NgoVuAnh_Task1.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1> Assignment 1 - Task 1 Solution </h1>\n",
    "\n",
    "Student name: Ngo Vu Anh </br>\n",
    "UOW ID:    </br>\n",
    "Tutorial Group: T02   "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2> 1. Import the data set </h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import csv\n",
    "import numpy as np\n",
    "\n",
    "test_file = 'customer_churn_dataset-testing-master.csv'\n",
    "train_file = 'customer_churn_dataset-training-master.csv'\n",
    "\n",
    "test_df = pd.read_csv(test_file)\n",
    "train_df = pd.read_csv(train_file)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> Describe the 2 data set imported </h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Usage Frequency</th>\n",
       "      <th>Support Calls</th>\n",
       "      <th>Payment Delay</th>\n",
       "      <th>Total Spend</th>\n",
       "      <th>Last Interaction</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>64374.000000</td>\n",
       "      <td>64374.000000</td>\n",
       "      <td>64374.000000</td>\n",
       "      <td>64374.000000</td>\n",
       "      <td>64374.000000</td>\n",
       "      <td>64374.000000</td>\n",
       "      <td>64374.000000</td>\n",
       "      <td>64374.000000</td>\n",
       "      <td>64374.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>32187.500000</td>\n",
       "      <td>41.970982</td>\n",
       "      <td>31.994827</td>\n",
       "      <td>15.080234</td>\n",
       "      <td>5.400690</td>\n",
       "      <td>17.133952</td>\n",
       "      <td>541.023379</td>\n",
       "      <td>15.498850</td>\n",
       "      <td>0.473685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>18583.317451</td>\n",
       "      <td>13.924911</td>\n",
       "      <td>17.098234</td>\n",
       "      <td>8.816470</td>\n",
       "      <td>3.114005</td>\n",
       "      <td>8.852211</td>\n",
       "      <td>260.874809</td>\n",
       "      <td>8.638436</td>\n",
       "      <td>0.499311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>16094.250000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>313.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>32187.500000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>534.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>48280.750000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>64374.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         CustomerID           Age        Tenure  Usage Frequency  \\\n",
       "count  64374.000000  64374.000000  64374.000000     64374.000000   \n",
       "mean   32187.500000     41.970982     31.994827        15.080234   \n",
       "std    18583.317451     13.924911     17.098234         8.816470   \n",
       "min        1.000000     18.000000      1.000000         1.000000   \n",
       "25%    16094.250000     30.000000     18.000000         7.000000   \n",
       "50%    32187.500000     42.000000     33.000000        15.000000   \n",
       "75%    48280.750000     54.000000     47.000000        23.000000   \n",
       "max    64374.000000     65.000000     60.000000        30.000000   \n",
       "\n",
       "       Support Calls  Payment Delay   Total Spend  Last Interaction  \\\n",
       "count   64374.000000   64374.000000  64374.000000      64374.000000   \n",
       "mean        5.400690      17.133952    541.023379         15.498850   \n",
       "std         3.114005       8.852211    260.874809          8.638436   \n",
       "min         0.000000       0.000000    100.000000          1.000000   \n",
       "25%         3.000000      10.000000    313.000000          8.000000   \n",
       "50%         6.000000      19.000000    534.000000         15.000000   \n",
       "75%         8.000000      25.000000    768.000000         23.000000   \n",
       "max        10.000000      30.000000   1000.000000         30.000000   \n",
       "\n",
       "              Churn  \n",
       "count  64374.000000  \n",
       "mean       0.473685  \n",
       "std        0.499311  \n",
       "min        0.000000  \n",
       "25%        0.000000  \n",
       "50%        0.000000  \n",
       "75%        1.000000  \n",
       "max        1.000000  "
      ]
     },
     "execution_count": 281,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Usage Frequency</th>\n",
       "      <th>Support Calls</th>\n",
       "      <th>Payment Delay</th>\n",
       "      <th>Total Spend</th>\n",
       "      <th>Last Interaction</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>440832.000000</td>\n",
       "      <td>440832.000000</td>\n",
       "      <td>440832.000000</td>\n",
       "      <td>440832.000000</td>\n",
       "      <td>440832.000000</td>\n",
       "      <td>440832.000000</td>\n",
       "      <td>440832.000000</td>\n",
       "      <td>440832.000000</td>\n",
       "      <td>440832.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>225398.667955</td>\n",
       "      <td>39.373153</td>\n",
       "      <td>31.256336</td>\n",
       "      <td>15.807494</td>\n",
       "      <td>3.604437</td>\n",
       "      <td>12.965722</td>\n",
       "      <td>631.616223</td>\n",
       "      <td>14.480868</td>\n",
       "      <td>0.567107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>129531.918550</td>\n",
       "      <td>12.442369</td>\n",
       "      <td>17.255727</td>\n",
       "      <td>8.586242</td>\n",
       "      <td>3.070218</td>\n",
       "      <td>8.258063</td>\n",
       "      <td>240.803001</td>\n",
       "      <td>8.596208</td>\n",
       "      <td>0.495477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>113621.750000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>480.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>226125.500000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>661.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>337739.250000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>830.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>449999.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          CustomerID            Age         Tenure  Usage Frequency  \\\n",
