• Dhruv Garg

The recruitment industry is rapidly evolving with the integration of Artificial Intelligence (AI), especially in resume screening and job matching. Traditional hiring methods are often inefficient and biased, relying heavily on manual resume reviews. This research addresses these challenges using AI-driven resume analysis with Natural Language Processing (NLP) techniques like spaCy, TF-IDF, and Cosine Similarity. Job descriptions and resumes are manually collected, and spaCy’s Named Entity Recognition (NER) is used to extract key skills and job titles. TF-IDF and Cosine Similarity then evaluate the match between resumes and job descriptions. This ensures an objective, data-driven, and accurate matching process. The expected outcome is a more efficient, fair, and automated hiring workflow. The use of spaCy enhances precision by reducing irrelevant data, improving overall candidate-job alignment. This paper concludes by emphasizing AI’s role in transforming recruitment through automation, reducing bias, and supporting a transparent, effective hiring ecosystem

Artificial Intelligence (AI), Natural Language Processing (NLP), TF-IDF, SpaCy, Named Entity Recognition (NER), Cosine Similarity.

"AI-Powered Resume Analyzer", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 5, page no.a534-a537, May-2025, Available :http://www.jetir.org/papers/JETIR2505058.pdf

"AI-Powered Resume Analyzer", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 5, page no. ppa534-a537, May-2025, Available at : http://www.jetir.org/papers/JETIR2505058.pdf