Cover
Vol. 1 No. 1 (2025)

Published: June 30, 2025

Pages: 31-37

Original Article

Utilizing Machine Learning Algorithms and SMOTE for Analyzing and Predicting Homicides

Hussain Younis 1 Ghazwan abdulnabi 2 Israa Hayder 3 Sani Salisu 4 Maged Nasser 5
1School of Computer Sciences, Universiti Sains Malaysia, Gelugor 11800, Malaysia, & College of Education for Women, University of Basrah, Basrah 61004, Iraq
2Department of Computer Science (Educational Science), University of Basrah, 61004, Basrah, Iraq
3Department of Computer Systems Techniques, Qurna Technique Institute, Southern Technical University, Basrah, 61016, Iraq
4Pakistan Department of International Relations, Faculty of Pakistan Studies, Lahore Garrison University, Lahore-Pakistan
5Positive Computing Research Group, Institute of Autonomous Systems, Department of Computing, Universiti Teknologi PETRONAS, 32610 Seri Iskandar, Perak, Malaysia
History
  • Received: June 14, 2025
  • Revised: June 19, 2025
  • Accepted: June 23, 2025
  • Available Online: June 30, 2025
DOI

https://doi.org/10.33971/ijmrai.1.1.5

Abstract

This study analyzes homicide data in the United States from 1980 to 2014 using machine learning techniques to predict crime resolution and classify victim gender. The dataset, obtained from the FBI Supplementary Homicide Report, contains 638,454 records. Data preprocessing involved cleaning, converting categorical features to numerical values, and addressing class imbalance using Synthetic Minority Oversampling Technique (SMOTE). Various classification algorithms were applied, including Decision Tree and Naïve Bayes. The results showed that the Decision Tree model achieved 95% accuracy in predicting crime resolution and 85% accuracy in classifying victim gender, while Naïve Bayes reached 92% accuracy in crime resolution prediction. The findings highlight the effectiveness of machine learning in crime pattern analysis and prediction, aiding law enforcement in making more informed investigative decisions.

Keywords

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