Skill Gap Analyzer

An end-to-end AI/ML system that analyzes a candidate’s resume, identifies skill gaps for a target role (ML Engineer), computes a job-fit score, and recommends a personalized learning path.

Built to simulate real-world hiring intelligence systems using NLP, Machine Learning, and API-based deployment.

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Features

• Resume analysis (PDF & text)

• Skill extraction using hybrid NLP:

  • Rule-based matching
  • Sentence embeddings (SBERT)

• Skill gap detection (missing / weak / strong skills)

• Job fit scoring (0–100)

• Personalized learning recommendations

• Production-style FastAPI backend

• Clean modular project structure

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Problem Statement

Students and early professionals often struggle to understand:

• How well their current skills match a target role
• What skills they are missing
• What to learn next in a structured way

This project solves that by automating resume evaluation and guidance using Machine Learning.

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System Architecture (High Level)

Resume (PDF/Text)
        ↓
Text Preprocessing (spaCy)
        ↓
Skill Extraction
  ├─ Rule-based (skills taxonomy)
  └─ Embedding-based (SBERT)
        ↓
Skill Scoring & Normalization
        ↓
Skill Gap Detection
        ↓
Job Fit Scoring
        ↓
Learning Recommendation Engine
        ↓
FastAPI REST API

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ML & NLP Techniques Used

Natural Language Processing

• Text cleaning & lemmatization (spaCy)
• Sentence embeddings using Sentence Transformers (SBERT)

Machine Learning Concepts

• Feature engineering (skill vectors)
• Similarity scoring (cosine similarity)
• Explainable rule + ML hybrid approach

Recommendation System

• Content-based recommendations
• Skill → learning resource mapping

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Tech Stack

• Language: Python 3.11
• NLP: spaCy, Sentence Transformers
• ML: scikit-learn, NumPy
• Backend: FastAPI
• PDF Parsing: PyMuPDF
• API Docs: Swagger (auto-generated)
• Version Control: Git

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Project Structure

skill-gap-analyzer/
│
├── api/                    # FastAPI entry point
│   └── main.py
│
├── src/
│   ├── preprocessing/      # Text & PDF processing
│   ├── extraction/         # Skill extraction logic
│   ├── scoring/            # Gap detection & fit scoring
│   └── recommendation/     # Learning recommendations
│
├── data/
│   ├── raw/                # Sample resumes & JDs
│   └── skills/             # Skill taxonomy & resources
│
├── tests/                  # Test scripts
├── requirements.txt
└── README.md

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How to Run Locally

1. Clone repository

git clone <repo-url>
cd skill-gap-analyzer

2. Setup environment

python -m venv venv
source venv/bin/activate
pip install -r requirements.txt
python -m spacy download en_core_web_sm

3. Start API

uvicorn api.main:app --reload

4. Open Swagger UI

http://127.0.0.1:8000/docs

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API Endpoints

POST /analyze-text

Analyze raw resume text.

POST /analyze-resume

Upload and analyze a PDF resume.

Returns:

• Job fit score
• Extracted skills with confidence
• Skill gaps
• Learning recommendations

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Example Output

{
  "job_fit_score": 62.4,
  "gaps": {
    "missing": ["docker", "model deployment"],
    "weak": ["pandas"],
    "strong": ["python", "scikit-learn"]
  },
  "recommendations": [
    {
      "skill": "docker",
      "resources": ["Docker fundamentals", "Docker for ML models"]
    }
  ]
}

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Use Cases

• Students preparing for ML/AI roles
• Resume screening tools
• EdTech platforms
• Career guidance systems

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Future Improvements

• Multiple target roles (Data Scientist, Backend, etc.)
• Proficiency prediction using supervised ML
• User dashboard & progress tracking
• Cloud deployment (Docker + AWS)
• ATS-style resume parsing improvements

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Author

Static Aspiring ML Engineer Focused on applied AI, NLP, and production-ready ML systems

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