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Python Feature Engineering Cookbook

You're reading from  Python Feature Engineering Cookbook

Product type Book
Published in Jan 2020
Publisher Packt
ISBN-13 9781789806311
Pages 372 pages
Edition 1st Edition
Languages
Author (1):
Soledad Galli Soledad Galli
Profile icon Soledad Galli
Toc

Table of Contents (13) Chapters close

Preface 1. Foreseeing Variable Problems When Building ML Models 2. Imputing Missing Data 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

Creating features from transactions with Featuretools

Featuretools is an open source Python library that allows us to automatically create features from time series and transactional databases with multiple transaction records for each specific entity, such as customers. With Featuretools, we can automatically create features at the transaction level. Such features include the day, month, and year from a datetime variable, the time between transactions, or if the transaction occurred on a weekend, as well as the cumulative sum or the difference in value between transactions.

Featuretools also aggregates existing and new features at the entity level—in our example, at the customer level—using mathematical and statistical operations, such as the ones we used in the Aggregating transactions with mathematical operations recipe of this chapter or by...

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