notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Problem Statement\n",
    "\n",
    "In the contemporary marketplace, businesses have cutthroat competition. Keeping a high consumer engagement rate is a top priority for businesses.\n",
    "\n",
    "Analyzing the vast amount of data collected from the consumers is a challenging task. However, it is crucial to understand the consumer's behavior and preferences to improve the product and services.\n",
    "\n",
    "Our Task is to decode the unseen patterns in customer preferences and behaviors through a series of advanced analytical techniques, including Exploratory Data Analysis, Univariate and Bivariate Analysis, and Unsupervised Learning. The ultimate goal is to leverage these insights to segment customers efficiently.\n",
    "\n",
    "- The dataset paints a picture of 2.240 indivuals who \n",
    "were approached and includes socio-demographic and firmographic features about them. Additionally, it flags those who were inspired by the compaign and decided to buy the product.\n",
    "\n",
    "## Customer Data Attributes\n",
    "\n",
    "1. **ClientID**: Customer's unique identifier\n",
    "2. **BirthYear**: Customer's birth year\n",
    "3. **AcademicLevel**: Customer's education level\n",
    "4. **RelationshipStatus**: Customer's marital status\n",
    "5. **AnnualIncome**: Customer's yearly household income\n",
    "6. **ChildrenAtHome**: Number of small children in customer’s household\n",
    "7. **TeensAtHome**: Number of teenagers in customer’s household\n",
    "8. **EnrollmentDate**: Date of customer’s enrollment with the company\n",
    "9. **LastPurchaseDays**: Number of days since customer’s last purchase\n",
    "10. **RecentComplaint**: 1 if the customer complained in the last 2 years, 0 otherwise\n",
    "11. **WineSpending**: Amount spent on wine in last 2 years\n",
    "12. **FruitSpending**: Amount spent on fruits in last 2 years\n",
    "13. **MeatSpending**: Amount spent on meat in last 2 years\n",
    "14. **FishSpending**: Amount spent on fish in last 2 years\n",
    "15. **SweetSpending**: Amount spent on sweets in last 2 years\n",
    "16. **GoldSpending**: Amount spent on gold in last 2 years\n",
    "17. **Campaign1Success**: If customer accepted the offer in the 1st campaign, 0 otherwise\n",
    "18. **Campaign2Success**: If customer accepted the offer in the 2nd campaign, 0 otherwise\n",
    "19. **Campaign3Success**: If customer accepted the offer in the 3rd campaign, 0 otherwise\n",
    "20. **Campaign4Success**: If customer accepted the offer in the 4th campaign, 0 otherwise\n",
    "21. **Campaign5Success**: If customer accepted the offer in the 5th campaign, 0 otherwise\n",
    "22. **LastCampaignResponse**: If customer accepted the offer in the last campaign, 0 otherwise\n",
    "23. **WebPurchases**: Number of purchases made through the company’s website\n",
    "24. **StorePurchases**: Number of purchases made directly in the store\n",
    "25. **CatalogPurchases**: Number of purchases made through a catalog\n",
    "26. **DiscountedPurchases**: Number of purchases made with a discount\n",
    "27. **WebVisitsMonth**: Number of visits to the website in the last month"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TABLE OF CONTENTS:\n",
    "* [LOAD DATA](#LOAD-DATA)\n",
    "* [DATA PREPROCESSING](#DATA-PREPROCESSING)\n",
    "* [FEATURE ENGINEERING](#FEATURE-ENGINEERING)\n",
    "* [UNIVARIATE ANALYSIS](#UNIVARIATE-ANALYSIS)\n",
    "* [BIVARIATE ANALYSIS](#BIVARIATE-ANALYSIS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing Libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "import sys\n",
    "import scipy.stats as stats\n",
    "# Ignoring Warnings\n",
    "if not sys.warnoptions:\n",
    "    warnings.simplefilter(\"ignore\")\n",
    "# Setting Seed for Reproducibility\n",
    "np.random.seed(123) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LOAD DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading data\n",
    "data = pd.read_csv(\"dataset\\dataset.csv\", sep=\"\\t\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ClientID</th>\n",
       "      <th>BirthYear</th>\n",
       "      <th>AcademicLevel</th>\n",
       "      <th>RelationshipStatus</th>\n",
       "      <th>AnnualIncome</th>\n",
       "      <th>ChildrenAtHome</th>\n",
       "      <th>TeensAtHome</th>\n",
       "      <th>EnrollmentDate</th>\n",
       "      <th>LastPurchaseDays</th>\n",
       "      <th>WineSpending</th>\n",
       "      <th>...</th>\n",
       "      <th>WebVisitsMonth</th>\n",
       "      <th>Campaign3Success</th>\n",
       "      <th>Campaign4Success</th>\n",
       "      <th>Campaign5Success</th>\n",
       "      <th>Campaign1Success</th>\n",
       "      <th>Campaign2Success</th>\n",
       "      <th>RecentComplaint</th>\n",
       "      <th>Z_CC</th>\n",
       "      <th>Z_R</th>\n",
       "      <th>LastCampaignResponse</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5524</td>\n",
       "      <td>1957</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Single</td>\n",
       "      <td>58138.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>04-09-2012</td>\n",
       "      <td>58</td>\n",
       "      <td>635</td>\n",
       "      <td>...</td>\n",
       "      <td>7</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>3</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2174</td>\n",
       "      <td>1954</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Single</td>\n",
       "      <td>46344.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>08-03-2014</td>\n",
       "      <td>38</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>5</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>3</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4141</td>\n",
       "      <td>1965</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Together</td>\n",
       "      <td>71613.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21-08-2013</td>\n",
       "      <td>26</td>\n",
       "      <td>426</td>\n",
       "      <td>...</td>\n",
       "      <td>4</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>3</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6182</td>\n",
       "      <td>1984</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Together</td>\n",
       "      <td>26646.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>10-02-2014</td>\n",
       "      <td>26</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>6</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>3</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5324</td>\n",
       "      <td>1981</td>\n",
       "      <td>PhD</td>\n",
       "      <td>Married</td>\n",
