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@article{187260,
        author = {Prasad Galande and Harshwardhan Gaikwad and Krish Gaikwad and Anushka Gaikwad and Prajakta Gaikwad and Hardik Game and Keshav Tambre},
        title = {AI Resume Analyzer},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {6},
        pages = {7281-7284},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=187260},
        abstract = {The recruitment industry faces increasing pressure to efficiently process a high volume of resumes while minimizing bias and ensuring accurate candidate-job matching. This paper presents an AI-powered Resume Analyzer that automates the evaluation of resumes using Natural Language Processing (NLP) and Machine Learning (ML) techniques. The system is designed to extract essential information from unstructured resume data—including skills, educational qualifications, work experience, and certifications—and compare it with a predefined job description to generate a compatibility score. The proposed solution accepts resumes in formats such as PDF and DOCX, extracts text using document parsers, and processes it with NLP techniques such as tokenization, named entity recognition (NER), and keyword matching. A similarity score is computed using vectorization methods like TF-IDF and cosine similarity to measure the alignment between resume content and job requirements. The frontend interface, built using a lightweight Python framework, allows recruiters to upload resumes and instantly receive detailed analysis and ranking based on job fit. The system also highlights matched and missing skills, giving both recruiters and applicants actionable feedback. This Resume Analyzer significantly reduces the time and effort involved in manual screening and ensures a more consistent and scalable recruitment process. The results obtained demonstrate that AI-based systems can effectively streamline candidate filtering, improve decision-making, and contribute to unbiased hiring. Future work includes enhancing the system with deep learning models such as BERT for contextual understanding and expanding the tool to support multilingual and domain-specific resumes.},
        keywords = {Artificial Intelligence, Candidate Scoring, Machine Learning, Natural Language Processing, Resume Analysis, Skill Extraction, Text Mining},
        month = {November},
        }