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Skill-Based Resume Evaluation and Job Recommendation System

  • Unique Paper ID: 175992
  • Volume: 11
  • Issue: 11
  • PageNo: 4416-4421
  • 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.
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Cite This Article

  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 11
  • PageNo: 4416-4421

Skill-Based Resume Evaluation and Job Recommendation System

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