       "count  440832.000000  440832.000000  440832.000000    440832.000000   \n",
       "mean   225398.667955      39.373153      31.256336        15.807494   \n",
       "std    129531.918550      12.442369      17.255727         8.586242   \n",
       "min         2.000000      18.000000       1.000000         1.000000   \n",
       "25%    113621.750000      29.000000      16.000000         9.000000   \n",
       "50%    226125.500000      39.000000      32.000000        16.000000   \n",
       "75%    337739.250000      48.000000      46.000000        23.000000   \n",
       "max    449999.000000      65.000000      60.000000        30.000000   \n",
       "\n",
       "       Support Calls  Payment Delay    Total Spend  Last Interaction  \\\n",
       "count  440832.000000  440832.000000  440832.000000     440832.000000   \n",
       "mean        3.604437      12.965722     631.616223         14.480868   \n",
       "std         3.070218       8.258063     240.803001          8.596208   \n",
       "min         0.000000       0.000000     100.000000          1.000000   \n",
       "25%         1.000000       6.000000     480.000000          7.000000   \n",
       "50%         3.000000      12.000000     661.000000         14.000000   \n",
       "75%         6.000000      19.000000     830.000000         22.000000   \n",
       "max        10.000000      30.000000    1000.000000         30.000000   \n",
       "\n",
       "               Churn  \n",
       "count  440832.000000  \n",
       "mean        0.567107  \n",
       "std         0.495477  \n",
       "min         0.000000  \n",
       "25%         0.000000  \n",
       "50%         1.000000  \n",
       "75%         1.000000  \n",
       "max         1.000000  "
      ]
     },
     "execution_count": 282,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.describe()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2> 2. Find missing value </h2>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> Test for missing value in testing data frame </h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CustomerID           False\n",
       "Age                  False\n",
       "Gender               False\n",
       "Tenure               False\n",
       "Usage Frequency      False\n",
       "Support Calls        False\n",
       "Payment Delay        False\n",
       "Subscription Type    False\n",
       "Contract Length      False\n",
       "Total Spend          False\n",
       "Last Interaction     False\n",
       "Churn                False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 283,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_missing = test_df.isnull().any() \n",
    "test_missing\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> Test for missing value in training data frame </h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CustomerID           True\n",
       "Age                  True\n",
       "Gender               True\n",
       "Tenure               True\n",
       "Usage Frequency      True\n",
       "Support Calls        True\n",
       "Payment Delay        True\n",
       "Subscription Type    True\n",
       "Contract Length      True\n",
       "Total Spend          True\n",
       "Last Interaction     True\n",
       "Churn                True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 284,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_missing = train_df.isnull().any() \n",
    "train_missing"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> Find where the missing value is </h3> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Check missing value train set\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Usage Frequency</th>\n",
       "      <th>Support Calls</th>\n",
       "      <th>Payment Delay</th>\n",
       "      <th>Subscription Type</th>\n",
       "      <th>Contract Length</th>\n",
       "      <th>Total Spend</th>\n",
       "      <th>Last Interaction</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>199295</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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      ],
      "text/plain": [
       "        CustomerID  Age Gender  Tenure  Usage Frequency  Support Calls  \\\n",
       "199295         NaN  NaN    NaN     NaN              NaN            NaN   \n",
       "\n",
       "        Payment Delay Subscription Type Contract Length  Total Spend  \\\n",
       "199295            NaN               NaN             NaN          NaN   \n",
       "\n",
       "        Last Interaction  Churn  \n",
       "199295               NaN    NaN  "
      ]
     },
     "execution_count": 285,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_missing = train_df.isnull().any() \n",
    "train_missing = train_df[train_df.isnull().any(axis=1)]\n",
    "\n",
    "print('Check missing value train set')\n",
    "\n",
    "train_missing"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> Handle the mssing value </h3>\n",
    "\n",
    "- The whole row is missing value \n",
    "- This is the only row with null value\n",
    "- It is less than 5% of the entire data frame \n",
    "\n",
    "-> Hence, we can remove this entire instance\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CustomerID           False\n",
       "Age                  False\n",
       "Gender               False\n",
       "Tenure               False\n",
       "Usage Frequency      False\n",
       "Support Calls        False\n",
       "Payment Delay        False\n",
       "Subscription Type    False\n",
       "Contract Length      False\n",
       "Total Spend          False\n",
       "Last Interaction     False\n",
       "Churn                False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 286,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = train_df.dropna()\n",
    "\n",
    "train_missing = train_df.isnull().any() \n",
    "train_missing\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After dropping the row with missing values, we will not see any missing value in our training data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Usage Frequency</th>\n",
