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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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Applying Mathematical Computations to Features

New features can be created by combining two or more variables. Variables can be combined automatically or by using domain knowledge of the data and the industry. For example, in finance, we combine information about the income and the acquired debt to determine the disposable income:

disposable income = income - total debt.

Similarly, if a client has debt across many financial products, for example, a car loan, a mortgage, and credit cards, we can determine the total debt by adding all of those variables up:

Total debt = car loan balance + credit card balance + mortgage balance

In the previous examples, the mathematical functions used to combine the existing variables are derived via domain knowledge of the industry. We can also combine variables automatically, by creating polynomial combinations of the existing variables in the...

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