Create end-to-end, reproducible feature engineering pipelines that can be deployed into production using open-source Python libraries

Key Features

Learn and implement feature engineering best practices

Reinforce your learning with the help of multiple hands-on recipes

Build end-to-end feature engineering pipelines that are performant and reproducible

Book Description

Feature engineering, the process of transforming variables and creating features, albeit time-consuming, ensures that your machine learning models perform seamlessly. This second edition of Python Feature Engineering Cookbook will take the struggle out of feature engineering 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 begins by addressing fundamental data challenges such as missing data and categorical values, before moving on to strategies for dealing with skewed distributions and outliers. The concluding chapters show you how to develop new features from various types of data, including text, time series, and relational databases. With the help of numerous open source Python libraries, you’ll learn how to implement each feature engineering 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 engineering 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 time with pandas and Feature-engine

Combine variables into new features

Extract features from text as well as from transactional data with Featuretools

Create features from time 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 engineers 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.