Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Haque, Mustafizul; Paralkar, Tejasvini Alok; Rajguru, Sudhir; Goyal, Adheer A.; Patil, Tanaya; Upreti, Kamal Title A name given to the resource Featuring Machine Learning Models to Evaluate Employee Attrition: A Comparative Analysis of Workforce Stability Relating Factors Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived International Research Journal of Multidisciplinary Scope;Volume;6;Issue;2;pp.862-873 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.47857/irjms.2025.v06i02.03512" target="_blank" rel="noreferrer noopener">https://doi.org/10.47857/irjms.2025.v06i02.03512</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105006820774?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105006820774?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Haque M., Centre for Online Learning, Dr. D. Y. Patil Vidyapeeth, Pune (Deemed to be University), Pune, India; Paralkar T.A., Symbiosis Centre for Management Studies, Symbiosis International (Deemed University), Nagpur Campus, India; Rajguru S., Department of Management, IIMT, Greater Noida, India; Goyal A.A., School of Commerce &amp; Management, GH Raisoni University, Saikheda, India; Patil T., Department of Management, Sanjivani University, Maharashtra, India; Upreti K., Department of Computer Science, CHRIST (Deemed to be University), Delhi NCR, Ghaziabad, India Description An account of the resource Employee attrition is a problem for most organizations as it affects morale, productivity, and business continuity. In addressing this, the study made use of machine learning techniques such as Clear AI, Random Forest, and logistic regression in designing a prediction model to predict who is the next to leave within an organization. The HR data relating to demographics, performance metrics, job roles, and conditions of work was sourced from publicly available website Kaggle.com for the study. Data preprocessing included scaling, outlier detection, and balancing the dataset using SMOTE. Multiple machine learning models were trained and evaluated by checking on accuracy, F1-score, and the ROC-AUC curve. The best model that was tested was Random Forest, which gave an accuracy of 85.71%. Additional insights from feature importance highlighted the significant effect of overtime, marital status, and stock options on attrition. Among the remaining key drivers are workload, work-life balance, and financial incentives. These findings suggest the need for focused HR strategies, such as reduction of overtime, mentorship programs, and career development opportunities, to reduce attrition rates and improve employee satisfaction. This study provides a robust methodology in predicting attrition and delivers actionable insights into designing interventions that improve workforce stability and organizational efficiency. 2025, Iquz Galaxy Publisher. All rights reserved. Subject The topic of the resource Employee Attrition; Features Importance; Human Resources; Machine Learning Models; Organizations Publisher An entity responsible for making the resource available Iquz Galaxy Publisher Relation A related resource ISSN: 2582631X; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Gold Open Access Format The file format, physical medium, or dimensions of the resource online