Predictive Crime Rate Analysis System

Abstract: Crime prediction plays a crucial role in enhancing urban safety and aiding law enforcement agencies in proactive decision-making. This study presents a predictive crime rate analysis system utilizing advanced machine learning techniques to analyse historical crime data and forecast future crime trends. The proposed approach integrates multiple models, including Long Short-Term Memory (LSTM), Support Vector Machines (SVM), Decision Trees, and Random Forest, to identify patterns and correlations across various crime types, time periods, and geographic locations. Feature engineering techniques are employed to preprocess data, handle missing values, and extract key insights. The system provides real-time crime risk assessments, helping law enforcement optimize resource allocation and strategic planning. Additionally, a web-based platform is developed to enhance accessibility, allowing users to visualize crime predictions based on time, location, and crime category. Experimental results demonstrate the effectiveness of the proposed model, achieving high accuracy in crime prediction and forecasting. This research contributes to improving urban safety by leveraging data-driven insights for crime prevention and law enforcement strategies.