Behavioral Analysis and Crime Severity Prediction using Machine Learning

Abstract

Crime analysis and prediction involve a systematic methodology for identifying patterns and anticipating potential criminal activity. This study presents a system capable of predicting regions with a high likelihood of crime occurrence and visualizing crime-prone areas. By leveraging data mining techniques, it becomes possible to uncover previously unknown yet valuable insights from unstructured datasets. These insights are derived by analyzing existing crime data to forecast future trends.

Crime remains a serious and widespread social issue, adversely impacting the quality of life, economic stability, and global reputation of nations. To ensure public safety and maintain social order, there is a growing need for intelligent and data-driven systems that can enhance crime analytics and support preventive measures.

This project proposes a solution designed to analyze, detect, and predict the probability of various types of crimes within specific regions. It highlights the use of multiple data mining methods for conducting different types of crime analysis and predictions. Through such an approach, communities and law enforcement agencies can be better equipped to address criminal activities with greater efficiency and effectiveness