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": 103,
   "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": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading data\n",
    "data = pd.read_csv(\"dataset\\dataset.csv\", sep=\"\\t\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
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       "<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",
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       "<p>5 rows × 29 columns</p>\n",
       "</div>"
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       "   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": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Looking at the first five rows\n",
    "data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
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       "      <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",
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       "      <td>7270</td>\n",
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       "      <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",
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       "    <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",
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       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
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       "      <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",
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       "      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": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>AnnualIncome</th>\n",
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       "      <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": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "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": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Checking for missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "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": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`AnnualIncome`  has `24` null values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "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": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['AnnualIncome'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7500.0"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mode_value = data['AnnualIncome'].mode()[0]\n",
    "mode_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "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": 114,
   "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": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.drop(columns =['AnnualIncome_mean','AnnualIncome_mode','AnnualIncome_median'])\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Change EnrollmentDate into suitable format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "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": 117,
   "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": 117,
     "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": 118,
   "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": 119,
   "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": 119,
     "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": 120,
   "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": 121,
   "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": 122,
   "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": 123,
   "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": 124,
   "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": 125,
   "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": 126,
   "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": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a new column 'Children'\n",
    "data['Children'] = data['ChildrenAtHome'] + data['TeensAtHome']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "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": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TotalPurchases column\n",
    "data['TotalPurchases'] = data['DiscountedPurchases'] + data['WebPurchases'] + data['CatalogPurchases'] + data['StorePurchases']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TotalSuccessfulCampaignsSuccess column\n",
    "data['TotalSuccessfulCampaignsSuccess'] = data['Campaign1Success'] + data['Campaign2Success'] + data['Campaign3Success'] + data['Campaign4Success'] + data['Campaign5Success']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Is_Parent column\n",
    "data['Is_Parent'] = np.where(data['Children'] > 0, 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping unnecessary column"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Single-valued functions are functions that produce only one output for each input i.e., having only one unique value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Z_CC', 'Z_R']"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "single_valued_columns = []\n",
    "for col in data.columns:\n",
    "    if data[col].nunique()==1:\n",
    "        single_valued_columns.append(col)\n",
    "single_valued_columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hence `Z_CC` and `Z_R` are Single-valued functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of duplicate rows : 0\n"
     ]
    }
   ],
   "source": [
