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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

Deriving Features from Dates and Time Variables

Date and time variables are those that contain information about dates, times, or date and time. In programming, we refer to these variables as datetime variables. Examples of the datetime variables are date of birth, time of the accident, and date of last payment. The datetime variables usually contain a multitude of different labels corresponding to a specific combination of date and time. We do not utilize the datetime variables in their raw format when building machine learning models. Instead, we enrich the dataset dramatically by deriving multiple features from these variables. In this chapter, we will learn how to derive a variety of new features from date and time.

This chapter will cover the following recipes:

  • Extracting date and time parts from a datetime variable
  • Deriving representations...
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