featurepreprocessing
Feature preprocessing is the process of transforming raw data into numerical features suitable for machine learning models. It includes cleaning, encoding, scaling, and transforming features so that algorithms can learn effectively. It is distinct from feature engineering, which creates new features; preprocessing focuses on preparing existing features for modeling.
Common techniques include handling missing values through imputation (such as mean, median, or constant replacement), encoding
Best practices emphasize reproducibility and avoiding data leakage. Preprocessing steps should be learned on the training