[P2-T1] Design & Implement Data Preprocessing Pipeline

[edaaydinea]

• Description: Develop a scikit-learn pipeline in src/features/build_features.py. This pipeline should handle all preprocessing steps identified in Phase 1.
  • Imputation: Strategy for missing values (e.g., Mean/Median for numerical, Mode for categorical).
  • Scaling: Standardize or normalize numerical features (e.g., StandardScaler).
  • Encoding: One-hot encode categorical features (e.g., Gender, Schooling).
• Acceptance Criteria: A reusable preprocessing pipeline object that can be saved and applied consistently to training, validation, and future data.