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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

Creating binary variables through one-hot encoding

In one-hot encoding, we represent a categorical variable as a group of binary variables, where each binary variable represents one category. The binary variable indicates whether the category is present in an observation (1) or not (0). The following table shows the one-hot encoded representation of the Gender variable with the categories of Male and Female:

Gender Female Male
Female 1 0
Male 0 1
Male 0 1
Female 1 0
Female 1 0

 

As shown in the table, from the Gender variable, we can derive the binary variable of Female, which shows the value of 1 for females, or the binary variable of Male, which takes the value of 1 for the males in the dataset.

For the categorical variable of Color with the values of red, blue, and green, we can create three variables called...

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