSKEmbAfsCc7v1ARgDPFEC52HAqH7OdQtwKEDZjrAt8DPgJ8Afl/YdgZ1X5wP1MddCYJKkN0naBtityRi/AXYs1wKqbRYPDEJtEREREcPeiN0DbPsZSbeWx5ddR/Vj/nmAgZNsPynpGWCppHnAJcCFwHclfQq4iWp/bH9cCFxUtk4sBY6wvUTShcClkuYD95Yanl/Nj9bbXLcCj1Jtl1gA3NPbALYfl3QacIuk16i2URyxmnVFREREtASt/Il7DDZJo4C1y0112wGzgXfafrXm0lbJ2O229/vOPKvuMiL65dpDDq67hIiIqJGku233+DCDEbsCPEysB9xUbjoTcEyrht+IiIiIkSIBeAAkfRA4s1vzo7an9NTf9ov08Bg1SXcC63ZrPsx216AUWtNcEREREa0gWyCi3zo6OtzZ2Vl3GRERERF9arYFYiQ/BSIiIiIi4g0SgCMiIiKirSQAR0RERERbSQCOiIiIiLaSp0BEv/3iucVM+e5P6i4j2szMT+5TdwkRETHCZAU4IiIiItpKAnBEREREtJUE4IiIiIhoKwnAEREREdFWEoAjIiIioq0kALcpSSdIul/SAkkzJI2uu6aIiIiIoTBiA7AqI/bzrQ5JWwHHAR22dwJGAZ+ut6qIiIiIoTGiAqKk8ZIekHQhcA/wH2WFs0vS1NJnA0mzJd1T2g9qOPdBSReXcy6XNFnSrZIelrRbk3k3kfQ9SfMl3SFpQmnfTNIPy1z/T9IvJY1rUnuf8zeZ62uSTmwYb4Gk8U0u11rAmyWtBawHPN5LXUdL6pTUueSF3zUZLiIiIqI1jKgAXLwL+BbwdWBrYCIwGThb0hbAK8AU2+8FDgD+SZLKudsD5wITgB2AzwD7ACcCX2ky52nAvbYnlH7fKu1/C9xY5poJbNtH7f2Zv7e5+s32b4BzgMeAJ4Dnbd/QS99ptjtsd6y70diBThUREREx7IzEAPxL23dQBccZtpfZXgTMAXYFBPyDpPnAj4CtgM3LuY/a7rK9HLgfmG3bQBcwvsmc+wCXAdi+EdhU0pjS/l+lfRbwXB+192f+3ubqN0kbAwcBbwO2BNaX9KcDGSMiIiKiVY3EAPxS+VO9vH8osBmwi+1JwCJgxQ1gSxr6LW94vZzmvza6p7ncpIbe9Gf+3uZayuu/n81uaptMFbZ/a/s14GpgrwHWGhEREdGSRmIAXuEWYKqkUZI2A/YF5gJjgKdsvybpAOCtgzTXoQCS9geetv0C8BPgj0v7/wE2XoNzLQTeW9rfS7W625vHgD0krVe2f7wfeGAQaouIiIgY9pqtara6mcCewDyqFdKTbD8p6XLgWkmdwH3Ag4Mw19eA6WVbxcvA4aX9NGBGuQFvDtV+2xfX0FzfBT4r6T7gLuCh3gawfaekq6huFFwK3AtMW826IiIiIlqCqi2msSZIWhdYZnuppD2Bfy/bLlrSxtvt4P3PurjuMqLNzPzkPnWXEBERLUjS3bY7enpvJK8ADwfbAleW5xG/CvxZzfVEREREtL0E4AGQdCRwfLfmW20f21N/2w8D7+k2xqbA7B66v9/2M4NSaA1zRURERLSKbIGIfuvo6HBnZ2fdZURERET0qdkWiJH8FIiIiIiIiDdIAI6IiIiItpIAHBERERFtJTfBRb/96nevctzMX9VdRgwj503Zpu4SIiIiBiwrwBERERHRVhKAIyIiIqKtJABHRERERFtJAI6IiIiItpIAHBERERFtJQF4DZD0cUmn1F1HM5JOkHS/pAWSZkgaXXdNEREREUOhbQKwKkPyeW1fY/uMoZhrVUjaCjgO6LC9EzAK+HS9VUVEREQMjREdgCWNl/SApAuBe4D/KCueXZKmlj4bSJot6Z7SflDDuQ9Kuricc7mkyZJulfSwpN2azHuEpPPL8eaSZkqaV772alJrn/NJ2kTS9yTNl3SHpAml/WuSTmwYb4Gk8U0uz1rAmyWtBawHPD6ASxsRERHRskZ0AC7eBXwL+DqwNTARmAycLWkL4BVgiu33AgcA/yRJ5dztgXOBCcAOwGeAfYATga/0c/7zgDm2JwLvBe5v0rc/850G3Gt7Qmn7Vj/r+F+2fwOcAzwGPAE8b/uGnvpKOlpSp6TO37/w7ECnioiIiBh22iEA/9L2HVRBcobtZbYXAXOAXQEB/yBpPvAjYCtg83Luo7a7bC+nCq6zbRvoAsb3c/4DgX8HKHM/36Rvf+bbB7isjHcjsKmkMf2sBQBJGwMHAW8DtgTWl/SnPfW1Pc12h+2ON2+0yUCmiYiIiBiW2iEAv1T+VC/vHwpsBuxiexKwCFhxQ9iShn7LG14vZ838Gun+zNfT5zCwlNd/P5vd1DaZKmz/1vZrwNVAj1szIiIiIkaadgjAK9wCTJU0StJmwL7AXGAM8JTt1yQdALx1kOedDRwDUObeaDXHu4UqtCNpf+Bp2y8AC6m2WCDpvVSru715DNhD0nplu8f7gQdWs66IiIiIltBOAXgmMB+YB9wInGT7SeByoENSJ1WwfHCQ5z0eOEBSF3A38IerOd7XqOqdD5wBHF7avwtsIuk+qsD9UG8D2L4TuIrqxsAuqn8Opq1mXREREREtQdUW04i+bb79BE89+3/qLiOGkfOmbFN3CRERET2SdLftjp7ea6cV4IiIiIiINXIjV9uQdCTVFodGt9o+tsk5m1LtC+7u/bafGeT6hmyuiIiIiFaRALwabE8Hpg/wnGeASWukoBrnioiIiGgVCcDRb9uMXSd7PiMiIqLlZQ9wRERERLSVBOCIiIiIaCsJwBERERHRVrIHOPrtd88t5eqrnq67jBgkBx8yru4SIiIiapEV4IiIiIhoKwnAEREREdFWEoAjIiIioq0kAEdEREREW0kAjoiIiIi2MmwDsKTFddfQSNIkSR9peH2opPnl6zZJE+usb6AkjZF0raR5ku6XdGTdNUVEREQMhWEbgIehScBHGl4/CuxnewLwd8C0OopaDccCP7U9Edgf+CdJ69RbUkRERMSaN+wDsCpnS1ogqUvS1Ib3Tipt8ySd0WSMSZLuKKu1MyVtXNp3LW23r5ijl/PXAU4Hpkq6T9JU27fZfq50uQPYuvQdL+lBSReXmi+XNFnSrZIelrRbkzq/JulSSTdIWijpYElnlc84S9Lapd/7Jd1b2r8pad3SvlDSuHLcIenmJpfWwIaSBGwAPAssbdI/IiIiYkQY9gEYOJhq9XUiMBk4W9IWkj4MfALYvaxintVkjG8BJ5fV2i7gb0v7dOALtvcElvV2su1XgVOBK2xPsn1Fty6fA65reL09cC4wAdgB+AywD3Ai8JU+Pu92wEeBg4BvAzfZ3hn4PfBRSaOBS4CppX0t4Jg+xuzJ+cC7gceprsnxtpd37yTpaEmdkjqff+GZVZgmIiIiYnhphQC8DzDD9jLbi4A5wK5UYXi67ZcBbD/b08mSxgBjbc8pTZcC+0oaC2xo+7bS/p+rUpykA6gC8MkNzY/a7iqB8n5gtm1TBc3xfQx5ne3XSt9RwKzSvuLcd5XxH2r8PKtQ+geB+4Atqf6Ccb6kjbp3sj3NdoftjjEbbboK00REREQML60QgNWk3Wtg3P4PIE0ALgYOst24PLqk4Xh5w+vl9P3rp5cAlPD8WgnOjec2q3spK7+no/uY50jgald+TrWneYc+zomIiIhoea0QgG+h2ns7StJmVKudc4EbgKMkrQcgaZOeTrb9PPCcpPeVpsOAOWX/7ouS9ijtn+6jjheBDVe8kLQtcDVwWMNq7FB4EBgvafvy+jCqVXGAhcAu5fiTfYzzGPB+AEmbU60sPzKolUZEREQMQ32tRg4HM4E9gXlUK74n2X4SmCVpEtAp6VXgB/S+v/Zw4KISlh+hWv2EauvCNyS9BNwMPN+kjpuAUyTdB/wj8AFgU+DC6j4yltruWMXP2G+2XymPLPuOpLWAu4CLytunAf8h6SvAnX0M9XfAJZK6qFaVT7b99JqqOyIiImK40MqfsLcfSRvYXlyOTwG2sH18zWUNW9tvN8lnnfmjusuIQXLwIePqLiEiImKNkXR3b4uTrbACvCZ9VNKXqa7DL4Ej6i0nIiIiIta0ERWAJV0A7N2t+Vzb03vqXx5n9rpHmkn6IHBmt66P2p4yiHUeCXRfab7V9rGDNUcdc0VERES0grbeAhED09HR4c7OzrrLiIiIiOhTsy0QrfAUiIiIiIiIQZMAHBERERFtJQE4IiIiItpKAnBEREREtJUR9RSIWLNefnop9178VN1lxGp6z+ffUncJERERtcoKcERERES0lQTgiIiIiGgrCcARERER0VYSgCMiIiKirSQAR0RERERbaesALGlx3TXURdLaki6V1CXpAUlfrrumiIiIiKGQx6C1r08B69reWdJ6wE8lzbC9sOa6IiIiItaotl4BXkGVsyUtKCuiUxveO6m0zZN0RpMxJkm6Q9J8STMlbVzady1tt6+Yo8kYR0j6nqRrJT0q6UuS/lLSvWXsTfqY62ZJHeV4nKSFTT62gfUlrQW8GXgVeKH/Vy0iIiKiNSUAVw4GJgETgcnA2ZK2kPRh4BPA7rYnAmc1GeNbwMm2JwBdwN+W9unAF2zvCSzrRy07AZ8BdgP+HnjZ9nuA24HP9jHXQFwFvAQ8ATwGnGP72e6dJB0tqVNS53MvPrMK00REREQMLwnAlX2AGbaX2V4EzAF2pQrD022/DNBTQASQNAYYa3tOaboU2FfSWGBD27eV9v/sRy032X7R9m+B54FrS3sXML63uQbwWVfYjSqQbwm8DfgrSW/v3sn2NNsdtjs23nDTVZgmIiIiYnhJAK6oSbvXwLjNLGk4Xt7wejl979leysrv6eg++n4GmGX7NdtPAbcCHQOsNSIiIqLlJABXbgGmSholaTOqFdW5wA3AUeUmMVbswe3O9vPAc5LeV5oOA+bYfg54UdIepf3Tq1tob3OV44XALuX4kD6Gegw4sOx/Xh/YA3hwdeuLiIiIGO7yFIjKTGBPYB7Viu9Jtp8EZkmaBHRKehX4AfCVXsY4HLiohOVHgCNL++eAb0h6CbiZalvD6uptrnOAKyUdBtzYxxgXUO1PXkC1Uj3d9vxBqC0iIiJiWJO9Oj/hj75I2sD24nJ8CrCF7eNrLmuV7Dh+ki//mxvqLiNW03s+/5a6S4iIiFjjJN1tu8ftnVkBXvM+Wn7JxFrAL4Ej6i0nIiIior0lAA+QpAuAvbs1n2t7ek/9bV8BXNFtjA8CZ3br+qjtKYNWaA1zRURERLSCbIGIfuvo6HBnZ2fdZURERET0qdkWiDwFIiIiIiLaSgJwRERERLSVBOCIiIiIaCsJwBERERHRVvIUiOi31xYt4clzfl53GbEa/uDE7esuISIionZZAY6IiIiItpIAHBERERFtJQE4IiIiItpKAnBEREREtJUE4IiIiIhoKwnAbUrSWZLul/SApPMkqe6aIiIiIoZCAnALkTRqkMbZC9gbmADsBOwK7DcYY0dEREQMdwnAw4ik70m6u6zMHl3aFks6XdKdwJ6SFko6U9Lc8rV96XeJpEMaxlrcZCoDo4F1gHWBtYFFvdR0tKROSZ3PLH52kD5pRERERH0SgIeXo2zvAnQAx0naFFgfWGB7d9s/Kf1esL0bcD7wrwOdxPbtwE3AE+XretsP9NJ3mu0O2x2bbrDJwD9RRERExDCTADy8HCdpHnAHsA3wDmAZ8N1u/WY0/LnnQCcpq8bvBrYGtgIOlLTvqhYdERER0UoSgIcJSfsDk4E9bU8E7qXapvCK7WXduruH46WU72e5oW2dJtNNAe6wvdj2YuA6YI/V/QwRERERrSABePgYAzxn+2VJO9A8kE5t+PP2crwQ2KUcH0S1r7c3jwH7SVpL0tpUN8D1uAUiIiIiYqRZq+4C4n/NAr4gaT7wM6ptEL1Zt9wU9ybgT0rbN4D/ljQXmA281OT8q4ADgS6qFeRZtq9dzfojIiIiWkIC8DBhewnw4R7e2qCHtgtsn9bt/EW8ftX4y03mWgb831WpMyIiIqLVZQtERERERLSVrAC3GNvj+9tX0s7AZd2al9jefVCLioiIiGghCcAjmO0uYNJgjbf25uvyByduP1jDRURERNQiWyAiIiIioq0kAEdEREREW0kAjoiIiIi2kj3A0W+vPfUii867ue4yWtLmx+1fdwkRERFRZAU4IiIiItpKAnBEREREtJUE4IiIiIhoKwnAEREREdFWEoAjIiIioq20XACWtLjuGhpJmiTpIw2vD5U0v3zdJmliaR8r6Yv1VfpGks6SdL+kBySdJ0l11xQRERGxprVcAB6GJgEfaXj9KLCf7QnA3wHTSvtYYNgEYEl7AXsDE4CdgF2B/WotKiIiImIItGwAVuVsSQskdUma2vDeSaVtnqQzmowxSdIdZbV2pqSNS/uupe32FXP0cv46wOnAVEn3SZpq+zbbz5UudwBbl+MzgO1Kv7N7GW9/SXMkXSnpIUlnlBXlueXzbFf6vVXS7FLjbEnblvZLJB3SMF6z1XIDo4F1gHWBtYFFTfpHREREjAgtG4CBg6lWXycCk4GzJW0h6cPAJ4DdbU8EzmoyxreAk8tqbRfwt6V9OvAF23sCy3o72farwKnAFbYn2b6iW5fPAdeV41OAX5R+f92kponA8cDOwGHAO23vBlwM/Hnpcz7wrVL35cB5TcbrrfbbgZuAJ8rX9bYf6N5P0tGSOiV1Prv4+YFOExERETHstHIA3geYYXuZ7UXAHKof408Gptt+GcD2sz2dLGkMMNb2nNJ0KbCvpLHAhrZvK+3/uSrFSTqAKgCfPMBT77L9hO0lwC+AG0p7FzC+HO/ZUNdlVNdioPVtD7ybaoV6K+BASft272d7mu0O2x2bbDBmoNNEREREDDutHIB7u2FLVD/eH+xx+z+ANIFqxfYg288M8PQlDcfLG14vp/dfXb3i8y6lfE/LDW3rNJlnCnCH7cW2F1OtVO8xwFojIiIiWk4rB+BbqPbejpK0GbAvMJdqxfQoSesBSNqkp5NtPw88J+l9pekwYE7Zv/uipBVh8NN91PEisOGKF2U/7tXAYbYf6q3farqtoa5DgZ+U44XALuX4IKp9vb15DNhP0lqS1qa6Ae4NWyAiIiIiRpreVhRbwUyqrQDzqFZAT7L9JDBL0iSgU9KrwA+Ar/QyxuHARSUsPwIcWdo/B3xD0kvAzUCzza83AadIug/4R+ADwKbAheWpYkvLFoJnJN1abqi7ro99wH05DvimpL8GfttQ9zeA/5Y0F5gNvNRkjKuAA6m2VhiYZfva1agpIiIioiXIXp3dAiOTpA3KtgAknQJsYfv4msuq3cRt3+UbTvx/dZfRkjY/bv+6S4iIiGgrku623dHTe628ArwmfVTSl6muzy+BI+otJyIiIiIGS1sEYEkXUP3Sh0bn2p7eU//yOLPXPdJM0geBM7t1fdT2lFWoZ2eqpzc0WmJ794GONZzmioiIiGgFbRGAbR87CGNcD1w/COVgu4vqGcZr3FDOFREREdEK2iIAx+BY+y0bZi9rREREtLxWfgxaRERERMSA5SkQ0W+SXgR+Vncdw8g44Om6ixgmci1eL9djpVyL18v1WCnX4vVyPVYarGvxVtub9fRGtkDEQPyst8eJtCNJnbkelVyL18v1WCnX4vVyPVbKtXi9XI+VhuJaZAtERERERLSVBOCIiIiIaCsJwDEQ0+ouYJjJ9Vgp1+L1cj1WyrV4vVyPlXItXi/XY6U1fi1yE1xEREREtJWsAEdEREREW0kAjj5J+pCkn0n6uaRT6q6nTpK2kXSTpAck3S/p+LprqpukUZLulfT9umupm6Sxkq6S9GD5Z2TPumuqk6QTyr8nCyTNkDS67pqGkqRvSnpK0oKGtk0k/VDSw+XPjeuscaj0ci3OLv+uzJc0U9LYGkscUj1dj4b3TpRkSePqqG2o9XYtJP15yR73SzprsOdNAI6mJI0CLgA+DOwI/ImkHeutqlZLgb+y/W5gD+DYNr8eAMcDD9RdxDBxLjDL9g7ARNr4ukjaCjgO6LC9EzAK+HS9VQ25S4APdWs7BZht+x3A7PK6HVzCG6/FD4GdbE8AHgK+PNRF1egS3ng9kLQN8AHgsaEuqEaX0O1aSDoAOAiYYPsPgXMGe9IE4OjLbsDPbT9i+1Xgv6j+oWxLtp+wfU85fpEq4GxVb1X1kbQ18FHg4rprqZukjYB9gf8AsP2q7d/VWlT91gLeLGktYD3g8ZrrGVK2bwGe7dZ8EHBpOb4U+MRQ1lSXnq6F7RtsLy0v7wC2HvLCatLLPxsA/wKcBLTNDVq9XItjgDNsLyl9nhrseROAoy9bAb9qeP1r2jjwNZI0HngPcGfNpdTpX6n+Y7285jqGg7cDvwWmly0hF0tav+6i6mL7N1SrNo8BTwDP276h3qqGhc1tPwHVX6iBt9Rcz3BxFHBd3UXUSdLHgd/Ynld3LcPAO4H3SbpT0hxJuw72BAnA0Rf10NY2fzPtjaQNgO8Cf2H7hbrrqYOkjwFP2b677lqGibWA9wL/bvs9wEu0z4+336DsbT0IeBuwJbC+pD+tt6oYjiR9lWp72eV111IXSesBXwVOrbuWYWItYGOqrYZ/DVwpqac8ssoSgKMvvwa2aXi9NW32Y8zuJK1NFX4vt3113fXUaG/g45IWUm2NOVDSt+stqVa/Bn5te8VPBK6iCsTtajLwqO3f2n4NuBrYq+aahoNFkrYAKH8O+o92W4mkw4GPAYe6vZ/Luh3VXxbnlf+mbg3cI+kPaq2qPr8GrnZlLtVPGQf1psAE4OjLXcA7JL1N0jpUN7FcU3NNtSl/A/0P4AHb/1x3PXWy/WXbW9seT/XPxY2223aFz/aTwK8kvas0vR/4aY0l1e0xYA9J65V/b95PG98U2OAa4PByfDjw3zXWUitJHwJOBj5u++W666mT7S7bb7E9vvw39dfAe8t/V9rR94ADASS9E1gHeHowJ0gAjqbKDQpfAq6n+p/Xlbbvr7eqWu0NHEa12nlf+fpI3UXFsPHnwOWS5gOTgH+ot5z6lJXwq4B7gC6q/9+01W+6kjQDuB14l6RfS/occAbwAUkPU93tf0adNQ6VXq7F+cCGwA/Lf0svqrXIIdTL9WhLvVyLbwJvL49G+y/g8MH+CUF+E1xEREREtJWsAEdEREREW0kAjoiIiIi2kgAcEREREW0lATgiIiIi2koCcERERES0lQTgiIiIiGgrCcAREbHGSFpX0o/Kc16nDtKYN0vqGIyxIqI9rVV3ARERMaK9B1jb9qS6C4mIWCErwBER0StJn5U0X9I8SZdJequk2aVttqRtS7/NJH1X0l3la29JbwG+DUwqK8Db9TD+hyVd2fB6f0nXluN/l9Qp6X5Jp/VS3+KG40MkXdJbPYN6YSKipSUAR0REjyT9IfBV4EDbE4HjqX597bdsTwAuB84r3c8F/sX2rsAngYttPwV8Hvix7Um2f9HDND8E9pC0fnk9FbiiHH/VdgcwAdhP0oQBlP+GegZwbkSMcNkCERERvTkQuMr20wC2n5W0J3Bwef8y4KxyPBnYUdKKczeStGFfE9heKmkW8EeSrgI+CpxU3v5jSUdT/b9qC2BHYH4/a++xHtsv9vP8iBjBEoAjIqI3AtxHnxXvvwnY0/bvXzfAygDazBXAscCzwF22X5T0NuBEYFfbz5WtDaObzE+393usJyICsgUiIiJ6N5tqFXZTAEmbALcBny7vHwr8pBzfAHxpxYmSJg1gnpuB9wJ/xsrtDxsBLwHPS9oc+HAv5y6S9G5JbwKmNLSvTj0RMcIlAEdERI9s3w/8PTBH0jzgn4HjgCMlzQcOo9oXTGnvKDfH/RT4wgDmWQZ8nyrkfr+0zQPuBe4Hvgnc2svpp5RzbgSeaGhf5XoiYuST3ddPtyIiIiIiRo6sAEdEREREW8lNcBERMSQkzQTe1q35ZNvX11FPRLSvbIGIiIiIiLaSLRARERER0VYSgCMiIiKirSQAR0RERERbSQCOiIiIiLaSABwRERERbeX/BzJJV0Ewc1nGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "top_15 = plot_feature_importances(top_feature_model.feature_importances_*100, top_n_featues.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 514
    },
    "id": "6Xr9cdgZrjhy",
    "outputId": "dd904f3c-1314-4fea-8d55-ba0165a9f5e7",
    "scrolled": false
