Main Article Content
Property crime is an increasing concern worldwide and one which causes trauma to victims and puts pressure on law enforcement agencies. One essential weapon in the fight against property crime is that of effective forecasting which helps law enforcement task forces to put in place effective preventative measures. Researchers and law enforcers now choose to harness the power of modern technology, namely artificial intelligence, to help them to predict property crime rates and to therefore create proactive preventative solutions. The objective of this study is to perform a comparative analysis on three different artificial intelligence techniques which are; Random Forest Classifier (RFC), Gradient Tree Boosting (GTB) and Support Vector Regression (SVR). These techniques are applied to four separate crime types in the USA in order to compare and contrast in terms of quantitative measurement of error. The results of the study show that GTB is the most effective method as it produced the lowest error measurements and the highest level of forecast accuracy in comparison to RFC and SVR
TURCOMAT publishes articles under the Creative Commons Attribution 4.0 International License (CC BY 4.0). This licensing allows for any use of the work, provided the original author(s) and source are credited, thereby facilitating the free exchange and use of research for the advancement of knowledge.
Detailed Licensing Terms
Attribution (BY): Users must give appropriate credit, provide a link to the license, and indicate if changes were made. Users may do so in any reasonable manner, but not in any way that suggests the licensor endorses them or their use.
No Additional Restrictions: Users may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.