Customer Churn Prediction Model

Developed a comprehensive customer churn prediction system using ensemble machine learning techniques. The model analyzes customer behavior patterns, transaction history, and engagement metrics to identify customers at risk of churning.

The solution combines multiple algorithms including Random Forest, XGBoost, and Neural Networks to achieve superior prediction accuracy. Feature engineering was performed on over 200 customer attributes, with automated feature selection reducing dimensionality while maintaining model performance.

The model is deployed in production with real-time scoring capabilities, enabling the customer success team to proactively engage with at-risk customers through targeted retention campaigns.