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In today’s IT world, the major concern is employee attrition rate. The attrition rate can be defined as the percentage of employees who left the organization. The aim of this research work is to analyse whether a particular employee will continue in the organization or not. The discontinuity of an employee can be done by either up to the individual or due to organization force. To predict attrition rate, we have used different machine learning techniques. The steps are dataset collection, pre-processing the data, training model using machine learning classification algorithms like Random Forest, decision tree classifier, etc. and result analysis. The results are evaluated using accuracy score and confusion matrix. In particular, the Random Forest classifier with feature reduction achieved an accuracy score of 85.3%, while the Decision tree classifier achieved 83%. Random Forest model giving best classification results. This work will help organizations to better understand the causes of attrition.

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Prediction of Employee Attirition Using Machine Learning Algorithms

  • 会议论文

  • 发表日期：

    2025年

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  • Ch. V. Satyanarayana,
  • S. N. Tirumala Rao,
  • Sireesha Moturi,
  • M. Sathyam Reddy,
  • Sneha Ananya Mallipeddi,
  • Sandeep Mallipeddi

摘要

In today’s IT world, the major concern is employee attrition rate. The attrition rate can be defined as the percentage of employees who left the organization. The aim of this research work is to analyse whether a particular employee will continue in the organization or not. The discontinuity of an employee can be done by either up to the individual or due to organization force. To predict attrition rate, we have used different machine learning techniques. The steps are dataset collection, pre-processing the data, training model using machine learning classification algorithms like Random Forest, decision tree classifier, etc. and result analysis. The results are evaluated using accuracy score and confusion matrix. In particular, the Random Forest classifier with feature reduction achieved an accuracy score of 85.3%, while the Decision tree classifier achieved 83%. Random Forest model giving best classification results. This work will help organizations to better understand the causes of attrition.