ABSTRACT: When applying for one position, recruitment firms frequently have to deal with hundreds or even thousands of resumes, which makes the process of going through them by hand time-consuming, repetitive and possibly inconsistent. This paper introduces an AI Resume Analyzer that makes use of Artificial Intelligence (AI), Machine Learning (ML) and Natural Language Processing (NLP) in order to assist with automated resume screening and job matching. The system takes resumes in forms such as PDF or DOCX, extracts and preprocesses the textual content, identifies features such as skills, education, certifications, projects and work experience, and then compares the candidate's profile with the job description. Techniques including TF-IDF, cosine similarity, Named Entity Recognition (NER), semantic representations and machine-learning classification can be employed to assess relevance. A hybrid method can combine matching of skills, semantic similarity, work experience and education in order to give an explainable outcome. The system is meant to act as a decision-support tool for recruiters and not as a substitute for human judgment. The main challenges involve the variety of resume formats, the problem of false matches, privacy concerns, biased training data and the requirement for fair and transparent recommendations
AI RESUME ANALYZER FOR HR & FRESHER
VARUN HARKESH BALMIKI