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Python Feature Engineering Cookbook

You're reading from   Python Feature Engineering Cookbook A complete guide to crafting powerful features for your machine learning models

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Product type Paperback
Published in Aug 2024
Publisher Packt
ISBN-13 9781835883587
Length 396 pages
Edition 3rd 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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Toc

Table of Contents (14) Chapters Close

Preface 1. Chapter 1: Imputing Missing Data 2. Chapter 2: Encoding Categorical Variables FREE CHAPTER 3. Chapter 3: Transforming Numerical Variables 4. Chapter 4: Performing Variable Discretization 5. Chapter 5: Working with Outliers 6. Chapter 6: Extracting Features from Date and Time Variables 7. Chapter 7: Performing Feature Scaling 8. Chapter 8: Creating New Features 9. Chapter 9: Extracting Features from Relational Data with Featuretools 10. Chapter 10: Creating Features from a Time Series with tsfresh 11. Chapter 11: Extracting Features from Text Variables 12. Index 13. Other Books You May Enjoy

Comparing features to reference variables

In the previous recipe, Combining features with mathematical functions, we created new features by applying mathematical or statistical functions, such as the sum or the mean, to a group of variables. Some mathematical operations, however, such as subtraction or division, are performed between features. These operations are useful to derive ratios, such as the debt-to-income ratio:

debt-to-income ratio = total debt / total income

These operations are also useful to compute differences, such as the disposable income:

disposable income = income - total debt

In this recipe, we will learn how to create new features by subtracting or dividing variables with pandas and feature-engine.

Note

In the recipe, we will show you how to create features with subtraction and division. We hope that the examples, relating to the financial sector, shed some light on how to use domain knowledge to decide which features to combine and how.

How...

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