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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 2. Chapter 2: Encoding Categorical Variables FREE CHAPTER 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

Creating Features from a Time Series with tsfresh

Throughout this book, we’ve discussed feature engineering methods and tools tailored for tabular and relational datasets. In this chapter, we will shift our focus to time-series data. A time series is a sequence of observations taken sequentially over time. Examples include energy generation and demand, temperature, air pollutant concentration, stock prices, and sales revenue. Each of these examples represents a variable and their values change over time.

The widespread availability of affordable sensors capable of measuring motion, movement, humidity, glucose, and other parameters has significantly increased the amount of temporally annotated data. These time series can be utilized in various classification tasks. For instance, by analyzing the electricity usage pattern of a household at a given time interval, we can infer whether a particular appliance was being used. Similarly, the signal of an ultrasound sensor can help...

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