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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 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

Extracting Features from Text Variables

Text can be one of the variables in our datasets. For example, in insurance, information describing the circumstances of an incident can come from free text fields in a form. If a company gathers customer reviews, this information will be collected as short pieces of text provided by the users. Text data does not show the tabular pattern of the datasets that we have worked with throughout this book. Instead, information in texts can vary in length and content, as well as writing style. We can extract a lot of information from text variables to use as predictive features in machine learning models. The techniques we will cover in this chapter belong to the realm of Natural Language Processing (NLP). NLP is a subfield of linguistics and computer science. It is concerned with the interactions between computer and human language, or, in other words, how to program computers to understand human language. NLP includes a multitude of techniques to understand...

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