SaaS Churn Prediction Model with Advanced Feature Engineering

Develop a comprehensive churn prediction model for a B2B SaaS platform using Python. Create a predictive pipeline that integrates customer usage data, interaction logs, and billing history from PostgreSQL. Implement advanced feature engineering techniques using pandas and scikit-learn, including time-based features, interaction frequency transformations, and machine learning classification models. The final model should provide probabilistic churn risk scores with interpretable feature importance and support real-time prediction via a Flask microservice endpoint.