https://github.com/priyanshu7639/data_visualization_dashboard
An Interactive data visualization tool that combines traditional plotting capabilities with modern AI assistance. It allows users to create and modify visualizations through natural language commands, making data exploration accessible to users of all skill levels.
https://github.com/priyanshu7639/data_visualization_dashboard
business-analytics data-analysis data-engineering data-exploration data-science data-visualization datapreprocessing datascience interactive-visualizations matplotlib plotly plotting python research-tool streamlit
Last synced: about 1 month ago
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An Interactive data visualization tool that combines traditional plotting capabilities with modern AI assistance. It allows users to create and modify visualizations through natural language commands, making data exploration accessible to users of all skill levels.
- Host: GitHub
- URL: https://github.com/priyanshu7639/data_visualization_dashboard
- Owner: Priyanshu7639
- License: mit
- Created: 2024-12-30T22:29:48.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-12-30T23:09:10.000Z (over 1 year ago)
- Last Synced: 2025-02-23T03:29:46.638Z (over 1 year ago)
- Topics: business-analytics, data-analysis, data-engineering, data-exploration, data-science, data-visualization, datapreprocessing, datascience, interactive-visualizations, matplotlib, plotly, plotting, python, research-tool, streamlit
- Language: Python
- Homepage:
- Size: 15.6 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
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README
# Advanced Data Visualization Dashboard 📊
An interactive data visualization tool with AI-powered customization capabilities. This application allows users to upload data, create various types of visualizations, and modify them using natural language commands.
## Features 🌟
### Data Handling
- Support for CSV and Excel file formats
- Automatic data type detection
- Basic data preprocessing options
- Missing value handling
- Feature scaling capabilities
### Visualizations
- Scatter Plots
- Line Plots
- Bar Charts
- Box Plots
- Violin Plots
- Histograms
- Correlation Heatmaps
### AI Assistant
- Natural language commands for visualization modification
- Interactive command interface
- Real-time visualization updates
- Example commands for easy reference
## Installation 🛠️
1. Clone the repository:
```bash
git clone https://github.com/Priyanshu7639/Data_Visualization_Dashboard.git
cd Data_Visualization_Dashboard
```
2. Create a virtual environment:
```bash
python -m venv venv
# Windows
venv\Scripts\activate
# macOS/Linux
source venv/bin/activate
```
3. Install required packages:
```bash
pip install -r requirements.txt
```
## Usage 💡
1. Start the application:
```bash
streamlit run main.py
```
2. Upload your data:
- Click "Browse files" in the sidebar
- Select your CSV or Excel file
3. Preprocess your data (optional):
- Remove duplicates
- Handle missing values
- Scale features
4. Create visualizations:
- Select visualization type
- Choose variables for axes
- Add color grouping if needed
5. Modify visualizations using AI:
- Enter commands in natural language
- Click "Apply Changes"
- See immediate updates to your visualization
## Example Commands 🗣️
- "Make the scatter plot points larger"
- "Add a trend line"
- "Change title to 'Sales Analysis'"
- "Make the axis labels bigger"
- "Show confidence intervals"
- "Add annotations for outliers"
## Dependencies 📚
- streamlit
- pandas
- numpy
- plotly
- seaborn
- matplotlib
- scikit-learn
- openpyxl
## Contributing 🤝
1. Fork the repository
2. Create a new branch
3. Make your changes
4. Submit a pull request
## License 📄
This project is licensed under the MIT License - see the LICENSE file for details.
## Acknowledgments 🙏
- Streamlit for the amazing web framework
- Plotly for interactive visualizations
- The open-source community for inspiration and support
### Additional Notes 📝
- Make sure you have Python 3.7+ installed
- The application works best with clean, structured data
- Large datasets might take longer to process
- Supported file formats: .csv, .xlsx, .xls
### Troubleshooting 🔧
1. If you encounter installation issues:
```bash
pip install --upgrade pip
pip install -r requirements.txt --force-reinstall
```
2. If visualizations don't load:
- Check your internet connection
- Ensure data format is correct
- Verify column names don't contain special characters
3. For memory issues with large datasets:
- Try reducing the dataset size
- Close other applications
- Increase system swap space
### Future Enhancements 🚀
- Additional visualization types
- More AI command capabilities
- Advanced data preprocessing options
- Export functionality for modified visualizations
- Custom theme support
- Real-time collaboration features
For more information or support, please open an issue in the GitHub repository.