Crime Pattern Analysis and Prediction Using Machine Learning Techniques

This study leverages historical data to examine trends in crime statistics and applies machine learning to forecast the types and occurrences of crimes. Algorithms such as Random Forest and k-Nearest Neighbors (k-NN) are used to categorize different kinds of crimes and predict their probability of occurrence. Law enforcement organizations can gain significant insights by using the model to anticipate high-risk regions by evaluating spatial and temporal crime data. Proactive police resource allocation in high-crime areas is made possible by this prediction technology. Through the integration of real-time data sources, the system can also offer current risk evaluations, enhancing community safety and assisting in the decrease of criminal activity.