       "      <th>Support Calls</th>\n",
       "      <th>Payment Delay</th>\n",
       "      <th>Subscription Type</th>\n",
       "      <th>Contract Length</th>\n",
       "      <th>Total Spend</th>\n",
       "      <th>Last Interaction</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>Female</td>\n",
       "      <td>25</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>27</td>\n",
       "      <td>Basic</td>\n",
       "      <td>Monthly</td>\n",
       "      <td>598</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>41</td>\n",
       "      <td>Female</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>7</td>\n",
       "      <td>13</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Monthly</td>\n",
       "      <td>584</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>47</td>\n",
       "      <td>Male</td>\n",
       "      <td>27</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>29</td>\n",
       "      <td>Premium</td>\n",
       "      <td>Annual</td>\n",
       "      <td>757</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>35</td>\n",
       "      <td>Male</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>17</td>\n",
       "      <td>Premium</td>\n",
       "      <td>Quarterly</td>\n",
       "      <td>232</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>53</td>\n",
       "      <td>Female</td>\n",
       "      <td>58</td>\n",
       "      <td>24</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Annual</td>\n",
       "      <td>533</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64369</th>\n",
       "      <td>64370</td>\n",
       "      <td>45</td>\n",
       "      <td>Female</td>\n",
       "      <td>33</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>21</td>\n",
       "      <td>Basic</td>\n",
       "      <td>Quarterly</td>\n",
       "      <td>947</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64370</th>\n",
       "      <td>64371</td>\n",
       "      <td>37</td>\n",
       "      <td>Male</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>22</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Annual</td>\n",
       "      <td>923</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64371</th>\n",
       "      <td>64372</td>\n",
       "      <td>25</td>\n",
       "      <td>Male</td>\n",
       "      <td>39</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>30</td>\n",
       "      <td>Premium</td>\n",
       "      <td>Monthly</td>\n",
       "      <td>327</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64372</th>\n",
       "      <td>64373</td>\n",
       "      <td>50</td>\n",
       "      <td>Female</td>\n",
       "      <td>18</td>\n",
       "      <td>19</td>\n",
       "      <td>7</td>\n",
       "      <td>22</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Monthly</td>\n",
       "      <td>540</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64373</th>\n",
       "      <td>64374</td>\n",
       "      <td>52</td>\n",
       "      <td>Female</td>\n",
       "      <td>45</td>\n",
       "      <td>15</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Monthly</td>\n",
       "      <td>696</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>64374 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       CustomerID  Age  Gender  Tenure  Usage Frequency  Support Calls  \\\n",
       "0               1   22  Female      25               14              4   \n",
       "1               2   41  Female      28               28              7   \n",
       "2               3   47    Male      27               10              2   \n",
       "3               4   35    Male       9               12              5   \n",
       "4               5   53  Female      58               24              9   \n",
       "...           ...  ...     ...     ...              ...            ...   \n",
       "64369       64370   45  Female      33               12              6   \n",
       "64370       64371   37    Male       6                1              5   \n",
       "64371       64372   25    Male      39               14              8   \n",
       "64372       64373   50  Female      18               19              7   \n",
       "64373       64374   52  Female      45               15              9   \n",
       "\n",
       "       Payment Delay Subscription Type Contract Length  Total Spend  \\\n",
       "0                 27             Basic         Monthly          598   \n",
       "1                 13          Standard         Monthly          584   \n",
       "2                 29           Premium          Annual          757   \n",
       "3                 17           Premium       Quarterly          232   \n",
       "4                  2          Standard          Annual          533   \n",
       "...              ...               ...             ...          ...   \n",
       "64369             21             Basic       Quarterly          947   \n",
       "64370             22          Standard          Annual          923   \n",
       "64371             30           Premium         Monthly          327   \n",
       "64372             22          Standard         Monthly          540   \n",
       "64373             25          Standard         Monthly          696   \n",
       "\n",
       "       Last Interaction  Churn  \n",
       "0                     9      1  \n",
       "1                    20      0  \n",
       "2                    21      0  \n",
       "3                    18      0  \n",
       "4                    18      0  \n",
       "...                 ...    ...  \n",
       "64369                14      1  \n",
       "64370                 9      1  \n",
       "64371                20      1  \n",
       "64372                13      1  \n",
       "64373                22      1  \n",
       "\n",
       "[64374 rows x 12 columns]"
      ]
     },
     "execution_count": 287,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2> 3. Using Z-Score normalization </h2>\n",
