To get the most out of this book

This book provides practical tools and techniques to streamline your feature engineering pipelines, allowing you to enhance code quality and simplify processes. The book explores methods to transform and create features to effectively train machine learning models with Python. Therefore, familiarity with machine learning and Python programming will benefit your understanding and application of the concepts presented.

The recipes have been tested in the following library versions:

• category-encoders == 2.6.3
• Feature-engine == 1.8.0
• featuretools == 1.31.0
• matplotlib==3.8.3
• nltk=3.8.1
• numpy==1.26.4
• pandas==2.2.1
• scikit-learn==1.5.0
• scipy==1.12.0
• seaborn==0.13.2
• tsfresh==0.20.0

Software/hardware covered in the book	OS requirements
Python 3.9 or greater	Windows, macOS, or Linux

Note that earlier or newer versions of the Python libraries may prevent code from running. If you are using newer versions, make sure to check their documentation for any recent updates, parameter name changes, or deprecation.

If you are using the digital version of this book, we advise you to type the code yourself or access the code via the GitHub repository (the link is available in the next section). Doing so will help you avoid any potential errors related to the copying and pasting of code.

Download the example code files

You can download the example code files for this book from GitHub at https://github.com/PacktPublishing/Python-Feature-Engineering-Cookbook-Third-Edition. If there’s an update to the code, it will be updated on the existing GitHub repository.

We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!