Customer Churn Prediction

Project Overview

This project focuses on predicting customer churn using machine learning while emphasizing model interpretability using SHAP (SHapley Additive exPlanations) values. We'll learn how to not just predict churn but understand the factors driving it.

Key Learning Objectives

• Understanding customer behavior patterns
• Implementing SHAP for model interpretability
• Feature importance analysis
• Building interpretable ML models
• Creating actionable insights from model explanations

Technologies Used

• Python
• SHAP library
• scikit-learn
• XGBoost
• pandas and numpy

Additional Resources

For detailed implementation guide, code examples, and step-by-step instructions, please visit the GitHub Project Repository.