<![CDATA[With the advancement of information technology and data analytics, new opportunities arise in decision-making within the field of human resources. The implementation of ensemble methods, such as extremely randomized trees, holds promise in predicting promotion decisions with higher accuracy. Despite numerous studies in this domain, there is a need for a deeper exploration in adapting ensemble methods to the context of employee promotions, considering various factors like skills, experience, and individual achievements. This research aims to develop an effective predictive model for employee promotions by integrating extremely randomized trees with relevant human resources data. The model derived from this study achieved an accuracy rate of 99% on the training data, indicating highly precise predictions. The use of test data confirmed the model's performance with an accuracy score of 98%, illustrating its consistent predictive capabilities. Factors such as avg_training_score, department, total_score, age, and sum_metric were identified as the most influential in shaping the model, providing crucial insights for human resources management.]]>
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