       "      <td>58293.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>19-01-2014</td>\n",
       "      <td>94</td>\n",
       "      <td>173</td>\n",
       "      <td>...</td>\n",
       "      <td>5</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>3</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   ClientID  BirthYear AcademicLevel RelationshipStatus  AnnualIncome  \\\n",
       "0      5524       1957    Graduation             Single       58138.0   \n",
       "1      2174       1954    Graduation             Single       46344.0   \n",
       "2      4141       1965    Graduation           Together       71613.0   \n",
       "3      6182       1984    Graduation           Together       26646.0   \n",
       "4      5324       1981           PhD            Married       58293.0   \n",
       "\n",
       "   ChildrenAtHome  TeensAtHome EnrollmentDate  LastPurchaseDays  WineSpending  \\\n",
       "0               0            0     04-09-2012                58           635   \n",
       "1               1            1     08-03-2014                38            11   \n",
       "2               0            0     21-08-2013                26           426   \n",
       "3               1            0     10-02-2014                26            11   \n",
       "4               1            0     19-01-2014                94           173   \n",
       "\n",
       "   ...  WebVisitsMonth  Campaign3Success  Campaign4Success  Campaign5Success  \\\n",
       "0  ...               7                 0                 0                 0   \n",
       "1  ...               5                 0                 0                 0   \n",
       "2  ...               4                 0                 0                 0   \n",
       "3  ...               6                 0                 0                 0   \n",
       "4  ...               5                 0                 0                 0   \n",
       "\n",
       "   Campaign1Success  Campaign2Success  RecentComplaint  Z_CC  Z_R  \\\n",
       "0                 0                 0                0     3   11   \n",
       "1                 0                 0                0     3   11   \n",
       "2                 0                 0                0     3   11   \n",
       "3                 0                 0                0     3   11   \n",
       "4                 0                 0                0     3   11   \n",
       "\n",
       "   LastCampaignResponse  \n",
       "0                     1  \n",
       "1                     0  \n",
       "2                     0  \n",
       "3                     0  \n",
       "4                     0  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Looking at the first five rows\n",
    "data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>ClientID</th>\n",
       "      <th>BirthYear</th>\n",
       "      <th>AcademicLevel</th>\n",
       "      <th>RelationshipStatus</th>\n",
       "      <th>AnnualIncome</th>\n",
       "      <th>ChildrenAtHome</th>\n",
       "      <th>TeensAtHome</th>\n",
       "      <th>EnrollmentDate</th>\n",
       "      <th>LastPurchaseDays</th>\n",
       "      <th>WineSpending</th>\n",
       "      <th>...</th>\n",
       "      <th>WebVisitsMonth</th>\n",
       "      <th>Campaign3Success</th>\n",
       "      <th>Campaign4Success</th>\n",
       "      <th>Campaign5Success</th>\n",
       "      <th>Campaign1Success</th>\n",
       "      <th>Campaign2Success</th>\n",
       "      <th>RecentComplaint</th>\n",
       "      <th>Z_CC</th>\n",
       "      <th>Z_R</th>\n",
       "      <th>LastCampaignResponse</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2235</th>\n",
       "      <td>10870</td>\n",
       "      <td>1967</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Married</td>\n",
       "      <td>61223.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>13-06-2013</td>\n",
       "      <td>46</td>\n",
       "      <td>709</td>\n",
       "      <td>...</td>\n",
       "      <td>5</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>3</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2236</th>\n",
       "      <td>4001</td>\n",
       "      <td>1946</td>\n",
       "      <td>PhD</td>\n",
       "      <td>Together</td>\n",
       "      <td>64014.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>10-06-2014</td>\n",
       "      <td>56</td>\n",
       "      <td>406</td>\n",
       "      <td>...</td>\n",
       "      <td>7</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>3</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2237</th>\n",
       "      <td>7270</td>\n",
       "      <td>1981</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>56981.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25-01-2014</td>\n",
       "      <td>91</td>\n",
       "      <td>908</td>\n",
       "      <td>...</td>\n",
       "      <td>6</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>3</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2238</th>\n",
       "      <td>8235</td>\n",
       "      <td>1956</td>\n",
       "      <td>Master</td>\n",
       "      <td>Together</td>\n",
       "      <td>69245.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>24-01-2014</td>\n",
       "      <td>8</td>\n",
       "      <td>428</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2239</th>\n",
       "      <td>9405</td>\n",
       "      <td>1954</td>\n",
       "      <td>PhD</td>\n",
       "      <td>Married</td>\n",
       "      <td>52869.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>15-10-2012</td>\n",
       "      <td>40</td>\n",
       "      <td>84</td>\n",
       "      <td>...</td>\n",
       "      <td>7</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>3</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      ClientID  BirthYear AcademicLevel RelationshipStatus  AnnualIncome  \\\n",
       "2235     10870       1967    Graduation            Married       61223.0   \n",
       "2236      4001       1946           PhD           Together       64014.0   \n",
       "2237      7270       1981    Graduation           Divorced       56981.0   \n",
       "2238      8235       1956        Master           Together       69245.0   \n",
       "2239      9405       1954           PhD            Married       52869.0   \n",
       "\n",
       "      ChildrenAtHome  TeensAtHome EnrollmentDate  LastPurchaseDays  \\\n",
       "2235               0            1     13-06-2013                46   \n",
       "2236               2            1     10-06-2014                56   \n",
       "2237               0            0     25-01-2014                91   \n",
