feature-engineering

Design and implement feature engineering pipelines for ML models. Covers numerical transformations (scaling, normalization, binning, polynomial), categorical encoding (one-hot, target, ordinal, hashing), text features (TF-IDF, embeddings, tokenization), image features (CNN extraction, augmentation), time-series features (lag, rolling, Fourier), missing value imputation (KNN, MICE), feature selection (mutual info, SHAP, RFE, L1), automated feature engineering (Featuretools, tsfresh), scikit-learn Pipelines, ColumnTransformer, dimensionality reduction (PCA, UMAP), and production feature pipeline best practices.

Version

1.0

License

Apache-2.0