python_files/case_study_classification_B.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "residential-frank",
   "metadata": {},
   "source": [
    "### Table of Contents\n",
    "\n",
    "* [Case study classification project](#casestudy)\n",
    "* [EDA - exploratory data analysis - get to know the data](#eda)\n",
    "* [Get the sweet summary stats on each column to understand the distribution better](#summary)\n",
    "* [Get a  representation of the numbers](#numbersrep)\n",
    "* [Outliers](#outliers)\n",
    "* [Box Plots](#boxplots)\n",
    "* [Outliers](#outliers)\n",
    "* [Pre-processing](#preprocessing)\n",
    "* [Split off the dependant variable (label) ](#split)\n",
    "* [TT split and get model](#ttsplit)\n",
    "* [Apply model, train model](#applymoded)\n",
    "* [Evaluate accuracy and test](#evaluate)\n",
    "* [Visualising accuracy - ROC / AUC](#rocauc)\n",
    "* [Data imbalanced -  SMOTE (oversample the minority class of the target label)](#smote)\n",
    "\n",
    "\n",
    "* [sample](#sample)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "frequent-insurance",
   "metadata": {},
   "source": [
    "## Case study classification project  - B<a class=\"anchor\" id=\"casestudy\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "forward-vampire",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries\n",
    "import pandas as pd \n",
    "import seaborn as sns \n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np \n",
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "lonely-offer",
   "metadata": {},
   "outputs": [],
   "source": [
    "# conda install -c conda-forge jupyter_contrib_nbextensions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "secure-poverty",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df = pd.read_csv('creditcardmarketing.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "occupied-friendship",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_original = pd.read_excel(r'data/creditcardmarketing.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "favorite-japanese",
   "metadata": {},
   "outputs": [],
   "source": [
    "# save as a csv file to use in the SQL task\n",
    "import csv\n",
    "df_original.to_csv(r'new_creditcardmarketing_original.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "pleased-lesbian",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# create a copy of the dataaet\n",
    "df = df_original"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "speaking-midwest",
   "metadata": {},
   "source": [
    "###  EDA - exploratory data analysis - get to know the data <a class=\"anchor\" id=\"eda\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "piano-riding",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 18000 entries, 0 to 17999\n",
      "Data columns (total 17 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   Customer Number       18000 non-null  int64  \n",
      " 1   Offer Accepted        18000 non-null  object \n",
      " 2   Reward                18000 non-null  object \n",
      " 3   Mailer Type           18000 non-null  object \n",
      " 4   Income Level          18000 non-null  object \n",
      " 5   # Bank Accounts Open  18000 non-null  int64  \n",
      " 6   Overdraft Protection  18000 non-null  object \n",
      " 7   Credit Rating         18000 non-null  object \n",
      " 8   # Credit Cards Held   18000 non-null  int64  \n",
      " 9   # Homes Owned         18000 non-null  int64  \n",
      " 10  Household Size        18000 non-null  int64  \n",
      " 11  Own Your Home         18000 non-null  object \n",
      " 12  Average Balance       17976 non-null  float64\n",
      " 13  Q1 Balance            17976 non-null  float64\n",
      " 14  Q2 Balance            17976 non-null  float64\n",
      " 15  Q3 Balance            17976 non-null  float64\n",
      " 16  Q4 Balance            17976 non-null  float64\n",
      "dtypes: float64(5), int64(5), object(7)\n",
      "memory usage: 2.3+ MB\n"
     ]
    }
   ],
   "source": [
    "# check information about the dataset\n",
    "#df.dtypes\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "adjacent-matthew",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18000, 17)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cheack shape\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "surprised-mexico",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Customer Number          0\n",
      "Offer Accepted           0\n",
      "Reward                   0\n",
      "Mailer Type              0\n",
      "Income Level             0\n",
      "# Bank Accounts Open     0\n",
      "Overdraft Protection     0\n",
      "Credit Rating            0\n",
      "# Credit Cards Held      0\n",
      "# Homes Owned            0\n",
      "Household Size           0\n",
      "Own Your Home            0\n",
      "Average Balance         24\n",
      "Q1 Balance              24\n",
      "Q2 Balance              24\n",
      "Q3 Balance              24\n",
      "Q4 Balance              24\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# print the sum of null values \n",
    "print(df.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "seeing-surge",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>customer_number</th>\n",
       "      <th>offer_accepted</th>\n",
       "      <th>reward</th>\n",
       "      <th>mailer_type</th>\n",
       "      <th>income_level</th>\n",
       "      <th>bank_accounts_open</th>\n",
       "      <th>overdraft_protection</th>\n",
       "      <th>credit_rating</th>\n",
       "      <th>credit_cards_held</th>\n",
       "      <th>homes_owned</th>\n",
       "      <th>household_size</th>\n",
       "      <th>own_your_home</th>\n",
       "      <th>average_balance</th>\n",
       "      <th>q1_balance</th>\n",
       "      <th>q2_balance</th>\n",
       "      <th>q3_balance</th>\n",
       "      <th>q4_balance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>Air Miles</td>\n",
       "      <td>Letter</td>\n",
       "      <td>High</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>High</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>No</td>\n",
       "      <td>1160.75</td>\n",
       "      <td>1669.0</td>\n",
       "      <td>877.0</td>\n",
       "      <td>1095.0</td>\n",
       "      <td>1002.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>No</td>\n",
       "      <td>Air Miles</td>\n",
       "      <td>Letter</td>\n",
       "      <td>Medium</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>Medium</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>Yes</td>\n",
       "      <td>147.25</td>\n",
       "      <td>39.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>366.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>No</td>\n",
       "      <td>Air Miles</td>\n",
       "      <td>Postcard</td>\n",
       "      <td>High</td>\n",
       "      <td>2</td>\n",
       "      <td>No</td>\n",
       "      <td>Medium</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>276.50</td>\n",
       "      <td>367.0</td>\n",
       "      <td>352.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>242.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>No</td>\n",
       "      <td>Air Miles</td>\n",
       "      <td>Letter</td>\n",
       "      <td>Medium</td>\n",
       "      <td>2</td>\n",
       "      <td>No</td>\n",
       "      <td>High</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>No</td>\n",
       "      <td>1219.00</td>\n",
       "      <td>1578.0</td>\n",
       "      <td>1760.0</td>\n",
       "      <td>1119.0</td>\n",
       "      <td>419.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>No</td>\n",
       "      <td>Air Miles</td>\n",
       "      <td>Letter</td>\n",
       "      <td>Medium</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>Medium</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1211.00</td>\n",
       "      <td>2140.0</td>\n",
       "      <td>1357.0</td>\n",
       "      <td>982.0</td>\n",
       "      <td>365.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   customer_number offer_accepted     reward mailer_type income_level  \\\n",
       "0                1             No  Air Miles      Letter         High   \n",
       "1                2             No  Air Miles      Letter       Medium   \n",
       "2                3             No  Air Miles    Postcard         High   \n",
       "3                4             No  Air Miles      Letter       Medium   \n",
       "4                5             No  Air Miles      Letter       Medium   \n",
       "\n",
       "   bank_accounts_open overdraft_protection credit_rating  credit_cards_held  \\\n",
       "0                   1                   No          High                  2   \n",
       "1                   1                   No        Medium                  2   \n",
       "2                   2                   No        Medium                  2   \n",
       "3                   2                   No          High                  1   \n",
       "4                   1                   No        Medium                  2   \n",
       "\n",
       "   homes_owned  household_size own_your_home  average_balance  q1_balance  \\\n",
       "0            1               4            No          1160.75      1669.0   \n",
       "1            2               5           Yes           147.25        39.0   \n",
       "2            1               2           Yes           276.50       367.0   \n",
       "3            1               4            No          1219.00      1578.0   \n",
       "4            1               6           Yes          1211.00      2140.0   \n",
       "\n",
       "   q2_balance  q3_balance  q4_balance  \n",
       "0       877.0      1095.0      1002.0  \n",
       "1       106.0        78.0       366.0  \n",
       "2       352.0       145.0       242.0  \n",
       "3      1760.0      1119.0       419.0  \n",
       "4      1357.0       982.0       365.0  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# standize the readers: replace spaces by _, put in lowercase, remove #\n",
    "df.columns = df.columns.str.replace(' ', '_')\n",
    "df.columns= df.columns.str.lower()\n",
    "df.columns= df.columns.str.replace('#_', '')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ethical-shark",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0013333333333333333"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find the percentage of missing values\n",
    "pct_missing=24/18000\n",
    "pct_missing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "peaceful-tokyo",
   "metadata": {},
   "source": [
    "We found few missing values (less than 20%). so we are not droping them.<br/>\n",
    "Since the balances columns are float, we will fill them with the mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "adjacent-scene",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # fill nulls with the means\n",
    "\n",
    "# col_miss_values= ['average_balance', 'q1_balance', 'q2_balance', 'q3_balance', 'q4_balance']\n",
    "\n",
    "# mean_average_balance = df['average_balance'].mean()\n",
    "# df['average_balance'] = df['average_balance'].fillna(mean_average_balance)\n",
    "\n",
    "# mean_average_balance = df['q1_balance'].mean()\n",
    "# df['q1_balance'] = df['q1_balance'].fillna(mean_average_balance)\n",
    "\n",
    "# mean_q2_balance = df['q2_balance'].mean()\n",
    "# df['q2_balance'] = df['q2_balance'].fillna(mean_q2_balance)\n",
    "\n",
    "# mean_q3_balance = df['q3_balance'].mean()\n",
    "# df['q3_balance'] = df['q3_balance'].fillna(mean_q3_balance)\n",
    "\n",
    "# mean_q4_balance = df['q4_balance'].mean()\n",
    "# df['q4_balance'] = df['q4_balance'].fillna(mean_q4_balance)\n",
    "                                           \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "assisted-lesson",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customer_number           int64\n",
       "offer_accepted           object\n",
       "reward                   object\n",
       "mailer_type              object\n",
       "income_level             object\n",
       "bank_accounts_open        int64\n",
       "overdraft_protection     object\n",
       "credit_rating            object\n",
       "credit_cards_held         int64\n",
       "homes_owned               int64\n",
       "household_size            int64\n",
       "own_your_home            object\n",
       "average_balance         float64\n",
       "q1_balance              float64\n",
       "q2_balance              float64\n",
       "q3_balance              float64\n",
       "q4_balance              float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_miss_values= ['average_balance', 'q1_balance', 'q2_balance', 'q3_balance', 'q4_balance']\n",
    "\n",
    "mean_average_balance = df['average_balance'].mean()\n",
    "df['average_balance'] = df['average_balance'].fillna(mean_average_balance)\n",