       "2238               0            1     24-01-2014                 8   \n",
       "2239               1            1     15-10-2012                40   \n",
       "\n",
       "      WineSpending  ...  WebVisitsMonth  Campaign3Success  Campaign4Success  \\\n",
       "2235           709  ...               5                 0                 0   \n",
       "2236           406  ...               7                 0                 0   \n",
       "2237           908  ...               6                 0                 1   \n",
       "2238           428  ...               3                 0                 0   \n",
       "2239            84  ...               7                 0                 0   \n",
       "\n",
       "      Campaign5Success  Campaign1Success  Campaign2Success  RecentComplaint  \\\n",
       "2235                 0                 0                 0                0   \n",
       "2236                 0                 1                 0                0   \n",
       "2237                 0                 0                 0                0   \n",
       "2238                 0                 0                 0                0   \n",
       "2239                 0                 0                 0                0   \n",
       "\n",
       "      Z_CC  Z_R  LastCampaignResponse  \n",
       "2235     3   11                     0  \n",
       "2236     3   11                     0  \n",
       "2237     3   11                     0  \n",
       "2238     3   11                     0  \n",
       "2239     3   11                     1  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>ClientID</th>\n",
       "      <th>BirthYear</th>\n",
       "      <th>AnnualIncome</th>\n",
       "      <th>ChildrenAtHome</th>\n",
       "      <th>TeensAtHome</th>\n",
       "      <th>LastPurchaseDays</th>\n",
       "      <th>WineSpending</th>\n",
       "      <th>FruitSpending</th>\n",
       "      <th>MeatSpending</th>\n",
       "      <th>FishSpending</th>\n",
       "      <th>...</th>\n",
       "      <th>WebVisitsMonth</th>\n",
       "      <th>Campaign3Success</th>\n",
       "      <th>Campaign4Success</th>\n",
       "      <th>Campaign5Success</th>\n",
       "      <th>Campaign1Success</th>\n",
       "      <th>Campaign2Success</th>\n",
       "      <th>RecentComplaint</th>\n",
       "      <th>Z_CC</th>\n",
       "      <th>Z_R</th>\n",
       "      <th>LastCampaignResponse</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2240.000000</td>\n",
       "      <td>2240.000000</td>\n",
       "      <td>2216.000000</td>\n",
       "      <td>2240.000000</td>\n",
       "      <td>2240.000000</td>\n",
       "      <td>2240.000000</td>\n",
       "      <td>2240.000000</td>\n",
       "      <td>2240.000000</td>\n",
       "      <td>2240.000000</td>\n",
       "      <td>2240.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2240.000000</td>\n",
       "      <td>2240.000000</td>\n",
       "      <td>2240.000000</td>\n",
       "      <td>2240.000000</td>\n",
       "      <td>2240.000000</td>\n",
       "      <td>2240.000000</td>\n",
       "      <td>2240.000000</td>\n",
       "      <td>2240.0</td>\n",
       "      <td>2240.0</td>\n",
       "      <td>2240.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5592.159821</td>\n",
       "      <td>1968.805804</td>\n",
       "      <td>52247.251354</td>\n",
       "      <td>0.444196</td>\n",
       "      <td>0.506250</td>\n",
       "      <td>49.109375</td>\n",
       "      <td>303.935714</td>\n",
       "      <td>26.302232</td>\n",
       "      <td>166.950000</td>\n",
       "      <td>37.525446</td>\n",
       "      <td>...</td>\n",
       "      <td>5.316518</td>\n",
       "      <td>0.072768</td>\n",
       "      <td>0.074554</td>\n",
       "      <td>0.072768</td>\n",
       "      <td>0.064286</td>\n",
       "      <td>0.013393</td>\n",
       "      <td>0.009375</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.149107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3246.662198</td>\n",
       "      <td>11.984069</td>\n",
       "      <td>25173.076661</td>\n",
       "      <td>0.538398</td>\n",
       "      <td>0.544538</td>\n",
       "      <td>28.962453</td>\n",
       "      <td>336.597393</td>\n",
       "      <td>39.773434</td>\n",
       "      <td>225.715373</td>\n",
       "      <td>54.628979</td>\n",
       "      <td>...</td>\n",
       "      <td>2.426645</td>\n",
       "      <td>0.259813</td>\n",
       "      <td>0.262728</td>\n",
       "      <td>0.259813</td>\n",
       "      <td>0.245316</td>\n",
       "      <td>0.114976</td>\n",
       "      <td>0.096391</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.356274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1893.000000</td>\n",
       "      <td>1730.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2828.250000</td>\n",
       "      <td>1959.000000</td>\n",
       "      <td>35303.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>23.750000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5458.500000</td>\n",
       "      <td>1970.000000</td>\n",
       "      <td>51381.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>173.500000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8427.750000</td>\n",
       "      <td>1977.000000</td>\n",
       "      <td>68522.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>504.250000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>232.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>11191.000000</td>\n",
       "      <td>1996.000000</td>\n",
       "      <td>666666.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>1493.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>1725.000000</td>\n",
       "      <td>259.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>20.000000</td>\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>1.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           ClientID    BirthYear   AnnualIncome  ChildrenAtHome  TeensAtHome  \\\n",
       "count   2240.000000  2240.000000    2216.000000     2240.000000  2240.000000   \n",
       "mean    5592.159821  1968.805804   52247.251354        0.444196     0.506250   \n",
       "std     3246.662198    11.984069   25173.076661        0.538398     0.544538   \n",
       "min        0.000000  1893.000000    1730.000000        0.000000     0.000000   \n",
       "25%     2828.250000  1959.000000   35303.000000        0.000000     0.000000   \n",
       "50%     5458.500000  1970.000000   51381.500000        0.000000     0.000000   \n",
       "75%     8427.750000  1977.000000   68522.000000        1.000000     1.000000   \n",
       "max    11191.000000  1996.000000  666666.000000        2.000000     2.000000   \n",
