📊 Job Market Analyzer

AI-Powered Career Analysis Tool with Personalized Learning Paths

Features • Demo • Installation • Usage • Architecture • Technologies

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🎯 Overview

Job Market Analyzer is an intelligent desktop application that helps job seekers identify skill gaps and provides personalized learning recommendations. Upload your resume, and within 60 seconds, receive:

• ✅ Detailed skill gap analysis against 50+ job postings
• ✅ AI-powered career path matching using K-Means clustering
• ✅ Personalized 4-week learning plan with real course links
• ✅ Interactive visualizations of your skill profile
• ✅ Job-specific matching percentages

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✨ Features

🤖 AI-Powered Analysis

• K-Means Clustering: Groups similar career paths to find your best fit
• NLP Skill Extraction: Identifies 100+ technical skills using OpenNLP
• Smart Matching: Goes beyond simple keyword matching

📄 Multi-Format Resume Support

• PDF files (text-based and scanned)
• Image formats (PNG, JPG, JPEG, BMP, TIFF)
• Advanced OCR with Tesseract (85-95% accuracy)

📚 Personalized Learning Paths

• 4-week structured learning plans
• 40+ curated courses from Udemy, Coursera, YouTube
• Domain-specific recommendations (Web Dev, Data Science, DevOps, etc.)
• Progress milestones and project suggestions

📊 Visual Analytics

• Skill match percentage charts
• Top missing skills visualization
• Job-by-job analysis with color-coded results
• Export results to text files

🌐 Real Job Market Data

• Fetches 50+ job postings via Adzuna API
• Domain-specific job filtering
• Intelligent fallback with sample jobs

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🎥 Demo

Main Upload Screen

Upload your resume and select your target job domain.

Analysis Results

View comprehensive skill analysis with interactive tabs:

• Summary: Matched vs. missing skills
• Job Postings: 50+ analyzed jobs with match percentages
• Learning Path: Your personalized 4-week plan
• Charts: Visual skill gap analysis

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🚀 Installation

Prerequisites

• Java JDK 21 or higher
• Maven 3.x
• Tesseract OCR (for image processing)

Step 1: Clone the Repository

git clone https://github.com/rakshanrk/Job_Market_Analyzer_Java.git
cd Job_Market_Analyzer_Java

Step 2: Install Tesseract OCR

Windows:

# Download installer from: https://github.com/UB-Mannheim/tesseract/wiki
# Install to: C:\Program Files\Tesseract-OCR
# Download tessdata: https://github.com/tesseract-ocr/tessdata
# Place eng.traineddata in C:\tessdata

macOS:

brew install tesseract

Linux:

sudo apt-get install tesseract-ocr
sudo apt-get install tesseract-ocr-eng

Step 3: Build with Maven

mvn clean install

Step 4: Run the Application

mvn javafx:run

Or run the JAR:

java -jar target/JobMarketAnalyzer-1.0-SNAPSHOT.jar

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📖 Usage

Basic Workflow

1. Launch Application

   • Run the application using Maven or the JAR file

2. Select Job Domain

   • Choose from: Software Developer, Data Scientist, Web Developer, etc.

3. Upload Resume

   • Click "Upload Resume" and select your PDF or image file
   • Maximum file size: 10MB

4. Wait for Analysis (30-60 seconds)

   • Text extraction
   • Skill identification using NLP
   • Job market analysis
   • AI-powered matching
   • Learning path generation

5. View Results

   • Explore 4 interactive tabs
   • View matched and missing skills
   • Check job-specific match percentages
   • Get your personalized 4-week learning plan

6. Export Results

   • Save analysis to text file for future reference

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🏗️ Architecture

Design Pattern: MVC-Inspired

├── models/              # Data structures (Skill, Job, Resume, etc.)
├── services/            # Business logic
│   ├── ResumeParser         # PDF & OCR processing
│   ├── SkillExtractor       # NLP-based skill identification
│   ├── JobFetcher           # API integration
│   ├── SkillAnalyzer        # K-Means clustering
│   └── LearningPathGenerator # Personalized recommendations
├── utils/               # Helper utilities
│   ├── FileValidator        # Input validation
│   └── ChartGenerator       # JFreeChart visualization
└── Main.java           # JavaFX GUI + Controller

Key Components

1. Resume Parser

• Extracts text from PDFs using Apache PDFBox
• OCR processing for images using Tesseract
• Text normalization and cleanup

2. Skill Extractor

• Dictionary-based matching (100+ technical skills)
• NLP tokenization and POS tagging with OpenNLP
• Filters out common words to prevent false positives

3. Job Fetcher

• Integrates with Adzuna Job Search API
• Intelligent fallback to domain-specific sample jobs
• Parses job descriptions to extract required skills

4. Skill Analyzer (AI Core)

• K-Means Clustering: Groups similar skill profiles
• Creates n-dimensional feature vectors for resumes and jobs
• Calculates match percentage based on cluster similarity
• Identifies skill gaps with priority ranking

