AUTOMATED RESUME SCREENING USING NLP AND MACHINE LEARNING

ABSTRACT: This project presents a machine learning-based resume screening system that extracts IT hard skills, soft skills, education, and experience from resumes and job descriptions. Using algorithms like Support Vector Classifier (SVC) and Random Forest, the system ensures accurate skill extraction and semantic matching. It provides personalized recommendations by highlighting key skills and relevant experience, helping job seekers make informed career decisions.The system supports multiple resume formats and adapts to changing market trends. A bidirectional semantic matching approach is used to assess the similarity between job descriptions and resumes. It leverages natural language processing, machine learning, and semantic web technologies, integrating data from occupational standards, social media, and web-scraped job listings. Additionally, the system offers feedback to improve job descriptions, reducing the complexity and time involved in traditional job matching processes. Keywords: Job Matching, Skill Extraction, Machine Learning, Resume Analysis, IT Hard Skills, Soft Skills, SVC, Random Forest, Career Exploration, Data Processing