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--- |
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title: SimpleML |
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emoji: 🤖 |
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colorFrom: blue |
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colorTo: purple |
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sdk: docker |
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app_port: 8501 |
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tags: |
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- streamlit |
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- machine-learning |
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- classification |
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- regression |
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- sklearn |
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pinned: false |
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short_description: Train ML models in seconds — no code required. |
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--- |
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# SimpleML |
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Interactive Streamlit dashboard to train machine learning models (classification or regression) from CSV files — no code required. |
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## Author |
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Eduardo Nacimiento García |
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📧 enacimie@ull.edu.es |
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📜 Apache 2.0 License |
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## Features |
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- Upload CSV or use built-in classification/regression demo datasets |
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- Auto-detect task type (classification vs regression) |
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- Encode categorical variables automatically |
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- Choose between models: |
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- Classification: Random Forest, Logistic Regression |
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- Regression: Random Forest, Linear Regression |
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- View performance metrics |
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- Confusion matrix (classification) or Predicted vs Actual plot (regression) |
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- Feature importance (for tree-based models) |
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- Interactive prediction form |
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## Demo Datasets |
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Two built-in demos: |
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- **Classification**: Predict “Purchase” (0/1) based on age, income, education, etc. |
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- **Regression**: Predict “Salary” based on experience, age, education, etc. |
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## Deployment |
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Ready for [Hugging Face Spaces](https://huggingface.co/spaces) (free tier). |
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> ⚠️ Uses `sdk: docker` — include `Dockerfile`. |
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## Requirements |
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- Python 3.8+ |
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- Streamlit, scikit-learn, pandas, numpy, plotly |
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--- |
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💡 Tip: After uploading your CSV, select target variable → features → model → see results + make predictions! |
