Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{175992,
        author = {Chavan Tushar Chandrakant and Prof. Chavan Ganesh Baban and Chavan Arati Chandrakant and Dadas Pranav Nana and Deshmukh Vaishnavi Vikas},
        title = {Skill-Based Resume Evaluation and Job Recommendation System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {4416-4421},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=175992},
        abstract = {In today’s competitive job market, manual resume screening is time-consuming, error-prone, and inefficient, especially when dealing with a large number of applicants. This paper presents an intelligent resume analysis and job recommendation system that leverages Natural Language Processing (NLP) and Machine Learning (ML) techniques to automate the screening process. The system extracts and preprocesses text from uploaded PDF resumes and job descriptions, identifies key skills using spaCy and custom mappings, and computes similarity scores using the TF-IDF vectorizer and cosine similarity. A K-Nearest Neighbors (KNN) classifier is then used to categorize candidates into Top, Average, and Not Selected. Additionally, the system suggests suitable job roles based on the candidate's extracted skillset. The results are presented via a user-friendly interface and can be downloaded in Excel format, making the tool practical and efficient for recruiters and hiring managers.},
        keywords = {K- Nearest Neighbor, Cosine Similarity, Natural Language Processing, Term frequence and Inverse Document frequence (TF- IDF).},
        month = {April},
        }