Clustering with k-means

k-means is one of the most popular clustering algorithms (if not the most popular) among data scientists due to its simplicity and high performance. Its origins date back as early as 1956, when a famous mathematician named Hugo Steinhaus laid its foundations, but it was a decade later that another researcher called James MacQueen named this approach k-means.

The objective of k-means is to group similar data points (or observations) together that will form a cluster. Think of it as grouping elements close to each other (we will define how to measure closeness later in this chapter). For example, if you were manually analyzing user behavior on a mobile app, you might end up grouping customers who log in quite frequently, or users who make bigger in-app purchases, together. This is the kind of grouping that clustering algorithms such as k-means will automatically find for you from the data.

In this chapter, we will be working with an open source dataset...