5. Learning Path Generator

• Prioritizes missing skills by job market demand
• Queries SQLite database for relevant courses
• Creates structured 4-week learning plan
• Includes milestones and project suggestions

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🛠️ Technologies

Core Technologies

• Java 21+: Core programming language
• JavaFX 23: Modern GUI framework
• Maven: Dependency management and build tool

AI & Machine Learning

• Weka 3.8+: K-Means clustering algorithm
• Apache OpenNLP 2.3+: NLP processing (tokenization, POS tagging)

Data Processing

• Apache PDFBox 3.0+: PDF text extraction
• Tesseract 5.x: OCR engine for images
• SQLite 3.46+: Embedded database

External Integrations

• Adzuna API: Real-time job market data
• Apache HttpClient 5.x: HTTP requests
• Gson 2.11+: JSON parsing

Visualization

• JFreeChart 1.5+: Chart generation

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📊 Algorithm: K-Means Clustering

How It Works

1. Feature Space Creation

   • Collects all unique skills from resume and jobs
   • Example: [Java, Python, SQL, Docker, AWS, React]

2. Vector Representation

   • Resume: [1, 1, 1, 0, 0, 0] (has Java, Python, SQL)
   • Job 1: [1, 0, 1, 1, 0, 0] (needs Java, SQL, Docker)
   • Job 2: [0, 1, 0, 0, 1, 1] (needs Python, AWS, React)

3. Clustering

   • Groups similar skill profiles into 3 clusters
   • Cluster 0: Backend Developers
   • Cluster 1: Data Scientists
   • Cluster 2: DevOps Engineers

4. Match Calculation

   • Identifies resume's cluster
   • Counts jobs in same cluster
   • Match % = (same cluster jobs / total jobs) × 100

Advantage: Considers overall skill profile, not just individual skill overlap

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📁 Database Schema

Tables

1. learning_resources

Stores 40+ curated courses mapped to skills

- skill_name (Java, Python, React, etc.)
- resource_title (Course name)
- resource_url (Link to Udemy, Coursera, YouTube)
- platform (Udemy, Coursera, YouTube)
- duration_weeks (Time to complete)
- difficulty_level (Beginner, Intermediate, Advanced)

2. analysis_history

Tracks past analyses for progress monitoring

- resume_filename
- extracted_skills
- missing_skills
- match_percentage
- jobs_analyzed
- analysis_date

3. learning_paths

Stores generated 4-week plans

- analysis_id (Foreign key)
- week_number (1-4)
- skill_focus (Skills for the week)
- resources (Course links)

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🔧 Configuration

API Setup (Optional)

To use real job data from Adzuna:

1. Sign up at Adzuna Developer Portal
2. Get your API credentials (App ID and App Key)
3. Update JobFetcher.java:

private static final String APP_ID = "your_app_id";
private static final String APP_KEY = "your_app_key";

Note: Application works perfectly with sample jobs if API is not configured.

Tesseract Path Configuration

Update ResumeParser.java if Tesseract is installed in a custom location:

tesseract.setDatapath("path/to/your/tessdata");

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📈 Performance

• Processing Time: 30-60 seconds per resume

  • Text extraction: 5-20 seconds (PDF) or 10-30 seconds (Image OCR)
  • Skill extraction: 2-5 seconds
  • Job analysis: 5-10 seconds
  • AI clustering: 5-10 seconds
  • Path generation: 1-2 seconds

• Accuracy:

  • PDF text extraction: ~99%
  • OCR (images): 85-95% (depends on image quality)
  • Skill detection: 85-95%

• Supported Files: PDF, PNG, JPG, JPEG, BMP, TIFF (max 10MB)

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🤝 Contributing

Contributions are welcome! Here's how you can help:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

Areas for Contribution

• Add more learning resources to database
• Improve OCR accuracy
• Support additional file formats
• Enhance UI/UX
• Add more job domains
• Implement additional ML algorithms

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🛣️ Roadmap

• Resume builder module
• LinkedIn profile import
• Job application tracker
• Skill verification quizzes
• Interview preparation questions
• Salary estimator based on skills
• Multiple career path suggestions
• Skill trend analysis dashboard
• Peer comparison feature
• Mobile app (iOS/Android)

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👨‍💻 Author

Rakshan RK

• GitHub: @rakshanrk
• LinkedIn: [https://www.linkedin.com/in/rakshanrk/]

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🙏 Acknowledgments

• Apache Software Foundation for OpenNLP, PDFBox, and HttpClient
• Weka Team for the machine learning library
• Tesseract Team for OCR engine
• Adzuna for job market API
• JFreeChart Team for visualization library
• Course providers: Udemy, Coursera, YouTube

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📞 Support

If you encounter any issues or have questions:

1. Check the Issues page
2. Create a new issue with detailed description
3. Reach out via email

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