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Leipzig AI Temperature Predictor

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Description

The Leipzig AI Temperature Predictor is an AI-powered web application that predicts hourly temperatures in Leipzig, Germany using neural networks. Trained on historical weather patterns, it provides:

  • Real-time model training visualization 📈
  • Interactive temperature predictions 🌡️
  • Detailed hourly temperature charts 📊
  • Persistent prediction history 📅

✨ Key Features

  • 🧠 Neural Network Training with Brain.js
  • ⏳ Real-Time Training Progress Visualization
  • 🔮 Temperature Prediction Interface
  • 📈 Chart.js Interactive Visualizations
  • 💾 LocalStorage Prediction History
  • 📱 Responsive Mobile-Friendly Design
  • ♿ Accessibility-First Approach
  • 🎨 Temperature Gradient Charts
  • ⚡ Web Worker Background Processing

🚀 Getting Started

  1. Live Demo: Click Here
  2. Local Usage:
    git clone https://github.com/salimov333/ai-temperature-predictor.git
    cd ai-temperature-predictor
    # Open index.html in modern browser

⚙️ Technologies Used

  • Brain.js - Neural Network Library
  • Chart.js - Data Visualization
  • Web Workers - Background Training
  • LocalStorage - Prediction History
  • Modern CSS - Responsive Design
  • ES6 JavaScript - Core Logic

🌐 Browser Support

Browser Version
Chrome 90+
Firefox 85+
Safari 14+
Edge 90+

🤝 Contributing

  1. Fork the repository
  2. Create feature branch (git checkout -b feature/AmazingFeature)
  3. Commit changes (git commit -m 'Add AmazingFeature')
  4. Push to branch (git push origin feature/AmazingFeature)
  5. Open Pull Request

📜 License

MIT License - See LICENSE for details

✍️ Author

Salem Helwani
GitHub

🙏 Acknowledgements

  • German Weather Service (DWD) for historical data
  • Brain.js team for neural network library
  • Chart.js for visualization capabilities
  • Leipzig climate researchers

Predict Leipzig's Temperatures with AI Precision! 🔮🌡️


About

This is an AI application that uses a neural network, simulating a simplified human brain, and machine learning principles to predict weather temperature based on the hour of the day and month of the year. It also generates a chart visualizing temperature variations throughout the day for a given month.

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