Smart Security Analysis System: A Machine Learning-Based Framework for Crime Prediction and Visualization

Abstract

Addressing the fluid and complex nature of urban crime requires sophisticated, data-centric approaches for the effective distribution of security assets. This research proposes a practical framework that blends open-source crime statistics with machine learning algorithms and geospatial mapping to facilitate operational command. We developed a cohesive Python-based interface capable of processing real-time inputs, performing predictive analytics, and visualizing spatial trends. By utilizing a Random Forest regression model, the system calculates a localized "Risk Index," forecasting crime density relative to specific environmental and temporal variables. Additionally, incidents are stratified by severity levels to refine situational assessment. The distinct value of this study lies not in algorithmic novelty, but in the engineering of standard analytical techniques into a functional decision-support mechanism suitable for security leaders. Experimental validation confirms that the prototype delivers actionable intelligence, underscoring the efficacy of machine learning-driven tools in shaping proactive security operations.