Crime Type Prediction in Saudi Arabia Based on Intelligence Gathering

Abstract

Abstract One of the biggest social problems currently facing major cities around the globe is the high rate of crime. The largest part of the social-economic loss globally is ascribed to criminal activities. Crime also has direct impacts on the nation’s economy, social constructs and country’s global repute. Inadequate policing capital is one of the biggest challenges facing many global economies. As a result, these resources have to be rationed. This implies that some areas will not be covered extensively thus providing favorable environs for perpetrators. To combat crime, more innovative security measures are needed. In this sense, traditional methods are being replaced with modern approaches of machine learning systems that can predict the occurrence of crime. These crime forecasts can be used by legislatures and law enforcers to make effective and informed approaches that can efficiently eradicate criminals and facilitate nation building. This paper seeks to review the literature on the application of machine learning models in crime prediction and to find the influences that have an impact on crimes in Saudi Arabia. The results show that after the four models were trained and tested, the random forest classifier had the highest accuracy of 97.84%.