Intelligent Crime Investigation Assistance Using Machine Learning Classifiers on Crime and Victim Information

In order to establish peace and justice in a society, it is essential to make proper and correct investigation of crime incidents. With the expansion of the utilization of computerized system to track crime and violence, computer applications can help law enforcement officers in a significant way. In most cases, crime incidents are kept in police database and these can be used for various helpful purpose. In this experiment, we have collected data of crime scenario from Bangladesh Police that had features such as area of crime, type of crime, number of victims and so on. Then we applied machine learning algorithms on the dataset for prediction of some attributes such as criminal age, sex, race, crime method etc. We used four different algorithms for our research: K-Nearest Neighbor (KNN), Logistic Regression (LR), Random Forest Classifier (RFC), Decision Tree Classifier (DTC). Using the aforementioned algorithms with 10 fold cross validation, we achieved different accuracy from all four attribute labels ranging from an average of approximate 75% to an average of approximate 90%. Despite the clear need of further improvement, the results give clear implications that it is possible to achieve well performing automated system for suspect attribute prediction with further work. Finally, we ended the research by comparing and analyzing all the achieved results.