Abstract
This study develops a data-driven framework for predicting vehicle theft using machine-learning techniques applied to a comprehensive crime dataset containing demographic, spatial ,and operational policing variables. The dataset, sourced from publicly available crime records, included 9,712 cases and 30 variables, with minimal missing data and balanced class distribution. After performing extensive preprocessing—including outlier analysis, missing-value imputation, categorical encoding, feature selection, and correlation testing—four supervised classification models were implemented: Logistic Regression, Random Forest, Support Vector Machine, and a Neural Network. Bivariate statistical tests, including Chi-square and Mann–Whitney U, revealed significant relationships between theft occurrence and factors such as crime area, luxury vehicle density, police response time, and distance to the nearest police station. These insights guided the feature selection process before model development. Model performance was evaluated using accuracy, precision, recall, F1-score, ROC curve, and confusion matrices. Among the four models, the Neural Network demonstrated the highest predictive performance, achieving an ac curacy of 92%,alongside strong precision and recall values. The model showed a well-balanced ability to correctly classify both theft and non-theft cases while minimizing false negatives, which is operationally critical in crime-prevention contexts. ROC analysis further confirmed its robustness, with an AUC score near optimal discrimination levels. The findings highlight the effectiveness of machine-learning approaches—particularly Neural Networks—in forecasting vehicle theft patterns. The study provides a practical, interpretable, and operationally relevant framework to support law-enforcement agencies in resource allocation, hotspot identification, and strategic crime-prevention planning.
Publication Date
12-2025
Document Type
Thesis
Student Type
Graduate
Degree Name
Data Science (MS)
Department, Program, or Center
Graduate Programs & Research
Advisor
Sanjay Modak
Advisor/Committee Member
Hammou Messatfa
Recommended Citation
Almutawa, Hamad, "achine Learning Approaches for Predicting and Preventing Vehicle Theft" (2025). Thesis. Rochester Institute of Technology. Accessed from
https://repository.rit.edu/theses/12377
Campus
RIT Dubai
