Interactive Streamlit application for Support Vector Machine classification across multiple domains.
- 3 Domain options: Medical, Fraud Detection, Classification
- 3 Kernel types: Linear, RBF, Polynomial
- Automated preprocessing
- Comprehensive visualizations
- Clone repository
- Create virtual environment:
python -m venv venv - Activate:
venv\Scripts\activate(Windows) orsource venv/bin/activate(Mac/Linux) - Install dependencies:
pip install -r requirements.txt
streamlit run src/app.py- Python 3.8+
- See requirements.txt for dependencies
Classic SVM/
│
├── venv/ # Virtual environment (auto-generated)
│
├── docs/ # Documentation
│ ├── project_features.md
│ ├── project_phases_overview.md
│ ├── phase1_setup_and_structure.md
│ ├── phase2_data_and_preprocessing.md
│ ├── phase3_svm_implementation.md
│ ├── phase4_visualizations.md
│ └── phase5_streamlit_ui.md
│
├── data/ # Datasets
│ ├── samples/ # Sample datasets
│ └── uploaded/ # User uploaded datasets (runtime)
│
├── src/ # Source code
│ ├── __init__.py
│ ├── app.py # Main Streamlit app
│ ├── data_handler.py # Data loading & preprocessing
│ ├── svm_models.py # SVM implementations
│ ├── visualizations.py # All visualization functions
│ └── utils.py # Utility functions
│
├── assets/ # Static assets
│ ├── images/ # Icons, logos
│ └── styles/ # Custom CSS
│
├── tests/ # Test files (optional)
│
├── requirements.txt # Dependency list
├── README.md # Project documentation
├── .gitignore # Git ignore file
└── config.py # Configuration settings (optional)
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