1.
Shobhanam Krishna
– Indian Institute Of Management Shillong, Shillong, India.
2.
Ashutosh Bishnu Murti And Rohit Dwivedi
– Indian Institute Of Management Shillong, Shillong, India.
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.]
