Feature engineering for ML models

Create advanced features for a churn prediction model. Techniques: 1. Temporal features (days since last purchase, purchase frequency). 2. Aggregations (total spend, average order value). 3. Categorical encoding (one-hot, target encoding). 4. Interaction features (tenure × monthly charges). 5. Feature selection using mutual information and correlation analysis. Document feature importance and business rationale for each engineered feature.