This mini-project focuses on three learning objectives: 1. How to visualize and understand geographical data in an interactive way with Python. 2. How the K-Means algorithm works, and some of the shortcomings it has. 3. Density-based clustering approaches, and how to deal with any outliers they may classify.
We take raw geographical data, and cluster it effectively using basic or more advanced density-based clustering techniques--- and determine the strength of a given clustering.