Predictive Churn Risk Modeling with Feature Engineering

Develop a comprehensive Python-based predictive churn risk model using advanced feature engineering techniques. Utilize pandas for data manipulation, scikit-learn for machine learning, and implement techniques like recursive feature elimination, cross-validation, and ensemble modeling. The script must automatically handle categorical variables, perform robust scaling, generate interpretable feature importance charts, and output a probabilistic churn risk score with confidence intervals.