International Journal on Science and Technology

E-ISSN: 2229-7677   •   Impact Factor: 9.88

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Analyzing Employee Attrition Drivers: The Impact of Burnout Through Predictive Models

Author(s) HENO MERLIN C P S, Dr. JAYASREE KRISHNAN
Country India
Abstract Employee burnout has become an organizational crisis because of its relation with increasing attrition and decreasing productivity. Predictive analytical modeling was used in the current study to capture the effects of burnout on employee turnover. Demographics include age, marital status, job type, and years of service, whereas burnout indicators consist of emotional exhaustion, physical fatigue, frustration, and job satisfaction. These variables entered the machine learning models developed in Python to identify the patterns and predictors of employee departure. With the data analysis framework approach, the modeling is started with data preprocessing and exploratory data analysis, followed by feature selection and classification. In this study, we analyze the predictive abilities of various algorithms: Random Forest, Logistic Regression, and Support Vector Machine. Much in line with several research works, it shows a quantitative relationship between burnout dimensions and self-reported turnover intentions. To facilitate decisions, an interactive Power BI dashboard was constructed visualizing the profile of high-risk employees and the burnout patterns leading to attrition; the results were then deployed to substantiate targeted interventions.
Keywords Employee Attrition, Employee Burnout, Predictive Analytics, Machine Learning, HR Analytics, Power BI
Published In Volume 16, Issue 2, April-June 2025
Published On 2025-06-15
DOI https://doi.org/10.71097/IJSAT.v16.i2.6248
Short DOI https://doi.org/g9qqzh
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