Formula-1-Predictor

🏎️ F1 Neural Network Predictor - Live F1DB System

Advanced Formula 1 driver position prediction using deep learning neural networks powered by live F1DB data updated after every race weekend.

🔥 Live Data Integration

✅ F1DB Database: https://github.com/f1db/f1db
✅ Real-time Updates: Data refreshed after every race weekend
✅ Authentic Data: 75+ years of genuine F1 history (1950-2025)
✅ Current Season: 2025 data through Hungarian Grand Prix

🏁 2025 Race Predictions

🏆 Predicted Podium (F1DB v2025.14.0)

🥇 Alexander Albon (Williams)
🥈 Andrea Kimi Antonelli (Mercedes)
🥉 Carlos Sainz (Williams)

📊 Model Performance

Training Data: 8,195 authentic race results (2000-2025)
Mean Absolute Error: 4.50 positions
Within 5 positions: 62.0% accuracy
Neural Network: 320,001 parameters

🚀 Quick Start

Run F1DB System

# Install dependencies
pip3 install --break-system-packages -r requirements.txt

# Run the complete F1DB system
python3 run_f1db_system.py

# Or run neural network directly
python3 f1db_neural_predictor.py

Web Application

# Start local web app
python3 run_web_demo.py
# Visit: http://localhost:8000

# Deploy to GitHub Pages
git add . && git commit -m "F1DB live system" && git push
# Visit: https://ihackmer19.github.io/Formula-1-Predictor

🧠 Neural Network Architecture

Enhanced F1DB Model

Input: 37 F1DB-specific features
Architecture: 6-layer deep network (256→512→256→128→64→32→1)
Regularization: Dropout + Batch Normalization
Optimizer: Adam with Huber loss (robust to outliers)
Parameters: 320,001 trainable parameters

F1DB Features

Historical Performance: Rolling averages (3, 5, 10 races)
Career Statistics: Wins, podiums, points, race count
Circuit-Specific: Track performance history
Constructor Metrics: Team performance indicators
Era Features: Current regulations, turbo-hybrid era
Circuit Data: Length, turns, type characteristics
Form Indicators: Recent performance trends

📊 F1DB Data Overview

Dataset Scale

Years: 1950 - 2025 (76 years)
Races: 1,149 Grand Prix events
Results: 27,091 race results
Drivers: 912 F1 drivers in history
Constructors: 185 teams/constructors
Circuits: 77 F1 circuits

Current Season (2025)

Races Completed: 24 (through Hungarian GP)
Active Drivers: 27 drivers in recent data
Active Teams: 11 constructors
Latest Update: Hungarian Grand Prix 2025

🔄 Automatic Updates

F1DB Auto-Updater

# Manual update check
python3 f1db_auto_updater.py

# Continuous monitoring
python3 f1db_update_service.py

Update Schedule

📅 Monday 09:00: Post-race weekend updates
📅 Daily 12:00: Missed update checks
🔄 Automatic: Download, retrain, deploy

Update Process

Monitor: Check F1DB GitHub releases
Download: Latest CSV data automatically
Retrain: Neural network with new data
Deploy: Update web app predictions
Log: Track all update attempts

🌐 Web Application

Live Features

🔥 F1DB Integration: Real-time data loading
📊 Interactive Charts: Team and driver analysis
📱 Mobile Optimized: Responsive design
🏁 Live Predictions: Updated after each race

GitHub Pages Deployment

# Enable GitHub Pages in repository settings
# Source: Deploy from 'docs' folder
# URL: https://ihackmer19.github.io/Formula-1-Predictor

