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Author(s):

Kazi Simran | Tamboli Aalmeen | Sayyed Afsha | Shaikh Sufiya
Keywords:

Smart Resume Analyzer, Applicant Tracking System (ATS), Natural Language Processing (NLP), Resume Parsing, SpaCy, Flask, Cosine Similarity, Job Description Matching
Abstract:
In Today's Competitive Job Market, Most Organizations Use Applicant Tracking Systems (ATS) To Filter Candidates Automatically. This Process, While Efficient For Employers, Often Results In Many Well- Qualified Applicants Being Rejected Simply Because Their Resumes Are Not Optimized For ATS Compatibility, Suffering From Poor Formatting Or Missing Keywords. This Paper Proposes The "Smart Resume Analyzer," An AI- Driven Web Application Designed To Bridge This Gap. The System Architecture Is Designed On Three Core Pillars: (1) A Frontend Interface For Users To Upload A Resume And Job Description (JD); (2) A Flask-based Backend To Process The Inputs; And (3) An NLP Engine, Using SpaCy, To Analyze The Resume, Extract Key Features, And Compute An "ATS Score." This Score, Generated Using A Semantic Similarity Algorithm, Indicates How Effectively A Resume Matches Job-specific Requirements