After cleaning and pre-processing, I implemented K-means clustering on the event locations. Using the Geopy library for geodesic distance and running the elbow method, we chose 4 as the optimal number of clusters. The results clearly grouped the country into different conflict zones based on event types. This week helped deepen my understanding of unsupervised learning and clustering concepts.
This week’s focus was on interpreting the clusters and understanding what each one represented. Northern regions mostly experienced violence against civilians, while central regions had more protests. I found it interesting how clustering highlighted patterns that might be hidden in raw data. We also visualized the clusters on maps, which made our results easier to communicate to others.