   },
   "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>coef_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>loc_ic_mou_8</th>\n",
       "      <td>15.730083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_ic_mou_8</th>\n",
       "      <td>12.683502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loc_ic_t2m_mou_8</th>\n",
       "      <td>11.529257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>av_rech_amt_call_data_8</th>\n",
       "      <td>9.639113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loc_ic_t2t_mou_8</th>\n",
       "      <td>7.152392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>arpu_diff</th>\n",
       "      <td>6.003313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_og_mou_diff</th>\n",
       "      <td>5.687578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>last_day_rch_amt_8</th>\n",
       "      <td>5.373289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_og_mou_8</th>\n",
       "      <td>5.088024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>roam_og_mou_8</th>\n",
       "      <td>4.687546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>roam_ic_mou_8</th>\n",
       "      <td>3.940443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loc_og_t2m_mou_8</th>\n",
       "      <td>3.432392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loc_og_mou_8</th>\n",
       "      <td>3.362933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>arpu_8</th>\n",
       "      <td>3.353677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loc_og_t2t_mou_8</th>\n",
       "      <td>2.336457</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         coef_value\n",
       "loc_ic_mou_8              15.730083\n",
       "total_ic_mou_8            12.683502\n",
       "loc_ic_t2m_mou_8          11.529257\n",
       "av_rech_amt_call_data_8    9.639113\n",
       "loc_ic_t2t_mou_8           7.152392\n",
       "arpu_diff                  6.003313\n",
       "total_og_mou_diff          5.687578\n",
       "last_day_rch_amt_8         5.373289\n",
       "total_og_mou_8             5.088024\n",
       "roam_og_mou_8              4.687546\n",
       "roam_ic_mou_8              3.940443\n",
       "loc_og_t2m_mou_8           3.432392\n",
       "loc_og_mou_8               3.362933\n",
       "arpu_8                     3.353677\n",
       "loc_og_t2t_mou_8           2.336457"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_15_ = top_15.sort_values(by='coef_value', ascending=False)[0:15]\n",
