XGBoost for Machine Learning Classification | Amazon SageMaker

🚀 Excited to share my latest machine learning project: Bank Customer Churn Prediction! I've built a comprehensive ML pipeline that predicts which bank customers are likely to churn with 87% accuracy using: 🔹 **Algorithms Used:** LightGBM, XGBoost, CatBoost 🔹 **Key Features:** Age, Balance, Geography, Activity Status 🔹 **Performance:** 0.872 ROC-AUC Score **Key Insights:** • German customers have 32% higher churn rate • Inactive members are 2.5x more likely to leave • Customers with 2 product