Crime Forecasting and Analysis: A Machine Learning Perspective

Predicting crime remains a major challenge for ensuring community safety, as traditional methods often fail to capture the complexities of modern urban environments. This research proposes a comprehensive machine learning and time series-based framework to enhance crime prediction capabilities. The study has three primary objectives: (1) to use supervised learning algorithms to predict crime types based on historical data, including features such as time, location, and previous crime patterns; (2) to apply spatial analysis techniques to identify and map high-risk crime zones using the Indian Crime Dataset; and (3) to forecast future crime trends through time series modeling, thereby supporting more effective resource allocation and strategic planning by law enforcement agencies. By integrating data-driven approaches, this research aims to improve the accuracy and reliability of crime forecasting models. The framework also evaluates the performance of different machine learning models to determine their suitability for large-scale urban crime analysis, with the ultimate goal of strengthening proactive crime prevention strategies and enhancing public safety outcomes.