Crime Pulse: Crime hot-spot detection using Machine Learning and Gen-AI*

The aim of this research is to determine the accuracy of machine learning algorithms in identifying high-crime regions. Traditional methods of locating crime hotspots, such manual analysis, are challenging and prone to errors. Machine learning offers a more accurate and effective approach. The study procedure includes preprocessing, determining pertinent characteristics, and collecting crime data. A range of machine learning algorithms that predict the likelihood of crime in different places will be trained using this data. The performance of these models will be evaluated using the pertinent metrics. The expected outcomes include demonstrating the value of machine learning in identifying crime hotspots, identifying suitable algorithms, gaining a better understanding of the factors that lead to hotspots, and developing a practical tool for law enforcement. The goal of this research is to make society safer and more secure by utilizing machine learning. Law enforcement organizations may more effectively deploy resources, discourage criminal activity, and eventually lower crime rates by accurately and efficiently identifying high-crime areas.