Customer Segmentation using KMeans Clustering

📌 Project Overview

This project applies KMeans clustering to segment customers based on their purchasing behavior using an online retail dataset. The goal is to identify actionable customer groups for better marketing and retention strategies.

📂 Dataset

The dataset contains transactional data from an online retail store, including:

• InvoiceNo: Unique transaction ID
• StockCode: Product code
• Description: Product description
• Quantity: Number of items purchased
• InvoiceDate: Date of transaction
• UnitPrice: Price per item
• CustomerID: Unique identifier for customers
• Country: Customer's location

The dataset is available at: Online Retail II Dataset

🚀 Methodology

1️⃣ Data Preprocessing

• Removed missing values and invalid transactions
• Filtered out refunds (negative quantities)
• Computed Monetary Value, Frequency, and Recency features

2️⃣ Feature Engineering

• Recency: Days since last purchase
• Frequency: Number of transactions
• Monetary Value: Total spending
• Applied StandardScaler for normalization

3️⃣ Clustering using KMeans

• Determined the optimal number of clusters using Elbow Method (Inertia) & Silhouette Score
• Applied KMeans clustering to segment customers

4️⃣ Cluster Analysis & Interpretation

• Used 3D scatter plots for visualization
• Identified three customer segments:
  • Retain: High-value, loyal customers
  • Re-Engage: Low-value, infrequent buyers
  • Nurture: Recent buyers with lower engagement

📊 Visualizations

• Elbow Method Plot to determine optimal clusters
• 3D Scatter Plot for customer segmentation
• Violin Plots to compare cluster distributions

🛠️ Technologies Used

• Python
• Pandas, NumPy
• Scikit-learn (KMeans, StandardScaler)
• Matplotlib, Seaborn

📈 Results & Insights

• High-value customers contribute the most to revenue → Priority for retention.
• Infrequent buyers need marketing efforts to increase engagement.
• Recent buyers have potential to be nurtured into loyal customers.

📌 How to Run the Project

Prerequisites

Ensure you have Python installed and the required libraries:

pip install pandas numpy scikit-learn matplotlib seaborn

Running the Script

python customer_segmentation.py

🔗 Future Improvements

• Explore Hierarchical Clustering and DBSCAN for comparison
• Implement Customer Lifetime Value (CLV) predictions
• Apply Deep Learning for more advanced segmentation

📜 License

This project is open-source under the MIT License. Feel free to contribute!

🤝 Contributing

Pull requests are welcome! For major changes, please open an issue first to discuss what you would like to change.

📬 Contact

For any queries, reach out via GitHub Issues.