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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 pandas, NumPy, Matplotlib, SciPy and scikit-learn Python libraries. These libraries are bundled in the free Anaconda Python distribution (https://www.anaconda.com/distribution/), which you can install as described in the Technical Requirements section of Chapter 1, Foreseeing Variable Problems when Building ML Models. 

We will also use the open source Python library Feature-engine, which can be installed using pip:

pip install feature-engine

We will use the Boston House Prices dataset from scikit-learn, which contains no missing data. When trying the recipes in your own dataset, make sure you impute the missing values with any of the techniques we covered in Chapter 2, Imputing Missing Data. 

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