A Comparative Study of Decision Tree and Naive Bayes Machine Learning Model for Crime Category Prediction in Chicago

Abstract

Crimes have a massive effect on individuals regards their minds, concern, and psyche, along with their indisputable impact on society. As a result, law enforcement keeps monitoring their control areas so they would notice any suspicious activity, which makes them more cautious and raises their ability to prevent potential criminal activity. This study aims to create a machine learning model that will be able to predict the potential crime category in a particular geographic area, by exploring and analyzing the existing repeated incidents data. Decision Tree and Naive Bayes are applied on a dataset, which was extracted from the Chicago Police Department's CLEAR. By applying these techniques, law enforcement agencies will highly benefit as they will be able to predict the potential crimes, which will increase the ability to prevent them. We have applied the two algorithms on the selected top 9 features from the chosen dataset. However, comparing the two algorithms, Decision Tree performed better than Naïve Bayes algorithm.