       "\n",
       "       LastPurchaseDays  WineSpending  FruitSpending  MeatSpending  \\\n",
       "count       2240.000000   2240.000000    2240.000000   2240.000000   \n",
       "mean          49.109375    303.935714      26.302232    166.950000   \n",
       "std           28.962453    336.597393      39.773434    225.715373   \n",
       "min            0.000000      0.000000       0.000000      0.000000   \n",
       "25%           24.000000     23.750000       1.000000     16.000000   \n",
       "50%           49.000000    173.500000       8.000000     67.000000   \n",
       "75%           74.000000    504.250000      33.000000    232.000000   \n",
       "max           99.000000   1493.000000     199.000000   1725.000000   \n",
       "\n",
       "       FishSpending  ...  WebVisitsMonth  Campaign3Success  Campaign4Success  \\\n",
       "count   2240.000000  ...     2240.000000       2240.000000       2240.000000   \n",
       "mean      37.525446  ...        5.316518          0.072768          0.074554   \n",
       "std       54.628979  ...        2.426645          0.259813          0.262728   \n",
       "min        0.000000  ...        0.000000          0.000000          0.000000   \n",
       "25%        3.000000  ...        3.000000          0.000000          0.000000   \n",
       "50%       12.000000  ...        6.000000          0.000000          0.000000   \n",
       "75%       50.000000  ...        7.000000          0.000000          0.000000   \n",
       "max      259.000000  ...       20.000000          1.000000          1.000000   \n",
       "\n",
       "       Campaign5Success  Campaign1Success  Campaign2Success  RecentComplaint  \\\n",
       "count       2240.000000       2240.000000       2240.000000      2240.000000   \n",
       "mean           0.072768          0.064286          0.013393         0.009375   \n",
       "std            0.259813          0.245316          0.114976         0.096391   \n",
       "min            0.000000          0.000000          0.000000         0.000000   \n",
       "25%            0.000000          0.000000          0.000000         0.000000   \n",
       "50%            0.000000          0.000000          0.000000         0.000000   \n",
       "75%            0.000000          0.000000          0.000000         0.000000   \n",
       "max            1.000000          1.000000          1.000000         1.000000   \n",
       "\n",
       "         Z_CC     Z_R  LastCampaignResponse  \n",
       "count  2240.0  2240.0           2240.000000  \n",
       "mean      3.0    11.0              0.149107  \n",
       "std       0.0     0.0              0.356274  \n",
       "min       3.0    11.0              0.000000  \n",
       "25%       3.0    11.0              0.000000  \n",
       "50%       3.0    11.0              0.000000  \n",
       "75%       3.0    11.0              0.000000  \n",
       "max       3.0    11.0              1.000000  \n",
       "\n",
       "[8 rows x 26 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2240 entries, 0 to 2239\n",
      "Data columns (total 29 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   ClientID              2240 non-null   int64  \n",
      " 1   BirthYear             2240 non-null   int64  \n",
      " 2   AcademicLevel         2240 non-null   object \n",
      " 3   RelationshipStatus    2240 non-null   object \n",
      " 4   AnnualIncome          2216 non-null   float64\n",
      " 5   ChildrenAtHome        2240 non-null   int64  \n",
      " 6   TeensAtHome           2240 non-null   int64  \n",
      " 7   EnrollmentDate        2240 non-null   object \n",
      " 8   LastPurchaseDays      2240 non-null   int64  \n",
      " 9   WineSpending          2240 non-null   int64  \n",
      " 10  FruitSpending         2240 non-null   int64  \n",
      " 11  MeatSpending          2240 non-null   int64  \n",
      " 12  FishSpending          2240 non-null   int64  \n",
      " 13  SweetSpending         2240 non-null   int64  \n",
      " 14  GoldSpending          2240 non-null   int64  \n",
      " 15  DiscountedPurchases   2240 non-null   int64  \n",
      " 16  WebPurchases          2240 non-null   int64  \n",
      " 17  CatalogPurchases      2240 non-null   int64  \n",
      " 18  StorePurchases        2240 non-null   int64  \n",
      " 19  WebVisitsMonth        2240 non-null   int64  \n",
      " 20  Campaign3Success      2240 non-null   int64  \n",
      " 21  Campaign4Success      2240 non-null   int64  \n",
      " 22  Campaign5Success      2240 non-null   int64  \n",
      " 23  Campaign1Success      2240 non-null   int64  \n",
      " 24  Campaign2Success      2240 non-null   int64  \n",
      " 25  RecentComplaint       2240 non-null   int64  \n",
      " 26  Z_CC                  2240 non-null   int64  \n",
      " 27  Z_R                   2240 non-null   int64  \n",
      " 28  LastCampaignResponse  2240 non-null   int64  \n",
      "dtypes: float64(1), int64(25), object(3)\n",
      "memory usage: 507.6+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Takeaway:**\n",
    "- We have `3 categorical` variables and `26 numerical` values\n",
    "- We have missing values for `AnnualIncome` Variable.\n",
    "- `EnrollmentDate` is in `object` format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Checking for missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ClientID                 0\n",
       "BirthYear                0\n",
       "AcademicLevel            0\n",
       "RelationshipStatus       0\n",
       "AnnualIncome            24\n",
       "ChildrenAtHome           0\n",
       "TeensAtHome              0\n",
       "EnrollmentDate           0\n",
       "LastPurchaseDays         0\n",
       "WineSpending             0\n",
       "FruitSpending            0\n",
       "MeatSpending             0\n",
       "FishSpending             0\n",
       "SweetSpending            0\n",
       "GoldSpending             0\n",
       "DiscountedPurchases      0\n",
       "WebPurchases             0\n",
       "CatalogPurchases         0\n",
       "StorePurchases           0\n",
       "WebVisitsMonth           0\n",
       "Campaign3Success         0\n",
       "Campaign4Success         0\n",
       "Campaign5Success         0\n",
       "Campaign1Success         0\n",
       "Campaign2Success         0\n",
       "RecentComplaint          0\n",
       "Z_CC                     0\n",
       "Z_R                      0\n",
       "LastCampaignResponse     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`AnnualIncome`  has `24` null values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      2216.000000\n",
       "mean      52247.251354\n",
