Unlock the Power of Your Data, From Start to Insight.

DataEclipseAI_** was built to solve the critical challenge of understanding and managing data flows. In today’s data-driven world, it’s just as important to know how data moves as it is to understand what it means. Our solution provides organizations with complete visibility into how data is created, accessed, moved, and used — with clear business context. The name DataEclipseAI reflects our mission: to remove the "eclipse" or shadow cast on the data analytics process, offering organizations a brighter, more transparent view of their entire data lifecycle.

By leveraging the power of AI and machine learning, DataEclipseAI automates data analysis, recommends optimal cleaning methods, and suggests the best models based on performance metrics. Users can then customize their approach, experimenting with different models and cleaning techniques to fine-tune results, ensuring security, performance, and compliance at every stage of the data lifecycle.

1. Clone the repository:

   git clone https://github.com/tanmay1501/ripple_hackUTD
   Backend:
     python app.py
   Frontend:
     npm install
     npm run dev
   

DataEclipseAI provides end-to-end visibility into the lifecycle of your data. It allows organizations to securely upload, clean, and analyze their data with AI-powered insights. By leveraging machine learning algorithms, the platform automates data preprocessing, including missing value imputation and outlier detection. It then applies predictive models to analyze and forecast outcomes, offering users the ability to visualize and customize each stage of the process. The platform enhances data governance by mapping the entire data journey—from creation to consumption—ensuring transparency and accuracy at every step.

Automated Data Cleaning: We’ve automated complex data cleaning tasks, reducing manual effort and increasing data quality.

Comprehensive Model Selection: By integrating multiple machine learning algorithms, we allow users to select and compare the best models for their specific needs. Real-Time Visualization: The ability to visualize data at each stage of the process—cleaning, processing, and prediction—offers a unique, actionable perspective. Customizability: Empowering users to choose from various algorithms and download outputs at any stage ensures flexibility in how they approach data analysis. Data Security & Compliance: With Pinata as the database, we ensure that user data is securely stored and handled in compliance with industry standards.

Contributions are welcome! Please follow these steps to contribute:

1. Fork the project.
2. Create a feature branch:

   git checkout -b feature/YourFeatureName

3. Commit your changes

   git commit -m "Add some feature"

4. Push to Branch

   git push origin feature/YourFeatureName

5. Open a pull request