    "df['average_balance'] = pd.to_numeric(df['average_balance'], errors = 'coerce')\n",
    "\n",
    "mean_average_balance = df['q1_balance'].mean()\n",
    "df['q1_balance'] = df['q1_balance'].fillna(mean_average_balance)\n",
    "df['q1_balance'] = pd.to_numeric(df['q1_balance'], errors = 'coerce')\n",
    "\n",
    "mean_q2_balance = df['q2_balance'].mean()\n",
    "df['q2_balance'] = df['q2_balance'].fillna(mean_q2_balance)\n",
    "df['q2_balance'] = pd.to_numeric(df['q2_balance'], errors = 'coerce')\n",
    "\n",
    "mean_q3_balance = df['q3_balance'].mean()\n",
    "df['q3_balance'] = df['q3_balance'].fillna(mean_q3_balance)\n",
    "df['q3_balance'] = pd.to_numeric(df['q3_balance'], errors = 'coerce')\n",
    "\n",
    "mean_q4_balance = df['q4_balance'].mean()\n",
    "df['q4_balance'] = df['q4_balance'].fillna(mean_q4_balance)\n",
    "df['q4_balance'] = pd.to_numeric(df['q4_balance'], errors = 'coerce')\n",
    "\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "nervous-fields",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "finnish-simpson",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "behavioral-result",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 18000 entries, 0 to 17999\n",
      "Data columns (total 17 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   customer_number       18000 non-null  int64  \n",
      " 1   offer_accepted        18000 non-null  object \n",
      " 2   reward                18000 non-null  object \n",
      " 3   mailer_type           18000 non-null  object \n",
      " 4   income_level          18000 non-null  object \n",
      " 5   bank_accounts_open    18000 non-null  int64  \n",
      " 6   overdraft_protection  18000 non-null  object \n",
      " 7   credit_rating         18000 non-null  object \n",
      " 8   credit_cards_held     18000 non-null  int64  \n",
      " 9   homes_owned           18000 non-null  int64  \n",
      " 10  household_size        18000 non-null  int64  \n",
      " 11  own_your_home         18000 non-null  object \n",
      " 12  average_balance       18000 non-null  float64\n",
      " 13  q1_balance            18000 non-null  float64\n",
      " 14  q2_balance            18000 non-null  float64\n",
      " 15  q3_balance            18000 non-null  float64\n",
      " 16  q4_balance            18000 non-null  float64\n",
      "dtypes: float64(5), int64(5), object(7)\n",
      "memory usage: 2.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "mighty-filename",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        vertical-align: middle;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_number</th>\n",
       "      <th>offer_accepted</th>\n",
       "      <th>reward</th>\n",
       "      <th>mailer_type</th>\n",
       "      <th>income_level</th>\n",
       "      <th>bank_accounts_open</th>\n",
       "      <th>overdraft_protection</th>\n",
       "      <th>credit_rating</th>\n",
       "      <th>credit_cards_held</th>\n",
       "      <th>homes_owned</th>\n",
       "      <th>household_size</th>\n",
       "      <th>own_your_home</th>\n",
       "      <th>average_balance</th>\n",
       "      <th>q1_balance</th>\n",
       "      <th>q2_balance</th>\n",
       "      <th>q3_balance</th>\n",
       "      <th>q4_balance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>Air Miles</td>\n",
       "      <td>Letter</td>\n",
       "      <td>High</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>High</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>No</td>\n",
       "      <td>1160.75</td>\n",
       "      <td>1669.0</td>\n",
       "      <td>877.0</td>\n",
       "      <td>1095.0</td>\n",
       "      <td>1002.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>No</td>\n",
       "      <td>Air Miles</td>\n",
       "      <td>Letter</td>\n",
       "      <td>Medium</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>Medium</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>Yes</td>\n",
       "      <td>147.25</td>\n",
       "      <td>39.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>366.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>No</td>\n",
       "      <td>Air Miles</td>\n",
       "      <td>Postcard</td>\n",
       "      <td>High</td>\n",
       "      <td>2</td>\n",
       "      <td>No</td>\n",
       "      <td>Medium</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>276.50</td>\n",
       "      <td>367.0</td>\n",
       "      <td>352.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>242.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>No</td>\n",
       "      <td>Air Miles</td>\n",
       "      <td>Letter</td>\n",
       "      <td>Medium</td>\n",
       "      <td>2</td>\n",
       "      <td>No</td>\n",
       "      <td>High</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>No</td>\n",
       "      <td>1219.00</td>\n",
       "      <td>1578.0</td>\n",
       "      <td>1760.0</td>\n",
       "      <td>1119.0</td>\n",
       "      <td>419.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>No</td>\n",
       "      <td>Air Miles</td>\n",
       "      <td>Letter</td>\n",
       "      <td>Medium</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>Medium</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1211.00</td>\n",
       "      <td>2140.0</td>\n",
       "      <td>1357.0</td>\n",
       "      <td>982.0</td>\n",
       "      <td>365.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   customer_number offer_accepted     reward mailer_type income_level  \\\n",
       "0                1             No  Air Miles      Letter         High   \n",
       "1                2             No  Air Miles      Letter       Medium   \n",
       "2                3             No  Air Miles    Postcard         High   \n",
       "3                4             No  Air Miles      Letter       Medium   \n",
       "4                5             No  Air Miles      Letter       Medium   \n",
       "\n",
       "   bank_accounts_open overdraft_protection credit_rating  credit_cards_held  \\\n",
       "0                   1                   No          High                  2   \n",
       "1                   1                   No        Medium                  2   \n",
       "2                   2                   No        Medium                  2   \n",
       "3                   2                   No          High                  1   \n",
       "4                   1                   No        Medium                  2   \n",
       "\n",
       "   homes_owned  household_size own_your_home  average_balance  q1_balance  \\\n",
       "0            1               4            No          1160.75      1669.0   \n",
       "1            2               5           Yes           147.25        39.0   \n",
       "2            1               2           Yes           276.50       367.0   \n",
       "3            1               4            No          1219.00      1578.0   \n",
       "4            1               6           Yes          1211.00      2140.0   \n",
       "\n",
       "   q2_balance  q3_balance  q4_balance  \n",
       "0       877.0      1095.0      1002.0  \n",
       "1       106.0        78.0       366.0  \n",
       "2       352.0       145.0       242.0  \n",
       "3      1760.0      1119.0       419.0  \n",
       "4      1357.0       982.0       365.0  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "israeli-actress",
   "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>customer_number</th>\n",
       "      <th>bank_accounts_open</th>\n",
       "      <th>credit_cards_held</th>\n",
       "      <th>homes_owned</th>\n",
       "      <th>household_size</th>\n",
       "      <th>average_balance</th>\n",
       "      <th>q1_balance</th>\n",
       "      <th>q2_balance</th>\n",
       "      <th>q3_balance</th>\n",
       "      <th>q4_balance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>18000.000000</td>\n",
       "      <td>18000.000000</td>\n",
       "      <td>18000.000000</td>\n",
       "      <td>18000.000000</td>\n",
       "      <td>18000.000000</td>\n",
       "      <td>18000.000000</td>\n",
       "      <td>18000.000000</td>\n",
       "      <td>18000.000000</td>\n",
       "      <td>18000.000000</td>\n",
       "      <td>18000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>9000.500000</td>\n",
       "      <td>1.255778</td>\n",
       "      <td>1.903500</td>\n",
       "      <td>1.203444</td>\n",
       "      <td>3.499056</td>\n",
       "      <td>940.515562</td>\n",
       "      <td>910.450656</td>\n",
       "      <td>999.392190</td>\n",
       "      <td>1042.033600</td>\n",
       "      <td>810.185803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5196.296758</td>\n",
       "      <td>0.472501</td>\n",
       "      <td>0.797009</td>\n",
       "      <td>0.427341</td>\n",
       "      <td>1.114182</td>\n",
       "      <td>350.064214</td>\n",
       "      <td>619.663515</td>\n",
       "      <td>457.097214</td>\n",
       "      <td>553.083487</td>\n",
       "      <td>558.628553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>48.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4500.750000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>787.937500</td>\n",
       "      <td>393.000000</td>\n",
       "      <td>664.000000</td>\n",
       "      <td>633.000000</td>\n",
       "      <td>363.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>9000.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1006.500000</td>\n",
       "      <td>773.000000</td>\n",
       "      <td>1032.000000</td>\n",
       "      <td>946.000000</td>\n",
       "      <td>704.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>13500.250000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1152.562500</td>\n",
       "      <td>1520.250000</td>\n",
       "      <td>1342.000000</td>\n",
       "      <td>1462.000000</td>\n",
       "      <td>1211.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>18000.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>3366.250000</td>\n",
       "      <td>3450.000000</td>\n",
       "      <td>3421.000000</td>\n",
       "      <td>3823.000000</td>\n",
       "      <td>4215.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       customer_number  bank_accounts_open  credit_cards_held   homes_owned  \\\n",
       "count     18000.000000        18000.000000       18000.000000  18000.000000   \n",
       "mean       9000.500000            1.255778           1.903500      1.203444   \n",
       "std        5196.296758            0.472501           0.797009      0.427341   \n",
       "min           1.000000            1.000000           1.000000      1.000000   \n",
       "25%        4500.750000            1.000000           1.000000      1.000000   \n",
       "50%        9000.500000            1.000000           2.000000      1.000000   \n",
       "75%       13500.250000            1.000000           2.000000      1.000000   \n",
       "max       18000.000000            3.000000           4.000000      3.000000   \n",
       "\n",
       "       household_size  average_balance    q1_balance    q2_balance  \\\n",
       "count    18000.000000     18000.000000  18000.000000  18000.000000   \n",
       "mean         3.499056       940.515562    910.450656    999.392190   \n",
       "std          1.114182       350.064214    619.663515    457.097214   \n",
       "min          1.000000        48.250000      0.000000      0.000000   \n",
       "25%          3.000000       787.937500    393.000000    664.000000   \n",
       "50%          3.000000      1006.500000    773.000000   1032.000000   \n",
       "75%          4.000000      1152.562500   1520.250000   1342.000000   \n",
       "max          9.000000      3366.250000   3450.000000   3421.000000   \n",
       "\n",
       "         q3_balance    q4_balance  \n",
       "count  18000.000000  18000.000000  \n",
       "mean    1042.033600    810.185803  \n",
       "std      553.083487    558.628553  \n",
       "min        0.000000      0.000000  \n",
       "25%      633.000000    363.750000  \n",
       "50%      946.000000    704.000000  \n",
       "75%     1462.000000   1211.000000  \n",
       "max     3823.000000   4215.000000  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "solid-container",
   "metadata": {},
   "source": [
    "### Get the sweet summary stats on each column to understand the distribution better <a class=\"anchor\" id=\"summary\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "august-heather",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_number</th>\n",
       "      <th>bank_accounts_open</th>\n",
       "      <th>credit_cards_held</th>\n",
       "      <th>homes_owned</th>\n",
       "      <th>household_size</th>\n",
       "      <th>average_balance</th>\n",
       "      <th>q1_balance</th>\n",
       "      <th>q2_balance</th>\n",
       "      <th>q3_balance</th>\n",
       "      <th>q4_balance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>18000.000000</td>\n",
