Data preparation is the process of transforming raw data into learning algorithms.

In some cases, data preparation is a required step in order to provide the data to an algorithm in its required input format. In other cases, the most appropriate representation of the input data is not known and must be explored in a trial-and-error manner in order to discover what works best for a given model and dataset.

Max Kuhn and Kjell Johnson have written a new book focused on this important topic of data preparation and how to get the most out of your data on a predictive modeling project with machine learning algorithms. The title of the book is “Feature Engineering and Selection: A Practical Approach for Predictive Models” and it was released in 2019.

In this post, you will discover my review and breakdown of the book “Feature Engineering and Selection” on the topic of data preparation for machine learning.

Let’s dive in!

Feature Engineering and Selection (Book Review)

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