       "std       25173.076661\n",
       "min        1730.000000\n",
       "25%       35303.000000\n",
       "50%       51381.500000\n",
       "75%       68522.000000\n",
       "max      666666.000000\n",
       "Name: AnnualIncome, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['AnnualIncome'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7500.0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mode_value = data['AnnualIncome'].mode()[0]\n",
    "mode_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['AnnualIncome_mean'] = data['AnnualIncome'].fillna(data['AnnualIncome'].mean()) # filling with missing value\n",
    "data['AnnualIncome_mode'] = data['AnnualIncome'].fillna(mode_value) #filling with mode value\n",
    "data['AnnualIncome_median'] = data['AnnualIncome'].fillna(data['AnnualIncome'].median())#filling with median value\n",
    "fig  = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "data['AnnualIncome'].plot(kind ='kde',ax  = ax ,color  =  'red')\n",
    "data['AnnualIncome_mean'].plot(kind ='kde',ax  = ax ,color  =  'blue')\n",
    "data['AnnualIncome_mode'].plot(kind ='kde',ax  = ax ,color  =  'green')\n",
    "data['AnnualIncome_median'].plot(kind ='kde',ax  = ax ,color  =  'pink')\n",
    "data['AnnualIncome'] = data['AnnualIncome'].fillna(mode_value)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After conducting a comparative analysis by plotting graphs with missing values filled using mean,\n",
    "mode, and median imputation techniques, it was determined that mode imputation yielded the most\n",
    "accurate results due to complete overlap. Consequently, we will proceed with \n",
    "filling missing values utilizing the mode value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>ClientID</th>\n",
       "      <th>BirthYear</th>\n",
       "      <th>AcademicLevel</th>\n",
       "      <th>RelationshipStatus</th>\n",
       "      <th>AnnualIncome</th>\n",
       "      <th>ChildrenAtHome</th>\n",
       "      <th>TeensAtHome</th>\n",
       "      <th>EnrollmentDate</th>\n",
       "      <th>LastPurchaseDays</th>\n",
       "      <th>WineSpending</th>\n",
       "      <th>...</th>\n",
       "      <th>WebVisitsMonth</th>\n",
       "      <th>Campaign3Success</th>\n",
       "      <th>Campaign4Success</th>\n",
       "      <th>Campaign5Success</th>\n",
       "      <th>Campaign1Success</th>\n",
       "      <th>Campaign2Success</th>\n",
       "      <th>RecentComplaint</th>\n",
       "      <th>Z_CC</th>\n",
       "      <th>Z_R</th>\n",
       "      <th>LastCampaignResponse</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5524</td>\n",
       "      <td>1957</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Single</td>\n",
       "      <td>58138.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>04-09-2012</td>\n",
       "      <td>58</td>\n",
       "      <td>635</td>\n",
       "      <td>...</td>\n",
       "      <td>7</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>3</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2174</td>\n",
       "      <td>1954</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Single</td>\n",
       "      <td>46344.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>08-03-2014</td>\n",
       "      <td>38</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>5</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>3</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4141</td>\n",
       "      <td>1965</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Together</td>\n",
       "      <td>71613.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21-08-2013</td>\n",
       "      <td>26</td>\n",
       "      <td>426</td>\n",
       "      <td>...</td>\n",
       "      <td>4</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>3</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6182</td>\n",
       "      <td>1984</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Together</td>\n",
       "      <td>26646.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>10-02-2014</td>\n",
       "      <td>26</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>6</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>3</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5324</td>\n",
       "      <td>1981</td>\n",
       "      <td>PhD</td>\n",
       "      <td>Married</td>\n",
       "      <td>58293.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>19-01-2014</td>\n",
       "      <td>94</td>\n",
       "      <td>173</td>\n",
       "      <td>...</td>\n",
       "      <td>5</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>3</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2235</th>\n",
       "      <td>10870</td>\n",
       "      <td>1967</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Married</td>\n",
       "      <td>61223.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>13-06-2013</td>\n",
       "      <td>46</td>\n",
       "      <td>709</td>\n",
       "      <td>...</td>\n",
       "      <td>5</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>3</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2236</th>\n",
       "      <td>4001</td>\n",
       "      <td>1946</td>\n",
       "      <td>PhD</td>\n",
       "      <td>Together</td>\n",
       "      <td>64014.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>10-06-2014</td>\n",
       "      <td>56</td>\n",
       "      <td>406</td>\n",
       "      <td>...</td>\n",
       "      <td>7</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>3</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2237</th>\n",
       "      <td>7270</td>\n",
       "      <td>1981</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>56981.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25-01-2014</td>\n",
       "      <td>91</td>\n",
       "      <td>908</td>\n",
       "      <td>...</td>\n",
       "      <td>6</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>3</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2238</th>\n",
       "      <td>8235</td>\n",
       "      <td>1956</td>\n",
       "      <td>Master</td>\n",
       "      <td>Together</td>\n",
       "      <td>69245.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>24-01-2014</td>\n",
       "      <td>8</td>\n",
       "      <td>428</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2239</th>\n",
       "      <td>9405</td>\n",
       "      <td>1954</td>\n",
       "      <td>PhD</td>\n",
       "      <td>Married</td>\n",
       "      <td>52869.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>15-10-2012</td>\n",
       "      <td>40</td>\n",
       "      <td>84</td>\n",
       "      <td>...</td>\n",
