You're reading from Python Feature Engineering Cookbook A complete guide to crafting powerful features for your machine learning models

Product type Paperback

Published in Aug 2024

Last Updated in Feb 2025

Publisher Packt

ISBN-13 9781835883587

Length 396 pages

Edition 3rd Edition

Languages

Python

Tools

Combine

Concepts

Data Science

Author (1):

Galli

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Table of Contents (14) Chapters

Preface

1. Chapter 1: Imputing Missing Data

2. Chapter 2: Encoding Categorical Variables FREE CHAPTER

3. Chapter 3: Transforming Numerical Variables

4. Chapter 4: Performing Variable Discretization

5. Chapter 5: Working with Outliers

6. Chapter 6: Extracting Features from Date and Time Variables

7. Chapter 7: Performing Feature Scaling

8. Chapter 8: Creating New Features

9. Chapter 9: Extracting Features from Relational Data with Featuretools

10. Chapter 10: Creating Features from a Time Series with tsfresh

11. Chapter 11: Extracting Features from Text Variables

12. Index

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Technical requirements

In this chapter, we will use pandas, matplotlib, and featuretools open source Python libraries. You can install featuretools with pip:

pip install featuretools

Additionally, you can do so with conda:

conda install -c conda-forge featuretools

These commands install the basic featuretools functionality, but we can install add-ons for creating features with natural language processing (NLP) or for using dask as the backend instead of pandas. For more information on how to install featuretools add-ons, including graphviz, check out their documentation here: https://docs.featuretools.com/en/v0.16.0/getting_started/install.html.

We will work with the Online Retail II dataset from the UCI Machine Learning Repository, which is available at https://archive.ics.uci.edu/ml/datasets/Online+Retail+II and licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license: https://creativecommons.org/licenses/by/4.0/legalcode. The corresponding...