A machine learning model achieving 85% accuracy in customer churn prediction with interpretable insights via SHAP analysis.
Developed a machine learning model to predict customer churn with 85% accuracy. Applied feature engineering and ensemble learning to improve performance metrics and demonstrated model interpretability using SHAP analysis to explain key churn drivers.