Abstract
The Smart AI Resume Analyzer project leverages Natural Language Processing (NLP) and Machine Learning (ML) to automatically parse, extract, and match candidate skills and experience against job descriptions, resulting in a data-driven compatibility score that significantly reduces screening time and minimizes human bias in the talent acquisition process. The recruitment process is often hindered by the manual, time-consuming, and subjective screening of a high volume of job applications. This traditional method is prone to human error, unconscious bias, and inefficiency, which can result in overlooking qualified candidates and delaying the time-to-hire. The manual screening of thousands of resumes for a single job opening is a time-consuming, resource-intensive, and inherently subjective process, often leading to potential human bias and the oversight of qualified candidates. This paper introduces a novel Smart AI Resume Analyzer (SARA) system designed to automate the initial screening phase. SARA leverages Natural Language Processing (NLP) and Machine Learning (ML) models to effectively parse, extract, and match candidate information against specific job description requirements. Key features include multi-attribute skill extraction, contextual keyword matching, and a data-driven candidate scoring mechanism. Experimental results demonstrate that SARA significantly reduces the screening time by over $80\%$ compared to manual methods, while achieving an accuracy of $92\%$ in identifying best-fit candidates. This system offers a scalable, efficient, and objective solution to optimize talent acquisition and recruitment processes.
Citations
IRE Journals:
Surwase Vinayak Bhagwat, Kumbhar Avinash Bansi, Prof. J. M. Shaikh, Prof. A. S. Hangargekar "Smart AI Resume Analyzer" Iconic Research And Engineering Journals Volume 9 Issue 5 2025 Page 2121-2126 https://doi.org/10.64388/IREV9I5-1712383
IEEE:
Surwase Vinayak Bhagwat, Kumbhar Avinash Bansi, Prof. J. M. Shaikh, Prof. A. S. Hangargekar
"Smart AI Resume Analyzer" Iconic Research And Engineering Journals, 9(5) https://doi.org/10.64388/IREV9I5-1712383
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