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Predicting Employee Attrition in an Organization Through Advanced Data Mining Technique
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 187 - Number 17 |
| Year of Publication: 2025 |
| Authors: Yasmin P. Zacarias, Millbert S. Secretario, Dexter C. Macaraeg, Tracy Anne M. Agbuya, Jenniea A. Olalia, Maynard Gel F. Carse |
10.5120/ijca2025925267
|
Yasmin P. Zacarias, Millbert S. Secretario, Dexter C. Macaraeg, Tracy Anne M. Agbuya, Jenniea A. Olalia, Maynard Gel F. Carse . Predicting Employee Attrition in an Organization Through Advanced Data Mining Technique. International Journal of Computer Applications. 187, 17 ( Jun 2025), 28-35. DOI=10.5120/ijca2025925267
Abstract
Employees play a big role in an organization, and they greatly contribute to its success and functioning. Every level from operational tasks to strategic decision-making, their collective efforts contribute to achieving the organization’s mission, vision, and objectives. If the attrition rate of employees continuously increases that will be a big problem for the company. Understanding and forecasting turnover at the firm and departmental levels is essential for reducing attrition as well as for effectively planning, budgeting, and recruiting in the human resource field [6]. Advanced data mining techniques help organizations predict attrition proactively to address workforce stability by leveraging insights derived from historical data. In this study, the proponents identified key predictors of employee attrition using feature selection methods, specifically Recursive Feature Elimination (RFE) and SelectKBest. After evaluating both methods with Random Forest and SVM models, the Random Forest model combined with RFE achieved the highest overall performance with an accuracy of 84.2% and a precision of 0.700. This combination offered the most reliable balance, making it a valuable tool for organizations to more accurately identify potential attrition risks.
References
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