    "top_15_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8. 6 Create logistic regression model on top 15 features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['loc_ic_mou_8',\n",
       " 'total_ic_mou_8',\n",
       " 'loc_ic_t2m_mou_8',\n",
       " 'av_rech_amt_call_data_8',\n",
       " 'loc_ic_t2t_mou_8',\n",
       " 'arpu_diff',\n",
       " 'total_og_mou_diff',\n",
       " 'last_day_rch_amt_8',\n",
       " 'total_og_mou_8',\n",
       " 'roam_og_mou_8',\n",
       " 'roam_ic_mou_8',\n",
       " 'loc_og_t2m_mou_8',\n",
       " 'loc_og_mou_8',\n",
       " 'arpu_8',\n",
       " 'loc_og_t2t_mou_8']"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_15_list = list(top_15_.index)\n",
    "top_15_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(X_train[top_15_list].corr())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will select top 9 best features with low multi-collinearity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_features = [\n",
    "    'loc_ic_mou_8', 'av_rech_amt_call_data_8', 'arpu_diff', 'total_og_mou_diff',\n",
    "    'last_day_rch_amt_8', 'total_og_mou_8', 'roam_ic_mou_8', 'roam_og_mou_8', 'arpu_8'\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Scaling the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_sc_best = scaler.fit_transform(X_train[best_features])\n",
    "\n",
    "X_test_sc_best = scaler.fit_transform(X_test[best_features])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will apply logistic regression with multiple parameters "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr_final = LogisticRegression(class_weight='balanced', random_state=42)\n",
    "\n",
    "params = {'penalty': ['l1','l2'], 'C': [0.001,0.01,0.1,1,10,100,1000]}\n",
    "\n",
    "model_lr_final = GridSearchCV(\n",
    "    estimator= lr_final,\n",
    "    param_grid = params,\n",
    "    cv = 5,\n",
    "    scoring='recall',\n",
    "    n_jobs = -1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=LogisticRegression(class_weight='balanced',\n",
       "                                          random_state=42),\n",
       "             n_jobs=-1,\n",
       "             param_grid={'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000],\n",
       "                         'penalty': ['l1', 'l2']},\n",
       "             scoring='recall')"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_lr_final.fit(X_train_sc_best, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 0.1, 'penalty': 'l2'}"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_lr_final.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "logistic_model = model_lr_final.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.73681702, -1.45380187, -0.63878603, -0.23739497, -0.34786856,\n",
