Analyzing Crime Patterns Using Data Science with Full Stack Web Development

Abstract

Crime analysis plays a crucial role in supporting law enforcement agencies by identifying trends, predicting risk zones, and assisting in strategic resource deployment. This project integrates data science techniques with full stack web development to build an intelligent, interactive crime analysis platform. The system collects crime data from public datasets, preprocesses it, and applies analytical and predictive models to uncover meaningful insights. Through visual dashboards, heatmaps, and temporal charts, users can explore crime patterns across locations and time. The platform aims to support decision-making, enhance public awareness, and facilitate preventive measures using data-driven intelligence. Overall, the system bridges advanced analytics with an accessible web interface to enable real-time and historical crime pattern exploration.