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Python Feature Engineering Cookbook

You're reading from  Python Feature Engineering Cookbook

Product type Book
Published in Jan 2020
Publisher Packt
ISBN-13 9781789806311
Pages 372 pages
Edition 1st Edition
Languages
Author (1):
Soledad Galli Soledad Galli
Profile icon Soledad Galli
Toc

Table of Contents (13) Chapters close

Preface 1. Foreseeing Variable Problems When Building ML Models 2. Imputing Missing Data 3. Encoding Categorical Variables 4. Transforming Numerical Variables 5. Performing Variable Discretization 6. Working with Outliers 7. Deriving Features from Dates and Time Variables 8. Performing Feature Scaling 9. Applying Mathematical Computations to Features 10. Creating Features with Transactional and Time Series Data 11. Extracting Features from Text Variables 12. Other Books You May Enjoy

Technical requirements

In this chapter, we will use the following Python libraries: pandas, NumPy, Matplotlib, and scikit-learn. I recommend installing the free Anaconda Python distribution, which contains all of these packages.

For details on how to install the Anaconda Python distribution, visit the Technical requirements section in Chapter 1, Foreseeing Variable Problems in Building ML Models

We will also use the open source Python library's feature-engine and category encoders, which can be installed using pip:

pip install feature-engine
pip install category_encoders

To learn more about Feature-engine, visit the following sites:

  • Home page: https://www.trainindata.com/feature-engine
  • GitHub: https://github.com/solegalli/feature_engine/
  • Documentation: https://feature-engine.readthedocs.io

To learn more about category encoders...

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