AI and HR Technologies
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Abstract
Abstract
Employee attrition remains a major challenge for modern organizations, particularly in competitive sectors like IT and healthcare.The evolving field of predictive analytics offers powerful tools for understanding and mitigating this issue.This research investigates the use of machine learning algorithms-including Logistic Regression, Decision Trees, Support Vector Machines (SVM), and XGBoost-to predict employee attrition with high accuracy.Using a sample of 50 professionals and applying supervised learning models in Python, the study identifies key predictors such as job satisfaction, career advancement, overtime status, compensation, and tenure.XGBoost demonstrated the highest accuracy (87%), affirming the potential of data-driven decisionmaking in Human Resource Management (HRM).The research highlights actionable predictors such as job satisfaction, career growth, overtime, and compensation, offering data-driven pathways for HR decisionmaking.This study underscores the transformative potential of predictive analytics in workforce planning and talent retention.
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@article{scollr2026PREDICTING,
title = {PREDICTING EMPLOYEE ATTRITION USING PREDICTIVE ANALYSIS},
journal = {International Research Journal of Modernization in Engineering Technology and Science},
year = {2026},
doi = {10.56726/irjmets79450},
url = {https://doi.org/10.56726/irjmets79450}
}FAQ
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