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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
Languages
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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 pandas, matplotlib, and scikit-learn Python libraries. We will also use NLTK, a comprehensive Python library for NLP and text analysis. You can find the instructions to install NLTK at http://www.nltk.org/install.html.

If you are using the Python Anaconda distribution, follow the instructions to install NLTK at https://anaconda.org/anaconda/nltk.

After you have installed NLTK, open up a Python console and execute the following:

import nltk
nltk.download('punkt')
nltk.download('stopwords')

These commands will download the necessary data for you to be able to run the recipes in this chapter successfully.

Note

If you haven’t downloaded these or the other data sources necessary for NLTK functionality, NLTK will raise an error. Read the error message carefully because it will direct you to download the data required to run the command that you are trying to execute.

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