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Python Feature Engineering Cookbook

Python Feature Engineering Cookbook

By : Galli
3.6 (9)
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Python Feature Engineering Cookbook

Python Feature Engineering Cookbook

3.6 (9)
By: Galli

Overview of this book

Feature engineering is invaluable for developing and enriching your machine learning models. In this cookbook, you will work with the best tools to streamline your feature engineering pipelines and techniques and simplify and improve the quality of your code. Using Python libraries such as pandas, scikit-learn, Featuretools, and Feature-engine, you’ll learn how to work with both continuous and discrete datasets and be able to transform features from unstructured datasets. You will develop the skills necessary to select the best features as well as the most suitable extraction techniques. This book will cover Python recipes that will help you automate feature engineering to simplify complex processes. You’ll also get to grips with different feature engineering strategies, such as the box-cox transform, power transform, and log transform across machine learning, reinforcement learning, and natural language processing (NLP) domains. By the end of this book, you’ll have discovered tips and practical solutions to all of your feature engineering problems.
Table of Contents (13 chapters)
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Creating features from transactions with Featuretools

Featuretools is an open source Python library that allows us to automatically create features from time series and transactional databases with multiple transaction records for each specific entity, such as customers. With Featuretools, we can automatically create features at the transaction level. Such features include the day, month, and year from a datetime variable, the time between transactions, or if the transaction occurred on a weekend, as well as the cumulative sum or the difference in value between transactions.

Featuretools also aggregates existing and new features at the entity level—in our example, at the customer level—using mathematical and statistical operations, such as the ones we used in the Aggregating transactions with mathematical operations recipe of this chapter or by...

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