Mall Customer Segmentation & Classification

This project performs customer segmentation on the Mall Customers dataset using K-Means clustering and classifies new customers into segments using supervised machine learning models.

📊 Project Overview

• Dataset: Mall_Customers.csv
• Techniques:
  • Outlier removal using IQR
  • Feature scaling with StandardScaler
  • Dimensionality reduction with PCA
  • Clustering using K-Means
  • Cluster evaluation using Silhouette Score & Davies-Bouldin Index
  • Classification using Random Forest and LightGBM

🔁 Workflow

1. Preprocessing: Handled nulls, duplicates, outliers, and label-encoded categorical features.
2. Scaling: Standardized numeric features.
3. PCA: Reduced features to 2D for visualization.
4. Clustering: Applied K-Means, determined optimal k using the Elbow method.
5. Evaluation: Used Silhouette Score and Davies-Bouldin Index to evaluate clustering.
6. Classification: Trained RandomForest and LightGBM classifiers to predict clusters.

📦 Requirements

• pandas
• seaborn
• matplotlib
• scikit-learn
• lightgbm

Install all dependencies with:

pip install -r requirements.txt