You're reading from Python Feature Engineering Cookbook Over 70 recipes for creating, engineering, and transforming features to build machine learning models

Product type Paperback

Published in Oct 2022

Publisher Packt

ISBN-13 9781804611302

Length 386 pages

Edition 2nd Edition

Languages

Python

Tools

Combine

Concepts

Machine Learning

Author (1):

Soledad Galli

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Table of Contents (14) Chapters

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

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Technical requirements

In this chapter, we will use the pandas, Matplotlib, and scikit-learn Python libraries. We will also use NLTK from Python, a comprehensive library for NLP and text analysis. You can find the instructions to install NLTK here: http://www.nltk.org/install.html. If you are using the Python Anaconda distribution, follow these instructions to install NLTK: 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 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 you are trying to execute.

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You're reading from Python Feature Engineering Cookbook Over 70 recipes for creating, engineering, and transforming features to build machine learning models

Table of Contents (14) Chapters

Technical requirements

Authors (1)

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You're reading from Python Feature Engineering Cookbook Over 70 recipes for creating, engineering, and transforming features to build machine learning models

Table of Contents (14) Chapters

Technical requirements

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