Abstract: This study aims to find the most accurate algorithm that is capable of predicting crimes in Dubai. It compares models on a dataset of sample crimes in the Emirate of Dubai, United Arab Emirates using the open-source data mining software WEKA, which enabled us to use Random Forest, KNN, SVM, ANN, Naïve Bayes and Decision Tree, We chose those algorithms as former studies that were effective used them. We have applied the algorithms on a dataset containing 13440 Major Crime in four categories occurred between 2014 and 2018. After comparing the models and analyzing their success rates, we identified the ideal algorithms and evaluated the effectiveness of variables in making predictions by measuring the correlation coefficients. One of the study's most crucial recommendations is to increase the variables and data, also adding more details about the crime, the criminal, and the victim. These variables make an impact on the analysis and the ultimate prediction.

Shaikha Khamis AlAbdouli, Ahmad Falah Alomosh, Ali Bou Nassif and Qassim Nasir. “Comparison of Machine Learning Algorithms for Crime Prediction in Dubai”. International Journal of Advanced Computer Science and Applications (IJACSA) 14.9 (2023). http://dx.doi.org/10.14569/IJACSA.2023.0140918

@article{AlAbdouli2023,
title = {Comparison of Machine Learning Algorithms for Crime Prediction in Dubai},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0140918},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0140918},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {9},
author = {Shaikha Khamis AlAbdouli and Ahmad Falah Alomosh and Ali Bou Nassif and Qassim Nasir}
}