       "      <td>7</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>3</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2240 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      ClientID  BirthYear AcademicLevel RelationshipStatus  AnnualIncome  \\\n",
       "0         5524       1957    Graduation             Single       58138.0   \n",
       "1         2174       1954    Graduation             Single       46344.0   \n",
       "2         4141       1965    Graduation           Together       71613.0   \n",
       "3         6182       1984    Graduation           Together       26646.0   \n",
       "4         5324       1981           PhD            Married       58293.0   \n",
       "...        ...        ...           ...                ...           ...   \n",
       "2235     10870       1967    Graduation            Married       61223.0   \n",
       "2236      4001       1946           PhD           Together       64014.0   \n",
       "2237      7270       1981    Graduation           Divorced       56981.0   \n",
       "2238      8235       1956        Master           Together       69245.0   \n",
       "2239      9405       1954           PhD            Married       52869.0   \n",
       "\n",
       "      ChildrenAtHome  TeensAtHome EnrollmentDate  LastPurchaseDays  \\\n",
       "0                  0            0     04-09-2012                58   \n",
       "1                  1            1     08-03-2014                38   \n",
       "2                  0            0     21-08-2013                26   \n",
       "3                  1            0     10-02-2014                26   \n",
       "4                  1            0     19-01-2014                94   \n",
       "...              ...          ...            ...               ...   \n",
       "2235               0            1     13-06-2013                46   \n",
       "2236               2            1     10-06-2014                56   \n",
       "2237               0            0     25-01-2014                91   \n",
       "2238               0            1     24-01-2014                 8   \n",
       "2239               1            1     15-10-2012                40   \n",
       "\n",
       "      WineSpending  ...  WebVisitsMonth  Campaign3Success  Campaign4Success  \\\n",
       "0              635  ...               7                 0                 0   \n",
       "1               11  ...               5                 0                 0   \n",
       "2              426  ...               4                 0                 0   \n",
       "3               11  ...               6                 0                 0   \n",
       "4              173  ...               5                 0                 0   \n",
       "...            ...  ...             ...               ...               ...   \n",
       "2235           709  ...               5                 0                 0   \n",
       "2236           406  ...               7                 0                 0   \n",
       "2237           908  ...               6                 0                 1   \n",
       "2238           428  ...               3                 0                 0   \n",
       "2239            84  ...               7                 0                 0   \n",
       "\n",
       "      Campaign5Success  Campaign1Success  Campaign2Success  RecentComplaint  \\\n",
       "0                    0                 0                 0                0   \n",
       "1                    0                 0                 0                0   \n",
       "2                    0                 0                 0                0   \n",
       "3                    0                 0                 0                0   \n",
       "4                    0                 0                 0                0   \n",
       "...                ...               ...               ...              ...   \n",
       "2235                 0                 0                 0                0   \n",
       "2236                 0                 1                 0                0   \n",
       "2237                 0                 0                 0                0   \n",
       "2238                 0                 0                 0                0   \n",
       "2239                 0                 0                 0                0   \n",
       "\n",
       "      Z_CC  Z_R  LastCampaignResponse  \n",
       "0        3   11                     1  \n",
       "1        3   11                     0  \n",
       "2        3   11                     0  \n",
       "3        3   11                     0  \n",
       "4        3   11                     0  \n",
       "...    ...  ...                   ...  \n",
       "2235     3   11                     0  \n",
       "2236     3   11                     0  \n",
       "2237     3   11                     0  \n",
       "2238     3   11                     0  \n",
       "2239     3   11                     1  \n",
       "\n",
       "[2240 rows x 29 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.drop(columns =['AnnualIncome_mean','AnnualIncome_mode','AnnualIncome_median'])\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Change EnrollmentDate into suitable format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2240 entries, 0 to 2239\n",
      "Data columns (total 29 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   ClientID              2240 non-null   int64  \n",
      " 1   BirthYear             2240 non-null   int64  \n",
      " 2   AcademicLevel         2240 non-null   object \n",
      " 3   RelationshipStatus    2240 non-null   object \n",
      " 4   AnnualIncome          2240 non-null   float64\n",
      " 5   ChildrenAtHome        2240 non-null   int64  \n",
      " 6   TeensAtHome           2240 non-null   int64  \n",
      " 7   EnrollmentDate        2240 non-null   object \n",
      " 8   LastPurchaseDays      2240 non-null   int64  \n",
      " 9   WineSpending          2240 non-null   int64  \n",
      " 10  FruitSpending         2240 non-null   int64  \n",
      " 11  MeatSpending          2240 non-null   int64  \n",
      " 12  FishSpending          2240 non-null   int64  \n",
      " 13  SweetSpending         2240 non-null   int64  \n",
      " 14  GoldSpending          2240 non-null   int64  \n",
      " 15  DiscountedPurchases   2240 non-null   int64  \n",
      " 16  WebPurchases          2240 non-null   int64  \n",
      " 17  CatalogPurchases      2240 non-null   int64  \n",
      " 18  StorePurchases        2240 non-null   int64  \n",
      " 19  WebVisitsMonth        2240 non-null   int64  \n",
      " 20  Campaign3Success      2240 non-null   int64  \n",
