Crime Analysis and Prediction using Machine Learning

Abstract

Data mining and machine learning have become a vital part of crime detection and prevention. The purpose of this paper is to evaluate data mining methods and their performances that can be used for analyzing the collected data about the past crimes. I identified the most appropriate data mining methods to analyze the collected data from sources specialized in crime prevention by comparing them theoretically and practically. Some attributes of this dataset are, gender, age, employment status, crime place. Methods are applied on these data to determine their effectiveness in analyzing and preventing crime. Evaluations on the data showed that the method with a higher performance is “Decision Tree”. This was achieved by some performance measures, such as the number of instances correctly classified, accuracy or precision and recall, that has brought better results compared to other methods. I come to the conclusion that the data mining methods contribute to the predictions on the possibility of occurrence of the crime and as a result in its prevention.