Comparative Crime Analysis and Prediction Using Machine Learning Algorithms: Assessing the Tools and Addressing the Threats

As the human population continues to grow, the need for proactive security strategies is becoming more urgent than before. The increasing number of violent crimes, including homicides, assaults, and battery, as well as property crimes like burglaries, thefts, and motor vehicle thefts, compounds this issue. Crime prediction is a promising field that can assist law enforcement in making tactical decisions to improve policing strategies and policies. This paper presents a comparative analysis of four predictive supervised learning algorithms that forecast crime by learning social-economic and demographic attributes from event reports in a community to forecast crime events falling under four categories: felonies, misdemeanors, infractions, and violations, for all months in the year 2022. The author also examined ethical concerns related to the adoption of crime prediction technology and the potential threats it poses to society.