Optimizing Safe Routes with Machine Learning: A Crime Prediction System for Urban Safety

In urban environments, ensuring safe routes for commuting and travel is a growing concern. This study introduces a machine learning-based crime prediction system aimed at optimizing safe routes in cities by forecasting crime trends. By utilizing historical crime data, which includes categories such as murder, rape, kidnapping, and theft, the system identifies crime-prone areas and generates safety scores for various routes. The proposed model leverages predictive analytics to assess future crime probabilities, enabling urban dwellers and authorities to make informed decisions about safe navigation. This tool is particularly useful for law enforcement to allocate resources efficiently and for residents seeking safer pathways during their daily routines. Ultimately, this crime prediction system provides a data-driven approach to enhance urban safety and community well-being.