ENHANCING PUBLIC SAFETY FORECASTING USING MACHINE LEARNING ALGORITHMS

Abstract

Enhancing public safety forecasting has become increasingly important with the rapid growth of urban populations and the complexity of modern security challenges. This study presents a machine learning-based approach to predict and analyze potential public safety risks using historical and real-time data. The proposed system leverages advanced algorithms such as Random Forest, Support Vector Machines, and Deep Learning models to identify patterns in crime data, environmental conditions, and social indicators. By integrating data preprocessing, feature engineering, and predictive modeling, the system provides accurate forecasts of high-risk areas and time periods.