The Stanford Open Policing Project (SOPP) aims to bring transparency to police interactions by collecting and analyzing data on traffic stops across the United States. It accumulates a vast dataset on traffic stops, encompassing details such as demographics, location, and outcomes. The primary goal is to utilize advanced data analysis techniques to reveal patterns and trends within the dataset. This involves employing clustering methods, such as K-means and Hierarchical Clustering, to categorize regions or demographic groups based on specific factors. These factors included but are not limited to race, time of day, location, and outcome of traffic stops.

▪ Thoroughly understood the structure of the dataset and familiarized myself with the provided data dictionary.

▪ Identified key variables that may contribute to understanding policing patterns.

▪ Implemented both K-means and Hierarchical Clustering algorithms to group similar demographic profiles.

▪ Evaluated the clusters based on relevant features and assess their significance.

▪ Utilized visualizations such as heatmaps, scatter plots, and bar charts to present my findings effectively.

▪ Provided insights into the characteristics of each cluster and highlighted notable disparities.

▪ Prepared a well-commented notebook containing the clustering models and the final list of clusters.

▪ Created a presentation summarizing the problem statement, the analysis approach, and the most crucial findings.

▪ Included visuals to enhance the presentation and make it accessible to a non-technical audience.

▪ Suggested areas for improvement in policing and areas where additional data collection may be beneficial.