Intelligent Applicant Resume Scoring and Job Fit Prediction System
DOI:
https://doi.org/10.15662/IJRAI.2026.0902006Keywords:
Resume Screening, Job Fit Prediction, Machine Learning, Natural Language Processing (NLP), Applicant Tracking System (ATS), Resume Parsing, Skill Extraction, Candidate Ranking, Recruitment Automation, Text Mining, Predictive Analytics, HR AnalyticsAbstract
Recruiters today receive hundreds of resumes for every job opening, making manual screening inefficient, subjective, and time-consuming. Traditional Applicant Tracking Systems (ATS) rely only on simple keyword matching, which often fails to identify the true suitability of candidates. This project proposes an Intelligent Applicant Resume Scoring and Job Fit Prediction System using Machine Learning (ML) and Natural Language Processing (NLP) to automate the resume evaluation process. The system extracts skills, experience, education, and other key elements from resumes, analyzes job descriptions, and calculates a Resume Fit Score to determine how well a candidate matches a job. The model classifies candidates into categories such as Excellent, Good, Average, or Poor fit. This system significantly reduces recruitment time, minimizes bias, increases hiring accuracy, and enables HR teams to make data-driven decisions. It offers an efficient, intelligent, and scalable solution for modern recruitment challenges
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