Predicting Employee Performance in Business Environments Using Effective Machine Learning Models

Predicting Employee Performance in Business Environments Using Effective Machine Learning Models

Registration ID: IJNRD_300317

Published ID: IJNRD2409098

DOI: https://doi.org/10.5281/zenodo.13771036

Himanshu Sinha

Employee performance, human resources dataset, extra trees, gradient boosting, machine learning, Bayesian optimization, optuna, randomize search

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.

Published Paper   E-Certificate

"Predicting Employee Performance in Business Environments Using Effective Machine Learning Models ", IJNRD - INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (www.IJNRD.org), ISSN:2456-4184, Vol.9, Issue 9, page no.a875-a881, September-2024, Available :https://ijnrd.org/papers/IJNRD2409098.pdf

Volume 9 Issue 9, September-2024

Pages : a875-a881

Paper Reg. ID: IJNRD_300317

Published Paper Id: IJNRD2409098

Downloads: 000121273

Research Area: Science and Technology

Country: Gwalior, MP, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2409098.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2409098

DOI: https://doi.org/10.5281/zenodo.13771036

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

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This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition

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