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

• Author(s):

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

• Keywords:

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

• 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.

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Other Details

• Paper id:

  IJSARTV12I3104721

• Published in:

  Volume: 12 Issue: 3 March 2026

• Publication Date:

  2026-03-15

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