In my "Resume Shortlisting" project, I created an automated system to filter and shortlist resumes using Python and MySQL. Resumes were stored and managed in a MySQL database, allowing for efficient retrieval and organization. Using Natural Language Processing (NLP), I extracted key skills, experience, and qualifications from each resume. Matching algorithms implemented in Python compared candidate profiles with job requirements.

• Languages & Tools: Python, MySQL, Pandas, NLTK, Scikit-learn
• Techniques: Resume parsing, NLP for skill extraction, cosine similarity for profile-job match
• Database Role: MySQL used for resume storage, indexing, and retrieval

• Automated parsing of resumes with keyword and skill extraction
• Intelligent shortlisting based on job descriptions and relevance scoring
• Ranked list of top-matching candidates for faster decision-making
• Structured and searchable resume database using MySQL

The system significantly reduced manual screening effort and ensured that only the most relevant resumes were considered for interviews. This project demonstrates how combining data science with backend databases can streamline recruitment processes effectively.