Intelligent Resume Analyzer: A Machine Learning Approach For Automated  Resume Screening And Candidate Evaluation

Mr. Dinesh; B Ajmal Shakeel; S Gowthamraj; K H Hamdan Mohammed; R Haynesh

Volume 12, Issue 3 (March 2026)

Intelligent Resume Analyzer: A Machine Learning Approach For Automated Resume Screening And Candidate Evaluation

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Volume 12 Issue 07

July 2026

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Author(s)

Mr. Dinesh B Ajmal Shakeel S Gowthamraj K H Hamdan Mohammed R Haynesh

Abstract

We Spent A Fair Amount Of Time Watching How Recruiters Actually Screen Resumes Before Building This. What Struck Us Was Not The Speed — Six To Eight Seconds Per Resume Is Well-documented [1] — But How Much Of That Time Went To Things That Had Nothing To Do With Whether The Candidate Could Do The Job. Things Like Font Choices, Gap Years, University Names. That Observation Is What Pushed Us Toward Building This. The System Pairs An NLP Extraction Layer With Five ML Models Running In Parallel. Logistic Regression Handles ATS Compatibility Scoring And Lands At 83%, Random Forest Picks Up Job Role Detection At 92%, Gradient Boosting Estimates Experience Level At 90%, Isolation Forest Catches Suspicious Submissions At 88%, And SVM Handles Quality Tiering At 86%. Eight Field Types Get Pulled From PDF And DOCX Files With 85 To 98 Percent Accuracy Depending On The Field. The Whole Pipeline Wraps Up In Around 6.2 Seconds — Which In Practice Cuts First- Pass Screening Time By About 65% Compared To Doing It By Hand.

Keywords

Machine Learning Resume Analysis NLP ATS Score Prediction Job Role Detection Fraud Detection Random Forest Gradient Boosting Feature Engineering

Paper ID

IJSARTV12I3104721

Publication Date

March 15, 2026

Research Area

Computer Science And Engineering

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