Most of the complexity in this example was just in determining our distance metric, and you know we intentionally got a little bit fancy there just to keep it interesting, but you can do anything else you want to. So, if you want fiddle around with this, I definitely encourage you to do so. Our choice of 10 for K was completely out of thin air, I just made that up. How does this respond to different K values? Do you get better results with a higher value of K? Or with a lower value of K? Does it matter?

If you really want to do a more involved exercise you can actually try to apply it to train/test, to actually find the value of K that most optimally can predict the rating of the given movie based on KNN. And, you can use different distance metrics, I kind of made that up too! So, play around the distance metric, maybe you can use different sources of information, or weigh things differently. It might be an interesting thing to do. Maybe, popularity isn't really as important as the genre information, or maybe it's the other way around. See what impact that has on your results too. So, go ahead and mess with these algorithms, mess with the code and run with it, and see what you can get! And, if you do find a significant way of improving on this, share that with your classmates.

That is KNN in action! So, a very simple concept but it can be actually pretty powerful. So, there you have it: similar movies just based on the genre and popularity and nothing else. Works out surprisingly well! And, we used the concept of KNN to actually use those nearest neighbors to predict a rating for a new movie, and that actually worked out pretty well too. So, that's KNN in action, very simple technique but often it works out pretty darn good!