ItemArticle

Featuring Machine Learning Models to Evaluate Employee Attrition: A Comparative Analysis of Workforce Stability Relating Factors

Title
Featuring Machine Learning Models to Evaluate Employee Attrition: A Comparative Analysis of Workforce Stability Relating Factors
Creator
Haque, Mustafizul; Paralkar, Tejasvini Alok; Rajguru, Sudhir; Goyal, Adheer A.; Patil, Tanaya; Upreti, Kamal
Description
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.
Source
International Research Journal of Multidisciplinary Scope;Volume;6;Issue;2;pp.862-873
Date
01-01-2025
Publisher
Iquz Galaxy Publisher
Subject
Employee Attrition; Features Importance; Human Resources; Machine Learning Models; Organizations
Coverage
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 & 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
Rights
All Open Access; Gold Open Access
Relation
ISSN: 2582631X;
Format
online
Language
English
Type
Article

Citation

Haque, Mustafizul; Paralkar, Tejasvini Alok; Rajguru, Sudhir; Goyal, Adheer A.; Patil, Tanaya; Upreti, Kamal, “Featuring Machine Learning Models to Evaluate Employee Attrition: A Comparative Analysis of Workforce Stability Relating Factors,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 19, 2026, https://archives.christuniversity.in/items/show/23632.

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