Job Market Analytics Platform

A comprehensive web scraping and data analytics platform that analyzes job market trends for technology professionals. This project scrapes job listings from the.protocol to identify the most in-demand technologies for Python developers in Poland, providing actionable insights for career development and skill prioritization.

🎯 Project Overview

This platform combines web scraping, data analysis, and visualization to deliver market intelligence for job seekers and professionals looking to understand current technology trends. By analyzing job postings, it helps users make informed decisions about which skills to develop for maximum career impact.

🚀 Key Features

🔍 Advanced Web Scraping

• Multi-technology scraping using Scrapy and Selenium for dynamic content
• Custom pipelines and middlewares for robust data extraction
• CSS and XPath selectors for precise data targeting
• Configurable position targeting (Python, Java, JavaScript, etc.)

📊 Data Analytics & Visualization

• Real-time data analysis using Pandas and Jupyter
• Interactive visualizations with Matplotlib
• Market trend identification and skill demand analysis
• Custom analytics pipeline for job market insights

🏗️ Scalable Architecture

• Microservices architecture with Docker containerization
• Asynchronous task processing with Celery and Redis
• RESTful API with Flask backend
• Nginx reverse proxy for production deployment
• MySQL database for metadata management

⚙️ DevOps & Infrastructure

• Docker Compose for easy deployment
• Automated scraping schedules with Celery Beat
• Environment-based configuration management
• Production-ready setup with proper logging and monitoring

🛠️ Technology Stack

Category	Technology	Version
Web Scraping	Scrapy, Selenium	2.11.2, 4.27.1
Backend	Flask	3.0.3
Data Processing	Pandas, NLTK	2.2.2
Visualization	Matplotlib	3.8.4
Database	MySQL, SQLAlchemy	9.0.0, 2.0.31
Task Queue	Celery, Redis	5.2.1
Containerization	Docker, Docker Compose	Latest
Web Server	Nginx	Latest

📈 Analytics Capabilities

The platform provides comprehensive analytics including:

• Skill Demand Analysis: Most requested technologies by experience level
• Market Trends: Employment types and contract preferences
• Geographic Insights: Job distribution across locations
• Technology Stack Analysis: Required vs. optional skills breakdown
• Career Path Guidance: Experience level requirements and progression

🚀 Quick Start

Prerequisites

• Docker and Docker Compose installed
• Git for version control

Installation

1. Clone the repository

   git clone https://github.com/bigdata5911/Jobsite-Scraper-and-Analyzer.git
   cd Jobsite-Scraper-and-Analyzer

2. Configure environment variables

   cp .env.sample .env
   # Edit .env file with your configuration

3. Launch the application

   docker-compose up --build

4. Access the application

   • Web Interface: http://localhost:8000/scraping/diagrams
   • API Documentation: Available in the web interface

📊 API Endpoints

Endpoint	Method	Description
/scraping/diagrams	GET	Initiates analytics task and returns task ID
/scraping/diagrams/{task_id}	GET	Retrieves analytics results by task ID

📋 Configuration

Position Targeting

Modify the POSITION variable in config.py to target different job roles:

POSITION = "python"  # Options: python, java, javascript, dev, etc.

Scraping Schedule

Adjust the scraping frequency in your environment configuration:

SCRAPING_EVERY_DAYS=7  # Scrape every 7 days

🎨 Sample Visualizations

🏗️ Project Structure

Jobsite-Scraper-and-Analyzer/
├── analyzing/           # Data analysis and visualization modules
├── scraping/           # Web scraping spiders and pipelines
├── web_server/         # Flask web application
├── db/                 # Database models and connections
├── main_celery/        # Celery task queue configuration
├── static/             # Static assets and visualizations
├── migrations/         # Database migration files
├── nginx/              # Nginx configuration
└── scripts/            # Utility scripts

🔧 Development

Key Learning Outcomes

• Web Scraping: Advanced techniques with Selenium, Scrapy, CSS/XPath selectors
• Data Analysis: Comprehensive data processing with Pandas, NLTK, and visualization
• Task Management: Distributed task processing with Celery and Redis
• Containerization: Production-ready Docker deployment
• API Development: RESTful API design with Flask
• DevOps: Nginx configuration and container orchestration

Areas for Future Enhancement

• Cloud Storage Integration: Migrate from local file storage to cloud solutions
• Database Optimization: Implement proper database storage for scraped data
• Container Optimization: Reduce Docker image sizes and improve build efficiency
• Code Quality: Implement comprehensive testing and SOLID principles
• Performance Monitoring: Add application performance monitoring and logging

🤝 Contributing

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

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

👨‍💻 Author

bigdata5911 - GitHub Profile

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Empowering developers with data-driven career insights

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