    "For column Last Interaction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         0.293052\n",
       "1        -0.986582\n",
       "2        -1.335574\n",
       "3         1.689016\n",
       "4         0.642043\n",
       "            ...   \n",
       "440828   -0.753922\n",
       "440829   -1.451904\n",
       "440830   -0.637591\n",
       "440831   -1.451904\n",
       "440832    0.758373\n",
       "Name: Last Interaction, Length: 440832, dtype: float64"
      ]
     },
     "execution_count": 288,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normalized_last_interaction = (train_df['Last Interaction'] - train_df['Last Interaction'].mean()) / train_df['Last Interaction'].std()\n",
    "\n",
    "normalized_last_interaction\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       -0.752318\n",
       "1        0.521061\n",
       "2        0.636822\n",
       "3        0.289537\n",
       "4        0.289537\n",
       "           ...   \n",
       "64369   -0.173509\n",
       "64370   -0.752318\n",
       "64371    0.521061\n",
       "64372   -0.289271\n",
       "64373    0.752584\n",
       "Name: Last Interaction, Length: 64374, dtype: float64"
      ]
     },
     "execution_count": 289,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normalized_last_interaction_test = (test_df['Last Interaction'] - test_df['Last Interaction'].mean()) / test_df['Last Interaction'].std()\n",
    "\n",
    "normalized_last_interaction_test"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> Show mean and variance of the calculated value </h3>\n",
    "\n",
    "Mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For train set\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1.0367239393712263e-16"
      ]
     },
     "execution_count": 290,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"For train set\")\n",
    "normalized_last_interaction.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For test set\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "3.3996203842251666e-17"
      ]
     },
     "execution_count": 291,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"For test set\")\n",
    "normalized_last_interaction_test.mean()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Variance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For train set\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1.0000000000005635"
      ]
     },
     "execution_count": 292,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"For train set\")\n",
    "normalized_last_interaction.var()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For test set\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9999999999995506"
      ]
     },
     "execution_count": 293,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"For test set\")\n",
    "normalized_last_interaction_test.var()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Append to train set and test set \n",
    "train_df['Normalized Last Interaction'] = normalized_last_interaction\n",
    "test_df['Normalized Last Interaction'] = normalized_last_interaction_test"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2> 4. Create 5 bins for the attribute Total Spend </h2> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Usage Frequency</th>\n",
       "      <th>Support Calls</th>\n",
       "      <th>Payment Delay</th>\n",
       "      <th>Subscription Type</th>\n",
       "      <th>Contract Length</th>\n",
       "      <th>Total Spend</th>\n",
       "      <th>Last Interaction</th>\n",
       "      <th>Churn</th>\n",
       "      <th>Normalized Last Interaction</th>\n",
       "      <th>Total Spend Bin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>39.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Annual</td>\n",
       "      <td>932.00</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.293052</td>\n",
       "      <td>Bin 5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>49.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Basic</td>\n",
       "      <td>Monthly</td>\n",
       "      <td>557.00</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.986582</td>\n",
       "      <td>Bin 3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>14.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>Basic</td>\n",
       "      <td>Quarterly</td>\n",
       "      <td>185.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.335574</td>\n",
       "      <td>Bin 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>38.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Monthly</td>\n",
       "      <td>396.00</td>\n",
       "      <td>29.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.689016</td>\n",
       "      <td>Bin 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Basic</td>\n",
       "      <td>Monthly</td>\n",
       "      <td>617.00</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.642043</td>\n",
       "      <td>Bin 3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440828</th>\n",
       "      <td>449995.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>54.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Premium</td>\n",
       "      <td>Annual</td>\n",
       "      <td>716.38</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.753922</td>\n",
       "      <td>Bin 4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440829</th>\n",
       "      <td>449996.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>8.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Premium</td>\n",
       "      <td>Annual</td>\n",
       "      <td>745.38</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.451904</td>\n",
       "      <td>Bin 4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440830</th>\n",
       "      <td>449997.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Quarterly</td>\n",
       "      <td>977.31</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.637591</td>\n",
       "      <td>Bin 5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440831</th>\n",
       "      <td>449998.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>55.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Quarterly</td>\n",
