Optimizing Employee Promotions: A Machine Learning Approach Using AdaBoost

For many companies, deciding on employee promotions is challenging due to subjective judgments and financial constraints. Many employees demand higher salaries and promotions after working for a few years; they are not satisfied with the status quo. However, companies are not willing to give promotions to employees easily, which will lead to a higher expenditure for the enterprise and greater economic pressures. This paper introduces a statistical analytics model to provide objective recommendations for employee promotions, thus minimizing biases and enhancing decision-making processes. The model can help the human resources department to recognize outstanding employees at the same time. With the accumulation of more data and the iteration of models, promotion strategies can be continuously adjusted and optimized to more accurately reflect employees' abilities and performance. Utilizing a comprehensive dataset, including age, service length, awards, and performance ratings, we applied multiple predictive modelsBagging, Random Forest, AdaBoost, Gradient Boosting, and Logistic Regression, to find significant correlations between specific employee attributes and promotion likelihood, highlighting the critical role of training scores. The implementation of automated predictive models has not only reduced the HR department's workload but also improved the overall efficiency of the promotion process by enabling continuous adjustments and optimizations based on data insights.