Volume 9, Issue 4 (April 2023)

Forecasting Employee Turnover

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Volume 12 Issue 10

October 2026

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Author(s)

P Sruthi Naeemali Ahamed Pavin shaji Ashid A P | Prof. S Kavitha

Abstract

Supervised Machine Learning Methods Are Described, Demonstrated And Assessed For The Prediction Of Employee Turnover Within An Organization. In Our Project, Numerical Experiments For Real And Simulated Human Resources Datasets Representing Organizations O

Keywords

Machine Learning Employee Turnover Random Forest Logistic Regression Attrition Rate

Paper ID

IJSARTV9I460404

Publication Date

April 10, 2023

Research Area

Computer Science

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