Dimension Reduction and Intuitive Feature Engineering for Machine Learning

Editor’s Note: This is the fourth in a four-part series on improving analytics output with feature engineering. Click here to read previous entries in the series. In the previous parts of this series, we looked at an overview of some popular tricks for feature engineering, and examined those tricks in greater detail. In this part, we continue our closer examination of these approaches with a deeper dive into the final techniques described in Part 1. The examples discussed in this article can be reproduced with the source code and datasets available here. Reducing Dimensionality As an analyst, you savor the scenario in which you have a lot of data. But, with a lot of data comes the added complexity of analyzing and making better sense of that data. Often, the variables within the…


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