Paper Title
Predicting Employee Performance in Business Environments Using Effective Machine Learning Models
Article Identifiers
Registration ID: IJNRD_300317
Published ID: IJNRD2409098
: https://doi.org/10.5281/zenodo.13771036
About Hard Copy and Transparent Peer Review Report
Authors
Keywords
Employee performance, human resources dataset, extra trees, gradient boosting, machine learning, Bayesian optimization, optuna, randomize search
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.
Downloads
How To Cite (APA)
Himanshu Sinha (September-2024). Predicting Employee Performance in Business Environments Using Effective Machine Learning Models . INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(9), a875-a881. https://doi.org/10.5281/zenodo.13771036
Issue
Volume 9 Issue 9, September-2024
Pages : a875-a881
Other Publication Details
Paper Reg. ID: IJNRD_300317
Published Paper Id: IJNRD2409098
Research Area: Science and Technology
Author Type: Indian Author
Country: Gwalior, MP, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2409098.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2409098
Crossref DOI: https://doi.org/10.5281/zenodo.13771036
About Publisher
Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)
UGC CARE JOURNAL PUBLICATION | ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016
An International UGC CARE JOURNAL PUBLICATION, Low Cost, Scholarly Open Access, 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
Publisher: IJNRD (IJ Publication) Janvi Wave | IJNRD.ORG | IJNRD.COM | IJPUB.ORG
Copyright & License
© 2026 - Authors hold the copyright of this article. This work is licensed under a Creative Commons Attribution 4.0 International License. and The Open Definition.
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0).
🛡️ Disclaimer: The content, data, and findings in this article are based on the authors’ research and have been peer-reviewed for academic purposes only. Readers are advised to verify all information before practical or commercial use.
The journal and its editorial board are not liable for any errors, losses, or consequences arising from its use.
Publication Timeline
Article Preview: View Full Paper
Call For Paper
IJNRD is a Scholarly Open Access, Peer-Reviewed, Refereed, and UGC CARE Journal Publication with a High Impact Factor of 8.76 (calculated by Google Scholar & Semantic Scholar | AI-Powered Research Tool). It is a Multidisciplinary, Monthly, Low-Cost, and Transparent Peer Review Journal Publication that adheres to the New UGC CARE Transparent Peer-Reviewed Journal Policy and aligns with Scopus Journal Publication standards to ensure the highest level of research quality and credibility.
IJNRD offers comprehensive Journal Publication Services including indexing in all major databases and metadata repositories, Digital Object Identifier (Crossref DOI) assignment for each published article with additional fees, citation generation tools, and full Open Access visibility to enhance global research reach and citation impact.
The INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (IJNRD) aims to advance applied, theoretical, and experimental research across diverse academic and professional fields. The journal promotes global knowledge exchange among researchers, developers, academicians, engineers, and practitioners, serving as a trusted platform for innovative, peer-reviewed journal publication and scientific collaboration.
Indexing Coverage: Google Scholar, SSRN, ResearcherID-Publons, Semantic Scholar (AI-Powered Research Tool), Microsoft Academic, Academia.edu, arXiv.org, ResearchGate, CiteSeerX, ResearcherID (Thomson Reuters), Mendeley, DocStoc, ISSUU, Scribd, and many other recognized academic repositories.
Transparent Peer Review Journal Publication: IJNRD operates a strict double-blind peer review system managed by 3000+ expert reviewers, ensuring ethical, unbiased, and high-quality review for every research paper.
For Indian Authors : Get a transparent peer review report from Scholar9.com for just ₹1000. View Sample Report
For Foreign Authors : A detailed peer review report is available through Scholar9.com for $20 USD. View Sample Report
Transparent Peer Review Journal Publication
⭐ Transparent Peer Review | 🕵️♂️ Double-Blind | 👨🏫 3000+ Expert Reviewers | 🇮🇳 Report for India Author ₹1000 | 🌐 Report for Foreign Author $20 | 📄 Sample Reports on Scholar9.com | 🌍 High Credibility | ⚖️ Ethical & Unbiased Evaluation
How to submit the paper?
By Our website
Click Here to Submit Paper Online
Recently, the UGC discontinued the UGC-CARE Journal List and introduced new parameters that allow publication in Transparent Peer-Reviewed (Refereed) Journals. IJNRD is Transparent Peer Review Journal Valid As per New UGC Notification.
You can now publish your research paper in IJNRD.ORG. IJNRD is a Transparent Peer-Reviewed Open Access (Refereed Journal), UGC and UGC CARE Approved, Crossref DOI, Multidisciplinary, Impact Factor calculate by Google Scholar. As an International, open-access, and online journal, Publishing with us ensures wider reach, academic credibility, and enhanced recognition for your work.
For more details, refer to the official notice: UGC Public Notice
⭐ Low Cost ₹1570 | 📚 UGC CARE Approved | 🔍 Peer-Reviewed | 🌐 Open Access | 🔗 Crossref DOI & Global Indexing | 📊 Google Scholar Impact Factor | 🧪 Multidisciplinary
Submit Paper Online Call for Paper About IJNRD UGC CARE Approval
Important Dates for Current issue
Paper Submission Open For: February 2026
Current Issue: Volume 11 | Issue 2 | February 2026
Impact Factor: 8.76
Last Date for Paper Submission: Till 28-Feb-2026
Notification of Review Result: Transparent peer review process - your paper is evaluated by experts, and you receive acceptance or rejection updates via email and SMS.
Publication of Paper: Once all documents are submitted, your paper is published without delay, and you can instantly download your certificate and confirmation letter online.
Frequency: Monthly (12 issue Annually).
Journal Type: IJNRD is an international open-access journal offering Low Cost Journal Publication, transparent Peer Review Journal Publication, Crossref DOI, and multidisciplinary research visibility under UGC CARE Approved Journal Publication.
Subject Category: Research Area
Approval, Licenses and Indexing: More Details
Call For Paper - Volume 11 | Issue 2 | February 2026
IJNRD.org offers low-cost journal publication starting at ₹1570 with UGC CARE Approved, refereed, peer-reviewed, open-access publishing. This multidisciplinary monthly journal, available in both online and print formats, features a strong Google Scholar-based impact factor of 8.76, Transparent Peer Review, CrossRef DOI, global indexing, fast publication, and complete metadata for maximum research visibility and citation impact across multidisciplinary domains.
Volume 11 | Issue 2 | February 2026 | IJNRD Transparent Peer Review Certificate | Submit Paper Online
⭐ UGC CARE Approved Refereed Journal | 🔍 Transparent Peer Review | 🌐 Open Access Publishing | 💰 Low-Cost ₹1570 | 🔗 CrossRef DOI & Global Indexing | 📊 Google Scholar Impact Factor 8.76 | 🧪 Multidisciplinary | Online & Print
Submit Paper Online
