This week, I focused on cleaning the dataset and beginning exploratory data analysis. After dropping or imputing missing values and normalizing the text entries, I moved into visualizations. It was enlightening to see trends appear—certain states had significantly more incidents, and specific demographic groups were overrepresented. This helped guide which features to include in the model and gave me a better grasp of the problem space. With clean data and exploratory insights, I began model building. I used logistic regression and decision trees to predict the likelihood of a fatal police shooting based on features like age, race, armed status, and location. It took some iteration to tune the models, but seeing prediction accuracy improve over time helped solidify my understanding of classification techniques and their evaluation metrics like precision and recall.