Nosa-autoStreamlit 🚀
📊 Automated Machine Learning and Data Visualization Framework
Nosa-autoStreamlit is an advanced Python framework that automates the creation of machine learning and data visualization Streamlit applications. Designed to simplify complex data science workflows with powerful, user-friendly tools.
✨ Features
🤖 Machine Learning Generator
- Supports classification and regression problems
- Advanced preprocessing techniques
- Multiple machine learning models
- Cross-validation
- Hyperparameter tuning
- Model saving and loading
📈 Data Visualization Generator
- Multiple visualization types
- Interactive Plotly plots
- Easy-to-use interface
🚀 Quick Start
Installation
Install via pip
pip install nosa-autostreamlit
Install from GitHub
pip install git+https://github.com/thesnak/nosa-autostreamlit.git
Local Installation
# Clone the repository
git clone https://github.com/yourusername/nosa-autostreamlit.git
cd nosa-autostreamlit
# Create virtual environment
python -m venv venv
source venv/bin/activate # On Windows use `venv\Scripts\activate`
# Install dependencies
pip install -r requirements.txt
🔗 Links
- PyPI Package: https://pypi.org/project/nosa-autostreamlit/
- GitHub Repository: https://github.com/thesnak/nosa-autostreamlit
Machine Learning Example
from nosa_autostreamlit.generators import AdvancedMachineLearningGenerator
# Create generator
generator = AdvancedMachineLearningGenerator()
# Load data
generator.load_data(
data,
target_column='target',
problem_type='classification'
)
# Preprocess and train models
generator.advanced_preprocessing()
generator.train_multiple_models()
generator.generate_model_comparison_report()
Data Visualization Example
from nosa_autostreamlit.generators import DataVisualizationGenerator
# Create generator
generator = DataVisualizationGenerator()
# Load data
generator.load_data(data)
# Create visualizations
generator.create_histogram()
generator.create_scatterplot()
generator.create_boxplot()
🛠 Key Components
machine_learning_generator.py: Core ML functionalitydata_visualization_generator.py: Visualization toolsadvanced_ml_comparison.py: Example ML workflowadvanced_data_viz_example.py: Example visualization workflow
📦 Dependencies
- Streamlit
- Pandas
- NumPy
- Scikit-learn
- Plotly
- Joblib
🤝 Contributing
Contributions are welcome! Please follow these steps:
- Fork the repository
- Create a new branch (git checkout -b feature/amazing-feature)
- Commit your changes (git commit -m 'Add some amazing feature')
- Push to the branch (git push origin feature/amazing-feature)
- Open a Pull Request
📄 License
Distributed under the MIT License. See LICENSE for more information.
📞 Contact
Your Name - mohamed.mahmoud0726@gmail.com
Project Link: https://github.com/thesnak/nosa-autostreamlit
Made with ❤️ by Mohamed Mahmoud
Metadata
Release files for nosa-autostreamlit 0.1.2
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| nosa_autostreamlit-0.1.2.tar.gz | 14.2 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| nosa_autostreamlit-0.1.2-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 27.7 kB
Release files / nosa_autostreamlit-0.1.2.tar.gz
| Download URL | nosa_autostreamlit-0.1.2.tar.gz |
|---|---|
| Size | 14.2 kB |
| Tags | Source |
|
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No |
| Uploaded via |
twine/6.0.1 CPython/3.11.9
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Release files / nosa_autostreamlit-0.1.2-py3-none-any.whl
| Download URL | nosa_autostreamlit-0.1.2-py3-none-any.whl |
|---|---|
| Size | 13.4 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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No |
| Uploaded via |
twine/6.0.1 CPython/3.11.9
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