Let's apply the concept of item-based collaborative filtering. To start with, movie similarities-figure out what movies are similar to other movies. In particular, we'll try to figure out what movies are similar to Star Wars, based on user rating data, and we'll see what we get out of it. Let's dive in!

Okay so, let's go ahead and compute the first half of item-based collaborative filtering, which is finding similarities between items. Download and open the SimilarMovies.ipynb file.

In this case, we're going to be looking at similarities between movies, based on user behavior. And, we're going to be using some real movie rating data from the GroupLens project. GroupLens.org provides real movie ratings data, by real people who are using the MovieLens.org website to rate movies and get recommendations back for new movies that they want to watch.

We have included the data files that you need from the GroupLens dataset with the course materials, and the first thing we need to do is import those into a Pandas DataFrame, and we're really going to see the full power of Pandas in this example. It's pretty cool stuff!