Matic Price Dynamics - Model Dashboard

Overview

The Matic Price Dynamics - Model Dashboard is an interactive web application designed to showcase a comparative analysis of machine learning models for forecasting Polygon (Matic) token prices. The dashboard highlights model descriptions, performance metrics, visualizations, and detailed results to provide an intuitive understanding of each model's behavior and suitability for time-series data.

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Features

1. Model Descriptions

• Comprehensive descriptions of four machine learning models:
  • Random Forest
  • Support Vector Machine (SVM)
  • LSTM
  • GNU+GRU Hybrid
• Each description includes:
  • A concise explanation of the model.
  • Key performance metrics like RMSE and R² scores.
  • Strengths and weaknesses of each model.

2. Model Performance Comparisons

• A detailed table comparing all models:
  • RMSE values for accuracy measurement.
  • R² scores for goodness-of-fit.
  • A summary of each model's strengths and weaknesses.

3. Visual Representations

• An interactive gallery showcasing:
  • Architecture diagrams.
  • Predictions for each model compared with actual data.

4. Results Section

• A summary of key findings:
  • Analysis of the best-performing model (GNU+GRU Hybrid).
  • Discussion of each model’s limitations and use cases.

5. Aesthetic UI

• A modern, responsive design with:
  • Gradient background.
  • Hover animations for interactivity.
  • Clean typography and well-structured layout.

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Live Preview

You can deploy this application on any web server or view it locally.

How to View Locally

1. Clone this repository:

   git clone https://github.com/your-repository/matic-price-dynamics.git

2. Navigate to the project folder:

   cd matic-price-dynamics

3. Open index.html in any browser to view the dashboard.

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Project Structure

matic-price-dynamics/
│
├── index.html          # Main HTML file for the dashboard
├── styles.css          # Custom CSS for the project
├── script.js           # JavaScript for rendering dynamic content
└── README.md           # Documentation

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Technologies Used

Frontend

• HTML5: Structure of the webpage.
• CSS3:
  • Responsive layout.
  • Animations and hover effects.
• Bootstrap 5: Predefined styling and grid system for responsiveness.

JavaScript

• Vanilla JS: Dynamic content rendering.
• Chart.js: For visualizing data in future iterations.

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Setup and Usage

Prerequisites

Ensure you have a modern web browser like Google Chrome, Firefox, or Microsoft Edge.

Steps to Run

1. Clone the repository using:

   git clone https://github.com/your-repository/matic-price-dynamics.git

2. Open the project directory:

   cd matic-price-dynamics

3. Double-click index.html to launch the application in your browser.

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Features Breakdown

1. Descriptions Section

• Displays detailed explanations for each model.
• Includes RMSE and R² metrics in an easily understandable list format.
• Provides insights into strengths and weaknesses to help the user understand the trade-offs of each model.

2. Comparison Table

• Highlights differences across models in a clean, structured table.
• Includes:
  • RMSE for accuracy comparison.
  • R² scores for model fit evaluation.
  • Strengths and weaknesses to guide model selection.

3. Visual Representations

• Showcases:
  • Architecture diagrams for understanding model pipelines.
  • Comparative prediction charts to highlight performance.
• Hover effects on images to enhance interactivity.

4. Results Section

• Concludes with a performance summary:
  • Discusses why the GNU+GRU Hybrid outperforms others.
  • Explains the applicability of different models to specific datasets.

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Screenshots

1. Architecture Diagram

2. Model Descriptions

3. Performance Comparison Table

4. Visual Representations

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Future Improvements

1. Enhanced Visualizations
   • Integrate dynamic charts using Chart.js to visualize RMSE trends.
   • Add interactive line graphs for model predictions.
2. Advanced Filtering
   • Allow users to filter and sort the table based on RMSE or R² scores.
3. Deployment
   • Host the application on GitHub Pages or Netlify for wider access.

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Contributing

Contributions are welcome! To contribute:

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

   git checkout -b feature-name

3. Commit your changes:

   git commit -m "Add a feature"

4. Push to your branch:

   git push origin feature-name

5. Submit a pull request.

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License

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

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