Crime Classification Using Machine Learning and Data Analytic

Crimes have both short-term and long-term effects on individuals and on society as a whole. Modern-day law enforcement agencies are making use of data analytics and machine learning algorithms for predictive policing in order to prevent crimes. There is a lack of crime reporting systems in developing countries like Pakistan. This needs improvement because the crime reporting systems serve as a backbone of generating crime data for analysis. To build a crime prediction system, it is advisable to analyze the minimalistic requirements in advance and use similar data sets from other countries for analysis and prediction. This can serve to provide the government and other authorities with insights into the benefits of correcting crime reporting systems and deploying crime prediction systems in the future. This study implements three machine learning algorithms namely Naïve Bayesian Classifier, Decision Tree Classifier, and Random Forest Classifier for predicting the primary crime type, to highlight potential data problems such as a high number of distinct values for features, class imbalance, and lack of independent features, which can be expected in Pakistan crime data in order to build crime prediction systems. The performance of the algorithms to predict crime type is analyzed on the datasets, where, the Random Forest Classifier performed outperformed Naïve Bayes and Decision tree with an accuracy of 55.03% respectively. In its entirety, this study demonstrates that the limitation of the feature set is the primary cause of the low accuracy of predictions and suggests the inclusion of more features to be extracted for improving the accuracy of predictions. Finally, this research work highlights the lack of structured crime data in the country and provides suggestions for the application of this study in Pakistan.