In this section, we reintroduce kernel density estimation (KDE). When using kernel density estimation, we are attempting to reveal the shape of a dataset with a limited amount of information. Also, in this section, we're going to investigate which movies in the dataset have the highest rating; we're going to compute the KDE of a select group of movies using their rating; and, finally, compute the KDE overlap of two movies.
Let's go back to our MovieLens dataset notebook and import Data.Maybe, as shown in the following example:
If you recall, this library is used in our KDE function. So, we are going ...
