AI-Powered Local Crime Prediction

The world has seen rising crime more than ever make the most of it, making old-fashioned means of crime identification and prevention insufficient. AI-driven crime prediction models are one such solution, capable of processing past and real-time data to predict potential crimes. This paper investigates the following models AI and machine leaning model like Random Forest, SVM and Neural Networks for crime prediction. We cover data preprocessing, model selection, evaluation metrics, as well as ethical implications of predictive policing. Experimental results show that the predictive accuracy and forecasting of crime trend has improved. This research's result recommendations that AI-based crime prediction systems can help law enforcement agencies deploy human resources and avert crime when it is committed. The future research directions concentrate on improving the interpretability of the models, minimizing bias, and incorporating new data streams like social media, IoT devices, etc., into crime forecasting models. This thus, is a manuscript to connect the dots between theoretical constructs proposed by AI models and real world implementation in predictive policing, thereby bringing a new capability to the law enforcement agencies across the world.