Machine Learning-Based Human Resource Analytics Framework For Employee Attrition Prediction Using Random Forest Model

Authors: Dr. Jermiah Anand Jupalli, Dr. Kiran Koduru

Abstract: The field of Human Resource Management (HRM) has experienced a significant shift in its landscape as Artificial Intelligence (AI) and Machine Learning (ML) technologies have been introduced.The Human Resource Management (HRM) landscape is undergoing significant transformation, with the introduction of Artificial Intelligence (AI) and Machine Learning (ML) technologies. Attrition becomes one of the key challenges in the organizations as it directly affects their productivity, operational efficiency and cost. Employee turnover is influenced by a complex interdependency between employee behavioral and organizational factors that makes it difficult to accurately predict employee turnover with traditional HR management approaches. The paper suggests a Machine Learning-Based Human Resource Analytics Framework based on Random Forest algorithm for intelligent prediction of employee attrition. The proposed framework is based on employee-related attributes including job satisfaction, monthly income, work experience, working overtime, performance rating, and work-life balance that could be used to classify employees likely to quit from the organization. Experiments are performed using the IBM HR Analytics Employee Attrition dataset. The performance of the suggested RF model is compared with Decision Tree, Logistic Regression and Support Vector Machine (SVM) models. Experimental results show that the proposed Random Forest model can accurately predict the disease and the obtained accuracy of 96.8% is better than other models in terms of precision, recall and f1 score. The suggested framework helps HR units in taking strategic decisions regarding employee retention and workforce management.

DOI: http://doi.org/10.5281/zenodo.20178703