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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
Languages
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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 FREE CHAPTER 2. Chapter 2: Encoding Categorical Variables 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

Index

As this ebook edition doesn't have fixed pagination, the page numbers below are hyperlinked for reference only, based on the printed edition of this book.

A

aggregate primitive 280, 300

features, creating 299-305

reference link 300

arbitrary intervals

variable, discretizing into 131-134

arbitrary value

used, for replacing missing values 16-19

AveOccup variable 92

B

Bag-of-Words (BoW)

used, for creating features 349-352

binary encoding 80

performing 80-82

binary variables

creating, through one-hot encoding 44-52

bins 113

Box-Cox transformation 99, 103

performing 103-105

performing, with Feature-engine 107, 108

performing, with scikit-learn 105, 107

boxplots 154

using, to visualize outliers 154

C

categorical encoding 43

categorical variables 43

imputing 12-16

nominal categorical variables 43

ordinal categorical variables 43

categories

replacing, with counts or frequency...

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