Predictive Modeling of Employee Attrition: Insights and Strategies for Enhanced Retention in the Era of the Great Renegotiation
Published: 2025
Author(s) Name: Shobhanam Krishna, Ashutosh Bishnu Murti and Rohit Dwivedi |
Author(s) Affiliation: Indian Institute of Management Shillong, Shillong, India.
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Abstract
The contemporary employment landscape, with traditional lifelong employment yields to heightened job mobility and diverse career trajectories. This paradigm shift has exacerbated employee attrition, presenting organizations worldwide with a complex and multifaceted challenge. This study aims to develop a predictive model employing decision tree algorithms to forecast employee attrition
and identify critical factors influencing turnover. Using a dataset from a Frenchbased manufacturer, the research applies supervised learning techniques to examine key predictors. The decision tree
model, optimized through GridSearchCV, achieved an exceptional ROC-AUC score of 0.97, demonstrating robust predictive capabilities. The findings reveal that prolonged tenure, absence of promotions,
and declining job satisfaction significantly contribute to employee turnover.]
Keywords: N.A.
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