       "        -0.36187259,  0.07796924,  0.20205376,  0.70156713]])"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logistic_model.coef_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have the coefficients, we will display them in a more presentable manner"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "intercept_df = pd.DataFrame(logistic_model.intercept_.reshape((1,1)), columns = ['intercept'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "# coefficients\n",
    "coefficients = logistic_model.coef_.reshape((9, 1)).tolist()\n",
    "coefficients = [val for sublist in coefficients for val in sublist]\n",
    "coefficients = [round(coefficient, 3) for coefficient in coefficients]\n",
    "\n",
    "logistic_features = list(X_train[best_features].columns)\n",
    "coefficients_df = pd.DataFrame(logistic_model.coef_, columns=logistic_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>intercept</th>\n",
       "      <td>-1.340263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loc_ic_mou_8</th>\n",
       "      <td>-1.736817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>av_rech_amt_call_data_8</th>\n",
       "      <td>-1.453802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>arpu_diff</th>\n",
       "      <td>-0.638786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_og_mou_diff</th>\n",
       "      <td>-0.237395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>last_day_rch_amt_8</th>\n",
       "      <td>-0.347869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_og_mou_8</th>\n",
       "      <td>-0.361873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>roam_ic_mou_8</th>\n",
       "      <td>0.077969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>roam_og_mou_8</th>\n",
       "      <td>0.202054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>arpu_8</th>\n",
       "      <td>0.701567</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                0\n",
       "intercept               -1.340263\n",
       "loc_ic_mou_8            -1.736817\n",
       "av_rech_amt_call_data_8 -1.453802\n",
       "arpu_diff               -0.638786\n",
       "total_og_mou_diff       -0.237395\n",
       "last_day_rch_amt_8      -0.347869\n",
       "total_og_mou_8          -0.361873\n",
       "roam_ic_mou_8            0.077969\n",
       "roam_og_mou_8            0.202054\n",
