Enhancing Employee Performance in Dynamic Business Environments Through Machine Learning Approaches

Abstract

Employee performance prediction is among the most significant undertakings within dynamic business environment to enable the organization to optimize the management of its human resources and improve its productivity. This paper presents an Extreme Gradient Boosting (XGBoost)-based predictive model on the Kaggle Human Resource dataset, which includes 35 variables in 1,470 participants. Data pretreatment procedures contain handling of outliers, missing, or negative values, one-hot encoding of categorical data, normalization to minmax, and feature selection using Principal Component Analysis (PCA). The data was split into two sets: training and testing, after the Synthetic Minority Oversampling Technique (SMOTE) was used to fix the problem of class imbalance. They used standard performance metrics including F1-score, recall, accuracy, precision, and ROC-AUC to train and evaluate the proposed XGBoost model. With an F1-score of 98.66, an accuracy of 97.87, and a precision and recall of 99.9%, the model achieved impressive results in the testing. This means it can outperform other predictive models. These results indicate the strength, scalability, and reliability of the suggested strategy in predicting employee performance, and providing meaningful actionable data to support data-driven human resource planning and organizational development.