Feature Engineering Pipeline

Create a comprehensive feature engineering pipeline for my tabular dataset.

Dataset information:
- Number of features: [N_FEATURES]
- Feature types: [NUMERICAL/CATEGORICAL/TEXT/DATE]
- Target variable: [TARGET DESCRIPTION]
- Missing data: [PERCENTAGE/PATTERN]

Pipeline requirements:
1. Missing value handling:
   - Imputation strategies by feature type
   - Missing indicator features
2. Categorical encoding:
   - One-hot, label, target encoding
   - High-cardinality handling
3. Numerical transformations:
   - Scaling (standard, minmax, robust)
   - Log/power transforms for skewed data
   - Binning strategies
4. Feature creation:
   - Polynomial features
   - Interaction terms
   - Date/time feature extraction
5. Feature selection:
   - Correlation analysis
   - Mutual information
   - Recursive feature elimination
6. Sklearn Pipeline integration
7. Persistence and versioning

Handle data leakage properly with fit/transform separation.