       "      <td>18000.000000</td>\n",
       "      <td>18000.000000</td>\n",
       "      <td>18000.000000</td>\n",
       "      <td>18000.000000</td>\n",
       "      <td>18000.000000</td>\n",
       "      <td>18000.000000</td>\n",
       "      <td>18000.000000</td>\n",
       "      <td>18000.000000</td>\n",
       "      <td>18000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>9000.500000</td>\n",
       "      <td>1.255778</td>\n",
       "      <td>1.903500</td>\n",
       "      <td>1.203444</td>\n",
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       "      <td>940.515562</td>\n",
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       "      <td>1042.033600</td>\n",
       "      <td>810.185803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5196.296758</td>\n",
       "      <td>0.472501</td>\n",
       "      <td>0.797009</td>\n",
       "      <td>0.427341</td>\n",
       "      <td>1.114182</td>\n",
       "      <td>350.064214</td>\n",
       "      <td>619.663515</td>\n",
       "      <td>457.097214</td>\n",
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       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4500.750000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
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       "      <td>393.000000</td>\n",
       "      <td>664.000000</td>\n",
       "      <td>633.000000</td>\n",
       "      <td>363.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>9000.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1006.500000</td>\n",
       "      <td>773.000000</td>\n",
       "      <td>1032.000000</td>\n",
       "      <td>946.000000</td>\n",
       "      <td>704.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>13500.250000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1152.562500</td>\n",
       "      <td>1520.250000</td>\n",
       "      <td>1342.000000</td>\n",
       "      <td>1462.000000</td>\n",
       "      <td>1211.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>18000.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>3366.250000</td>\n",
       "      <td>3450.000000</td>\n",
       "      <td>3421.000000</td>\n",
       "      <td>3823.000000</td>\n",
       "      <td>4215.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       customer_number  bank_accounts_open  credit_cards_held   homes_owned  \\\n",
       "count     18000.000000        18000.000000       18000.000000  18000.000000   \n",
       "mean       9000.500000            1.255778           1.903500      1.203444   \n",
       "std        5196.296758            0.472501           0.797009      0.427341   \n",
       "min           1.000000            1.000000           1.000000      1.000000   \n",
       "25%        4500.750000            1.000000           1.000000      1.000000   \n",
       "50%        9000.500000            1.000000           2.000000      1.000000   \n",
       "75%       13500.250000            1.000000           2.000000      1.000000   \n",
       "max       18000.000000            3.000000           4.000000      3.000000   \n",
       "\n",
       "       household_size  average_balance    q1_balance    q2_balance  \\\n",
       "count    18000.000000     18000.000000  18000.000000  18000.000000   \n",
       "mean         3.499056       940.515562    910.450656    999.392190   \n",
       "std          1.114182       350.064214    619.663515    457.097214   \n",
       "min          1.000000        48.250000      0.000000      0.000000   \n",
       "25%          3.000000       787.937500    393.000000    664.000000   \n",
       "50%          3.000000      1006.500000    773.000000   1032.000000   \n",
       "75%          4.000000      1152.562500   1520.250000   1342.000000   \n",
       "max          9.000000      3366.250000   3450.000000   3421.000000   \n",
       "\n",
       "         q3_balance    q4_balance  \n",
       "count  18000.000000  18000.000000  \n",
       "mean    1042.033600    810.185803  \n",
       "std      553.083487    558.628553  \n",
       "min        0.000000      0.000000  \n",
       "25%      633.000000    363.750000  \n",
       "50%      946.000000    704.000000  \n",
       "75%     1462.000000   1211.000000  \n",
       "max     3823.000000   4215.000000  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#statistical distribution\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "traditional-shanghai",
   "metadata": {},
   "source": [
    "### Get a  representation of the numbers  <a class=\"anchor\" id=\"numbersrep\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "numerical-interpretation",
   "metadata": {},
   "outputs": [],
   "source": [
    "# using pandas\n",
    "# hist = df.hist(figsize=(15,10),layout=(3,4), grid=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "composed-filling",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # check relationships with seaborn\n",
    "# sns.pairplot(df)\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "sonic-utilization",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "steady-backup",
   "metadata": {},
   "source": [
    "Only the household_size has a normal distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "postal-compiler",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # check for multicollinearity \n",
    "# corr_matrix=df.corr(method='pearson')  # default\n",
    "# fig, ax = plt.subplots(figsize=(10, 8))\n",
    "# ax = sns.heatmap(corr_matrix, annot=True)\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ceramic-layer",
   "metadata": {},
   "source": [
    "The graph proves the absence of multicollinearity. \n",
    "\n",
    "\"The linear model makes the following assumptions\n",
    "- Require a linear relationship between the dependent and independent variables.\n",
    "- The error terms (residuals) need to be normally distributed. \n",
    "- If not weights can be introduced to penalize some observations in the error metric.\n",
    "Independent variables are normally distributed.\n",
    "If not (apply transformation Box-Cox)\n",
    "- Absence of multicollinearity.\n",
    "- Independent observations (not repeated)\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "printable-novelty",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>offer_accepted</th>\n",
       "      <th>reward</th>\n",
       "      <th>income_level</th>\n",
       "      <th>bank_accounts_open</th>\n",
       "      <th>overdraft_protection</th>\n",
       "      <th>credit_rating</th>\n",
       "      <th>credit_cards_held</th>\n",
       "      <th>homes_owned</th>\n",
       "      <th>household_size</th>\n",
       "      <th>own_your_home</th>\n",
       "      <th>q2_balance</th>\n",
       "      <th>q3_balance</th>\n",
       "      <th>q4_balance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>No</td>\n",
       "      <td>Air Miles</td>\n",
       "      <td>High</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>High</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>No</td>\n",
       "      <td>877.0</td>\n",
       "      <td>1095.0</td>\n",
       "      <td>1002.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>No</td>\n",
       "      <td>Air Miles</td>\n",
       "      <td>Medium</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>Medium</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>Yes</td>\n",
       "      <td>106.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>366.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>No</td>\n",
       "      <td>Air Miles</td>\n",
       "      <td>High</td>\n",
       "      <td>2</td>\n",
       "      <td>No</td>\n",
       "      <td>Medium</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>352.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>242.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>No</td>\n",
       "      <td>Air Miles</td>\n",
       "      <td>Medium</td>\n",
       "      <td>2</td>\n",
       "      <td>No</td>\n",
       "      <td>High</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>No</td>\n",
       "      <td>1760.0</td>\n",
       "      <td>1119.0</td>\n",
       "      <td>419.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>No</td>\n",
       "      <td>Air Miles</td>\n",
       "      <td>Medium</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>Medium</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1357.0</td>\n",
       "      <td>982.0</td>\n",
       "      <td>365.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>No</td>\n",
       "      <td>Air Miles</td>\n",
       "      <td>Medium</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>High</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>No</td>\n",
       "      <td>1365.0</td>\n",
       "      <td>750.0</td>\n",
       "      <td>497.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>No</td>\n",
       "      <td>Air Miles</td>\n",
       "      <td>Medium</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>Medium</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>No</td>\n",
       "      <td>188.0</td>\n",
       "      <td>347.0</td>\n",
       "      <td>132.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>No</td>\n",
       "      <td>Cash Back</td>\n",
       "      <td>Low</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>Medium</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Yes</td>\n",
       "      <td>391.0</td>\n",
       "      <td>285.0</td>\n",
       "      <td>306.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>No</td>\n",
       "      <td>Air Miles</td>\n",
       "      <td>Medium</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>Low</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Yes</td>\n",
       "      <td>891.0</td>\n",
       "      <td>882.0</td>\n",
       "      <td>1353.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>No</td>\n",
       "      <td>Air Miles</td>\n",
       "      <td>High</td>\n",
       "      <td>2</td>\n",
       "      <td>No</td>\n",
       "      <td>Low</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1454.0</td>\n",
       "      <td>514.0</td>\n",
       "      <td>115.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  offer_accepted     reward income_level  bank_accounts_open  \\\n",
       "0             No  Air Miles         High                   1   \n",
       "1             No  Air Miles       Medium                   1   \n",
       "2             No  Air Miles         High                   2   \n",
       "3             No  Air Miles       Medium                   2   \n",
       "4             No  Air Miles       Medium                   1   \n",
       "5             No  Air Miles       Medium                   1   \n",
       "6             No  Air Miles       Medium                   1   \n",
       "7             No  Cash Back          Low                   1   \n",
       "8             No  Air Miles       Medium                   1   \n",
       "9             No  Air Miles         High                   2   \n",
       "\n",
       "  overdraft_protection credit_rating  credit_cards_held  homes_owned  \\\n",
       "0                   No          High                  2            1   \n",
       "1                   No        Medium                  2            2   \n",
       "2                   No        Medium                  2            1   \n",
       "3                   No          High                  1            1   \n",
       "4                   No        Medium                  2            1   \n",
       "5                   No          High                  3            1   \n",
       "6                   No        Medium                  2            1   \n",
       "7                   No        Medium                  4            1   \n",
       "8                   No           Low                  2            1   \n",
       "9                   No           Low                  3            2   \n",
       "\n",
       "   household_size own_your_home  q2_balance  q3_balance  q4_balance  \n",
       "0               4            No       877.0      1095.0      1002.0  \n",
       "1               5           Yes       106.0        78.0       366.0  \n",
       "2               2           Yes       352.0       145.0       242.0  \n",
       "3               4            No      1760.0      1119.0       419.0  \n",
       "4               6           Yes      1357.0       982.0       365.0  \n",
       "5               4            No      1365.0       750.0       497.0  \n",
       "6               3            No       188.0       347.0       132.0  \n",
       "7               4           Yes       391.0       285.0       306.0  \n",
       "8               4           Yes       891.0       882.0      1353.0  \n",
       "9               4           Yes      1454.0       514.0       115.0  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check for more claning\n",
    "# df.head(20)\n",
    "# df.tail(20)\n",
