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Author(s):

Kamale D.S. | Kamale D.S. | Nagargoje D.G. | Jagadale M.S. | Shelke M.R.
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Abstract:
The Rapid Digitalization Of Recruitment Has Created The Need For Automated, Accurate, And Unbiased Resume Screening Systems. This Research Presents An AI-powered Resume Analyzer That Utilizes Natural Language Processing (NLP), Machine Learning (ML), And Semantic Matching Techniques To Evaluate Resumes Efficiently. The System Extracts Key Information Such As Skills, Experience, Education, And Achievements Using Text-processing Algorithms And Transforms Them Into Structured Data. A Machine-learning–based Relevance Model Then Compares Candidate Profiles With Job Descriptions To Generate A Match Score, Highlight Missing Skills, And Provide Improvement Suggestions. The Proposed System Reduces Manual Screening Time, Enhances Decision-making Accuracy, And Minimizes Human Bias. Experimental Results Demonstrate That The AI Resume Analyzer Improves Candidate–job Matching Efficiency And Delivers Consistent, Objective Evaluations, Making It A Valuable Tool For Modern Recruitment Workflows.