Machine Learning Algorithms for Unbalanced Dataset Promotion Prediction for Employees

Abstract

The practice of giving and accepting promotions is one of the most important in human resources. Management can keep morale and productivity high with the use of a merit-based, open promotion system inside the company. The prospect of a promotion is one of several extraneous reasons that drives many employees. maintaining the worker engaged and committed to the organization is crucial to maintaining his current productivity levels intact. It's a vital tool for motivating workers and monitoring their output. The promotion process takes into account a wide range of factors, including an employee's seniority, performance, competencies, age, awards, training score, and organizational devotion. Workers' promotion needs will be evaluated using Machine Learning methods such Support Vector Machine, Artificial Neural Network, and Random Forest, and a prediction strategy will be explored based on the findings. Accuracy, precision, and recall all improved by 1%, and the f1-score reached a perfect 98% when the ROS technique was combined with the Random Forest algorithm. Human resource professionals and business leaders might use the findings of this study to make more accurate predictions about who and when promotions will occur.

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