       "arpu_8                   0.701567"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# concatenate dataframes\n",
    "coefficients = pd.concat([intercept_df, coefficients_df], axis=1).T\n",
    "coefficients"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fvk-vJOTMhBv"
   },
   "source": [
    "## 9. Observations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- `loc_ic_mou_8` - reduce in the number of minutes of usage in August increase the probability of churning a customer.<br><br>\n",
    "- `av_rech_amt_call_data_8` - reduce in the average of recharges of call and data in the action phase i.e. August month,  is a strong indicator that the customer is likely to churn. <br><br>\n",
    "- `arpu_diff` - reduce in the average revenue per user increases the likelyhood of churn. <br><br>\n",
    "- `total_og_mou_diff` - denotes the difference between previous month and current month of outgoing minutes of call. A drop in minutes of usage means rise in the probability of churn. <br><br>\n",
    "- `last_day_rch_amt_8` - if the customer doesn't recharge in the action phase, this means he is likely to churn. <br><br>\n",
    "- `total_og_mou_8` - decrease in outgoing calls means the customer is not happy with the service and hence he is likely to churn. <br><br>\n",
    "- `roam_ic_mou_8` and `roam_og_mou_8` - increase in the amount of roaming minutes in month of august suggests that the customer has changed his location and hence will likely churn. <br><br>\n",
    "- `arpu_8` - increase in average revenue per user in month of august means the company is generating more revenue from the user and the user might feel that the recharge amount are getting expensive. Hence he is more likely to churn."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GXWkqs9O-Psp"
   },
   "source": [
    "## 10. Recommendations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Attractive low rates on calls will increase the minutes of usage of user. Thus reducing the likelyhood of churn.\n",
    "- Give rewards for high usage of service.\n",
    "- Provide discounts on data and internet recharge packs\n",
    "- Offer free access or discounted rates on smartphone applications. The increase in regular use by customers seems like an enticing bonus for many customers.\n",
    "- Send surveys to users who are likely to churn, asking them what caused them to reduce the usage.\n",
    "- Expanding on services offered and giving better rates or discounts to the client often improves customer retention rates."
   ]
  }
 ],
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