Web-Based Employee Promotion Prediction Using Random Forest Classifier

Employee promotion pays a significant role toward contributing to the success and downfall of any organization. Ensuring a fair promotion prediction system is a key for proper employees’ retention and career development. Employee promotion plays an important extrinsic motivation for employees’ engagement and commitment to the organization which indirectly contributes to the continuity of their performance. For this reason, we adapted machine learning algorithms to be used by human resource (HR) managers or any person responsible for making decisions related to employee promotion. In this work, we used employee prediction dataset and applied four machine learning algorithms: random forest (RF), support vector machine (SVM), decision tree classifier (DTC), and logistic regressor (LR). Random forest returned the highest performance with 95% accuracy, 96% precision, 96% recall, and 95% F1-score.