High Impact Factor : 7.883
Submit your paper here

Impact Factor

7.883


Call For Paper

Volume 10 Issue 12 December 2024


Download Paper Format


Copyright Form


Share on

FORECASTING EMPLOYEE TURNOVER

  • Author(s):

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

  • Keywords:

    Machine Learning, Employee Turnover, Random Forest, Logistic Regression, Attrition Rate

  • 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

Other Details

  • Paper id:

    IJSARTV9I460404

  • Published in:

    Volume: 9 Issue: 4 April 2023

  • Publication Date:

    2023-04-10


Download Article