📊 Automated Resume Relevance Check System

🚀 Problem Statement

At Innomatics Research Labs, resume evaluation is currently manual, inconsistent, and time-consuming. Placement teams handle 18–20 job requirements every week, with thousands of resumes to review.

This leads to:

• ⏳ Delays in shortlisting candidates
• ❌ Inconsistent evaluations
• 📉 Heavy workload on placement staff

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🎯 Objective

The Automated Resume Relevance Check System solves these problems by:

• Automatically evaluating resumes against job descriptions (JD)
• Generating a Relevance Score (0–100)
• Providing a Fit Verdict (High / Medium / Low)
• Highlighting missing keywords/skills
• Offering AI-powered improvement suggestions
• Enabling recruiters to download reports and analyze via dashboard

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🛠️ Tech Stack

• Frontend/Interface: Streamlit
• Backend: Python
• Libraries:
  • PyPDF2, docx2txt → Resume & JD parsing
  • pandas, numpy → Data analysis
  • matplotlib, plotly → Charts & visualizations
  • sentence-transformers → AI-powered semantic similarity
• Export: Excel report download

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📌 Features

✅ Upload Job Description (PDF/DOCX)
✅ Upload Multiple Resumes (PDF/DOCX)
✅ Extract & analyze resume content
✅ Relevance Score & Verdict (High/Medium/Low)
✅ Matched vs Missing Keywords
✅ Interactive Charts (bar chart, pie chart)
✅ Downloadable Excel Report
✅ AI-powered semantic matching (not just keyword-based)

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⚙️ Workflow

1. Upload JD
2. Upload Resumes
3. System extracts text & analyzes
4. Keyword Match + AI Semantic Similarity
5. Relevance Score + Verdict generated
6. Results shown in dashboard with charts
7. Download report

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