       "      <td>602.55</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.451904</td>\n",
       "      <td>Bin 3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440832</th>\n",
       "      <td>449999.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>48.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>Premium</td>\n",
       "      <td>Quarterly</td>\n",
       "      <td>567.77</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.758373</td>\n",
       "      <td>Bin 3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>440832 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        CustomerID   Age  Gender  Tenure  Usage Frequency  Support Calls  \\\n",
       "0              2.0  30.0  Female    39.0             14.0            5.0   \n",
       "1              3.0  65.0  Female    49.0              1.0           10.0   \n",
       "2              4.0  55.0  Female    14.0              4.0            6.0   \n",
       "3              5.0  58.0    Male    38.0             21.0            7.0   \n",
       "4              6.0  23.0    Male    32.0             20.0            5.0   \n",
       "...            ...   ...     ...     ...              ...            ...   \n",
       "440828    449995.0  42.0    Male    54.0             15.0            1.0   \n",
       "440829    449996.0  25.0  Female     8.0             13.0            1.0   \n",
       "440830    449997.0  26.0    Male    35.0             27.0            1.0   \n",
       "440831    449998.0  28.0    Male    55.0             14.0            2.0   \n",
       "440832    449999.0  31.0    Male    48.0             20.0            1.0   \n",
       "\n",
       "        Payment Delay Subscription Type Contract Length  Total Spend  \\\n",
       "0                18.0          Standard          Annual       932.00   \n",
       "1                 8.0             Basic         Monthly       557.00   \n",
       "2                18.0             Basic       Quarterly       185.00   \n",
       "3                 7.0          Standard         Monthly       396.00   \n",
       "4                 8.0             Basic         Monthly       617.00   \n",
       "...               ...               ...             ...          ...   \n",
       "440828            3.0           Premium          Annual       716.38   \n",
       "440829           20.0           Premium          Annual       745.38   \n",
       "440830            5.0          Standard       Quarterly       977.31   \n",
       "440831            0.0          Standard       Quarterly       602.55   \n",
       "440832           14.0           Premium       Quarterly       567.77   \n",
       "\n",
       "        Last Interaction  Churn  Normalized Last Interaction Total Spend Bin  \n",
       "0                   17.0    1.0                     0.293052           Bin 5  \n",
       "1                    6.0    1.0                    -0.986582           Bin 3  \n",
       "2                    3.0    1.0                    -1.335574           Bin 1  \n",
       "3                   29.0    1.0                     1.689016           Bin 2  \n",
       "4                   20.0    1.0                     0.642043           Bin 3  \n",
       "...                  ...    ...                          ...             ...  \n",
       "440828               8.0    0.0                    -0.753922           Bin 4  \n",
       "440829               2.0    0.0                    -1.451904           Bin 4  \n",
       "440830               9.0    0.0                    -0.637591           Bin 5  \n",
       "440831               2.0    0.0                    -1.451904           Bin 3  \n",
       "440832              21.0    0.0                     0.758373           Bin 3  \n",
       "\n",
       "[440832 rows x 14 columns]"
      ]
     },
     "execution_count": 295,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "column_to_bin = 'Total Spend'\n",
    "\n",
    "# Create 5 bins for the selected column\n",
    "bin_labels = ['Bin 1', 'Bin 2', 'Bin 3', 'Bin 4', 'Bin 5']\n",
    "train_df['Total Spend Bin'] = pd.cut(train_df[column_to_bin], bins=5, labels=bin_labels)\n",
    "\n",
    "# Display the DataFrame with the new bin column\n",
    "train_df\n",
    "\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2> 5. Conduct one hot encoding </h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Usage Frequency</th>\n",
       "      <th>Support Calls</th>\n",
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       "      <th>Contract Length</th>\n",
       "      <th>Total Spend</th>\n",
       "      <th>Last Interaction</th>\n",
       "      <th>Churn</th>\n",
       "      <th>Normalized Last Interaction</th>\n",
       "      <th>Total Spend Bin</th>\n",
       "      <th>Contract Length_Annual</th>\n",
       "      <th>Contract Length_Monthly</th>\n",
       "      <th>Contract Length_Quarterly</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>39.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Annual</td>\n",
       "      <td>932.00</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>Bin 5</td>\n",
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       "      <th>1</th>\n",
       "      <td>3.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>49.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Basic</td>\n",
       "      <td>Monthly</td>\n",
       "      <td>557.00</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.986582</td>\n",
       "      <td>Bin 3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>14.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>Basic</td>\n",
       "      <td>Quarterly</td>\n",
       "      <td>185.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.335574</td>\n",
       "      <td>Bin 1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>38.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Monthly</td>\n",
       "      <td>396.00</td>\n",
       "      <td>29.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.689016</td>\n",
