🎯 Project Overview

This project implements a K-Nearest Neighbors (KNN) model to classify customers based on a marketing campaign dataset. The goal is to predict whether customers will purchase a product in the company’s 6th marketing campaign, enabling efficient resource allocation and targeted marketing strategies.

General Details

• Project Name: Customer Classification Using KNN
• Objective: Predict purchase decisions (1 for purchase, 0 for decline)
• Data Source: Marketing Campaign Dataset from Kaggle

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Problem Definition

Business Context

The company is launching its 6th marketing campaign to promote a new product line and aims to:

• Identify customers likely to buy for focused marketing efforts.
• Exclude unlikely buyers to minimize costs.
• Understand factors driving purchase decisions.

Machine Learning Task

• Task Type: Binary classification
• Input: Customer attributes (e.g., income, past campaign responses, online purchases)
• Output: Binary label: 1 (purchase) or 0 (decline)

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Dataset Description

• Source: Marketing Campaign dataset from Kaggle
• Size: 2,240 customers from a phone-based pilot campaign
• Labels: 1 (purchase), 0 (decline)

Field Name	Meaning
ID	Customer ID
Year_birth	Year of birth
Education	Education level
Marital_status	Marital status
Income	Annual household income
Kidhome	Number of children in the household
Teenhome	Number of teenagers in the household
DtCustomer	Date when the customer information was first recorded in the system
Recency	Number of days since the last purchase
MntWines	Amount spent on wine products in the past 2 years
MntFishProducts	Amount spent on fish products in the past 2 years
MntMeatProducts	Amount spent on meat products in the past 2 years
MntFruits	Amount spent on fruit products in the past 2 years
MntSweetProducts	Amount spent on sweet products in the past 2 years
MntGoldProds	Amount spent on luxury products in the past 2 years
NumDealsPurchases	Number of purchases with promotions
NumStorePurchases	Number of in-store purchases
NumCatalogPurchases	Number of purchases made via catalog
NumWebPurchases	Number of purchases made via the website
NumWebVisitsMonth	Number of website visits in the last month
AcceptedCmp1	1 if the customer agreed to purchase in the first campaign, 0 if declined
AcceptedCmp2	1 if the customer agreed to purchase in the second campaign, 0 if declined
AcceptedCmp3	1 if the customer agreed to purchase in the third campaign, 0 if declined
AcceptedCmp4	1 if the customer agreed to purchase in the fourth campaign, 0 if declined
AcceptedCmp5	1 if the customer agreed to purchase in the fifth campaign, 0 if declined
Complain	1 if the customer has complained about a product/service in the past 2 years, 0 otherwise
Response (target)	1 if the customer accepted the last campaign, 0 if declined

Data Preparation

• Splitting: Divided into 80% training (X_train_scaled, y_train) and 20% validation (X_val_scaled, y_val) sets.
• Normalization: Scaled features for KNN’s Euclidean distance metric.
• Feature Selection: Retained 30 relevant features based on initial analysis.

Exploratory Data Analysis (EDA)

• Purpose: Analyzed the dataset to identify patterns and potential predictors of purchase behavior before modeling.
• Approach: Examined distributions of numerical features (Income, NumWebPurchases, Age) and categorical features (AcceptedCmp1-5, Education) using summary statistics and visualizations.
• Hypotheses:
  • AcceptedCmp1-5: Prior campaign acceptance signals higher purchase likelihood.
  • Income: Greater income may increase buying potential.
  • NumWebPurchases: Frequent online purchases suggest receptiveness.
  • Age: Certain age groups may be more inclined to buy.
• Findings:
  • Positive AcceptedCmp5 responses strongly correlated with purchases.
  • Higher NumWebPurchases linked to increased buying tendency.
  • Income showed a moderate positive trend with purchases; Age varied by group (e.g., 30-50 more likely).

Methodology

Algorithm

• K-Nearest Neighbors (KNN):
  • Distance metric: Euclidean (metric='euclidean)
  • Weighting: Tested uniform (equal) and distance (distance-based)

Implementation Steps

• Preprocessing: Split data 80/20 and scaled features.
• Training: Tested k from 1 to 30, focusing on k=9 and k=17.
• Tuning: Evaluated performance, noting overfitting at k=17 with weights='distance'; preferred k=9 with weights='uniform'.
• Feature Analysis: Assessed feature impact on validation accuracy.

Tools and Technologies

• Python, scikit-learn (KNN, scaling), pandas, numpy (data handling), matplotlib, seaborn (visualization)
• Environment: Jupyter Notebook

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Results

Model Performance

• At k=9 with weights='uniform': Balanced accuracy across training and validation (specific values pending refinement), minimal overfitting.
• At k=17 with weights='distance': 99% training accuracy, 87% validation accuracy (8% gap, indicating overfitting).

Visualization:

• Train vs. Validation Accuracy Plot (at k=9)

• Train vs. Validation Accuracy Plot (k= 17 ****weights='distance')

Key Features

Feature	Importance Score	Interpretation
AcceptedCmp5	0.008929	Top predictor (past engagement)
AcceptedCmp1	0.006696	Prior campaign response
Age	0.006696	Demographic factor
AcceptedCmp3	0.004464	Additional campaign influence
Income	0.002232	Financial capacity
NumWebPurchases	0.002232	Online purchase behavior

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Model Evaluation

Strengths of the KNN Model

• Simplicity: Easy to implement and interpret, suitable for small datasets like this (2,240 samples).
• EDA Alignment: Feature importance results matched EDA hypotheses, enhancing reliability.
• Flexibility: Adjustable k and weighting options allow performance tuning.

Weaknesses of the KNN Model

• Overfitting Risk: Evident at k=17 with weights='distance' (8% accuracy gap), requiring careful parameter selection.
• Scalability: Computationally intensive for larger datasets due to distance calculations.
• Sensitivity to Noise: Performance may degrade with irrelevant or noisy features.