    "print('Number of duplicate rows :', data.duplicated().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "data  = data.drop(columns = {'Z_CC', 'Z_R'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "New DataFrame Overview:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "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>Campaign2Success</th>\n",
       "      <th>RecentComplaint</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>0</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>0</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 × 34 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",
       "   ...  Campaign2Success  RecentComplaint  LastCampaignResponse  Age  \\\n",
       "0  ...                 0                0                     1   67   \n",
       "1  ...                 0                0                     0   70   \n",
       "2  ...                 0                0                     0   59   \n",
       "3  ...                 0                0                     0   40   \n",
       "4  ...                 0                0                     0   43   \n",
       "\n",
       "   TotalSpent  Children  CustomerSince  TotalPurchases  \\\n",
       "0        1617         0             12              25   \n",
       "1          27         2             10               6   \n",
       "2         776         0             11              21   \n",
       "3          53         1             10               8   \n",
       "4         422         1             10              19   \n",
       "\n",
       "   TotalSuccessfulCampaignsSuccess  Is_Parent  \n",
       "0                                0          0  \n",
       "1                                0          1  \n",
       "2                                0          0  \n",
       "3                                0          1  \n",
       "4                                0          1  \n",
       "\n",
       "[5 rows x 34 columns]"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### UNIVARIATE ANALYSIS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plots For The Engineered Features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Engineered features :- ['Age', 'TotalSpent', 'Children', 'CustomerSince', 'TotalPurchases', 'TotalSuccessfulCampaignsSuccess', 'Is_Parent' ,'AnnualIncome' ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize=(12, 6))\n",
    "sns.histplot(data=data['Age'], ax=axes[0] , kde = True)\n",
    "axes[0].set_title('KDE Plot')\n",
    "sns.boxplot(data=data['Age'], ax=axes[1],  palette='Set3', orient  = 'h')  # Corrected line\n",
    "axes[1].set_title('Boxplot')\n",
    "plt.tight_layout()  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "91.14640479922893 19.24198805791393\n",
      "number of outliers: 3 ,Outliers are effectively capped\n"
     ]
    }
   ],
   "source": [
    "upper_limit = data['Age'].mean() + 3*data['Age'].std() \n",
    "lower_limit = data['Age'].mean() - 3*data['Age'].std()\n",
    "x = data[(data['Age']>upper_limit) | (data['Age']<lower_limit)]\n",
    "data['Age'] = np.where(data['Age'] > upper_limit,upper_limit, \n",
    "                              np.where(data['Age'] < lower_limit,lower_limit , data['Age'] ))\n",
    "print(upper_limit ,lower_limit) \n",
    "print('number of outliers:',x.shape[0] ,',Outliers are effectively capped')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axes = plt.subplots(1,2, figsize=(12, 6))\n",
    "sns.histplot(data=data['Age'], ax=axes[0] , kde = True)\n",
    "axes[0].set_title('KDE Plot')\n",
    "sns.boxplot(data=data['Age'], ax=axes[1] ,orient ='h',palette = 'Set3')\n",
    "axes[1].set_title('Boxplot')\n",
    "plt.tight_layout()  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1,2, figsize=(12, 6))\n",
    "sns.histplot(data=data['TotalSpent'], ax=axes[0] , kde = True)\n",
    "axes[0].set_title('KDE Plot')\n",
    "sns.boxplot(data=data['TotalSpent'], ax=axes[1] ,orient ='h',palette = 'Set3')\n",
    "axes[1].set_title('Boxplot')\n",
    "plt.tight_layout()  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2412.5460783186177 -1200.9496497471891\n",
      "number of outliers : 5 , Outliers are effectively capped\n"
     ]
    }
   ],
   "source": [
    "upper_limit = data['TotalSpent'].mean() + 3*data['TotalSpent'].std() \n",
    "lower_limit = data['TotalSpent'].mean() - 3*data['TotalSpent'].std()\n",
    "x = data[(data['TotalSpent']>upper_limit) | (data['TotalSpent']<lower_limit)]\n",
    "data['TotalSpent'] = np.where(data['TotalSpent'] > upper_limit,upper_limit, \n",
    "                              np.where(data['TotalSpent'] < lower_limit,lower_limit , data['TotalSpent'] ))\n",
    "print(upper_limit ,lower_limit )\n",
    "print('number of outliers :',x.shape[0] , ', Outliers are effectively capped')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1,2, figsize=(12, 6))\n",
    "sns.histplot(data=data['TotalSpent'], ax=axes[0] , kde = True)\n",
    "axes[0].set_title('KDE Plot')\n",
    "sns.boxplot(data=data['TotalSpent'], ax=axes[1] ,orient ='h',palette = 'Set3')\n",
    "axes[1].set_title('Boxplot')\n",
    "plt.tight_layout()  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1,2, figsize=(12, 6))\n",
    "sns.histplot(data=data['Children'], ax=axes[0] , kde = True)\n",
    "axes[0].set_title('KDE Plot')\n",
    "sns.boxplot(data=data['Children'], ax=axes[1] ,orient ='h',palette = 'Set3')\n",
    "axes[1].set_title('Boxplot')\n",
    "plt.tight_layout()  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Children\n",
       "1    1128\n",
       "0     638\n",
       "2     421\n",
