Modern Real - Time Resume Analysis and Job Suggestion System Using NLP and Machine Learning Algorithm

This paper is about in today's highly competitive job market job seekers face significant challenges in optimizing their resumes to pass Applicant Tracking Systems (ATS) and align with job requirements. Many resumes are rejected due to missing keywords, improper formatting, or a lack of ATS-friendly structures, making it difficult for qualified candidates to secure interviews. To address this issue, we present an AI-powered resume analysis system that enhances job matching efficiency by leveraging natural language processing (NLP) and machine learning. This system extracts key skills, qualifications, and experience from job descriptions and compares them with resumes to identify gaps. By providing automated keyword suggestions, ATS optimization insights, and personalized resume recommendations, the model improves resume-job relevance and significantly increases the likelihood of passing ATS filters. The results demonstrate that integrating AI in the resume screening process enhances job application success rates, reduces manual effort for both job seekers and recruiters, and accelerates the hiring process.