Abstract
The rapid growth of digital recruitment platforms has increased the need for efficient, accurate, and unbiased resume screening mechanisms. This research presents an AI- powered Resume Analyzer designed to automate the evaluation of resumes using advanced Natural Language Processing (NLP) and Machine Learning techniques. The system extracts, preprocesses, and analyses resume content to identify key attributes such as skills, education, experience, and certifications, and matches them against job requirements. By leveraging techniques including text classification, keyword extraction, semantic analysis, and similarity scoring, the proposed model enhances candidate–job alignment while significantly reducing manual effort and screening time. Additionally, the system supports structured analytics and reporting to provide actionable insights for recruiters. Experimental results demonstrate improved accuracy, consistency, and scalability compared to traditional manual screening approaches. The proposed solution aims to assist recruiters in making data-driven hiring decisions while promoting efficiency and fairness in the recruitment process.
Keywords
Resume Parsing, Flask, Gemini API, Skill Gap Analysis, Job Matching, NLP, Resume Scoring
Citations
IRE Journals:
Shrushti Washimkar, Kashish Gour, Kanak Gour, Hushali Bokade, Shrikant Utane; Dr. P. S. Prasad "AI-Powered Resume Analyzer" Iconic Research And Engineering Journals Volume 9 Issue 8 2026 Page 322-328 https://doi.org/10.64388/IREV9I8-1714127
IEEE:
Shrushti Washimkar, Kashish Gour, Kanak Gour, Hushali Bokade, Shrikant Utane; Dr. P. S. Prasad
"AI-Powered Resume Analyzer" Iconic Research And Engineering Journals, 9(8) https://doi.org/10.64388/IREV9I8-1714127
About IRE Journals
IRE Journals is an open access online journals established with an aim to publish high quality of research work in various diciplines. We have more than 6 years in publications. Over 2500+ papers already published.
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