A comprehensive personal workspace showcasing my learning journey in software development. Features multiple projects including AI/ML, document processing, and analytics, along with reusable test utilities and development tools. Built with Python, FastAPI, and modern development practices.
Machine learning model for cryptocurrency price prediction using historical data and market indicators.
Document processing system using OCR and machine learning for automated data extraction.
Data analytics platform for university marketing campaign performance tracking and insights.
- Comprehensive test utilities
- Database and Redis utilities
- Mock implementations for testing
- JWT token generation
- Data validation schemas
- Development tools and configurations
- Python 3.10+
- FastAPI
- SQLAlchemy
- Redis
- PyTest
- Pydantic
- Docker
# Clone repository
git clone https://github.com/cosmaslabs/personal-workspace.git
cd personal-workspace
# Install dependencies
pip install -r requirements.txt
# Install development dependencies
pip install -r requirements-dev.txt
# Install pre-commit hooks
pre-commit install# Run all tests
pytest tests/
# Run with coverage
pytest --cov=src tests/# Format code
black .
# Sort imports
isort .
# Type checking
mypy .
# Lint code
flake8 ..
├── projects/ # Project implementations
│ ├── AI_Crypto_Price_Predictor/
│ ├── Document_Digitization_OCR_System/
│ └── University_Marketing_Analytics_Tool/
├── tests/ # Test suites
│ ├── utils/ # Test utilities
│ ├── unit/ # Unit tests
│ ├── integration/ # Integration tests
│ └── e2e/ # End-to-end tests
├── docs/ # Documentation
├── scripts/ # Utility scripts
└── requirements/ # Dependencies
- Create feature branch (
git checkout -b feature/amazing-feature) - Commit changes (
git commit -m 'feat: add amazing feature') - Push branch (
git push origin feature/amazing-feature) - Open pull request
This project is licensed under the MIT License - see the LICENSE file for details.