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Predictive Analytics for Employee Performance Evaluation using Machine Learning Models: A Data-Driven Approach

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Predictive Analytics for Employee Performance Evaluation using Machine Learning Models: A Data-Driven Approach


Priyanka Ramji Soni



Priyanka Ramji Soni "Predictive Analytics for Employee Performance Evaluation using Machine Learning Models: A Data-Driven Approach" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Special Issue | Advancements and Emerging Trends in Computer Applications - Innovations, Challenges, and Future Prospects, March 2025, pp.1492-1498, URL: https://www.ijtsrd.com/papers/ijtsrd80033.pdf

Conventional employee performance reviews are based on recurring evaluations and subjective assessments, which frequently result in prejudice, inefficiencies, and inconsistent workforce management. Although machine learning and predictive analytics have demonstrated promise in HR analytics, little is known about how they may be used in performance evaluation. The accuracy, objectivity, and real-time decision-making in employee assessments can be improved by using sophisticated machine learning models, such as decision trees, random forests, support vector machines (SVM), and deep learning, according to this study.This study focuses on examining key performance indicators (KPIs), such as productivity measures, peer feedback, and behavioural patterns, to close the gap between conventional evaluation techniques and AI-driven insights. It also looks at how explainable AI (XAI) may guarantee fairness and openness in automated performance evaluations. The results show that incorporating predictive analytics into frameworks for performance evaluations can result in better workforce efficiency, less bias, and more data-driven decision-making. To implement AI-powered HR analytics, this study offers firms a methodical strategy that facilitates more equitable evaluations, improved talent retention, and smart workforce planning.

Predictive Analytics, Machine Learning, Employee Performance Evaluation, Human Resource Analytics, Explainable Artificial Intelligence, Workforce Optimization, and Talent Management are Some of the Index Terms.


IJTSRD80033
Special Issue | Advancements and Emerging Trends in Computer Applications - Innovations, Challenges, and Future Prospects, March 2025
1492-1498
IJTSRD | www.ijtsrd.com | E-ISSN 2456-6470
Copyright © 2019 by author(s) and International Journal of Trend in Scientific Research and Development Journal. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)

International Journal of Trend in Scientific Research and Development - IJTSRD having online ISSN 2456-6470. IJTSRD is a leading Open Access, Peer-Reviewed International Journal which provides rapid publication of your research articles and aims to promote the theory and practice along with knowledge sharing between researchers, developers, engineers, students, and practitioners working in and around the world in many areas like Sciences, Technology, Innovation, Engineering, Agriculture, Management and many more and it is recommended by all Universities, review articles and short communications in all subjects. IJTSRD running an International Journal who are proving quality publication of peer reviewed and refereed international journals from diverse fields that emphasizes new research, development and their applications. IJTSRD provides an online access to exchange your research work, technical notes & surveying results among professionals throughout the world in e-journals. IJTSRD is a fastest growing and dynamic professional organization. The aim of this organization is to provide access not only to world class research resources, but through its professionals aim to bring in a significant transformation in the real of open access journals and online publishing.

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