The Use of K-Nearest Neighbour Approach for Crime Rate Prediction in Public Universities: Case Study Adekunle Ajasin University, Akungba–Akoko (AAUA), Ondo State, Nigeria

Abstract

The prediction of crime rates has been a challenging problem for law enforcement agencies and policymakers. The main challenge in developing a predictive model for crime rate is the unavailability and quality of data. Crime data is often incomplete, inconsistent, and biased, which has always affected the accuracy of the scholars’ designed model. For example, the use of traditional methods such as manual crime analysis and statistical analysis are often limited by the volume and complexity of the data. The need for a more accurate and efficient predictive model that can handle large and complex datasets is increasing. This research developed a predictive model using the KNN algorithm that is into accurate prediction of crime rates and helps policymakers to make better decisions. The KNN algorithm was implemented using Python programming language with scikit-learn libraries. The primary dataset was collected from security units of Adekunle Ajasin University, Akungba Akoko and Akungba police station. The Algorithm was implemented on a Laboratory Local Area Network at the Information and Communication Technology Application Centre (ICTAC), Adekunle Ajasin University, Akungba Akoko. The performance evaluation was based on precision, recall, and F1 score. The results show that the KNN algorithm can accurately predict crime rates with an average accuracy of 88%. The model demonstrates that the KNN algorithm can be a useful tool for predicting crime rates. In addition, the model is hoped to enhance crime prevention strategies, reduce crime rates, and ultimately improve the quality of life in university communities. Keywords: Crime, Crime Rate, Prediction, K-Nearest Neighbour, Public Universities. Akinwumi, D.A. (2023): The Use of K-Nearest Neighbour Approach for Crime Rate Prediction in Public Universities: Case Study Adekunle Ajasin University, Akungba–Akoko (AAUA), Ondo State, Nigeria. Journal of Advances in Mathematical & Computational Science. Vol. 11, No. 2. Pp 11-24. dx.doi.org/10.22624/AIMS/MATHS/V11N2P1. Available online at www.isteams.net/mathematics-computationaljournal.