Crime is a social menace that impacts negatively on social economic development of a nation. Crime has been in existence from time immemorial and violent crime is the main enemy of the society. One of the primary responsibilities of any government is security of life and properties which translates to reduction of crime rate and provisioning of adequate security to its citizenry. To this end, government must wake up to its responsibilities by reducing crime rate and provide adequate security to its citizenry through effective, efficient and proactive policing. Any research in this direction that can help in analyzing and predicting the future occurrence of violent crime by using crime dataset is laudable. Predicting future occurrence of crime from crime dataset is well reported in literature, therefore it has become imperative to come up with an overview of the present state of the art on crime prediction and control. The systematic review present in this study focuses on crime prediction and data mining as well as the techniques employed in the past studies. The existing work is classified and grouped into different categories and are presented by using visualization approach. It is found that more studies adopted supervised learning approaches to crime prediction and control compared to other methods. The challenges encountered were also reported. Crime prediction has become hot research area in recent time because of its intending benefits to socio-economic development of a nation.

Crime rate tends to be on the increase across the globe, and crime data analysis becomes imperative to aid predictive policing in tackling incidence of crime. In this paper data mining approach was applied to violent crime dataset for predicting next occurrence of violent crime. Previous researchers have used different supervised learning algorithms for crime prediction with accuracy results left to be improved upon. Consequently, this study particularly apply decision tree C5.0 algorithm on violent crime dataset in order to determine the probability of next occurrence of violent crime in Lagos metropolis. The data used was derived from Nigerian Police statistic department Obalende Lagos, pre-processed and applied on decision tree model built. The model was evaluated using the six violent crime types (murder, arm robbery, kidnapping, rape, non-negligent assault and man slaughter) dataset. The results obtained were evaluated using confusion matric and found to return an accuracy of 76.4% (percent). Based on this result, the model could be used by the Police authority to strategize and plan towards mitigating crime rate in the country.

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