Feature Engineering and Feature Selection with Python
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With recent developments in big data, we’ve been given more access to data in general and high-dimensional data.
Consequently, the performance of machine learning models has improved by a large margin.
On the other hand, there are significant features often collected or generated by different sensors and methods that can influence the model accuracy in a harmful way that needs careful consideration, not only that, but these features can demand a lot of computational resources to build and maintain the model.
For that, we need handy processes that contribute to the machine learning pipeline to build great models even with these kinds of features.
In this book about feature engineering and feature selection techniques for machine learning with python in a hands-on approach, we will explore pretty much all you’ll need to know about feature engineering and feature selection.
Specifically, we’ll learn how to modify dataset variables to extract meaningful information to capture as much insight as possible, filter out unneeded features leaving datasets and their variables ready to be used in machine learning algorithms.
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