    "# drop customer_number\n",
    "df=df.drop(['customer_number', 'mailer_type', 'average_balance', 'q1_balance'], axis=1)\n",
    "df.head(10)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "hairy-entertainment",
   "metadata": {},
   "source": [
    "It seems that there is no need to make more cleaning. Neither for Bucketing features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "united-privacy",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['offer_accepted', 'reward', 'income_level', 'bank_accounts_open',\n",
       "       'overdraft_protection', 'credit_rating', 'credit_cards_held',\n",
       "       'homes_owned', 'household_size', 'own_your_home', 'q2_balance',\n",
       "       'q3_balance', 'q4_balance'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "polar-poland",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 18000 entries, 0 to 17999\n",
      "Data columns (total 13 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   offer_accepted        18000 non-null  object \n",
      " 1   reward                18000 non-null  object \n",
      " 2   income_level          18000 non-null  object \n",
      " 3   bank_accounts_open    18000 non-null  int64  \n",
      " 4   overdraft_protection  18000 non-null  object \n",
      " 5   credit_rating         18000 non-null  object \n",
      " 6   credit_cards_held     18000 non-null  int64  \n",
      " 7   homes_owned           18000 non-null  int64  \n",
      " 8   household_size        18000 non-null  int64  \n",
      " 9   own_your_home         18000 non-null  object \n",
      " 10  q2_balance            18000 non-null  float64\n",
      " 11  q3_balance            18000 non-null  float64\n",
      " 12  q4_balance            18000 non-null  float64\n",
      "dtypes: float64(3), int64(4), object(6)\n",
      "memory usage: 1.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "taken-fever",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\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>bank_accounts_open</th>\n",
       "      <th>credit_cards_held</th>\n",
       "      <th>homes_owned</th>\n",
       "      <th>household_size</th>\n",
       "      <th>q2_balance</th>\n",
       "      <th>q3_balance</th>\n",
       "      <th>q4_balance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>18000.000000</td>\n",
       "      <td>18000.000000</td>\n",
       "      <td>18000.000000</td>\n",
       "      <td>18000.000000</td>\n",
       "      <td>18000.000000</td>\n",
       "      <td>18000.000000</td>\n",
       "      <td>18000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.255778</td>\n",
       "      <td>1.903500</td>\n",
       "      <td>1.203444</td>\n",
       "      <td>3.499056</td>\n",
       "      <td>999.392190</td>\n",
       "      <td>1042.033600</td>\n",
       "      <td>810.185803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.472501</td>\n",
       "      <td>0.797009</td>\n",
       "      <td>0.427341</td>\n",
       "      <td>1.114182</td>\n",
       "      <td>457.097214</td>\n",
       "      <td>553.083487</td>\n",
       "      <td>558.628553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>664.000000</td>\n",
       "      <td>633.000000</td>\n",
       "      <td>363.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1032.000000</td>\n",
       "      <td>946.000000</td>\n",
       "      <td>704.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1342.000000</td>\n",
       "      <td>1462.000000</td>\n",
       "      <td>1211.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>3421.000000</td>\n",
       "      <td>3823.000000</td>\n",
       "      <td>4215.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       bank_accounts_open  credit_cards_held   homes_owned  household_size  \\\n",
       "count        18000.000000       18000.000000  18000.000000    18000.000000   \n",
       "mean             1.255778           1.903500      1.203444        3.499056   \n",
       "std              0.472501           0.797009      0.427341        1.114182   \n",
       "min              1.000000           1.000000      1.000000        1.000000   \n",
       "25%              1.000000           1.000000      1.000000        3.000000   \n",
       "50%              1.000000           2.000000      1.000000        3.000000   \n",
       "75%              1.000000           2.000000      1.000000        4.000000   \n",
       "max              3.000000           4.000000      3.000000        9.000000   \n",
       "\n",
       "         q2_balance    q3_balance    q4_balance  \n",
       "count  18000.000000  18000.000000  18000.000000  \n",
       "mean     999.392190   1042.033600    810.185803  \n",
       "std      457.097214    553.083487    558.628553  \n",
       "min        0.000000      0.000000      0.000000  \n",
       "25%      664.000000    633.000000    363.750000  \n",
       "50%     1032.000000    946.000000    704.000000  \n",
       "75%     1342.000000   1462.000000   1211.000000  \n",
       "max     3421.000000   3823.000000   4215.000000  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aggregate-necessity",
   "metadata": {},
   "source": [
    "### Outliers <a class=\"anchor\" id=\"outliers\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "brutal-revision",
   "metadata": {},
   "source": [
    "### Box Plots <a class=\"anchor\" id=\"boxplots\"></a>\n",
    "A boxplot is a standardized way of displaying the distribution of data based on a five number summary (“minimum”, first quartile (Q1), median, third quartile (Q3), and “maximum”). It can tell you about your outliers and what their values are. It can also tell you if your data is symmetrical, how tightly your data is grouped, and if and how your data is skewed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "passive-springfield",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Q1 of the column  q2_balance : 663.25\n",
      "Q3 of the column  q2_balance : 1341.0\n",
      "IRQ of the column  q2_balance : 677.75\n",
      "lower_whisker:  -353.375 , upper whisker:  2357.625\n",
      "Q1 of the column  q3_balance : 633.0\n",
      "Q3 of the column  q3_balance : 1461.0\n",
      "IRQ of the column  q3_balance : 828.0\n",
      "lower_whisker:  -609.0 , upper whisker:  2703.0\n",
      "Q1 of the column  q4_balance : 363.0\n",
      "Q3 of the column  q4_balance : 1210.0\n",
      "IRQ of the column  q4_balance : 847.0\n",
      "lower_whisker:  -907.5 , upper whisker:  2480.5\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(17990, 13)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find quartiles\n",
    "cols_outliers = ['q2_balance', 'q3_balance', 'q4_balance']\n",
    "\n",
    "for col in cols_outliers:\n",
    "    Q1 = df[col].quantile(0.25)\n",
    "    Q3 = df[col].quantile(0.75)\n",
    "    IQR = Q3 - Q1\n",
    "    print('Q1 of the column ', col,':', Q1)\n",
    "    print('Q3 of the column ', col,':', Q3)\n",
    "    print('IRQ of the column ', col,':', IQR)\n",
    "    Lower_Whisker = Q1 - 1.5 * IQR\n",
    "    Upper_Whisker = Q3 + 1.5 * IQR\n",
    "    print('lower_whisker: ', Lower_Whisker, ',' , 'upper whisker: ', Upper_Whisker)\n",
    "    #  Outlier Treatment \n",
    "    df = df[df[col]< Upper_Whisker]\n",
    "    df = df[df[col]> Lower_Whisker]\n",
    "df.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "unavailable-polyester",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "known-forward",
   "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>offer_accepted</th>\n",
       "      <th>reward</th>\n",
       "      <th>income_level</th>\n",
       "      <th>bank_accounts_open</th>\n",
       "      <th>overdraft_protection</th>\n",
       "      <th>credit_rating</th>\n",
       "      <th>credit_cards_held</th>\n",
       "      <th>homes_owned</th>\n",
       "      <th>household_size</th>\n",
       "      <th>own_your_home</th>\n",
       "      <th>q2_balance</th>\n",
       "      <th>q3_balance</th>\n",
       "      <th>q4_balance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>No</td>\n",
       "      <td>Air Miles</td>\n",
       "      <td>High</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>High</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>No</td>\n",
       "      <td>877.0</td>\n",
       "      <td>1095.0</td>\n",
       "      <td>1002.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>No</td>\n",
       "      <td>Air Miles</td>\n",
       "      <td>Medium</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>Medium</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>Yes</td>\n",
       "      <td>106.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>366.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>No</td>\n",
       "      <td>Air Miles</td>\n",
       "      <td>High</td>\n",
       "      <td>2</td>\n",
       "      <td>No</td>\n",
       "      <td>Medium</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>352.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>242.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>No</td>\n",
       "      <td>Air Miles</td>\n",
       "      <td>Medium</td>\n",
       "      <td>2</td>\n",
       "      <td>No</td>\n",
       "      <td>High</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>No</td>\n",
       "      <td>1760.0</td>\n",
       "      <td>1119.0</td>\n",
       "      <td>419.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>No</td>\n",
       "      <td>Air Miles</td>\n",
       "      <td>Medium</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>Medium</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1357.0</td>\n",
       "      <td>982.0</td>\n",
       "      <td>365.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  offer_accepted     reward income_level  bank_accounts_open  \\\n",
       "0             No  Air Miles         High                   1   \n",
       "1             No  Air Miles       Medium                   1   \n",
       "2             No  Air Miles         High                   2   \n",
       "3             No  Air Miles       Medium                   2   \n",
       "4             No  Air Miles       Medium                   1   \n",
       "\n",
       "  overdraft_protection credit_rating  credit_cards_held  homes_owned  \\\n",
       "0                   No          High                  2            1   \n",
       "1                   No        Medium                  2            2   \n",
       "2                   No        Medium                  2            1   \n",
       "3                   No          High                  1            1   \n",
       "4                   No        Medium                  2            1   \n",
       "\n",
       "   household_size own_your_home  q2_balance  q3_balance  q4_balance  \n",
       "0               4            No       877.0      1095.0      1002.0  \n",
       "1               5           Yes       106.0        78.0       366.0  \n",
       "2               2           Yes       352.0       145.0       242.0  \n",
       "3               4            No      1760.0      1119.0       419.0  \n",
