Python Feature Engineering Cookbook: Over 70 recipes for creating, engineering, and transforming features to build machine learning models

by Soledad Galli

Extract accurate information from data to train and improve machine learning models using NumPy, SciPy, pandas, and scikit-learn libraries

Key FeaturesDiscover solutions for feature generation, feature extraction, and feature selectionUncover the end-to-end feature engineering process across continuous, discrete, and unstructured datasetsImplement modern feature extraction techniques using Python's pandas, scikit-learn, SciPy and NumPy librariesBook DescriptionFeature engineering is invaluable for developing and enriching your machine learning models. In this cookbook, you will work with the best tools to streamline your feature engineering pipelines and techniques and simplify and improve the quality of your code.

Using Python libraries such as pandas, scikit-learn, Featuretools, and Feature-engine, you’ll learn how to work with both continuous and discrete datasets and be able to transform features from unstructured datasets. You will develop the skills necessary to select the best features as well as the most suitable extraction techniques. This book will cover Python recipes that will help you automate feature engineering to simplify complex processes. You’ll also get to grips with different feature engineering strategies, such as the box-cox transform, power transform, and log transform across machine learning, reinforcement learning, and natural language processing (NLP) domains.

By the end of this book, you’ll have discovered tips and practical solutions to all of your feature engineering problems.

What you will learnSimplify your feature engineering pipelines with powerful Python packagesGet to grips with imputing missing valuesEncode categorical variables with a wide set of techniquesExtract insights from text quickly and effortlesslyDevelop features from transactional data and time series dataDerive new features by combining existing variablesUnderstand how to transform, discretize, and scale your variablesCreate informative variables from date and timeWho this book is forThis book is for machine learning professionals, AI engineers, data scientists, and NLP and reinforcement learning engineers who want to optimize and enrich their machine learning models with the best features. Knowledge of machine learning and Python coding will assist you with understanding the concepts covered in this book.

Table of ContentsForeseeing Variable Problems When Building ML ModelsImputing Missing DataEncoding Categorical VariablesTransforming Numerical VariablesPerforming Variable DiscretisationWorking with OutliersDeriving Features from Dates and Time VariablesPerforming Feature ScalingApplying Mathematical Computations to FeaturesCreating Features with Transactional and Time Series DataExtracting Features from Text Variables

652 pages, Kindle Edition

Published January 22, 2020

Reviews

[Andrey Lukyanenko · Rating 4 out of 5]

Overall, this book presents a straightforward, hands-on approach to feature engineering in Python. It serves as a convenient reference for anyone who wants quick code examples tailored to specific tasks—especially beginners looking to build up their skills step by step.

The book is quite comprehensive, covering common feature engineering techniques such as handling missing data, encoding categorical variables, and scaling numerical features. The abundance of code snippets is a major plus; you can easily copy, adapt, and experiment with them in your own projects. I found the examples clear and practical, making it a solid resource for day-to-day data preprocessing work.

One small downside is that the reading experience sometimes feels akin to scrolling through a Jupyter notebook with added commentary. For some readers—particularly those who prefer a more narrative or conceptual flow—this might come across as slightly disjointed. However, if you enjoy following code-based demonstrations step by step, you may actually find this style convenient and engaging.

In summary, Python Feature Engineering Cookbook is a good pick for people new to data preprocessing or anyone wanting a quick “go-to” guide for different feature engineering tasks.

[Wallis Chan · Rating 5 out of 5]

Excellent. All the recipes in this book were well explained and easy to follow. There are tons of recipes, with many of them showing how to do certain things using 3 different libraries. It's a great reference book that I'll sure to return to very often.

[justin · Rating 4 out of 5]

Decent, probably geared more towards beginners and note that it uses feature engine, the authors own open source package (seems well maintained, so not a knock, but still worth pointing out) so the page count is a bit inflated since it repeats steps for multiple things.