I’m currently working on drafting a comprehensive report highlighting key findings and implications from my in-depth analysis of the Washington Post’s police shooting dataset. This report aims to provide impactful punchlines on what the data indicates about this societally important issue. In my analysis so far, I have utilized various statistical techniques and machine learning algorithms to uncover insights within the expansive dataset. Specifically, I used K-nearest neighbors (KNN) analysis to identify factors that may predict fatal police shooting occurrences. My report will highlight notable punchlines from this analytical work using KNN and other methods, such as quantifying the significance of factors like race, mental illness, and location on predicting shooting outcomes. I will also outline punchy implications from the data for policy and policing practice reform. However, I want to ensure punchlines are substantiated by the rigorous methodology undertaken. The report will include details on the data cleaning, validation, hypothesis testing, and modeling processes that lend credibility to the conclusions reached. My goal is a report that delivers punchy, data-driven insights on where reform efforts should be targeted, while transparently conveying the analytical work done. Impactful punchlines grounded in statistical rigor have the power to convince stakeholders and drive real change.