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Python Feature Engineering Cookbook

You're reading from Python Feature Engineering Cookbook

Product type Book

Published in Jan 2020

Publisher Packt

ISBN-13 9781789806311

Pages 372 pages

Edition 1st Edition

Languages

Concepts

Machine Learning

Author (1):

Soledad Galli

Table of Contents (13) Chapters

Preface

1. Foreseeing Variable Problems When Building ML Models

2. Imputing Missing Data

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

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Technical requirements

In this chapter, we will use the Python libraries, pandas and NumPy, and the built-in Python library, datetime. Visit the requirements.txt file in the accompanying Jupyter Notebook to check the library versions that we are using in the recipes.

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Authors (1)

Soledad Galli

Soledad Galli is a lead data scientist with more than 10 years of experience in world-class academic institutions and renowned businesses. She has researched, developed, and put into production machine learning models for insurance claims, credit risk assessment, and fraud prevention. Soledad received a Data Science Leaders' award in 2018 and was named one of LinkedIn's voices in data science and analytics in 2019. She is passionate about enabling people to step into and excel in data science, which is why she mentors data scientists and speaks at data science meetings regularly. She also teaches online courses on machine learning in a prestigious Massive Open Online Course platform, which have reached more than 10,000 students worldwide.

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