Learn imputation, variable encoding, discretization, feature extraction, how to work with datetime, outliers, and more.

Learn filter, wrapper, and embedded methods, recursive feature elimination, exhaustive search, feature shuffling & more.

Learn grid and random search, Bayesian optimization, multi-fidelity models, Optuna, Hyperopt, Scikit-Optimize & more.

Learn to over-sample and under-sample your data, apply SMOTE, ensemble methods, and cost-sensitive learning.

Create lag, window and seasonal features, perform imputation and encoding, extract datetime variables, remove outliers, and more.