Optimizing Employee Promotion Decisions: A Novel Machine Learning Framework for Predictive Analysis by using GBM CatBoost

In the evolving landscape of human resource management, making informed promotion decisions is crucial for organizational success. Traditional methods often rely on subjective assessments, leading to potential biases and inefficiencies. This paper introduces a novel machine learning framework for optimizing employee promotion decisions using Gradient Boosting Machines (GBM) with a focus on CatBoost. The proposed approach leverages the powerful capabilities of CatBoost, which excels in handling categorical features and offers robustness against overfitting, to predict promotion outcomes with high accuracy. We conduct an extensive analysis on a real-world dataset, where features such as employee performance metrics, tenure, and behavioral indicators are incorporated to build a predictive model. The framework demonstrates superior performance in identifying promotionworthy candidates by reducing classification errors and improving decision-making transparency. Our results indicate that the CatBoost-based model significantly outperforms traditional methods and other machine learning algorithms in terms of predictive accuracy, precision, and recall. Furthermore, the model’s ability to provide interpretable insights into the factors influencing promotion decisions makes it a valuable tool for HR professionals seeking to enhance fairness and efficiency in promotion processes. This study highlights the potential of machine learning frameworks like GBM with CatBoost in transforming HR practices and contributing to a more data-driven organizational culture. The results indicate that the CatBoost model significantly outperforms other GBM models like XGBoost and LightGBM, as well as traditional models like Logistic Regression and Random Forest, in predicting employee promotions