Customer Segmentation Using Machine Learning

Overview

This project segments customers using marketing campaign data to identify meaningful customer groups for targeted marketing and business insights.

Two clustering models were implemented:

• K-Means Clustering
• Hierarchical Clustering

Dataset

Marketing campaign dataset containing customer demographics, spending behavior, and purchase history.

Technologies Used

• Python
• Pandas
• NumPy
• Scikit-learn
• Matplotlib
• SciPy

Workflow

1. Automatic numerical feature extraction
2. Low variance and correlation-based feature filtering
3. Feature scaling using StandardScaler
4. K-Means and Hierarchical clustering
5. Model evaluation using Silhouette Score
6. Cluster interpretation
7. Export segmented dataset

Output

Final segmented dataset

Business Use Case

This segmentation can help businesses:

• Identify high-value customers
• Improve targeted marketing campaigns
• Personalize offers
• Increase customer retention