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Python Feature Engineering Cookbook

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

Publisher Packt

ISBN-13 9781835883587

Length 396 pages

Edition 3rd Edition

Languages

Python

Tools

Combine

Concepts

Data Science

Author (1):

Soledad Galli

View More author details

Table of Contents (14) Chapters

Preface

1. Chapter 1: Imputing Missing Data FREE CHAPTER

2. Chapter 2: Encoding Categorical Variables

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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13. Other Books You May Enjoy

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...

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Authors (1)

Soledad Galli

Soledad Galli

Soledad Galli is a bestselling data science instructor, author, and open-source Python developer. As the leading instructor at Train in Data, she teaches intermediate and advanced courses in machine learning that have enrolled over 64,000 students worldwide and continue to receive positive reviews. Sole is also the developer and maintainer of the Python open-source library Feature-engine, which provides an extensive array of methods for feature engineering and selection. With extensive experience as a data scientist in finance and insurance sectors, Sole has developed and deployed machine learning models for assessing insurance claims, evaluating credit risk, and preventing fraud. She is a frequent speaker at podcasts, meetups, and webinars, sharing her expertise with the broader data science community.

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