Predicting Employee Performance in Business Environments Using Effective Machine Learning Models

—The management of companies places great emphasison human resources, seeking to choose highly skilled employeeswho can perform above and beyond expectations. As managersand decision-makers attempt to devise plans for locating anddeveloping exceptional talent, human resources management(HRM) has become a crucial area of interest. A key concern liesin enhancing the performance of employees through professionalskill development programs. The goal of employee performancereviews is to gauge each employee's level of dedication to thebusiness. A company's ability to forecast employee performance iscritical to its success. This study's objective was to investigate thefactors influencing employee performance prediction in theworkplace using ML techniques. This project aims to provideimproved employee performance forecast accuracy andperformance via the use of state-of-the-art ML techniques.Utilising a Human Resources dataset from Kaggle, the researchinvolves meticulous data preprocessing steps, including balancingis conducted using SMOTE. Two machine learning models—Gradient Boosting and Extra Trees—are implemented andevaluated with hyperparameter optimisation techniques such asOptuna, Bayesian optimisation, and Randomized Search. Thecomparative analysis reveals that both models achieve highperformance metrics, with Gradient Boosting slightlyoutperforming with an accuracy0.962, precision0.955,recall0.967, and F1-score0.961. This study offers significantinsights for future research, demonstrating an effectiveness ofusing sophisticated ML algorithms for optimising and forecastingemployee performance in human resource management.