Feature Engineering Made Easy

Feature Construction

In the previous chapter, we worked with the Pima Indian Diabetes Prediction dataset to get a better understanding of which given features in our dataset are most valuable. Working with the features that were available to us, we identified missing values within our columns and employed techniques of dropping missing values, imputing, and normalizing/standardizing our data to improve the accuracy of our machine learning model.

It is important to note that, up to this point, we have only worked with features that are quantitative. We will now shift into dealing with categorical data, in addition to the quantitative data that has missing values. Our main focus will be to work with our given features to construct entirely ...