       "      <td>Bin 2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Basic</td>\n",
       "      <td>Monthly</td>\n",
       "      <td>617.00</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.642043</td>\n",
       "      <td>Bin 3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440828</th>\n",
       "      <td>449995.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>54.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Premium</td>\n",
       "      <td>Annual</td>\n",
       "      <td>716.38</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.753922</td>\n",
       "      <td>Bin 4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440829</th>\n",
       "      <td>449996.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>8.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Premium</td>\n",
       "      <td>Annual</td>\n",
       "      <td>745.38</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.451904</td>\n",
       "      <td>Bin 4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>440830</th>\n",
       "      <td>449997.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Quarterly</td>\n",
       "      <td>977.31</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.637591</td>\n",
       "      <td>Bin 5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440831</th>\n",
       "      <td>449998.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>55.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Quarterly</td>\n",
       "      <td>602.55</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.451904</td>\n",
       "      <td>Bin 3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>440832</th>\n",
       "      <td>449999.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>48.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>Premium</td>\n",
       "      <td>Quarterly</td>\n",
       "      <td>567.77</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.758373</td>\n",
       "      <td>Bin 3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>440832 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        CustomerID   Age  Gender  Tenure  Usage Frequency  Support Calls  \\\n",
       "0              2.0  30.0  Female    39.0             14.0            5.0   \n",
       "1              3.0  65.0  Female    49.0              1.0           10.0   \n",
       "2              4.0  55.0  Female    14.0              4.0            6.0   \n",
       "3              5.0  58.0    Male    38.0             21.0            7.0   \n",
       "4              6.0  23.0    Male    32.0             20.0            5.0   \n",
       "...            ...   ...     ...     ...              ...            ...   \n",
       "440828    449995.0  42.0    Male    54.0             15.0            1.0   \n",
       "440829    449996.0  25.0  Female     8.0             13.0            1.0   \n",
       "440830    449997.0  26.0    Male    35.0             27.0            1.0   \n",
       "440831    449998.0  28.0    Male    55.0             14.0            2.0   \n",
       "440832    449999.0  31.0    Male    48.0             20.0            1.0   \n",
       "\n",
       "        Payment Delay Subscription Type Contract Length  Total Spend  \\\n",
       "0                18.0          Standard          Annual       932.00   \n",
       "1                 8.0             Basic         Monthly       557.00   \n",
       "2                18.0             Basic       Quarterly       185.00   \n",
       "3                 7.0          Standard         Monthly       396.00   \n",
       "4                 8.0             Basic         Monthly       617.00   \n",
       "...               ...               ...             ...          ...   \n",
       "440828            3.0           Premium          Annual       716.38   \n",
       "440829           20.0           Premium          Annual       745.38   \n",
       "440830            5.0          Standard       Quarterly       977.31   \n",
       "440831            0.0          Standard       Quarterly       602.55   \n",
       "440832           14.0           Premium       Quarterly       567.77   \n",
       "\n",
       "        Last Interaction  Churn  Normalized Last Interaction Total Spend Bin  \\\n",
       "0                   17.0    1.0                     0.293052           Bin 5   \n",
       "1                    6.0    1.0                    -0.986582           Bin 3   \n",
       "2                    3.0    1.0                    -1.335574           Bin 1   \n",
       "3                   29.0    1.0                     1.689016           Bin 2   \n",
       "4                   20.0    1.0                     0.642043           Bin 3   \n",
       "...                  ...    ...                          ...             ...   \n",
       "440828               8.0    0.0                    -0.753922           Bin 4   \n",
       "440829               2.0    0.0                    -1.451904           Bin 4   \n",
       "440830               9.0    0.0                    -0.637591           Bin 5   \n",
       "440831               2.0    0.0                    -1.451904           Bin 3   \n",
       "440832              21.0    0.0                     0.758373           Bin 3   \n",
       "\n",
       "        Contract Length_Annual  Contract Length_Monthly  \\\n",
       "0                            1                        0   \n",
       "1                            0                        1   \n",
       "2                            0                        0   \n",
       "3                            0                        1   \n",
       "4                            0                        1   \n",
       "...                        ...                      ...   \n",
       "440828                       1                        0   \n",
       "440829                       1                        0   \n",
       "440830                       0                        0   \n",
       "440831                       0                        0   \n",
       "440832                       0                        0   \n",
       "\n",
