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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

Applying Mathematical Computations to Features

New features can be created by combining two or more variables. Variables can be combined automatically or by using domain knowledge of the data and the industry. For example, in finance, we combine information about the income and the acquired debt to determine the disposable income:

disposable income = income - total debt.

Similarly, if a client has debt across many financial products, for example, a car loan, a mortgage, and credit cards, we can determine the total debt by adding all of those variables up:

Total debt = car loan balance + credit card balance + mortgage balance

In the previous examples, the mathematical functions used to combine the existing variables are derived via domain knowledge of the industry. We can also combine variables automatically, by creating polynomial combinations of the existing variables in the...

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