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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

Extracting Features from Text Variables

Text can be part of the variables in our datasets. For example, in insurance, some variables that capture information about an incident may come from a free text field in a form. In data from a website that collects customer reviews or feedback, we may also encounter variables that contain short descriptions provided by text that has been entered manually by the users. Text is unstructured, that is, it does not follow a pattern, like the tabular pattern of the datasets we have worked with throughout this book. Text may also vary in length and content, and the writing style may be different. How can we extract information from text variables to inform our predictive models? This is the question we are going to address in this chapter.

The techniques we will cover in this chapter belong to the realm of Natural Language Processing (NLP...

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