Customer Churn Prediction Model

Developed and evaluated multiple machine learning models to predict customer churn with 89% accuracy. Implemented comprehensive feature engineering, data preprocessing, and model explainability using SHAP values. The project includes exploratory data analysis, feature importance ranking, hyperparameter tuning, and cross-validation. Deployed the model with a web interface for real-time predictions and business insights. Used advanced techniques like ensemble methods and automated feature selection to optimize model performance.