       "4               6           Yes      1357.0       982.0       365.0  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "natural-chosen",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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cVSdlutDJR/upmHdX1ZOrqpK8YGYaAAAA1mkt/5rgd5M8NcmxVXV7klcmuTDJJVX1wiR/nuS5SdJau6GqLklyY5J7k5zXWruvz+pFuf9fE7y33wAAANgHew1zrbXnrfDQ01YY/4IkF8xp35nk8evqHQAAAHMd6AugAAAAsAGEOQAAgAEJcwAAAAPa62/mAABgXx31uB15wsU7Ft2NDXPU45LkrEV3gy1CmAMA4KC5+6YLs+vCrRNutu24bNFdYAtxmiUAAMCAhDkAAIABCXMAAAADEuYAAAAGJMwBAAAMSJgDAAAYkDAHAAAwIGEOAABgQMIcAADAgIQ5AACAAQlzAAAAAxLmAAAABiTMAQAADEiYAwAAGJAwBwAAMCBhDgAAYEDCHAAAwICEOQAAgAEJcwAAAAMS5gAAAAYkzAEAAAxImAMAABiQMAcAADAgYQ4AAGBAwhwAAMCAhDkAAIABCXMAAAADEuYAAAAGJMwBAAAMSJgDAAAYkDAHAAAwIGEOAABgQMIcAADAgIQ5AACAAQlzAAAAAzp80R0AAADGdNTjduQJF+9YdDc2zFGPS5KzFt2NrxLmAACAfXL3TRdm14WHTrg52LbtuGzRXXgAp1kCAAAMSJgDAAAYkDAHAAAwIGEOAABgQC6AAgAcEg61CwscTEcfecSiuwBsAsIcALBwi7oa3rYdl22pK/EBm4vTLAEAAAYkzAEAAAxImAMAABiQ38wBAHBQubgNHBzCHAAAB42L28DB4zRLAACAAQlzAAAAAxLmAAAABiTMAQAADEiYAwAAGJAwBwAAMCBhDgAAYEDCHAAAwICEOQAAgAEJcwAAAAMS5gAAAAYkzAEAAAxImAMAABiQMAcAADAgYQ4AAGBA+xXmqmpXVV1XVddU1c7edkxVXVFVn+r3D5sZ/+VVdUtV3VxVz9jfzgMAAGxVB+LI3He11k5rrW3vf+9IcmVr7eQkV/a/U1WnJDk7yalJzkzyuqo67AAsHwAAYMs5GKdZPivJxX344iTPnml/S2vtS621W5PckuSMg7B8AACATW9/w1xLcnlVXV1V5/a2R7TW7kySfv/w3n58kttmpr29t+2hqs6tqp1VtXP37t372UUAAIDN5/D9nP4prbU7qurhSa6oqk+uMm7NaWvzRmytvSHJG5Jk+/btc8cBAADYyvbryFxr7Y5+/7kkv5/ptMnPVtUjk6Tff66PfnuSE2cmPyHJHfuzfAAAgK1qn8NcVX1dVR21NJzk6UmuT3JpknP6aOckeVcfvjTJ2VX14Ko6KcnJST66r8sHAADYyvbnNMtHJPn9qlqaz++01v6gqj6W5JKqemGSP0/y3CRprd1QVZckuTHJvUnOa63dt1+9BwAA2KL2Ocy11j6d5Elz2v8yydNWmOaCJBfs6zIBAACYHIx/TQAAAMBBJswBAAAMSJgDAAAYkDAHAAAwIGEOAABgQMIcAADAgIQ5AACAAQlzAAAAAxLmAAAABiTMAQAADEiYAwAAGJAwBwAAMCBhDgAAYEDCHAAAwICEOQAAgAEdvugOAAAA49q247JFd2HDHH3kEYvuwgMIcwAAwD7ZdeFZC1nuth2XLWzZhxKnWQIAAAxImAMAABiQMAcAADAgYQ4AAGBAwhwAAMCAhDkAAIABCXMAAAADEuYAAAAGJMwBAAAMSJgDAAAYkDAHAAAwIGEOAABgQMIcAADAgIQ5AACAAQlzAAAAAxLmAAAABiTMAQAADEiYAwAAGJAwBwAAMCBhDgAAYEDCHAAAwICEOQAAgAEJcwAAAAMS5gAAAAYkzAEAAAxImAMAABiQMAcAADAgYQ4AAGBAwhwAAMCAhDkAAIABCXMAAAADEuYAAAAGJMwBAAAMSJgDAAAYkDAHAAAwIGEOAABgQMIcAADAgIQ5AACAAQlzAAAAAxLmAAAABiTMAQAADEiYAwAAGJAwBwAAMCBhDgAAYEDCHAAAwICEOQAAgAEJcwAAAAMS5gAAAAYkzAEAAAxImAMAABjQhoe5qjqzqm6uqluqasdGLx8A1qKq9rgBwKFkQ8NcVR2W5FeT/OMkpyR5XlWdspF9OBT5wLC5qe/mpr6b00p1VF8ADiUbfWTujCS3tNY+3Vr72yRvSfKsDe7DIcUHhs1NfTc39QUAFunwDV7e8Ulum/n79iTfusF9OCS11r467IPg5qO+m5v6HvoOZF3WO6/Z7QMADqTayDeZqnpukme01n68//38JGe01v7FsvHOTXJukjzqUY86/TOf+cyG9XHJEy5+woYvc5GuO+e6RXdhw2y12ibqu9ltlfqqLStZ5JcowvrBpbabm/quXVVd3Vrbvkf7Boe5b0tyfmvtGf3vlydJa+0/rjTN9u3b286dOzeohxtvaSOe983+aBsZe1LfzU19N6/VPmCoLQAbbaUwt9G/mftYkpOr6qSq+pokZye5dIP7cEhy8YTNTX03N/UFABZhQ38z11q7t6penOR9SQ5L8sbW2g0b2YdDTWtt7gdA3/xuDuq7uanv5qW2AIxgoy+Aktbae5K8Z6OXeyjz4WBzU9/NTX03L7UF4FC34f80HAAAgP0nzAEAAAxImAMAABiQMAcAADAgYQ4AAGBAwhwAAMCAhDkAAIABCXMAAAADEuYAAAAGJMwBAAAMSJgDAAAYkDAHAAAwIGEOAABgQMIcAADAgIQ5AACAAVVrbdF9WFVV7U7ymUX3Y4Mcm+Tzi+4EB436bm7qu3mp7eamvpuX2m5uW62+j26tHbe88ZAPc1tJVe1srW1fdD84ONR3c1PfzUttNzf13bzUdnNT34nTLAEAAAYkzAEAAAxImDu0vGHRHeCgUt/NTX03L7Xd3NR381LbzU194zdzAAAAQ3JkDgAAYEDCHAAAwICGD3NVta2qrj8A8/nRqvqVA9Gnjdb7/o2L7sfoquqL/f4bq+ptffi0qnrmBvbhqVX17g2Y5qKq+oEDMa9FOlD7/2ZRVedX1csW3Y/9saiaLu3/6xh/7rre1/5X1YfXO81WU1W/UFWfrKpPVNXvV9XXrzLuut/Tq2pXVR273x1l3arq53tdr6mqy1f7TKO246qql1VVW60W6rt+w4c5kiQ/mkSYm6OqDl/vNK21O1prS0HntCQHLcxV1WEHa97AOFpr377oPgzgiiSPb609Mcn/TPLyBfeHA+cXWmtPbK2dluTdSf7dgvvDAVZVJyb5R0n+fNF92Ww2S5g7vKou7t/qvK2qvraq/l1Vfayqrq+qN1RVJUlV/UlVvaqqPlpV/7Oq/sHymVXVWVV11Uopv6q+r6r+rKr+R1X9YVU9orc/pKp+q6qu6335p739zKr6eFVdW1VX9rZjquqdfbyPVNUTe/sDvu3t/d/WbzdV1W9U1Q39m6sj+9GV7Une3L/ROrKqLqyqG/u8f3GllVZVj66qK/t4V1bVo3r7RVX1a1X1wb6Ovre3H9a/Gf1Yn+YnevtT+3p9W//W9M1L63sjVNULen+urarf7v1/dVX9cZJXVdVjquoPqurq/pwe26c7qdf5Y1X18zPz29bX+9ck+bkkP9TX7Q+tsPyV6v76qtrZ6/WzM+Pv6tvnh5I8t28fn+x//5OZ8b6zL/eavq0dtcpqeMi89V9Vp1fV+/tzf19VPXJO/+cufyCHzdkvTuv71dI3+A9Lvrr/v6aqPtD3p79fVe+oqk9V1b9fmmFV/UhNrxHXVNWv923/sL5tXd9r/S9X6tC85VfVw6vq6v74k2r6dnJpn/tfNb1uXVRVr62qD1fVp2vm6GlV/auZfW92e3pFVd1cVX+Y5O8ehPW7COut6fY+fGxV7erDp87U8BNVdXJv36O2Swutqgtqeh35SN3/uj73dXJW38+uraqrkpy32hNbpV9LZwb83Mx+/xdV9Vt76/dmNLtdV9XvVtXLWmuXt9bu7aN8JMkJe5nNiTW99t9cVa+cmfc7+2viDVV17grLnztOVX1xhe3kEX27vLbfvr23b6m6rcUKtf2bmVG+Lsners6ntoeoefXtD70myU9n77VN1Hd9WmtD35Jsy7RhPKX//cYkL0tyzMw4v53k+/rwnyT5pT78zCR/2Id/NMmvJHlOkg8medgqy3xY7r8S6I/PzO9VSf7zsvGOS3JbkpN62zH9/peTvLIPf3eSa/rw+UleNjOP6/tz3Jbk3iSn9fZLkvzIzHPavjT/JDfP9O/rV3ke/z3JOX34nyV5Zx++KMkfZAr7Jye5PcnfSXJukn/Tx3lwkp1JTkry1CR3ZXpjfVCSq5J8xwbV/9T+fI+def4XZfpm77DedmWSk/vwtyb5oz58aZIX9OHzknxxZpu6fna72Esf9qj7slof1mv0xP73riQ/3Yf/Tt8+Tk5Sva7vnqnP0nb9kCSHr7D8ues/yRFJPpzkuD7eDyV540yNf2C15Y9wW2m/SPKJJN/Z235uqT69Dq/qwy9JckeSR/bt+fYk35DkcX3dH9HHe12SFyQ5PckVM8tebd9aafk3JHlokhcn+ViSH07y6CRXzdTlrb2OpyS5pbc/PdMlmKs/9u4k/7D36bokX9vne0tmXj9GvO1jTZde/45NsqsP/3KSH+7DX5PkyJVq24db7n+f+E+5/7VupdfJ85fW9bK+/UL668cKz2+PfvXhLy4b7+g+39NX6/dmvK1lu+7r40dWmcePJrkz0z59ZKb30q++T/b7pfZv6H/vysx7yQrjrLSd/F6Sl/bhw3r9tlTd9re2SS7I9H50ffr7ltqOdVupvkm+P8l/WV4L9T0wt3WfgnaIuq219qd9+L8l+ckkt1bVT2faoI7J9CHqv/dx3tHvr870wWHJd2U6yvX09sBviZY7Icnv1XSU42uS3NrbvyfJ2Usjtda+UFXfl+QDrbVbe9tf9Ye/I8k/7W1/VFXfUFVH7+V53tpau2aFvi/5myT/L8l/rarLMn3oW8m35f4jMb+daeNecklr7StJPlVVn07y2EwfKJ9Y9x8tODpTCPjbJB9trd2eJFV1Te/bh/byfA6E707yttba55Np/dZ0UOqtrbX7quohSb49yVvr/oOFD+73T0mvQabn/6p97MMede+DP9i/FTo8U2A4JdOHs2R68Uim9Xpra+1TSVJV/y1TaE6SP03y6qp6c5J3LK3fFcxb/3+d5PFJrujP/bBML5CzVlv+KJbvF4/JFLTe39suzhSQllza769LckNr7c4k6dv5iZn2zdOTfKyvtyOTfC7T68c3VdUvJ7ksyeXzOtP345WW/+FM290/TPIfkpyZKaB9cGYW7+z73o1L3xxm2veenuR/9L8fkmnfOyrJ77fW/m9f9qXZHNZb03muSvKKqjoh0/7zqap6WubXNplex5ZeL6/OdDpQsvrr5Lx6/3aSf7yefi0foabOvTnJa1prV1fVi1fp92b0D7LKdl1Vr8gU+N+8l/lc0Vr7yz7NOzLt2zuT/GRVPaePc2Kmfekvl0270jgrbSffnelLn7TW7ktyV1U9P1urbmuxYm1ba6/ItG+8PNMXXq9cZT5qe2iaV9+vTfKKTO9ha6W+67BZwlyb8/frMiX526rq/ExHIJZ8qd/flweug08n+aYk35xpo1nJLyd5dWvt0qp6aqZvaJPpQ9nyvsxrW2qf9zzuzQNPf53X76W+H7nHDFq7t6rOSPK0TAHjxZk21LVoKwwv/V1J/kVr7X2zD/R1sLxvG7VtrbR+/0+/f1CSv27TefjzzJt2v/tQVSdl+jbq7/dQf1EeWMv/MzM8tw+ttQt7IH9mko9U1fe01j65Qh/mrf/KFFa+bS/9PxDrYJGWP/evX+P4X1k27Vdy/3q7uLW2x+9xqupJSZ6R6UjuD2Y6UrMeH8z0ZvfoJO9K8jOZ1v/sly6zfaqZ+//YWvv1Zf15acav3zzrqensa+ZX97HW2u9U1Z8lOSvJ+6rqx7NKbZN8ufWvYLP6a9haX+PnTzynX621P1o22vlJbm+t/dbMMlbq92Y1d51W1TlJvjfJ02bqtdZ5tP5+9T1Jvq219n+r6k/ywNfm7GWctW4nydas21rsrW6/k+kLs9XCnNoeuuZ9fjwpybU9GJ2Q5ONVdUZr7X+vdR7qu7LN8pu5R1XV0gfW5+X+I0Kf70dm9rhq3wo+k+kb2DdV1amrjHd0kr/ow+fMtF+eKTwlSWr6TcdVSb6zf7hPVR3TH/5AplOslja+z/ejgbuSfEtv/5ZMO8De3J3pG/r053t0a+09SV6a6QIeK/lw7j+i9MN54JG051bVg6rqMZkC7s1J3pfkRVV1RF/WN1fV162hfwfTlZmOgH1D79Mxsw/2dXprVT23P179A3kyHfmaff7zfHXdrmJe3R+aKbDd1Y+urPRN/SeTnNTXczJtv0vzeUxr7brW2qsyfbnw2L30Y7mbkxy3tG9U1RFztusVlz+wu5J8oe7/Pezzk7x/lfGXuzLJD1TVw5Ov/r710TX9hvZBrbW3J/m36fvpcq211Zb/gUynDH6qH337q0xh/U/3mNEDvS/JP+v7d6rq+N6/DyR5Tk2/KTsqyfet43mOZLV1uivTN6jJzGt9VX1Tkk+31l6b6WjsE7NCbfey7NVeJ9Na++tM+/l3zIyzohX6Nfv492b6xvgnZ5r3pd8jm7tdV9WZmb4A+f6lb/734h/1dXVkkmdn2s+OTvKF/kHvsUmePGe6tYyz3JVJXtT7eVhVPTRbr25rsVJtT54Z5/szvTetRm0PTfPqe09r7eGttW2ttW2ZftLwLasEuUR912WzhLmbkpxTVZ/IdErl65P8RqbTqN6Z6bcpa9JauznTm/FbZz7gLnd+f/yDST4/0/7vkzyspgskXJvku1pruzOdtvaO3vZ7M/PY3vt8Ye4PhW9PckxNp8q9KNMVu/bmoiS/1qc5Ksm7+3zfn2TFizRk+rDwY33c52f6DdGSm/v0703yz1tr/y/Jf01yY6ZvVK5P8utZ8NHd1toNmc6zf39fv6+eM9oPJ3lhf/yGJM/q7S9Jcl5VfSzTC8A8f5zklFrlAiiZX/drM50Sd0Om33HO/bDe1+u5SS6r6QIkn5l5+KUz87wnUy3WrLX2t5k+3L6qz+OaTKecrnX5IzsnyS/0bfu0TL+xWpPW2o1J/k2Sy/v0V2Q6Tfb4JH/S97OLsvqV9OYuv7W2qz/+gX7/oUxHjr+wfAbL+nR5pm+rr6qq65K8LclRrbWPZ3pNuSbTa8cHV5zJ+Faq6S9m+pLpw5l+M7fkh5Jc3+v12CRvWqW2q1ntdXLJjyX51ZougHLPXua3R7+WPf5Tma5OvPTD+5/bx34Pa5Xt+lcyvcdd0dfNr+1lVh/KdNrrNUne3lrbmen34If39fjzmS6kstxaxlnuJUm+q++fVyc5davVbS1Wqe2F/f3uE5lOx5u3n81S20PQAXxPUt91WLpIBnxVTacEvru19rZF9wWAra2mn0p8sbW24tWZGZPabm7quzE2y5E5AACALcWRuVXUdMWs5y5rfmtr7YJF9GdfbZbnsWhV9WPZ89SPP22trfp/pQ7g8p+Q6bSDWV9qrX3rRiyfPVXVr2a6OuWs/zJz4Qq2qKp6Rva8Qu6trbXnzBuffWM9b15qu7mp74EjzAEAAAzIaZYAAAADEuYAAAAGJMwBAAAMSJgDAAAYkDAHAAAwoP8Pp4Z+PqUAdvwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check outliers\n",
    "df.boxplot(grid=False, figsize= (15,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "applied-james",
   "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>offer_accepted</th>\n",
       "      <th>reward</th>\n",
       "      <th>income_level</th>\n",
       "      <th>overdraft_protection</th>\n",
       "      <th>credit_rating</th>\n",
       "      <th>own_your_home</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>No</td>\n",
       "      <td>Air Miles</td>\n",
       "      <td>High</td>\n",
       "      <td>No</td>\n",
       "      <td>High</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>No</td>\n",
       "      <td>Air Miles</td>\n",
       "      <td>Medium</td>\n",
       "      <td>No</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>No</td>\n",
       "      <td>Air Miles</td>\n",
       "      <td>High</td>\n",
       "      <td>No</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>No</td>\n",
       "      <td>Air Miles</td>\n",
       "      <td>Medium</td>\n",
       "      <td>No</td>\n",
       "      <td>High</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>No</td>\n",
       "      <td>Air Miles</td>\n",
       "      <td>Medium</td>\n",
       "      <td>No</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Yes</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17995</th>\n",
       "      <td>No</td>\n",
       "      <td>Cash Back</td>\n",
       "      <td>High</td>\n",
       "      <td>No</td>\n",
       "      <td>Low</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17996</th>\n",
       "      <td>No</td>\n",
       "      <td>Cash Back</td>\n",
       "      <td>High</td>\n",
       "      <td>No</td>\n",
       "      <td>Low</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17997</th>\n",
       "      <td>No</td>\n",
       "      <td>Cash Back</td>\n",
       "      <td>High</td>\n",
       "      <td>No</td>\n",
       "      <td>Low</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17998</th>\n",
       "      <td>No</td>\n",
       "      <td>Cash Back</td>\n",
       "      <td>Medium</td>\n",
       "      <td>No</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17999</th>\n",
       "      <td>No</td>\n",
       "      <td>Cash Back</td>\n",
       "      <td>Low</td>\n",
       "      <td>No</td>\n",
       "      <td>Medium</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17990 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      offer_accepted     reward income_level overdraft_protection  \\\n",
       "0                 No  Air Miles         High                   No   \n",
       "1                 No  Air Miles       Medium                   No   \n",
       "2                 No  Air Miles         High                   No   \n",
       "3                 No  Air Miles       Medium                   No   \n",
       "4                 No  Air Miles       Medium                   No   \n",
       "...              ...        ...          ...                  ...   \n",
       "17995             No  Cash Back         High                   No   \n",
       "17996             No  Cash Back         High                   No   \n",
       "17997             No  Cash Back         High                   No   \n",
       "17998             No  Cash Back       Medium                   No   \n",
       "17999             No  Cash Back          Low                   No   \n",
       "\n",
       "      credit_rating own_your_home  \n",
       "0              High            No  \n",
       "1            Medium           Yes  \n",
       "2            Medium           Yes  \n",
       "3              High            No  \n",
       "4            Medium           Yes  \n",
       "...             ...           ...  \n",
       "17995           Low           Yes  \n",
       "17996           Low           Yes  \n",
       "17997           Low            No  \n",
       "17998        Medium           Yes  \n",
       "17999        Medium            No  \n",
       "\n",
       "[17990 rows x 6 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# label / encode any categorical columns \n",
    "\n",
    "cat = df.select_dtypes(include = np.object)\n",
    "cat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "indonesian-joyce",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "auburn-sunrise",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # checking all the categorical columns\n",
    "# cols_cat = ['offer_accepted', 'reward', 'mailer_type', 'income_level', 'overdraft_protection', 'credit_rating', 'own_your_home']\n",
    "# for col in cols_cat:\n",
    "#     print(\"Frequency analysis of column: \",col)\n",
    "#     my_data = df[col].value_counts().reset_index()\n",
    "#     my_data.drop(my_data.index[0])\n",
    "#     ax = sns.barplot(x=col, y=\"index\", data = my_data).set_title(col.upper())\n",
    "#     plt.figure()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "ethical-shepherd",
   "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>bank_accounts_open</th>\n",
       "      <th>credit_cards_held</th>\n",
       "      <th>homes_owned</th>\n",
       "      <th>household_size</th>\n",
       "      <th>q2_balance</th>\n",
       "      <th>q3_balance</th>\n",
       "      <th>q4_balance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>17990.000000</td>\n",
       "      <td>17990.000000</td>\n",
       "      <td>17990.000000</td>\n",
       "      <td>17990.000000</td>\n",
       "      <td>17990.000000</td>\n",
       "      <td>17990.000000</td>\n",
       "      <td>17990.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.255809</td>\n",
       "      <td>1.903558</td>\n",
       "      <td>1.203446</td>\n",
       "      <td>3.499444</td>\n",
       "      <td>998.663002</td>\n",
       "      <td>1041.206437</td>\n",
       "      <td>809.064339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.472418</td>\n",
       "      <td>0.797031</td>\n",
       "      <td>0.427356</td>\n",
       "      <td>1.114030</td>\n",
       "      <td>455.640240</td>\n",
       "      <td>551.868613</td>\n",
       "      <td>556.540495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>663.250000</td>\n",
       "      <td>633.000000</td>\n",
       "      <td>363.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1031.000000</td>\n",
       "      <td>946.000000</td>\n",
       "      <td>703.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1341.000000</td>\n",
       "      <td>1461.000000</td>\n",
       "      <td>1210.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>2234.000000</td>\n",
       "      <td>2576.000000</td>\n",
       "      <td>2475.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       bank_accounts_open  credit_cards_held   homes_owned  household_size  \\\n",
       "count        17990.000000       17990.000000  17990.000000    17990.000000   \n",
       "mean             1.255809           1.903558      1.203446        3.499444   \n",
