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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

Combining features with mathematical functions

New features can be created by combining existing variables with mathematical and statistical functions. Taking an example from the finance industry, we can calculate the total debt of a person by summing up their debt across individual financial products, such as car loan, mortgage, or credit card debt:

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

We can also derive other insightful features using alternative statistical operations. For example, we can determine the maximum debt of a customer across financial products or the average time a user spends on a website:

maximum debt = max(car loan balance, credit card balance, mortgage balance)

average time on website = mean(time spent on homepage, time spent on about page, time spent on FAQ page)

We can, in principle, use any mathematical or statistical operation to create new features, such as the product, mean, standard deviation, or maximum or minimum values...

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