🌌 FrostByte - Exoplanet Discovery Platform

An advanced AI-powered platform for exoplanet detection and space exploration, featuring interactive dashboards, intelligent chatbots, and immersive space games.

🚀 🌐 VISIT LIVE DEMO →

✨ Features

🤖 AI Chatbot

• Intelligent conversations about exoplanets and space science
• Real-time responses in English with scientific accuracy
• Interactive interface with conversation history
• Educational content about detection methods and discoveries

📊 ML Dashboard

• Advanced exoplanet prediction using Random Forest AI model
• Interactive visualizations with Plotly charts
• Real-time parameter analysis and classification
• Professional space-themed interface
• Multiple input modes: Manual, Examples, Random generation

🎮 Space Game

• Interactive cosmic exploration game
• Educational planet discovery mechanics
• Immersive space environment

🚀 Quick Start

1. Clone the repository:

   git clone https://github.com/Promis229/FrostByte.git
   cd FrostByte

2. Start the HTTP server:

   python3 -m http.server 8000

3. Open your browser: Navigate to http://localhost:8000

4. Explore the features:

   • Click "Chatbot" for AI conversations
   • Click "ML Dashboard" for exoplanet predictions
   • Click "Space Game" for interactive exploration

📁 Project Structure

FrostByte/
├── index.html              # Main landing page
├── chatbot_test.html        # AI chatbot interface
├── dashboard.html           # ML prediction dashboard
├── api.js                   # Client-side ML simulation
├── game/                    # Space exploration game
│   ├── index.html
│   ├── game.js
│   ├── styles.css
│   └── images/
├── best_exoplanet_model.pkl # Trained ML model
├── exoplanet_scaler.pkl     # Data scaler
├── exoplanet_label_encoder.pkl # Label encoder
├── data.csv                 # NASA Kepler dataset
└── README.md               # This file

🔬 Technology Stack

• Frontend: HTML5, CSS3, JavaScript (ES6+)
• ML Framework: Python scikit-learn (Random Forest)
• Visualization: Plotly.js for interactive charts
• Data: NASA Kepler Exoplanet Archive
• Deployment: Static HTTP server (no dependencies)

🌟 Key Features

Exoplanet Detection

• 9 core parameters from NASA Kepler data
• Feature engineering for enhanced accuracy
• Real-time classification: Confirmed, Candidate, False Positive
• Confidence scoring and probability distributions

AI Responses

• Educational content about exoplanets
• Detection methods explanation
• Space mission information (Kepler, TESS, JWST)
• Habitability analysis and biosignatures

Interactive Experience

• Responsive design for all devices
• Dark space theme with cosmic aesthetics
• Smooth animations and visual effects
• Professional scientific interface

📈 Model Performance

• Accuracy: ~95% on NASA Kepler dataset
• Algorithm: Random Forest Classifier
• Features: 14 engineered parameters
• Classes: 3 (Confirmed, Candidate, False Positive)

🌍 Educational Value

Perfect for:

• Students learning about exoplanets
• Educators teaching space science
• Enthusiasts exploring astronomy
• Researchers visualizing exoplanet data

🤝 Contributing

1. Fork the repository
2. Create your feature branch
3. Commit your changes
4. Push to the branch
5. Open a Pull Request

📄 License

This project is open source and available under the MIT License.

🔭 Acknowledgments

• NASA Exoplanet Archive for providing the dataset
• Kepler Space Telescope mission for the discoveries
• Python scikit-learn for machine learning capabilities
• Plotly.js for interactive visualizations

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Made with ❤️ for space exploration and scientific education

🌌 "The cosmos is within us. We are made of star-stuff." - Carl Sagan