Abstract

This project aims to develop a predictive system capable of identifying crime types and predicting their occurrences based on historical crime data. The system uses advanced machine learning techniques to analyze factors such as geographic location, time, and other socio-economic variables, enabling authorities to better understand crime patterns and trends. By training models on vast datasets of past criminal activities, the system predicts not only the likely occurrence of specific crime types but also identifies high-risk locations and times, empowering law enforcement to allocate resources more effectively and proactively prevent crime. Various machine learning algorithms, including Decision Trees, Random Forests, and Support Vector Machines (SVM), are employed to ensure accuracy and robustness. The system's predictions can significantly enhance crime management and law enforcement strategies by offering insights into crime prevention and mitigation efforts. This approach facilitates informed decision-making and fosters safer communities by providing accurate forecasts based on data-driven insights. The project underscores the potential of machine learning in public safety and law enforcement, transforming crime prediction and management from reactive to proactive.