       "3      53\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Children'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"The maximum allowable number of children is restricted to three, ensuring the absence of outliers.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1,2, figsize=(12, 6))\n",
    "sns.histplot(data=data['CustomerSince'], ax=axes[0] , kde = True)\n",
    "axes[0].set_title('KDE Plot')\n",
    "sns.boxplot(data=data['CustomerSince'], ax=axes[1] ,orient ='h',palette = 'Set3')\n",
    "axes[1].set_title('Boxplot')\n",
    "plt.tight_layout()  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13.025537957522413 8.918212042477588\n",
      "number of outliers : 0  , no outliers\n"
     ]
    }
   ],
   "source": [
    "upper_limit = data['CustomerSince'].mean() + 3*data['CustomerSince'].std() \n",
    "lower_limit = data['CustomerSince'].mean() - 3*data['CustomerSince'].std()\n",
    "x = data[(data['CustomerSince']>upper_limit) | (data['CustomerSince']<lower_limit)]\n",
    "data['CustomerSince'] = np.where(data['CustomerSince'] > upper_limit,upper_limit, \n",
    "                              np.where(data['CustomerSince'] < lower_limit,lower_limit , data['CustomerSince'] ))\n",
    "print(upper_limit ,lower_limit)\n",
    "print('number of outliers :' ,x.shape[0] ,' , no outliers')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1,2, figsize=(12, 6))\n",
    "sns.histplot(data=data['TotalPurchases'], ax=axes[0] , kde = True)\n",
    "axes[0].set_title('KDE Plot')\n",
    "sns.boxplot(data=data['TotalPurchases'], ax=axes[1] ,orient ='h',palette = 'Set3')\n",
    "axes[1].set_title('Boxplot')\n",
    "plt.tight_layout()  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "37.89357139595621 -8.169464253099065\n",
      "number of outliers: 3 , Outliers are effectively capped\n"
     ]
    }
   ],
   "source": [
    "upper_limit = data['TotalPurchases'].mean() + 3*data['TotalPurchases'].std() \n",
    "lower_limit = data['TotalPurchases'].mean() - 3*data['TotalPurchases'].std() \n",
    "x = data[(data['TotalPurchases']>upper_limit) | (data['TotalPurchases']<lower_limit)]\n",
    "data['TotalPurchases'] = np.where(data['TotalPurchases'] > upper_limit,upper_limit, \n",
    "                              np.where(data['TotalPurchases'] < lower_limit,lower_limit , data['TotalPurchases'] ))\n",
    "print(upper_limit ,lower_limit)\n",
    "print('number of outliers:', x.shape[0] , ', Outliers are effectively capped')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1,2, figsize=(12, 6))\n",
    "sns.histplot(data=data['TotalPurchases'], ax=axes[0] , kde = True)\n",
    "axes[0].set_title('KDE Plot')\n",
    "sns.boxplot(data=data['TotalPurchases'], ax=axes[1] ,orient ='h',palette = 'Set3')\n",
    "axes[1].set_title('Boxplot')\n",
    "plt.tight_layout()  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Is_Parent\n",
       "1    1602\n",
       "0     638\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " data['Is_Parent'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"The variable 'Is_Parent' is constrained to binary values of 0 and 1, thereby precluding the presence of outliers.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TotalSuccessfulCampaignsSuccess\n",
       "0    1777\n",
       "1     325\n",
       "2      83\n",
       "3      44\n",
       "4      11\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[ 'TotalSuccessfulCampaignsSuccess'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"The 'TotalSuccessfulCampaignsSuccess' variable is limited to a maximum value of 4, ensuring the absence of outliers.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1,2, figsize=(12, 6))\n",
    "sns.histplot(data=data['AnnualIncome'], ax=axes[0] , kde = True)\n",
    "axes[0].set_title('KDE Plot')\n",
    "sns.boxplot(data=data['AnnualIncome'], ax=axes[1] ,orient ='h',palette = 'Set3')\n",
    "axes[1].set_title('Boxplot')\n",
    "plt.tight_layout()  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "118641.375 -15629.625\n",
      "number of outliers: 8 , Outliers are effectively capped\n"
     ]
    }
   ],
   "source": [
    "iqr  = data['AnnualIncome'].quantile(0.75) - data['AnnualIncome'].quantile(0.25)\n",
    "upper_limit = data['AnnualIncome'].quantile(0.75) + 1.5*iqr\n",
    "lower_limit = data['AnnualIncome'].quantile(0.25) - 1.5*iqr\n",
    "x = data[(data['AnnualIncome']>upper_limit) | (data['AnnualIncome']<lower_limit)]\n",
    "data['AnnualIncome'] = np.where(data['AnnualIncome'] > upper_limit,upper_limit, \n",
    "                              np.where(data['AnnualIncome'] < lower_limit,lower_limit , data['AnnualIncome'] ))\n",
    "print(upper_limit ,lower_limit )\n",
    "print('number of outliers:', x.shape[0] , ', Outliers are effectively capped')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1,2, figsize=(12, 6))\n",
    "sns.histplot(data=data['AnnualIncome'], ax=axes[0] , kde = True)\n",
    "axes[0].set_title('KDE Plot')\n",
    "sns.boxplot(data=data['AnnualIncome'], ax=axes[1] ,orient ='h',palette = 'Set3')\n",
    "axes[1].set_title('Boxplot')\n",
    "plt.tight_layout()  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"Hence, all engineered features are devoid of outliers.\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## BIVARIATE ANALYSIS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "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": 158,
   "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: 0.026259416449905735\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": []
  },
  {
   "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
}