Data-Driven Crime Detection and Prevention Model for Smart Cities Using Random Forest

Rising crime rates leading to detection is a priority for law enforcement. Shortfalls in existing methods are less accuracy and efficient allocation of resources. The proposed machine learning-based crime detection and prevention system aims to improve incident reporting, resource deployment, and crime forecasting. Random Forest, known for its robustness and ability to handle large datasets with multiple variables, was chosen due to its strong predictive power and capacity to minimize overfitting in complex data environments and results show that there is significant improvement in crime prediction, leading to better resource allocation and quicker responses and better accuracy. Moreover, this system fosters transparency and community trust by offering law enforcement and supports the development of smart cities, where technology enhances urban safety and efficiency.