So what I want you to do for an activity is to try a different value of k and see what you end up with. Just eyeballing the preceding graph, it looks like four would work well. Does it really? What happens if I increase k too large? What happens to my results? What does it try to split things into, and does it even make sense? So, play around with it, try different values of k. So in the n_clusters() function, change the 5 to something else. Run all through it again and see you end up with.

That's all there is to k-means clustering. It's just that simple. You can just use scikit-learn's KMeans thing from cluster. The only real gotcha: make sure you scale the data, normalize it. You want to make sure the things that you're using k-means on are comparable to each other, and the scale() function will do that for you. So those are the main things for k-means clustering. Pretty simple concept, even simpler to do it using scikit-learn.

That's all there is to it. That's k-means clustering. So if you have a bunch of data that is unclassified and you don't really have the right answers ahead of time, it's a good way to try to naturally find interesting groupings of your data, and maybe that can give you some insight into what that data is. It's a good tool to have. I've used it before in the real world and it's really not that hard to use, so keep that in your tool chest.