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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

Technical requirements

In this chapter, we will use the open source tsfresh Python library. You can install tsfresh with pip by executing pip install tsfresh.

Note

If you have an old Microsoft operating system, you may need to update the Microsoft C++ build tools to proceed with the tsfresh package’s installation. Follow the steps in this thread to do so: https://stackoverflow.com/questions/64261546/how-to-solve-error-microsoft-visual-c-14-0-or-greater-is-required-when-inst.

We will work with the Occupancy Detection dataset from the UCI Machine Learning Repository, available at http://archive.ics.uci.edu/ml/datasets/Occupancy+Detection and licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license: https://creativecommons.org/licenses/by/4.0/legalcode. The corresponding citation for this data is as follows:

Candanedo, Luis. (2016). Occupancy Detection. UCI Machine Learning Repository. https://doi.org/10.24432/C5X01N.

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