Smart Resume Screening System
M. Rama Raju,
Huda Kousar,
D. Soumya,
A. Ashok
This paper presents the development of a Smart Resume Screening System that leverages Machine
Learning and Natural Language Processing (NLP) to automate and enhance the candidate short
listing process. The system enables recruiters to input job descriptions and efficiently analyze multiple
resumes to identify the most relevant candidates. It employs NLP techniques such as text preprocessing,
tokenization, TF-IDF vectorization, and cosine similarity to evaluate resumes based on their alignment
with required skills, qualifications, and job criteria. Furthermore, the integration of Large Language
Models (LLMs) allows the system to generate concise, human-readable summaries of candidate profiles,
providing meaningful insights for informed decision-making. The application presents a ranked list
of top candidates, significantly reducing manual effort and improving the speed and accuracy of
recruitment. Overall, the proposed system offers a scalable, unbiased, and efficient solution for modern
hiring processes across various domains.
