DataCraft

Dashboard and Analytics

This open-source project allows you to upload a CSV file and automatically generate a comprehensive dashboard with various visualizations and plots. It leverages powerful data visualization libraries to provide insights and analytics from your data.

Features

• CSV File Upload: Users can upload CSV files directly through the web interface.
• Automatic Dashboard Creation: Upon uploading a CSV file, the application generates a dashboard with various visualizations.
• Interactive Visualizations: The dashboard includes a range of plots and charts that are interactive, allowing users to explore the data in depth.
• Analytics and Insights: The application provides key analytics based on the uploaded data, offering insights into trends, distributions, and correlations.

Installation

To run this project locally, follow these steps:

1. Clone the repository:

2. Install dependencies:

   Ensure you have Python installed. Then, set up a virtual environment and install the required packages:

   python3 -m venv fastapi-env
   source fastapi-env/bin/activate
   pip install -r requirements.txt

3. Start the FastAPI server:

   Run the FastAPI backend to handle CSV uploads and data processing:

4. Run the frontend:

   Ensure you have Node.js installed. Then, navigate to the frontend directory and start the development server:

5. Open the application:

   Visit http://localhost:3000 in your web browser to access the application.

Usage

1. Upload a CSV File:

   • Use the "Input File" button to select and upload a CSV file from your local system.

2. View the Dashboard:

   • Once uploaded, the application will process the CSV file and automatically generate a dashboard with various plots and visualizations.

3. Explore the Data:

   • Interact with the visualizations to explore the data. Hover over charts to see detailed information, filter data, and adjust visualization settings as needed.

Visualization Types

The dashboard may include the following types of visualizations, depending on the data:

• Bar Charts: For categorical data comparison.
• Line Plots: To show trends over time.
• Scatter Plots: To visualize correlations between variables.
• Histograms: For understanding the distribution of a single variable.
• Pie Charts: To represent parts of a whole.
• Heatmaps: To visualize relationships between variables using color intensity.

Contributions

Contributions are welcome! If you have ideas for new features, find a bug, or want to improve the documentation, please submit an issue or a pull request.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Acknowledgments

This project utilizes the following technologies:

• FastAPI: For the backend API and data processing.
• NextJS: For the frontend interface.
• NextUI: For the user interface components.
• Plotly.js: For creating interactive data visualizations.
• Pandas: For data manipulation and analysis.

Contact

For any inquiries or issues, please contact [[email protected]].