       "std              0.472418           0.797031      0.427356        1.114030   \n",
       "min              1.000000           1.000000      1.000000        1.000000   \n",
       "25%              1.000000           1.000000      1.000000        3.000000   \n",
       "50%              1.000000           2.000000      1.000000        3.000000   \n",
       "75%              1.000000           2.000000      1.000000        4.000000   \n",
       "max              3.000000           4.000000      3.000000        9.000000   \n",
       "\n",
       "         q2_balance    q3_balance    q4_balance  \n",
       "count  17990.000000  17990.000000  17990.000000  \n",
       "mean     998.663002   1041.206437    809.064339  \n",
       "std      455.640240    551.868613    556.540495  \n",
       "min        0.000000      0.000000      0.000000  \n",
       "25%      663.250000    633.000000    363.000000  \n",
       "50%     1031.000000    946.000000    703.000000  \n",
       "75%     1341.000000   1461.000000   1210.000000  \n",
       "max     2234.000000   2576.000000   2475.000000  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "sonic-arrest",
   "metadata": {},
   "outputs": [],
   "source": [
    "# a =df['average_balance'].value_counts().reset_index()\n",
    "# a.drop(a.index[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "remarkable-shuttle",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aboriginal-conducting",
   "metadata": {},
   "outputs": [],
   "source": [
    "# locate the target column in the last position\n",
    "cols = list(df.columns)\n",
    "cols = cols[1:]+[cols[0]]\n",
    "df = df[cols]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "educational-jerusalem",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dimensional-guidance",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "headed-burning",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "instant-gabriel",
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert balances to float\n",
    "\n",
    "df['q2_balance'] = pd.to_numeric(df.q2_balance, errors='coerce')\n",
    "df['q3_balance'] = pd.to_numeric(df.q3_balance, errors='coerce')\n",
    "df['q4_balance'] = pd.to_numeric(df.q4_balance, errors='coerce')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "earned-temple",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "offer_accepted           object\n",
       "reward                   object\n",
       "income_level             object\n",
       "bank_accounts_open        int64\n",
       "overdraft_protection     object\n",
       "credit_rating            object\n",
       "credit_cards_held         int64\n",
       "homes_owned               int64\n",
       "household_size            int64\n",
       "own_your_home            object\n",
       "q2_balance              float64\n",
       "q3_balance              float64\n",
       "q4_balance              float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "returning-married",
   "metadata": {},
   "source": [
    "### Density Plots\n",
    "\n",
    "A density plot is a representation of the distribution of a numeric variable. It uses a kernel density estimate to show the probability density function of the variable. It is a smoothed version of the histogram and is used in the same concept.\n",
    "\n",
    "Kernel Density Estimation(KDE): Kernel density estimation is a really useful statistical tool with an intimidating name. Often shortened to KDE, it’s a technique that let’s you create a smooth curve given a set of data. Kernel Density Estimation (KDE) is a way to estimate the probability density function of a continuous random variable. It is used for non-parametric analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "union-wallet",
   "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>bank_accounts_open</th>\n",
       "      <th>credit_cards_held</th>\n",
       "      <th>homes_owned</th>\n",
       "      <th>household_size</th>\n",
       "      <th>q2_balance</th>\n",
       "      <th>q3_balance</th>\n",
       "      <th>q4_balance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>877.0</td>\n",
       "      <td>1095.0</td>\n",
       "      <td>1002.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>106.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>366.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>352.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>242.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1760.0</td>\n",
       "      <td>1119.0</td>\n",
       "      <td>419.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1357.0</td>\n",
       "      <td>982.0</td>\n",
       "      <td>365.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   bank_accounts_open  credit_cards_held  homes_owned  household_size  \\\n",
       "0                   1                  2            1               4   \n",
       "1                   1                  2            2               5   \n",
       "2                   2                  2            1               2   \n",
       "3                   2                  1            1               4   \n",
       "4                   1                  2            1               6   \n",
       "\n",
       "   q2_balance  q3_balance  q4_balance  \n",
       "0       877.0      1095.0      1002.0  \n",
       "1       106.0        78.0       366.0  \n",
       "2       352.0       145.0       242.0  \n",
       "3      1760.0      1119.0       419.0  \n",
       "4      1357.0       982.0       365.0  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create numericala\n",
    "numerics = ['int64', 'float64']\n",
    "df_num = df.select_dtypes(include=numerics)\n",
    "df_num.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "worth-strain",
   "metadata": {},
   "outputs": [],
   "source": [
    "# for col in df_num.columns:\n",
    "#     ax = sns.kdeplot(data= df_num, x= col, shade= True)\n",
    "#     plt.figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "innovative-pavilion",
   "metadata": {},
   "outputs": [],
   "source": [
    "# for col in df_num.columns:\n",
    "#     plt.figure(figsize=(10,6)) \n",
    "#     sns.kdeplot(df[col][df.offer_accepted  == 'Yes'], shade=True)\n",
    "#     sns.kdeplot(df[col][df.offer_accepted  == 'No'], shade=True)\n",
    "#     plt.legend(['offer_accepted ', 'offer_rejected'])\n",
    "#     plt.title(col)\n",
    "#     plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "affiliated-subcommittee",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "binary-astrology",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check outliers\n",
    "df.boxplot(grid=False, figsize= (15,8))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "played-pottery",
   "metadata": {},
   "source": [
    "Note: the quartiles didn't removed all the outliers for average_balance. It could be improved."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "juvenile-trinidad",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df.hist(figsize=(15,10),layout=(3,4), grid=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "special-hunger",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "victorian-staff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(17990, 13)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "latest-cathedral",
   "metadata": {},
   "source": [
    "### Pre-processing <a class=\"anchor\" id=\"preprocessing\"></a>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "established-mechanism",
   "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>offer_accepted</th>\n",
       "      <th>reward</th>\n",
       "      <th>income_level</th>\n",
       "      <th>overdraft_protection</th>\n",
       "      <th>credit_rating</th>\n",
       "      <th>own_your_home</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>No</td>\n",
       "      <td>Air Miles</td>\n",
       "      <td>High</td>\n",
       "      <td>No</td>\n",
       "      <td>High</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>No</td>\n",
       "      <td>Air Miles</td>\n",
       "      <td>Medium</td>\n",
       "      <td>No</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>No</td>\n",
       "      <td>Air Miles</td>\n",
       "      <td>High</td>\n",
       "      <td>No</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>No</td>\n",
       "      <td>Air Miles</td>\n",
       "      <td>Medium</td>\n",
       "      <td>No</td>\n",
       "      <td>High</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>No</td>\n",
       "      <td>Air Miles</td>\n",
       "      <td>Medium</td>\n",
       "      <td>No</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  offer_accepted     reward income_level overdraft_protection credit_rating  \\\n",
       "0             No  Air Miles         High                   No          High   \n",
       "1             No  Air Miles       Medium                   No        Medium   \n",
       "2             No  Air Miles         High                   No        Medium   \n",
       "3             No  Air Miles       Medium                   No          High   \n",
       "4             No  Air Miles       Medium                   No        Medium   \n",
       "\n",
       "  own_your_home  \n",
       "0            No  \n",
       "1           Yes  \n",
       "2           Yes  \n",
       "3            No  \n",
       "4           Yes  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat.head()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "alleged-liver",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['offer_accepted', 'reward', 'income_level', 'overdraft_protection',\n",
       "       'credit_rating', 'own_your_home'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "anonymous-measurement",
   "metadata": {},
   "outputs": [],
   "source": [
    "# reset index\n",
    "df = df.reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "flush-skating",
   "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>offer_accepted</th>\n",
       "      <th>reward</th>\n",
       "      <th>income_level</th>\n",
       "      <th>bank_accounts_open</th>\n",
       "      <th>overdraft_protection</th>\n",
       "      <th>credit_rating</th>\n",
       "      <th>credit_cards_held</th>\n",
       "      <th>homes_owned</th>\n",
       "      <th>household_size</th>\n",
       "      <th>own_your_home</th>\n",
       "      <th>q2_balance</th>\n",
       "      <th>q3_balance</th>\n",
       "      <th>q4_balance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>No</td>\n",
       "      <td>Air Miles</td>\n",
       "      <td>High</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>High</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>No</td>\n",
       "      <td>877.0</td>\n",
       "      <td>1095.0</td>\n",
       "      <td>1002.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>No</td>\n",
       "      <td>Air Miles</td>\n",
       "      <td>Medium</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>Medium</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>Yes</td>\n",
       "      <td>106.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>366.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>No</td>\n",
       "      <td>Air Miles</td>\n",
       "      <td>High</td>\n",
       "      <td>2</td>\n",
       "      <td>No</td>\n",
       "      <td>Medium</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>352.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>242.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>No</td>\n",
       "      <td>Air Miles</td>\n",
       "      <td>Medium</td>\n",
       "      <td>2</td>\n",
       "      <td>No</td>\n",
       "      <td>High</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>No</td>\n",
       "      <td>1760.0</td>\n",
       "      <td>1119.0</td>\n",
       "      <td>419.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>No</td>\n",
       "      <td>Air Miles</td>\n",
       "      <td>Medium</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>Medium</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1357.0</td>\n",
       "      <td>982.0</td>\n",
       "      <td>365.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  offer_accepted     reward income_level  bank_accounts_open  \\\n",
       "0             No  Air Miles         High                   1   \n",
       "1             No  Air Miles       Medium                   1   \n",
       "2             No  Air Miles         High                   2   \n",
       "3             No  Air Miles       Medium                   2   \n",
       "4             No  Air Miles       Medium                   1   \n",
       "\n",
       "  overdraft_protection credit_rating  credit_cards_held  homes_owned  \\\n",
       "0                   No          High                  2            1   \n",
       "1                   No        Medium                  2            2   \n",
       "2                   No        Medium                  2            1   \n",
