Crime data analysis and prediction of arrest using machine learning

Sankul, Revanth and Aruva, Tejaswi Reddy and Kankal, Sai Varun and Arrapogula, Greeshma and Mohammed, Shoeib Khan (2025) Crime data analysis and prediction of arrest using machine learning. World Journal of Advanced Research and Reviews, 25 (2). pp. 498-506. ISSN 2581-9615

In order to forecast future criminal activity and improve law enforcement tactics, crime data analysis and arrest prediction entails looking at past crime data to find patterns and trends. This area analyzes a variety of variables, including time, place, demography, and the kinds of crimes committed, using statistical methods, machine learning algorithms, and data mining. The objective is to give law enforcement organizations useful information so they may better allocate resources, determine crime, and enhance public safety. It entails combining data from multiple sources, such as arrest logs, crime reports, socioeconomic information, and even environmental elements like urbanization and weather trends. To comprehend how crime trends change over time, sophisticated analytical methods such as random forest is used in predicting the arrests.