Skip to content

📊 Develop an interactive credit scoring dashboard and enhance NLP insights by comparing BERT and MiniLM models for e-commerce product classification.

License

Notifications You must be signed in to change notification settings

pizzzi/realisation_dashboard_veille_technique

Repository files navigation

🚀 realisation_dashboard_veille_technique - Your Easy Credit Scoring Dashboard

📥 Download Now

Download

📋 Overview

The realisation_dashboard_veille_technique is a user-friendly dashboard designed to help you score credit effectively. It also offers a comparison of Natural Language Processing (NLP) models, specifically BERT and MiniLM, for classifying eCommerce products. This application uses Streamlit, providing an interactive and visual representation of data, making analysis straightforward.

🛠️ Features

  • Credit Scoring: Evaluate credit scores using explainable AI.
  • NLP Comparison: Analyze and compare BERT vs MiniLM for eCommerce classification tasks.
  • User-Friendly Dashboard: Easy to understand interface, perfect for non-technical users.
  • Real-Time Data Visualization: View results through dynamic graphs and charts.
  • Compatible with Multiple Platforms: Run on Windows, macOS, and Linux.

🌐 System Requirements

To successfully run the application, your system should meet the following requirements:

  • Operating System: Windows 10 or later, macOS 10.13 or later, or any Linux distribution.
  • Memory: At least 4GB RAM.
  • Processor: A dual-core processor or better.
  • Video Card: Any card that supports OpenGL 3.0 or later.

🚀 Getting Started

  1. Visit the Download Page: Click the link below to access the releases page and download the application.

    Download the App

  2. Choose Your Version: Look for the latest release. Ensure you select the correct version for your operating system.

  3. Download the Application: Click on the file to start the download.

  4. Install the App: Follow the installation prompts:

    • For Windows, run the .exe file.
    • For macOS, drag the application to your Applications folder.
    • For Linux, unzip the package and run the executable.
  5. Running the Dashboard: After installation, launch the application from your list of installed software.

🔄 Using the Dashboard

  1. Input Your Data: Use the dashboard to input the data necessary for scoring credit or classifying products.
  2. View Results: The dashboard will display results using charts and tables.
  3. Interact with the Data: Adjust input parameters to see how scores change in real time.

🌍 Community Contributions

We welcome contributions from the community. If you want to help improve this dashboard, please feel free to submit your suggestions or code. To contribute, please:

  • Fork the repository.
  • Create a new branch for your feature.
  • Make your changes and test thoroughly.
  • Submit a pull request for review.

📞 Support

If you need assistance, please reach out through the GitHub Issues page. Your questions are important, and we aim to help you understand how to use the dashboard effectively.

📄 License

This project is licensed under the MIT License. You can use, modify, and distribute this software freely, provided that you include credit to the original authors.

📄 Changelog

Version 1.0

  • Initial release with core features: credit scoring and NLP comparison.

Future Updates

  • Potential integration of additional models and improved data visualization options.

🔗 Further Reading

For more information about the technologies used, consider the following:

  • Streamlit Documentation: Learn about Streamlit for building interactive web applications.
  • BERT vs MiniLM Studies: Discover articles comparing these NLP models for deeper insight into their effectiveness.

🔗 Important Links

Thank you for using realisation_dashboard_veille_technique! Enjoy your experience.

About

📊 Develop an interactive credit scoring dashboard and enhance NLP insights by comparing BERT and MiniLM models for e-commerce product classification.

Topics

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Contributors 2

  •  
  •