       "3                   No          High                  1            1   \n",
       "4                   No        Medium                  2            1   \n",
       "\n",
       "   household_size own_your_home  q2_balance  q3_balance  q4_balance  \n",
       "0               4            No       877.0      1095.0      1002.0  \n",
       "1               5           Yes       106.0        78.0       366.0  \n",
       "2               2           Yes       352.0       145.0       242.0  \n",
       "3               4            No      1760.0      1119.0       419.0  \n",
       "4               6           Yes      1357.0       982.0       365.0  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "eleven-turkey",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 17990 entries, 0 to 17989\n",
      "Data columns (total 10 columns):\n",
      " #   Column                Non-Null Count  Dtype \n",
      "---  ------                --------------  ----- \n",
      " 0   offer_accepted        17990 non-null  object\n",
      " 1   reward                17990 non-null  object\n",
      " 2   income_level          17990 non-null  object\n",
      " 3   bank_accounts_open    17990 non-null  int64 \n",
      " 4   overdraft_protection  17990 non-null  object\n",
      " 5   credit_rating         17990 non-null  object\n",
      " 6   credit_cards_held     17990 non-null  int64 \n",
      " 7   homes_owned           17990 non-null  int64 \n",
      " 8   household_size        17990 non-null  int64 \n",
      " 9   own_your_home         17990 non-null  object\n",
      "dtypes: int64(4), object(6)\n",
      "memory usage: 1.4+ MB\n"
     ]
    }
   ],
   "source": [
    "# categorical = pd.get_dummies(cat, columns =['reward', 'mailer_type', 'income_level',\n",
    "#        'overdraft_protection', 'credit_rating', 'own_your_home',\n",
    "#        'average_balance', 'offer_accepted'],drop_first=True)\n",
    "\n",
    "cat = df.select_dtypes(exclude = ['float64'])\n",
    "cat.info()\n",
    "\n",
    "# categorical.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "vocal-innocent",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['offer_accepted',\n",
       " 'reward',\n",
       " 'income_level',\n",
       " 'bank_accounts_open',\n",
       " 'overdraft_protection',\n",
       " 'credit_rating',\n",
       " 'credit_cards_held',\n",
       " 'homes_owned',\n",
       " 'household_size',\n",
       " 'own_your_home']"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a list of the column names\n",
    "cat_col=cat.columns.to_list()\n",
    "cat_col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "floating-resource",
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical = pd.get_dummies(cat, columns =['reward',\n",
    " 'income_level',\n",
    " 'bank_accounts_open',\n",
    " 'overdraft_protection',\n",
    " 'credit_rating',\n",
    " 'credit_cards_held',\n",
    " 'homes_owned',\n",
    " 'household_size',\n",
    " 'own_your_home',\n",
    " 'offer_accepted'],drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "digital-jamaica",
   "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",
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       "    }\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>reward_Cash Back</th>\n",
       "      <th>reward_Points</th>\n",
       "      <th>income_level_Low</th>\n",
       "      <th>income_level_Medium</th>\n",
       "      <th>bank_accounts_open_2</th>\n",
       "      <th>bank_accounts_open_3</th>\n",
       "      <th>overdraft_protection_Yes</th>\n",
       "      <th>credit_rating_Low</th>\n",
       "      <th>credit_rating_Medium</th>\n",
       "      <th>credit_cards_held_2</th>\n",
       "      <th>...</th>\n",
       "      <th>homes_owned_3</th>\n",
       "      <th>household_size_2</th>\n",
       "      <th>household_size_3</th>\n",
       "      <th>household_size_4</th>\n",
       "      <th>household_size_5</th>\n",
       "      <th>household_size_6</th>\n",
       "      <th>household_size_8</th>\n",
       "      <th>household_size_9</th>\n",
       "      <th>own_your_home_Yes</th>\n",
       "      <th>offer_accepted_Yes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>...</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   reward_Cash Back  reward_Points  income_level_Low  income_level_Medium  \\\n",
       "0                 0              0                 0                    0   \n",
       "1                 0              0                 0                    1   \n",
       "2                 0              0                 0                    0   \n",
       "3                 0              0                 0                    1   \n",
       "4                 0              0                 0                    1   \n",
       "\n",
       "   bank_accounts_open_2  bank_accounts_open_3  overdraft_protection_Yes  \\\n",
       "0                     0                     0                         0   \n",
       "1                     0                     0                         0   \n",
       "2                     1                     0                         0   \n",
       "3                     1                     0                         0   \n",
       "4                     0                     0                         0   \n",
       "\n",
       "   credit_rating_Low  credit_rating_Medium  credit_cards_held_2  ...  \\\n",
       "0                  0                     0                    1  ...   \n",
       "1                  0                     1                    1  ...   \n",
       "2                  0                     1                    1  ...   \n",
       "3                  0                     0                    0  ...   \n",
       "4                  0                     1                    1  ...   \n",
       "\n",
       "   homes_owned_3  household_size_2  household_size_3  household_size_4  \\\n",
       "0              0                 0                 0                 1   \n",
       "1              0                 0                 0                 0   \n",
       "2              0                 1                 0                 0   \n",
       "3              0                 0                 0                 1   \n",
       "4              0                 0                 0                 0   \n",
       "\n",
       "   household_size_5  household_size_6  household_size_8  household_size_9  \\\n",
       "0                 0                 0                 0                 0   \n",
       "1                 1                 0                 0                 0   \n",
       "2                 0                 0                 0                 0   \n",
       "3                 0                 0                 0                 0   \n",
       "4                 0                 1                 0                 0   \n",
       "\n",
       "   own_your_home_Yes  offer_accepted_Yes  \n",
       "0                  0                   0  \n",
       "1                  1                   0  \n",
       "2                  1                   0  \n",
       "3                  0                   0  \n",
       "4                  1                   0  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "amazing-cheese",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    16967\n",
       "1     1023\n",
       "Name: offer_accepted_Yes, dtype: int64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical['offer_accepted_Yes'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "afraid-alpha",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.05683333333333333"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# percentage\n",
    "pct_offer_accepted = 1023/18000\n",
    "pct_offer_accepted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "italic-depression",
   "metadata": {},
   "outputs": [],
   "source": [
    "# numerical scaling \n",
    "\n",
    "from sklearn.preprocessing import Normalizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "varied-intake",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_num are the numerical\n",
    "\n",
    "X = df_num.drop(['bank_accounts_open', 'credit_cards_held', 'homes_owned', \"household_size\"], axis=1)\n",
    "transformer = Normalizer().fit(X)\n",
    "X_normalised= transformer.transform(X)\n",
    "X_normalised = pd.DataFrame(X_normalised)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "public-painting",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.508702</td>\n",
       "      <td>0.635153</td>\n",
       "      <td>0.581208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.272534</td>\n",
       "      <td>0.200544</td>\n",
       "      <td>0.941014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.780311</td>\n",
       "      <td>0.321435</td>\n",
       "      <td>0.536464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.827347</td>\n",
       "      <td>0.526024</td>\n",
       "      <td>0.196965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.791553</td>\n",
       "      <td>0.572812</td>\n",
       "      <td>0.212909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>17985</th>\n",
       "      <td>0.158740</td>\n",
       "      <td>0.173393</td>\n",
       "      <td>0.971976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17986</th>\n",
       "      <td>0.655076</td>\n",
       "      <td>0.657167</td>\n",
       "      <td>0.372836</td>\n",
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       "    <tr>\n",
       "      <th>17987</th>\n",
       "      <td>0.374739</td>\n",
       "      <td>0.571635</td>\n",
       "      <td>0.729934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17988</th>\n",
       "      <td>0.472796</td>\n",
       "      <td>0.416041</td>\n",
       "      <td>0.776771</td>\n",
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       "    <tr>\n",
       "      <th>17989</th>\n",
       "      <td>0.554538</td>\n",
       "      <td>0.795992</td>\n",
       "      <td>0.242661</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17990 rows × 3 columns</p>\n",
       "</div>"
      ],
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       "              0         1         2\n",
       "0      0.508702  0.635153  0.581208\n",
       "1      0.272534  0.200544  0.941014\n",
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       "3      0.827347  0.526024  0.196965\n",
       "4      0.791553  0.572812  0.212909\n",
       "...         ...       ...       ...\n",
       "17985  0.158740  0.173393  0.971976\n",
       "17986  0.655076  0.657167  0.372836\n",
       "17987  0.374739  0.571635  0.729934\n",
       "17988  0.472796  0.416041  0.776771\n",
       "17989  0.554538  0.795992  0.242661\n",
       "\n",
       "[17990 rows x 3 columns]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_normalised\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "obvious-musical",
   "metadata": {},
   "source": [
    "### Split off the dependant variable (label) <a class=\"anchor\" id=\"split\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "helpful-container",
   "metadata": {},
   "outputs": [
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      ],
      "text/plain": [
       "       reward_Cash Back  reward_Points  income_level_Low  income_level_Medium  \\\n",
       "0                     0              0                 0                    0   \n",
       "1                     0              0                 0                    1   \n",
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       "\n",
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       "       household_size_4  household_size_5  household_size_6  household_size_8  \\\n",
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       "\n",
       "       household_size_9  own_your_home_Yes  \n",
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       "\n",
       "[17990 rows x 22 columns]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#sort out the categoricals \n",
    "y= categorical['offer_accepted_Yes']\n",
    "categorical=categorical.drop(['offer_accepted_Yes'], axis=1)\n",
    "categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "defined-myrtle",
   "metadata": {},
   "outputs": [
    {
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  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "municipal-yesterday",
   "metadata": {},
   "outputs": [
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>...</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17985</th>\n",
       "      <td>1</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17986</th>\n",
       "      <td>1</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17987</th>\n",
       "      <td>1</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17988</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17989</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17990 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       reward_Cash Back  reward_Points  income_level_Low  income_level_Medium  \\\n",
