Detecting and Predicting Crimes using Data Mining Techniques: Comparative Study

Crime is a major problem in our society where the highest priority is concerned with individuals, society, and government. Thus, it seems important to study factors and relations between the occurrence of different crimes to avoid more upcoming crimes. Crime prediction is a method of trying to study the causes and motives of crime and predict the times and places of its occurrence to reduce the commission of crimes that are expected to occur in the future. Data mining is an important way to facilitate the solution of future crime problems by investigating hidden crime patterns and historical crime data. Therefore, this study aims to analyze and discuss the various factors affecting the commission of crimes and the methods that are applied to predict future crimes and analyze their results. In this study, the model of crime prediction is proposed which is based on some classification algorithms such as (NB, KNN, Decision Tree, random forest, Linear Regression, Logistic Regression, SVM), these classification algorithms are applied to four real data sets (Chicago dataset, Los Angeles dataset, Egypt dataset, United States dataset), Egypt data set was extracted primarily from the online website (Zabatak.com) and comparing between their scores. The experimental results showed that the Random Forest classifier achieves a high score on four data sets compared with other classifiers. Random Forest achieves %88 on the Los Angeles dataset, %92 on the Egypt dataset, %97 on the Chicago dataset, and 81.7% on the United States dataset. Keywords— Crime prediction, data mining, classification, clustering, KNN, NB, SVM