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Python Feature Engineering Cookbook

You're reading from   Python Feature Engineering Cookbook Over 70 recipes for creating, engineering, and transforming features to build machine learning models

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Product type Paperback
Published in Jan 2020
Publisher Packt
ISBN-13 9781789806311
Length 372 pages
Edition 1st Edition
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Author (1):
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Soledad Galli Soledad Galli
Author Profile Icon Soledad Galli
Soledad Galli
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Table of Contents (13) Chapters Close

Preface 1. Foreseeing Variable Problems When Building ML Models 2. Imputing Missing Data FREE CHAPTER 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:

To learn more about category encoders...

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