This week, I implemented K-Means clustering on the spatial data. I used the geopy package to calculate real-world distances and applied the elbow method to find the ideal number of clusters. The curve flattened around k=4, so I proceeded with that. After fitting the model, I interpreted the clusters: one group concentrated in Northern U.S., another in Central, one in the South, and one covering scattered high-density zones. I assigned colors to each cluster and visualized them using matplotlib. This confirmed geographic differences in the type and intensity of political events across regions. It was exciting to finally see structure emerge from the noise.