Preface

Python Feature Engineering Cookbook, Second Edition, covers almost every aspect of feature engineering for tabular data, including missing data imputation, categorical encoding, variable transformation, discretization, scaling, and the handling of outliers. It also discusses how to extract features from date and time, text, time series, and relational datasets.

This book will take the pain out of feature engineering by showing you how to use open source Python libraries to accelerate the feature engineering process via multiple practical, hands-on recipes. Throughout the book, you will transform and create new features utilizing pandas, scikit-learn, and also the four major open source feature engineering libraries: Feature-engine, Category Encoders, Featuretools, and tsfresh.