       "0                     0              0                 0                    0   \n",
       "1                     0              0                 0                    1   \n",
       "2                     0              0                 0                    0   \n",
       "3                     0              0                 0                    1   \n",
       "4                     0              0                 0                    1   \n",
       "...                 ...            ...               ...                  ...   \n",
       "17985                 1              0                 0                    0   \n",
       "17986                 1              0                 0                    0   \n",
       "17987                 1              0                 0                    0   \n",
       "17988                 1              0                 0                    1   \n",
       "17989                 1              0                 1                    0   \n",
       "\n",
       "       bank_accounts_open_2  bank_accounts_open_3  overdraft_protection_Yes  \\\n",
       "0                         0                     0                         0   \n",
       "1                         0                     0                         0   \n",
       "2                         1                     0                         0   \n",
       "3                         1                     0                         0   \n",
       "4                         0                     0                         0   \n",
       "...                     ...                   ...                       ...   \n",
       "17985                     0                     0                         0   \n",
       "17986                     0                     0                         0   \n",
       "17987                     0                     0                         0   \n",
       "17988                     0                     0                         0   \n",
       "17989                     1                     0                         0   \n",
       "\n",
       "       credit_rating_Low  credit_rating_Medium  credit_cards_held_2  ...  \\\n",
       "0                      0                     0                    1  ...   \n",
       "1                      0                     1                    1  ...   \n",
       "2                      0                     1                    1  ...   \n",
       "3                      0                     0                    0  ...   \n",
       "4                      0                     1                    1  ...   \n",
       "...                  ...                   ...                  ...  ...   \n",
       "17985                  1                     0                    0  ...   \n",
       "17986                  1                     0                    0  ...   \n",
       "17987                  1                     0                    1  ...   \n",
       "17988                  0                     1                    0  ...   \n",
       "17989                  0                     1                    1  ...   \n",
       "\n",
       "       homes_owned_2  homes_owned_3  household_size_2  household_size_3  \\\n",
       "0                  0              0                 0                 0   \n",
       "1                  1              0                 0                 0   \n",
       "2                  0              0                 1                 0   \n",
       "3                  0              0                 0                 0   \n",
       "4                  0              0                 0                 0   \n",
       "...              ...            ...               ...               ...   \n",
       "17985              0              0                 0                 0   \n",
       "17986              0              0                 0                 1   \n",
       "17987              0              0                 0                 0   \n",
       "17988              1              0                 1                 0   \n",
       "17989              0              0                 0                 1   \n",
       "\n",
       "       household_size_4  household_size_5  household_size_6  household_size_8  \\\n",
       "0                     1                 0                 0                 0   \n",
       "1                     0                 1                 0                 0   \n",
       "2                     0                 0                 0                 0   \n",
       "3                     1                 0                 0                 0   \n",
       "4                     0                 0                 1                 0   \n",
       "...                 ...               ...               ...               ...   \n",
       "17985                 0                 1                 0                 0   \n",
       "17986                 0                 0                 0                 0   \n",
       "17987                 1                 0                 0                 0   \n",
       "17988                 0                 0                 0                 0   \n",
       "17989                 0                 0                 0                 0   \n",
       "\n",
       "       household_size_9  own_your_home_Yes  \n",
       "0                     0                  0  \n",
       "1                     0                  1  \n",
       "2                     0                  1  \n",
       "3                     0                  0  \n",
       "4                     0                  1  \n",
       "...                 ...                ...  \n",
       "17985                 0                  1  \n",
       "17986                 0                  1  \n",
       "17987                 0                  0  \n",
       "17988                 0                  1  \n",
       "17989                 0                  0  \n",
       "\n",
       "[17990 rows x 22 columns]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "ethical-california",
   "metadata": {},
   "outputs": [],
   "source": [
    "# bring the numerical data (scaled)in with the x cat\n",
    "X = np.concatenate((X_normalised, categorical),axis=1)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "peaceful-spirit",
   "metadata": {},
   "source": [
    "### TT split, get model <a class=\"anchor\" id=\"ttsplit\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "about-upper",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "encouraging-plymouth",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test =train_test_split(X, y, test_size= 0.4, random_state=40)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "noticed-staff",
   "metadata": {},
   "source": [
    "### Apply model and train model <a class=\"anchor\" id=\"applymodel\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "horizontal-worthy",
   "metadata": {},
   "outputs": [],
   "source": [
    "classification = LogisticRegression(random_state=0, solver='lbfgs',max_iter=400,\n",
    "                  multi_class='ovr').fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fewer-converter",
   "metadata": {},
   "source": [
    "### Evaluate accuracy and test <a class=\"anchor\" id=\"evaluate\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "consolidated-benefit",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score \n",
    "predictions = classification.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "loose-momentum",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 0, 0, 0], dtype=uint8)"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "joint-interaction",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9434408004446915"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "manufactured-withdrawal",
   "metadata": {},
   "outputs": [],
   "source": [
    "# next steps would be to visualise the accuracy of the predictions in some ways \n",
    "\n",
    "# can i improve my model ?? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "suitable-subdivision",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "surface-fraction",
   "metadata": {},
   "source": [
    "### Visualising accuracy - ROC / AUC  <a class=\"anchor\" id=\"rocauc\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "broke-indie",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7248244106397\n"
     ]
    }
   ],
   "source": [
    "import sklearn.metrics as metrics \n",
    "probs = classification.predict_proba(X_test)\n",
    "preds =probs[:,1]\n",
    "fpr, tpr, threshold = metrics.roc_curve(y_test, preds)\n",
    "roc_auc = metrics.auc(fpr, tpr)\n",
    "print(roc_auc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "generic-sense",
   "metadata": {},
   "outputs": [],
   "source": [
    "#definitions \n",
    "# tpr = true positive rate \n",
    "# fpr = false positive rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "specific-ghost",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# method to plot AUC visually \n",
    "plt.title('Receiver Operating Characteristic')\n",
    "plt.plot(fpr, tpr, 'b', label = 'AUC = %0.2f' % roc_auc)\n",
    "plt.legend(loc = 'lower right')\n",
    "plt.plot([0, 1], [0, 1],'r--')\n",
    "plt.xlim([0, 1])\n",
    "plt.ylim([0, 1])\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "unsigned-complex",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# confusion matrix \n",
    "\n",
    "from sklearn.metrics import confusion_matrix \n",
    "from sklearn.metrics import plot_confusion_matrix\n",
    "\n",
    "accuracy_score(y_test,predictions)\n",
    "confusion_matrix(y_test,predictions)\n",
    "plot_confusion_matrix(classification, X_test, y_test)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "opened-trace",
   "metadata": {},
   "source": [
    "### Data imbalanced -  SMOTE (oversample the minority class of the target label) <a class=\"anchor\" id=\"smote\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "vocational-arrest",
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.over_sampling import SMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "devoted-hudson",
   "metadata": {},
   "outputs": [],
   "source": [
    "smote = SMOTE()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "reliable-terminal",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    16967\n",
       "1    16967\n",
       "Name: offer_accepted_Yes, dtype: int64"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_sm, y_sm = smote.fit_resample(X, y)\n",
    "y_sm.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "composed-appraisal",
   "metadata": {},
   "outputs": [],
   "source": [
    "#re do train test split It2\n",
    "\n",
    "X_train, X_test, y_train, y_test =train_test_split(X_sm, y_sm, test_size= 0.4, random_state=40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "northern-arctic",
   "metadata": {},
   "outputs": [],
   "source": [
    "# apply model It2 \n",
    "classification = LogisticRegression(random_state=0, solver='lbfgs',max_iter=400,\n",
    "                  multi_class='ovr').fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "aggressive-driver",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6934580816266391"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#accuracy assessment It2\n",
    "predictions = classification.predict(X_test)\n",
    "accuracy_score(y_test, predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "bound-doctor",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "#Conf matrix It2 \n",
    "confusion_matrix(y_test,predictions)\n",
    "plot_confusion_matrix(classification, X_test, y_test)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "italic-pound",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "continued-champion",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "sweet-malta",
   "metadata": {},
   "source": [
    "#### conclusion on imbalance:\n",
    "\n",
    "- The SMOTE technique solved the imbalanced but the accuracy got too low (71%), but the TOMEK doesn't.\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "frozen-lincoln",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "tutorial-terminal",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "careful-delhi",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "significant-crack",
   "metadata": {},
   "source": [
    "### References\n",
    "- lectures\n",
    "- women who code: munibootcamp of data science\n",
    "- https://machinelearningmastery.com/confusion-matrix-machine-learning/\n",
    "- https://medium.com/analytics-vidhya/outlier-treatment-9bbe87384d02\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "accomplished-architecture",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "underlying-effect",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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