Transparent Peer Review By Scholar9
Predicting Employee Performance in Business Environments Using Effective Machine Learning Models
Abstract
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 high-performance 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. Keywords—Employee performance, human resources dataset, extra trees, gradient boosting, machine learning, Bayesian optimization, optuna, randomize search
Aravind Ayyagari Reviewer
13 Sep 2024 10:07 AM
Not Approved
Relevance and Originality:
The study is highly relevant to human resource management (HRM), focusing on predicting employee performance using advanced machine learning techniques. The integration of Gradient Boosting and Extra Trees, combined with hyperparameter optimization, offers an original approach to enhancing the accuracy of performance forecasts. The use of a Kaggle dataset and sophisticated methods highlights an innovative application of ML in HRM, addressing a critical need for effective talent management.
Methodology:
The methodology is robust, involving detailed data preprocessing with SMOTE for balancing and the implementation of Gradient Boosting and Extra Trees models. The use of hyperparameter optimization techniques such as Optuna, Bayesian Optimization, and Randomized Search adds rigor to the evaluation process. However, more detailed information on how these techniques specifically influence model performance and a discussion on the choice of hyperparameters would strengthen the methodology section.
Validity & Reliability:
The study's validity is supported by the use of a well-known dataset and state-of-the-art ML models, with high performance metrics reported. Reliability is bolstered by the application of multiple hyperparameter optimization techniques. To enhance the validity and reliability further, the study could include cross-validation results and a comparison with baseline models to demonstrate the robustness of the predictions.
Clarity and Structure:
The article is clearly structured, presenting objectives, methodology, and results in a coherent manner. However, improving clarity by providing more detailed explanations of technical terms and including visual aids like charts or tables would enhance understanding. Better sectioning and summaries of key findings would also aid in presenting complex information more effectively.
Result Analysis:
The result analysis is thorough, showing that Gradient Boosting slightly outperforms Extra Trees with impressive metrics. For deeper insight, the analysis could include a comparison of these results with other models or approaches and discuss the implications of the performance metrics in real-world HRM contexts. Addressing any limitations and the potential impact of model improvements would provide a more comprehensive view of the study's contributions.
IJ Publication Publisher
Ok Sir
Aravind Ayyagari Reviewer