      " 21  Campaign4Success      2240 non-null   int64  \n",
      " 22  Campaign5Success      2240 non-null   int64  \n",
      " 23  Campaign1Success      2240 non-null   int64  \n",
      " 24  Campaign2Success      2240 non-null   int64  \n",
      " 25  RecentComplaint       2240 non-null   int64  \n",
      " 26  Z_CC                  2240 non-null   int64  \n",
      " 27  Z_R                   2240 non-null   int64  \n",
      " 28  LastCampaignResponse  2240 non-null   int64  \n",
      "dtypes: float64(1), int64(25), object(3)\n",
      "memory usage: 507.6+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    04-09-2012\n",
       "1    08-03-2014\n",
       "2    21-08-2013\n",
       "3    10-02-2014\n",
       "4    19-01-2014\n",
       "Name: EnrollmentDate, dtype: object"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['EnrollmentDate'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "current datatype of EnrollmentDate is object type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n",
      "RangeIndex: 2240 entries, 0 to 2239\n",
      "Series name: EnrollmentDate\n",
      "Non-Null Count  Dtype         \n",
      "--------------  -----         \n",
      "2240 non-null   datetime64[ns]\n",
      "dtypes: datetime64[ns](1)\n",
      "memory usage: 17.6 KB\n"
     ]
    }
   ],
   "source": [
    "data['EnrollmentDate'] =  pd.to_datetime(data['EnrollmentDate'],format='mixed')\n",
    "data['EnrollmentDate'].info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   2012-04-09\n",
       "1   2014-08-03\n",
       "2   2013-08-21\n",
       "3   2014-10-02\n",
       "4   2014-01-19\n",
       "Name: EnrollmentDate, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['EnrollmentDate'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "now EnrollmentDate is in timedate formate and we can extract day, month and year as a  separate column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check for homogeneity in feature values, drop any feature if they do not hold any discriminatory power."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### FEATURE ENGINEERING"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- We will create a new column `Age` by subtracting `BirthYear` from the current year.\n",
    "<!-- - We will convert `AcademicLevel` values to Undergraduate and Postgraduate. -->\n",
    "- We will create a new column `Children` by adding `ChildrenAtHome` and `TeensAtHome`.\n",
    "- We will create a new column `TotalSpent` by adding all the `Spending` columns.\n",
    "- We will create a new column `TotalPurchases` by adding all the `Purchases` columns.\n",
    "- We will create a new column `TotalSuccessfulCampaigns` by adding all the `Campaign Success` columns.\n",
    "- We will create a new column `CustomerSince` by subtracting `EnrollmentDate` from the current date.\n",
    "- We will create a new column `IsParent` by converting the `Children` column to `1` and `0`.\n",
    "- We will convert `RelationshipStatus` to `Relationship` and `Single`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a new column `Age` by subtracting `BirthYear` from the current year.\n",
    "\n",
    "data['Age'] = 2024 - data['BirthYear']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert AcademicLevel values to Undergraduate and Postgraduate.\n",
    "\n",
    "# data['AcademicLevel'] = data['AcademicLevel'].replace(['Graduation', 'PhD', 'Master', '2n Cycle'], \"Post Graduate\")\n",
    "# data['AcademicLevel'] = data['AcademicLevel'].replace([\"Basic\"], \"Under Graduate\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting RelationshipStatus to Understandable Format\n",
    "data[\"RelationshipStatus\"] = data[\"RelationshipStatus\"].replace(['Single', 'Divorced', 'Widow', 'Alone','Absurd', 'YOLO'], \"Single\")\n",
    "data[\"RelationshipStatus\"] = data[\"RelationshipStatus\"].replace([\"Married\", \"Together\"], \"Relationship\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a new column 'TotalSpent'\n",
    "data['TotalSpent'] = data['WineSpending'] + data['FruitSpending'] + data['MeatSpending'] + data['FishSpending'] + data['SweetSpending'] + data['GoldSpending']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting RelationshipStatus to Understandable Format\n",
    "data[\"RelationshipStatus\"] = data[\"RelationshipStatus\"].replace(['Single', 'Divorced', 'Widow', 'Alone','Absurd', 'YOLO'], \"Single\")\n",
    "data[\"RelationshipStatus\"] = data[\"RelationshipStatus\"].replace([\"Married\", \"Together\"], \"Relationship\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a new column 'TotalSpent'\n",
    "data['TotalSpent'] = data['WineSpending'] + data['FruitSpending'] + data['MeatSpending'] + data['FishSpending'] + data['SweetSpending'] + data['GoldSpending']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a new column 'Children'\n",
    "data['Children'] = data['ChildrenAtHome'] + data['TeensAtHome']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# CustomerSince column\n",
    "years = []\n",
    "for date in data['EnrollmentDate']:\n",
    "    years.append(2024 - date.year)\n",
    "data['CustomerSince'] = years  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TotalPurchases column\n",
    "data['TotalPurchases'] = data['DiscountedPurchases'] + data['WebPurchases'] + data['CatalogPurchases'] + data['StorePurchases']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TotalSuccessfulCampaignsSuccess column\n",
    "data['TotalSuccessfulCampaignsSuccess'] = data['Campaign1Success'] + data['Campaign2Success'] + data['Campaign3Success'] + data['Campaign4Success'] + data['Campaign5Success']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Is_Parent column\n",
    "data['Is_Parent'] = np.where(data['Children'] > 0, 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping unnecessary columns\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "New DataFrame Overview:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>ClientID</th>\n",
       "      <th>BirthYear</th>\n",
       "      <th>AcademicLevel</th>\n",
       "      <th>RelationshipStatus</th>\n",
       "      <th>AnnualIncome</th>\n",
       "      <th>ChildrenAtHome</th>\n",
       "      <th>TeensAtHome</th>\n",
       "      <th>EnrollmentDate</th>\n",
       "      <th>LastPurchaseDays</th>\n",
       "      <th>WineSpending</th>\n",
       "      <th>...</th>\n",
       "      <th>Z_CC</th>\n",
       "      <th>Z_R</th>\n",
       "      <th>LastCampaignResponse</th>\n",
       "      <th>Age</th>\n",
       "      <th>TotalSpent</th>\n",
       "      <th>Children</th>\n",
       "      <th>CustomerSince</th>\n",
       "      <th>TotalPurchases</th>\n",
