We're obsessed with grouping things together. We self-select each other into groups based on which political candidate we support, which sports team we root for, and which arbitrary country we're born in. People also spend hours on the internet arguing over "tiers", or groupings, of their favorite athletes and sports teams. For example, which NBA players are "elite" vs. "great" vs. just "good"? Did Carmelo Anthony belong on the Banana Boat ? When engaging in these arguments, we typically use statistics like points or rebounds per game to back up our points, but at the end of the day, the groups are more or less kind of arbitrary. But what if there was a way to algorithmically sort observations into groups based on shared characteristics using machine learning methods? Enter clustering , which is the methodology of grouping similar observations into groups, or "clusters", using a mathematical distance metric derived from a set