Predicting Employee Performance in Business Environments Using Effective Machine Learning Models

—The management of companies places great emphasis
on human resources, seeking to choose highly skilled employees
who can perform above and beyond expectations. As managers
and decision-makers attempt to devise plans for locating and
developing exceptional talent, human resources management
(HRM) has become a crucial area of interest. A key concern lies
in enhancing the performance of employees through professional
skill development programs. The goal of employee performance
reviews is to gauge each employee's level of dedication to the
business. A company's ability to forecast employee performance is
critical to its success. This study's objective was to investigate the
factors influencing employee performance prediction in the
workplace using ML techniques. This project aims to provide
improved employee performance forecast accuracy and
performance via the use of state-of-the-art ML techniques.
Utilising a Human Resources dataset from Kaggle, the research
involves meticulous data preprocessing steps, including balancing
is conducted using SMOTE. Two machine learning models—
Gradient Boosting and Extra Trees—are implemented and
evaluated with hyperparameter optimisation techniques such as
Optuna, Bayesian optimisation, and Randomized Search. The
comparative analysis reveals that both models achieve highperformance metrics, with Gradient Boosting slightly
outperforming with an accuracy0.962, precision0.955,
recall0.967, and F1-score0.961. This study offers significant
insights for future research, demonstrating an effectiveness of
using sophisticated ML algorithms for optimising and forecasting
employee performance in human resource management.