       "      <th>TotalSuccessfulCampaignsSuccess</th>\n",
       "      <th>Is_Parent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5524</td>\n",
       "      <td>1957</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Single</td>\n",
       "      <td>58138.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2012-04-09</td>\n",
       "      <td>58</td>\n",
       "      <td>635</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>67</td>\n",
       "      <td>1617</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2174</td>\n",
       "      <td>1954</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Single</td>\n",
       "      <td>46344.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2014-08-03</td>\n",
       "      <td>38</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>27</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4141</td>\n",
       "      <td>1965</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Relationship</td>\n",
       "      <td>71613.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2013-08-21</td>\n",
       "      <td>26</td>\n",
       "      <td>426</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>59</td>\n",
       "      <td>776</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6182</td>\n",
       "      <td>1984</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Relationship</td>\n",
       "      <td>26646.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2014-10-02</td>\n",
       "      <td>26</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5324</td>\n",
       "      <td>1981</td>\n",
       "      <td>PhD</td>\n",
       "      <td>Relationship</td>\n",
       "      <td>58293.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2014-01-19</td>\n",
       "      <td>94</td>\n",
       "      <td>173</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>43</td>\n",
       "      <td>422</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   ClientID  BirthYear AcademicLevel RelationshipStatus  AnnualIncome  \\\n",
       "0      5524       1957    Graduation             Single       58138.0   \n",
       "1      2174       1954    Graduation             Single       46344.0   \n",
       "2      4141       1965    Graduation       Relationship       71613.0   \n",
       "3      6182       1984    Graduation       Relationship       26646.0   \n",
       "4      5324       1981           PhD       Relationship       58293.0   \n",
       "\n",
       "   ChildrenAtHome  TeensAtHome EnrollmentDate  LastPurchaseDays  WineSpending  \\\n",
       "0               0            0     2012-04-09                58           635   \n",
       "1               1            1     2014-08-03                38            11   \n",
       "2               0            0     2013-08-21                26           426   \n",
       "3               1            0     2014-10-02                26            11   \n",
       "4               1            0     2014-01-19                94           173   \n",
       "\n",
       "   ...  Z_CC  Z_R  LastCampaignResponse  Age  TotalSpent  Children  \\\n",
       "0  ...     3   11                     1   67        1617         0   \n",
       "1  ...     3   11                     0   70          27         2   \n",
       "2  ...     3   11                     0   59         776         0   \n",
       "3  ...     3   11                     0   40          53         1   \n",
       "4  ...     3   11                     0   43         422         1   \n",
       "\n",
       "   CustomerSince  TotalPurchases  TotalSuccessfulCampaignsSuccess  Is_Parent  \n",
       "0             12              25                                0          0  \n",
       "1             10               6                                0          1  \n",
       "2             11              21                                0          0  \n",
       "3             10               8                                0          1  \n",
       "4             10              19                                0          1  \n",
       "\n",
       "[5 rows x 36 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### UNIVARIATE ANALYSIS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plots For The Engineered Features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## BIVARIATE ANALYSIS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# AnnualIncome by AcademicLevel\n",
    "analysisData = pd.DataFrame({\n",
    "    'AcademicLevel': data['AcademicLevel'],\n",
    "    'AnnualIncome': data['AnnualIncome'],\n",
    "})\n",
    "plt.figure(figsize=(10, 10))\n",
    "sns.boxplot(x='AcademicLevel', y='AnnualIncome', data=analysisData)\n",
    "plt.title('Distribution of AnnualIncome across AcademicLevels')\n",
    "plt.xlabel('Academic Level')\n",
    "plt.ylabel('Annual Income ($)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Null Hypothesis: The distributions of AnnualIncome for PhD and Master degree owners are the same.\n",
      "Alternate Hypothesis: The distributions of AnnualIncome for PhD and Master degree owners are different.\n",
      "\n",
      "Performing the Mann-Whitney U Test to check the hypothesis...\n",
      "Statistic: 97874.5\n",
      "p-value: 0.026259430805670697\n",
      "Significance: 0.05\n",
      "\n",
      "Null hypothesis is rejected. There is a significant difference in average AnnualIncome between PHD and Master degree owners.\n"
     ]
    }
   ],
   "source": [
    "# Statistically Test If Average of AnnualIncome is the same for both PHD Owners and Master Owners.\n",
    "\n",
    "phd_income = data[data['AcademicLevel'] == 'PhD']['AnnualIncome']\n",
    "master_income = data[data['AcademicLevel'] == 'Master']['AnnualIncome']\n",
    "\n",
    "statistic, p_value = stats.mannwhitneyu(phd_income, master_income)\n",
    "\n",
    "alpha = 0.05\n",
    "\n",
    "print(\"Null Hypothesis: The distributions of AnnualIncome for PhD and Master degree owners are the same.\")\n",
    "print(\"Alternate Hypothesis: The distributions of AnnualIncome for PhD and Master degree owners are different.\\n\")\n",
    "\n",
    "print(\"Performing the Mann-Whitney U Test to check the hypothesis...\")\n",
    "print(\"Statistic:\", statistic)\n",
    "print(\"p-value:\", p_value)\n",
    "print(\"Significance:\", alpha)\n",
    "\n",
    "if p_value < alpha:\n",
    "    print(\"\\nNull hypothesis is rejected. There is a significant difference in average AnnualIncome between PHD and Master degree owners.\")\n",
    "else:\n",
    "    print(\"Cannot reject Null hypothesis. There is no significant difference in average AnnualIncome between PHD and Master degree owners.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}