Forecasting Employees’ Promotion Based on Personal Indicators by Using a Machine Learning Algorithm

Promotion is a tool to motivate employees to improve themselves and take on the burden and responsibility of the position assigned to them. Due to the fairness and measurability of promotions conducted by traditional methods needing to be quantifiable, different methods are required. In recent years, with the widespread use of information systems in companies, much information, such as performance data of employees, has started to be stored digitally. Additionally, with the development of data sciences and their application in many fields, machine learning and artificial intelligence algorithms in evaluating this data have become widespread. This study aims to establish a robust framework to predict employee promotions based on various features. These features include but are not limited to the number of training sessions attended, previous year ratings, tenure, awards received, and average training scores. The study aims to provide organizations with a reliable tool to make informed promotion decisions and demonstrate that this framework can be generalized to other prediction problems. Experimental results show that the XGBoost model is the most efficient in terms of accuracy. XGBoost is considered a superior algorithm with 94% accuracy, 94% ROC AUC, 94% sensitivity, and 94% precision, excelling in memory usage efficiency, accuracy, and runtime.