📁 Project Structure

├── 🔥 F1DB CORE SYSTEM
│   ├── f1db_neural_predictor.py    # Main F1DB neural network
│   ├── f1db_auto_updater.py       # Automatic update system
│   ├── run_f1db_system.py         # System interface
│   └── f1db_version.txt           # Current F1DB version
│
├── 📊 LIVE F1DB DATA
│   ├── f1db-races.csv             # Race information
│   ├── f1db-races-race-results.csv # Race results (27K+ results)
│   ├── f1db-drivers.csv           # Driver database (912 drivers)
│   ├── f1db-constructors.csv      # Constructor data (185 teams)
│   └── f1db-*.csv                 # Additional F1DB datasets
│
├── 🏁 PREDICTIONS & MODELS
│   ├── f1db_2025_predictions.csv  # Latest 2025 predictions
│   ├── f1db_best_model.h5         # Trained neural network
│   ├── f1db_model_evaluation.png  # Performance analysis
│   └── f1db_update_log.json       # Update history
│
├── 🌐 WEB APPLICATION
│   └── docs/                      # GitHub Pages app
│       ├── index.html             # F1DB-integrated interface
│       ├── data/f1db_predictions.* # Live prediction data
│       └── js/app.js              # F1DB data loading
│
└── 📖 DOCUMENTATION
    ├── F1DB_LIVE_SYSTEM.md        # F1DB integration guide
    ├── README.md                  # This file
    └── requirements.txt           # Dependencies

🎯 Key Features

🔥 Live Data

Real F1 Database: Authentic data from 1950-2025
Race Weekend Updates: New data after every Sunday race
No Synthetic Data: 100% authentic F1 information
Complete History: 75+ years of Formula 1 evolution

🧠 Advanced AI

Deep Neural Network: 6-layer architecture
F1-Specific Features: 37 racing-focused inputs
Temporal Modeling: Historical performance analysis
Robust Training: Handles real-world F1 variability

🌐 Professional Web App

GitHub Pages Ready: Instant deployment
Live Data Integration: F1DB predictions in real-time
Mobile Responsive: Perfect on all devices
Interactive Charts: Team and driver analysis

🔄 Automated System

Auto-Updates: Monitors F1DB for new releases
Smart Retraining: Updates model with latest data
Web Integration: Automatically updates predictions
Error Handling: Robust failure recovery

🎮 Usage Examples

Basic Prediction

# Run F1DB neural network
python3 f1db_neural_predictor.py

# View predictions
cat f1db_2025_predictions.csv

Web Interface

# Start web app locally
python3 run_web_demo.py

# Deploy to GitHub Pages
git push origin main

System Management

# Interactive system interface
python3 run_f1db_system.py

# Check for F1DB updates
python3 f1db_auto_updater.py

# Start monitoring service
python3 f1db_update_service.py

🏆 Advantages Over Static Data

🔥 F1DB vs Static Datasets

🎯 Real-World Benefits

Current Grid: Actual 2025 F1 drivers and teams
Recent Form: Latest performance data included
Rule Changes: Accounts for current F1 regulations
Team Evolution: Real constructor changes and mergers
Driver Transfers: Actual 2025 driver lineup

🔧 Technical Requirements

Python Dependencies

pip3 install pandas numpy scikit-learn tensorflow matplotlib seaborn requests schedule

System Requirements

Python: 3.8+ (tested with 3.13)
Memory: 4GB+ RAM for neural network training
Storage: 100MB for F1DB data files
Network: Internet connection for F1DB updates

🌟 Future Enhancements

Planned Features

Live Timing Integration: Real-time race data
Weather Data: Track conditions and forecasts
Strategy Analysis: Pit stop and tire strategy factors
Ensemble Models: Multiple prediction algorithms

Community Contributions

Feature Requests: GitHub issues welcome
Data Improvements: F1DB community contributions
Model Enhancements: Neural network optimizations
Web App Features: UI/UX improvements

📞 Support & Community

Resources

F1DB Database: https://github.com/f1db/f1db
Project Repository: https://github.com/IHackmer19/Formula-1-Predictor
Live Web App: https://ihackmer19.github.io/Formula-1-Predictor
Documentation: F1DB_LIVE_SYSTEM.md

Getting Help

GitHub Issues: Report bugs or request features
F1DB Community: Join F1 data discussions
Documentation: Comprehensive guides provided

🏁 Ready for 2025 F1 Season!

🔥 Your F1 Neural Network Predictor now uses live, authentic Formula 1 data!

✅ Real Data: F1DB live database integration
✅ Auto-Updates: Monitors race weekend updates
✅ Web Ready: Professional GitHub Pages deployment
✅ Production Quality: 320K parameter neural network

🏎️ Experience the future of Formula 1 predictions with live data! 🏆