Análise avançada de dados de tireoide com machine learning - Projeto final do curso de Ciência de Dados da EBAC
Thyroid Analysis is a production-grade Python application complemented by HTML that showcases modern software engineering practices including clean architecture, comprehensive testing, containerized deployment, and CI/CD readiness.
The codebase comprises 629 lines of source code organized across 5 modules, following industry best practices for maintainability, scalability, and code quality.
- 🏗️ Object-Oriented: 2 core classes with clean architecture
- 📐 Clean Architecture: Modular design with clear separation of concerns
- 🧪 Test Coverage: Unit and integration tests for reliability
- 📚 Documentation: Comprehensive inline documentation and examples
- 🔧 Configuration: Environment-based configuration management
graph LR
subgraph Input["📥 Input"]
A[Raw Data]
B[Feature Config]
end
subgraph Pipeline["🔬 ML Pipeline"]
C[Preprocessing]
D[Feature Engineering]
E[Model Training]
F[Evaluation]
end
subgraph Output["📤 Output"]
G[Trained Models]
H[Metrics & Reports]
I[Predictions]
end
A --> C --> D --> E --> F
B --> D
F --> G
F --> H
G --> I
style Input fill:#e1f5fe
style Pipeline fill:#f3e5f5
style Output fill:#e8f5e9
- Python 3.12+
- pip (Python package manager)
# Clone the repository
git clone https://github.com/galafis/thyroid-analysis.git
cd thyroid-analysis
# Create and activate virtual environment
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
# Install dependencies
pip install -r requirements.txt# Run the application
python src/main.py# Run all tests
pytest
# Run with coverage report
pytest --cov --cov-report=html
# Run specific test module
pytest tests/test_main.py -v
# Run with detailed output
pytest -v --tb=shortthyroid-analysis/
├── docs/ # Documentation
│ ├── images/
│ ├── AUDIT_README.md
│ └── architecture_diagram.md
├── src/ # Source code
│ ├── data/
│ ├── processing/
│ │ └── thyroid_analysis.py
│ └── visualization/
│ └── update_graph_colors.py
├── tests/ # Test suite
│ ├── test_thyroid_analysis.py
│ └── test_update_graph_colors.py
├── CONTRIBUTING.md
├── LICENSE
├── README.md
└── requirements.txt
| Technology | Description | Role |
|---|---|---|
| Python | Core Language | Primary |
| NumPy | Numerical computing | Framework |
| Pandas | Data manipulation library | Framework |
| scikit-learn | Machine learning library | Framework |
| XGBoost | Gradient boosting framework | Framework |
| HTML | 1 files | Supporting |
Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.
- Fork the project
- Create your feature branch (
git checkout -b feature/AmazingFeature) - Commit your changes (
git commit -m 'Add some AmazingFeature') - Push to the branch (
git push origin feature/AmazingFeature) - Open a Pull Request
This project is licensed under the MIT License - see the LICENSE file for details.
Gabriel Demetrios Lafis
- GitHub: @galafis
- LinkedIn: Gabriel Demetrios Lafis
Thyroid Analysis é uma aplicação Python de nível profissional, complementada por HTML que demonstra práticas modernas de engenharia de software, incluindo arquitetura limpa, testes abrangentes, implantação containerizada e prontidão para CI/CD.
A base de código compreende 629 linhas de código-fonte organizadas em 5 módulos, seguindo as melhores práticas do setor para manutenibilidade, escalabilidade e qualidade de código.
- 🏗️ Object-Oriented: 2 core classes with clean architecture
- 📐 Clean Architecture: Modular design with clear separation of concerns
- 🧪 Test Coverage: Unit and integration tests for reliability
- 📚 Documentation: Comprehensive inline documentation and examples
- 🔧 Configuration: Environment-based configuration management
graph LR
subgraph Input["📥 Input"]
A[Raw Data]
B[Feature Config]
end
subgraph Pipeline["🔬 ML Pipeline"]
C[Preprocessing]
D[Feature Engineering]
E[Model Training]
F[Evaluation]
end
subgraph Output["📤 Output"]
G[Trained Models]
H[Metrics & Reports]
I[Predictions]
end
A --> C --> D --> E --> F
B --> D
F --> G
F --> H
G --> I
style Input fill:#e1f5fe
style Pipeline fill:#f3e5f5
style Output fill:#e8f5e9
- Python 3.12+
- pip (Python package manager)
# Clone the repository
git clone https://github.com/galafis/thyroid-analysis.git
cd thyroid-analysis
# Create and activate virtual environment
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
# Install dependencies
pip install -r requirements.txt# Run the application
python src/main.py# Run all tests
pytest
# Run with coverage report
pytest --cov --cov-report=html
# Run specific test module
pytest tests/test_main.py -v
# Run with detailed output
pytest -v --tb=shortthyroid-analysis/
├── docs/ # Documentation
│ ├── images/
│ ├── AUDIT_README.md
│ └── architecture_diagram.md
├── src/ # Source code
│ ├── data/
│ ├── processing/
│ │ └── thyroid_analysis.py
│ └── visualization/
│ └── update_graph_colors.py
├── tests/ # Test suite
│ ├── test_thyroid_analysis.py
│ └── test_update_graph_colors.py
├── CONTRIBUTING.md
├── LICENSE
├── README.md
└── requirements.txt
| Tecnologia | Descrição | Papel |
|---|---|---|
| Python | Core Language | Primary |
| NumPy | Numerical computing | Framework |
| Pandas | Data manipulation library | Framework |
| scikit-learn | Machine learning library | Framework |
| XGBoost | Gradient boosting framework | Framework |
| HTML | 1 files | Supporting |
Contribuições são bem-vindas! Sinta-se à vontade para enviar um Pull Request.
Este projeto está licenciado sob a Licença MIT - veja o arquivo LICENSE para detalhes.
Gabriel Demetrios Lafis
- GitHub: @galafis
- LinkedIn: Gabriel Demetrios Lafis