| Name | nosa-autostreamlit JSON |
| Version |
0.1.2
JSON |
| download |
| home_page | https://github.com/thesnak/nosa-autostreamlit |
| Summary | Automated Machine Learning and Data Visualization Framework |
| upload_time | 2024-12-20 00:02:25 |
| maintainer | None |
| docs_url | None |
| author | Mohamed Mahmoud |
| requires_python | >=3.8 |
| license | MIT License Copyright (c) 2024 Mohamed Mahmoud Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
| keywords |
machine-learning
data-visualization
streamlit
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
streamlit
pandas
numpy
plotly
scikit-learn
joblib
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
<p align="center">
<img src="https://raw.githubusercontent.com/thesnak/nosa-autostreamlit/main/assets/nosa_autostreamlit_logo.webp" alt="Nosa-autoStreamlit Logo" width="200"/>
</p>
# 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
```bash
pip install nosa-autostreamlit
```
### Install from GitHub
```bash
pip install git+https://github.com/thesnak/nosa-autostreamlit.git
```
### Local Installation
```bash
# 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
```python
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
```python
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 functionality
- **`data_visualization_generator.py`**: Visualization tools
- **`advanced_ml_comparison.py`**: Example ML workflow
- **`advanced_data_viz_example.py`**: Example visualization workflow
### 📦 Dependencies
- Streamlit
- Pandas
- NumPy
- Scikit-learn
- Plotly
- Joblib
### 🤝 Contributing
Contributions are welcome! Please follow these steps:
1. Fork the repository
2. Create a new branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. 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](https://github.com/thesnak)
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"description": "<p align=\"center\">\r\n <img src=\"https://raw.githubusercontent.com/thesnak/nosa-autostreamlit/main/assets/nosa_autostreamlit_logo.webp\" alt=\"Nosa-autoStreamlit Logo\" width=\"200\"/>\r\n</p>\r\n\r\n# Nosa-autoStreamlit \ud83d\ude80\r\n\r\n## \ud83d\udcca Automated Machine Learning and Data Visualization Framework\r\n\r\nNosa-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.\r\n\r\n\r\n\r\n\r\n\r\n## \u2728 Features\r\n\r\n### \ud83e\udd16 Machine Learning Generator\r\n- Supports classification and regression problems\r\n- Advanced preprocessing techniques\r\n- Multiple machine learning models\r\n- Cross-validation\r\n- Hyperparameter tuning\r\n- Model saving and loading\r\n\r\n### \ud83d\udcc8 Data Visualization Generator\r\n- Multiple visualization types\r\n- Interactive Plotly plots\r\n- Easy-to-use interface\r\n\r\n## \ud83d\ude80 Quick Start\r\n\r\n## Installation\r\n\r\n### Install via pip\r\n\r\n```bash\r\npip install nosa-autostreamlit\r\n```\r\n### Install from GitHub\r\n```bash\r\npip install git+https://github.com/thesnak/nosa-autostreamlit.git\r\n```\r\n\r\n### Local Installation\r\n\r\n```bash\r\n# Clone the repository\r\ngit clone https://github.com/yourusername/nosa-autostreamlit.git\r\ncd nosa-autostreamlit\r\n\r\n# Create virtual environment\r\npython -m venv venv\r\nsource venv/bin/activate # On Windows use `venv\\Scripts\\activate`\r\n\r\n# Install dependencies\r\npip install -r requirements.txt\r\n```\r\n\r\n### \ud83d\udd17 Links\r\n- PyPI Package: https://pypi.org/project/nosa-autostreamlit/\r\n- GitHub Repository: https://github.com/thesnak/nosa-autostreamlit\r\n\r\n\r\n### Machine Learning Example\r\n\r\n```python\r\nfrom nosa_autostreamlit.generators import AdvancedMachineLearningGenerator\r\n\r\n# Create generator\r\ngenerator = AdvancedMachineLearningGenerator()\r\n\r\n# Load data\r\ngenerator.load_data(\r\n data, \r\n target_column='target', \r\n problem_type='classification'\r\n)\r\n\r\n# Preprocess and train models\r\ngenerator.advanced_preprocessing()\r\ngenerator.train_multiple_models()\r\ngenerator.generate_model_comparison_report()\r\n```\r\n\r\n### Data Visualization Example\r\n\r\n```python\r\nfrom nosa_autostreamlit.generators import DataVisualizationGenerator\r\n\r\n# Create generator\r\ngenerator = DataVisualizationGenerator()\r\n\r\n# Load data\r\ngenerator.load_data(data)\r\n\r\n# Create visualizations\r\ngenerator.create_histogram()\r\ngenerator.create_scatterplot()\r\ngenerator.create_boxplot()\r\n```\r\n\r\n### \ud83d\udee0 Key Components\r\n- **`machine_learning_generator.py`**: Core ML functionality\r\n- **`data_visualization_generator.py`**: Visualization tools\r\n- **`advanced_ml_comparison.py`**: Example ML workflow\r\n- **`advanced_data_viz_example.py`**: Example visualization workflow\r\n\r\n\r\n### \ud83d\udce6 Dependencies\r\n- Streamlit\r\n- Pandas\r\n- NumPy\r\n- Scikit-learn\r\n- Plotly\r\n- Joblib\r\n\r\n### \ud83e\udd1d Contributing\r\nContributions are welcome! Please follow these steps:\r\n\r\n1. Fork the repository\r\n2. Create a new branch (git checkout -b feature/amazing-feature)\r\n3. Commit your changes (git commit -m 'Add some amazing feature')\r\n4. Push to the branch (git push origin feature/amazing-feature)\r\n5. Open a Pull Request\r\n\r\n### \ud83d\udcc4 License\r\nDistributed under the MIT License. See LICENSE for more information.\r\n\r\n### \ud83d\udcde Contact\r\nYour Name - mohamed.mahmoud0726@gmail.com\r\n\r\nProject Link: https://github.com/thesnak/nosa-autostreamlit\r\n\r\n\r\nMade with \u2764\ufe0f by [Mohamed Mahmoud](https://github.com/thesnak)\r\n",
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