To get the most out of this book

Python Feature Engineering Cookbook will give you the practice, tools, and techniques to streamline your feature engineering pipelines and simplify and improve the quality of your code. The book discusses feature engineering methods to transform and create features to train machine learning models using Python. Therefore, some knowledge of machine learning and Python programming will be an asset.

The recipes have been tested in the following library versions: category-encoders == 2.4.0, Feature-engine == 1.4.0, featuretools == 1.4.0, 1.5.0, matplotlib==3.4.2, numpy==1.22.0, pandas==1.5.0, scikit-learn==1.1.0, scipy==1.7.0, seaborn==0.11.1, statsmodels==0.12.2, and tsfresh==0.19.0.

Software/hardware covered in the book	OS requirements
Python 3.3 or greater	Windows, macOS, or Linux
Jupyter Notebook	Windows, macOS, or Linux

Note that earlier versions or newer versions than those displayed in the table may prevent code from running. If you are using newer versions, make sure to check their documentation online for changes in parameter names. That usually solves the issue.

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 (link 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-Second-Edition. If there’s an update to the code, it will be updated in 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!