Compare popular ML classification algorithms side-by-side with interactive visualizations and real-time hyperparameter tuning.
ML Classification Lab is an interactive web application that enables users to compare 7 popular classification algorithms on any dataset — built-in or uploaded. Tune hyperparameters in real time, visualize performance with Plotly charts, and export results as CSV. Features a polished dark/light theme with industrial-scientific aesthetics.
| Feature | Description |
|---|---|
| 7 Algorithms | Logistic Regression, SVM, Naive Bayes, Random Forest, XGBoost, KNN, Decision Tree |
| Flexible Data | 4 built-in datasets (Iris, Breast Cancer, Wine, Titanic), single CSV upload with split, or separate train/test CSVs |
| Live Tuning | Interactive hyperparameter widgets per algorithm (C, kernel, n_estimators, learning_rate, etc.) |
| Rich Metrics | Accuracy, Precision, Recall, F1 (macro/weighted), ROC-AUC with sortable comparison table |
| Plotly Visualizations | Confusion matrices, ROC curves, PR curves, feature importance, class distribution |
| Preprocessing | Auto missing value handling, one-hot/label encoding, StandardScaler toggle |
| Dark/Light Theme | Polished dark-first UI with one-click theme toggle |
| Export | Download metrics comparison as CSV |
| Category | Technology |
|---|---|
| Frontend | Streamlit, Custom CSS (Outfit + JetBrains Mono fonts) |
| Visualization | Plotly, Matplotlib, Seaborn |
| ML Framework | scikit-learn, XGBoost |
| Data | Pandas, NumPy |
| Package Manager | uv |
| Language | Python 3.13+ |
┌─────────────────────────────────────────────────┐
│ Streamlit UI │
│ (app.py + theme.py) │
│ ┌──────────┬──────────┬──────────┬───────────┐ │
│ │ Data │ Training │ Metrics │ Viz │ │
│ │ Tab │ Tab │ Tab │ Tab │ │
│ └──────────┴──────────┴──────────┴───────────┘ │
├─────────────────────────────────────────────────┤
│ Application Layer │
│ ┌─────────────┐ ┌────────────┐ ┌───────────┐ │
│ │ data_loader │ │ models │ │ viz │ │
│ │ .py │ │ .py │ │ .py │ │
│ │ │ │ │ │ │ │
│ │ Load/Split │ │ Build/ │ │ Plotly │ │
│ │ Preprocess │ │ Train/ │ │ Charts │ │
│ │ Encode │ │ Evaluate │ │ Themed │ │
│ └─────────────┘ └────────────┘ └───────────┘ │
├─────────────────────────────────────────────────┤
│ ML / Data Libraries │
│ scikit-learn · XGBoost · Pandas · NumPy │
└─────────────────────────────────────────────────┘
mlclassification/
├── .streamlit/
│ └── config.toml # Streamlit theme configuration
├── sample_data/
│ └── titanic.csv # Bundled example dataset (90 rows)
├── app.py # Main Streamlit entry point & tab orchestration
├── models.py # 7 ML algorithms, hyperparameter specs, training, evaluation
├── visualizations.py # Themed Plotly chart functions
├── data_loader.py # Dataset loading, preprocessing, encoding, splitting
├── theme.py # Dark/light theme engine, CSS injection, UI components
├── pyproject.toml # uv-managed dependencies
├── uv.lock # Dependency lock file
└── .python-version # Python 3.13
- Python 3.13+
- uv package manager
- libomp (macOS only, for XGBoost):
brew install libomp
# Clone the repository
git clone https://github.com/alfredang/mlclassification.git
cd mlclassification
# Install dependencies
uv syncuv run streamlit run app.pyThe app will open at http://localhost:8501
- Data Tab — Select a built-in dataset or upload your own CSV. Configure target/feature columns, encoding, and scaling.
- Training Tab — Select algorithms from the sidebar, tune hyperparameters, and click TRAIN MODELS.
- Metrics Tab — View the sortable comparison table with highlighted best performers. Download as CSV.
- Visualizations Tab — Explore confusion matrices, ROC/PR curves, feature importance, and classification reports.
Contributions are welcome!
- Fork the repository
- Create a feature branch (
git checkout -b feature/amazing-feature) - Commit your changes (
git commit -m 'Add amazing feature') - Push to the branch (
git push origin feature/amazing-feature) - Open a Pull Request
Tertiary Infotech Academy Pte Ltd
- Streamlit — App framework
- scikit-learn — ML algorithms and metrics
- XGBoost — Gradient boosting
- Plotly — Interactive visualizations
- uv — Fast Python package manager
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