       "        Contract Length_Quarterly  \n",
       "0                               0  \n",
       "1                               0  \n",
       "2                               1  \n",
       "3                               0  \n",
       "4                               0  \n",
       "...                           ...  \n",
       "440828                          0  \n",
       "440829                          0  \n",
       "440830                          1  \n",
       "440831                          1  \n",
       "440832                          1  \n",
       "\n",
       "[440832 rows x 17 columns]"
      ]
     },
     "execution_count": 296,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For train set\n",
    "\n",
    "column_encode = ['Contract Length']\n",
    "\n",
    "encoded_contract_length = pd.get_dummies(train_df, columns = column_encode)\n",
    "\n",
    "# Append to data frame \n",
    "\n",
    "to_add_train = ['Contract Length_Annual', 'Contract Length_Monthly', 'Contract Length_Quarterly']\n",
    "train_df = pd.concat([train_df, encoded_contract_length[to_add_train]], axis=1)\n",
    "\n",
    "train_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
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       "      <td>Monthly</td>\n",
       "      <td>598</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.752318</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>41</td>\n",
       "      <td>Female</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>7</td>\n",
       "      <td>13</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Monthly</td>\n",
       "      <td>584</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0.521061</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>47</td>\n",
       "      <td>Male</td>\n",
       "      <td>27</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>29</td>\n",
       "      <td>Premium</td>\n",
       "      <td>Annual</td>\n",
       "      <td>757</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>0.636822</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>35</td>\n",
       "      <td>Male</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>17</td>\n",
       "      <td>Premium</td>\n",
       "      <td>Quarterly</td>\n",
       "      <td>232</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.289537</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>53</td>\n",
       "      <td>Female</td>\n",
       "      <td>58</td>\n",
       "      <td>24</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Annual</td>\n",
       "      <td>533</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.289537</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64369</th>\n",
       "      <td>64370</td>\n",
       "      <td>45</td>\n",
       "      <td>Female</td>\n",
       "      <td>33</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>21</td>\n",
       "      <td>Basic</td>\n",
       "      <td>Quarterly</td>\n",
       "      <td>947</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.173509</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64370</th>\n",
       "      <td>64371</td>\n",
       "      <td>37</td>\n",
       "      <td>Male</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>22</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Annual</td>\n",
       "      <td>923</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.752318</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64371</th>\n",
       "      <td>64372</td>\n",
       "      <td>25</td>\n",
       "      <td>Male</td>\n",
       "      <td>39</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>30</td>\n",
       "      <td>Premium</td>\n",
       "      <td>Monthly</td>\n",
       "      <td>327</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>0.521061</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64372</th>\n",
       "      <td>64373</td>\n",
       "      <td>50</td>\n",
       "      <td>Female</td>\n",
       "      <td>18</td>\n",
       "      <td>19</td>\n",
       "      <td>7</td>\n",
       "      <td>22</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Monthly</td>\n",
       "      <td>540</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.289271</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64373</th>\n",
       "      <td>64374</td>\n",
       "      <td>52</td>\n",
       "      <td>Female</td>\n",
       "      <td>45</td>\n",
       "      <td>15</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Monthly</td>\n",
       "      <td>696</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>0.752584</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>64374 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       CustomerID  Age  Gender  Tenure  Usage Frequency  Support Calls  \\\n",
       "0               1   22  Female      25               14              4   \n",
       "1               2   41  Female      28               28              7   \n",
       "2               3   47    Male      27               10              2   \n",
       "3               4   35    Male       9               12              5   \n",
       "4               5   53  Female      58               24              9   \n",
       "...           ...  ...     ...     ...              ...            ...   \n",
       "64369       64370   45  Female      33               12              6   \n",
       "64370       64371   37    Male       6                1              5   \n",
       "64371       64372   25    Male      39               14              8   \n",
       "64372       64373   50  Female      18               19              7   \n",
       "64373       64374   52  Female      45               15              9   \n",
       "\n",
       "       Payment Delay Subscription Type Contract Length  Total Spend  \\\n",
       "0                 27             Basic         Monthly          598   \n",
       "1                 13          Standard         Monthly          584   \n",
       "2                 29           Premium          Annual          757   \n",
