Python Feature Engineering Cookbook: Over 70 recipes for creating, engineering, and transforming features, 2nd Edition

This second edition of Python Feature Eeering Cookbook will take the struggle out of feature eeering by showing you how to use open source Python libraries to accelerate the process via a plethora of practical, hands-on recipes.

This updated edition bs by addressing fundamental data challenges such as missing data and categorical values, before moving on to strats for dealing with skewed distributions and outliers. The concluding chapters show you how to develop new features from various types of data, including text, series, and relational databases. With the help of numerous open source Python libraries, you'll learn how to implement each feature eeering method in a performant, reproducible, and elegant manner.

By the end of this Python book, you will have the tools and expertise needed to confidently build end-to-end and reproducible feature eeering pipelines that can be deployed into production.
What you will learn

Impute missing data using various univariate and multivariate methods
Encode categorical variables with one-hot, ordinal, and count encoding
Handle highly cardinal categorical variables
Transform, discretize, and scale your variables
Create variables from date and with pandas and Feature-ee
Combine variables into new features
Extract features from text as well as from transactional data with Featuretools
Create features from series data with tsfresh

Who this book is for

This book is for machine learning and data science students and professionals, as well as software eeers working on machine learning model deployment, who want to learn more about how to transform their data and create new features to train machine learning models in a better way.



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