This week, Final tuning and testing of the model were done this week. I compared multiple models and evaluated them using confusion matrices and ROC curves. We selected the best-performing model and generated predictions for unseen data. It was interesting to note how model performance varied depending on the features included. By now, the results were consolidated into a clean report with graphs and interpretation. Project 1 is complete!,
For Project 2, I began analyzing the ACLED US dataset on political violence. I noticed right away that it was much larger and more diverse in terms of event types. The data required geolocation checks, and several columns needed encoding or transformation. I also started thinking about which clustering method would suit our problem—K-means seemed like a strong candidate due to the spatial nature of the data.