       "3                 17           Premium       Quarterly          232   \n",
       "4                  2          Standard          Annual          533   \n",
       "...              ...               ...             ...          ...   \n",
       "64369             21             Basic       Quarterly          947   \n",
       "64370             22          Standard          Annual          923   \n",
       "64371             30           Premium         Monthly          327   \n",
       "64372             22          Standard         Monthly          540   \n",
       "64373             25          Standard         Monthly          696   \n",
       "\n",
       "       Last Interaction  Churn  Normalized Last Interaction  \\\n",
       "0                     9      1                    -0.752318   \n",
       "1                    20      0                     0.521061   \n",
       "2                    21      0                     0.636822   \n",
       "3                    18      0                     0.289537   \n",
       "4                    18      0                     0.289537   \n",
       "...                 ...    ...                          ...   \n",
       "64369                14      1                    -0.173509   \n",
       "64370                 9      1                    -0.752318   \n",
       "64371                20      1                     0.521061   \n",
       "64372                13      1                    -0.289271   \n",
       "64373                22      1                     0.752584   \n",
       "\n",
       "       Contract Length_Annual  Contract Length_Monthly  \\\n",
       "0                           0                        1   \n",
       "1                           0                        1   \n",
       "2                           1                        0   \n",
       "3                           0                        0   \n",
       "4                           1                        0   \n",
       "...                       ...                      ...   \n",
       "64369                       0                        0   \n",
       "64370                       1                        0   \n",
       "64371                       0                        1   \n",
       "64372                       0                        1   \n",
       "64373                       0                        1   \n",
       "\n",
       "       Contract Length_Quarterly  \n",
       "0                              0  \n",
       "1                              0  \n",
       "2                              0  \n",
       "3                              1  \n",
       "4                              0  \n",
       "...                          ...  \n",
       "64369                          1  \n",
       "64370                          0  \n",
       "64371                          0  \n",
       "64372                          0  \n",
       "64373                          0  \n",
       "\n",
       "[64374 rows x 16 columns]"
      ]
     },
     "execution_count": 297,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For test set\n",
    "\n",
    "column_encode = ['Contract Length']\n",
    "\n",
    "encoded_contract_length_test = pd.get_dummies(test_df, columns = column_encode)\n",
    "\n",
    "# Append to data frame \n",
    "\n",
    "to_add_test = ['Contract Length_Annual', 'Contract Length_Monthly', 'Contract Length_Quarterly']\n",
    "test_df = pd.concat([test_df, encoded_contract_length_test[to_add_test]], axis=1)\n",
    "\n",
    "test_df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Suggest new feature "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make new feature: \n",
    "\n",
    "support_per_usage_ratio = Support Calls / Usage Frequency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "\n",
    "class SupportPerUsageRatioTransformer(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self):\n",
    "        pass\n",
    "\n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "\n",
    "    def transform(self, X):\n",
    "        X_transformed = X.copy()\n",
    "\n",
    "        # Calculate the 'support_per_usage_ratio'\n",
    "        X_transformed['support_per_usage_ratio'] = X_transformed['Usage Frequency'] / X_transformed['Support Calls']\n",
    "\n",
    "        return X_transformed\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Initialize the custom transformer\n",
    "transformer = SupportPerUsageRatioTransformer()\n",
    "\n",
    "# Apply the transformer to add the new feature to the DataFrame\n",
    "train_df = transformer.transform(train_df)\n",
    "test_df = transformer.transform(test_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize why I choose this attribute\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "train_df = pd.DataFrame(train_df)\n",
    "\n",
    "checkSupport_gt_5 = train_df[train_df['support_per_usage_ratio'] > 5.0]\n",
    "churnCount_gt_5 = checkSupport_gt_5[checkSupport_gt_5['Churn'] == 1.0].shape[0]\n",
    "\n",
    "total_gt_5 = checkSupport_gt_5.shape[0]\n",
    "ratio_gt_5 = churnCount_gt_5 / total_gt_5\n",
    "total_lte_5 = train_df[train_df['support_per_usage_ratio'] <= 5.0].shape[0]\n",
    "ratio_lte_5 = (train_df[train_df['support_per_usage_ratio'] <= 5.0]['Churn'] == 1.0).mean()\n",
    "\n",
    "# Plot the comparison using a bar chart\n",
    "plt.bar(['> 5.0', '<= 5.0'], [ratio_gt_5, ratio_lte_5])\n",
    "plt.xlabel('Support Per Usage Ratio')\n",
    "plt.ylabel('Churn = 1 Ratio')\n",
    "plt.title('Comparison of Churn = 1 Ratio for Support Per Usage Ratio')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the reason why I created this attribute. By domain knowledge, I predict that the customer that contact customer support more frequently, have closer contact with the customer service will have higher chance to continue using, which the probability of Churn is lower. Based on the graph, we can also see that for customer that use more Support Calls,  the amount of Churn = 1 is significantly lower than the customer use less Support Calls"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}