Abstract

This study explores the issue of employee attrition within the military police force, with a focus on predicting which employees are at risk of leaving. Attrition in law enforcement is costly and disruptive, especially in roles that require long training periods and operational readiness. The research specifically targets the internal organizational factors influencing turnover in military police environments and aims to leverage machine learning models to support the early identification of high-risk individuals. Guided by the CRISP-DM framework, the study followed a structured process across six stages: business understanding, data exploration, preparation, modeling, evaluation, and insight generation. A simulated dataset of 2,500 military employee records was analyzed, including variables such as age, rank, years of service, job satisfaction, fitness readiness, and promotion history. Statistical tests like Chi-Square and Mann-Whitney U were used to identify significant features contributing to attrition. Five machine learning models were built—Logistic Regression, Random Forest, Support Vector Machine, Artificial Neural Network, and XGBoost. Among these, XGBoost outperformed others in recall, F1-score, and accuracy, making it the best model for predicting attrition. Key findings showed that salary, years of service, and educational level were the strongest predictors of attrition, while promotion and fitness readiness played a smaller role than expected. The study concludes that machine learning can be a powerful tool for supporting HR decision-making in law enforcement settings. The models developed in this research can help organizations implement early interventions, tailor retention strategies, and better allocate resources. However, the study was limited to internal data and did not include external factors like economic trends or personal motivations. Future research could expand on this by including broader variables and real institutional data from other military entities across the GCC region.

Library of Congress Subject Headings

Labor turnover--Data processing; Employment forecasting; United Arab Emirates--Officials and employees--Turnover--Data processing

Publication Date

5-2025

Document Type

Thesis

Student Type

Graduate

Degree Name

Professional Studies (MS)

Department, Program, or Center

Graduate Programs & Research

Advisor

Sanjay Modak

Advisor/Committee Member

Hammou Messatfa

Campus

RIT Dubai

Plan Codes

PROFST-MS

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