Proceedings of the International Conference on Advanced Materials, Manufacturing and Sustainable Development (ICAMMSD 2024)

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ML-Driven Predictive Analytics for Effective Hiring Under HR Practices

Authors
B. Swathi1, *, T. Swathi2, M. Rudra Kumar3
1Assistant Professor, Department of CSE, G. Pulla Reddy Engineering College, Kurnool, AP, India
2Associate Professor, Department of CSE, G. Pulla Reddy Engineering College, Kurnool, AP, India
3Professor, Mahatma Gandhi Institute of Technology, Hyderabad, Telangana, India
*Corresponding author. Email: bswathi.cse@gprec.ac.in
Corresponding Author
B. Swathi
Available Online 17 March 2025.
DOI
10.2991/978-94-6463-662-8_35How to use a DOI?
Keywords
XGBoost; Machine Learning; Human Resources (HR); Effective Hiring; Artificial Intelligence; Natural Language Processing
Abstract

This article explores the transformative potential of ML-Driven Predictive Analytics for Effective Hiring in modern Human Resources practices. In today’s dynamic recruitment landscape, traditional methods often face challenges of inefficiency and bias, necessitating innovative solutions to streamline talent acquisition processes. Leveraging machine learning algorithms, this approach offers a comprehensive and data-driven framework for candidate evaluation, surpassing conventional criteria to assess holistic attributes such as soft skills, cultural fit, and long-term potential. By mitigating bias and enhancing efficiency, ML-driven predictive analytics not only accelerates the hiring process but also elevates the quality of talent acquired, fostering diversity, equity, and inclusion within organizations. This article underscores the significance of embracing ML-driven approaches as a strategic imperative for HR professionals, poised to revolutionize hiring practices and drive organizational success in the digital era.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Advanced Materials, Manufacturing and Sustainable Development (ICAMMSD 2024)
Series
Advances in Engineering Research
Publication Date
17 March 2025
ISBN
978-94-6463-662-8
ISSN
2352-5401
DOI
10.2991/978-94-6463-662-8_35How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - B. Swathi
AU  - T. Swathi
AU  - M. Rudra Kumar
PY  - 2025
DA  - 2025/03/17
TI  - ML-Driven Predictive Analytics for Effective Hiring Under HR Practices
BT  - Proceedings of the International Conference on Advanced Materials, Manufacturing and Sustainable Development (ICAMMSD 2024)
PB  - Atlantis Press
SP  - 417
EP  - 426
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-662-8_35
DO  - 10.2991/978-94-6463-662-8_35
ID  - Swathi2025
ER  -