Customer Segmentation Project

📌 Overview

This project uses clustering techniques to segment customers based on demographic and behavioral data.

🎯 Objective

To identify distinct customer groups that can support targeted marketing and business strategy.

🛠️ Tools & Technologies

• Python (Pandas, NumPy)
• Data Visualization (Matplotlib, Seaborn)
• Machine Learning (K-Means Clustering)
• Scikit-learn

📊 Key Steps

1. Data Preparation
2. Exploratory Data Analysis
3. Feature Scaling
4. Clustering (K-Means)
5. Cluster Evaluation (Elbow Method)
6. Visualization and Interpretation

📈 Results

• Identified distinct customer segments
• Revealed differences in income and spending behavior

🧠 Key Insights

• High-income, high-spending customers represent valuable segments
• Lower-spending groups may require targeted engagement strategies
• Customer segmentation can improve marketing effectiveness

🚀 Future Improvements

• Test other clustering methods (Hierarchical, DBSCAN)
• Include additional features (e.g., purchase history)
• Apply clustering to real-world datasets

📁 Project Structure

customer-segmentation-project/
├── data/
├── notebooks/
├── src/
└── requirements.txt

👤 Author

Hamzat Afe Isede

🔗 Related Projects

• Customer Churn Prediction
• Sales Forecasting (Time Series)
• Sales Dashboard (Power BI)