🏎️ F1 Prediction Hub

Proof of Concept: F1 Prediction Hub is a state-of-the-art Formula 1 analytics dashboard powered by machine learning, providing comprehensive race predictions, historical insights, and driver analysis.

✨ Features

🎯 Race Predictions Engine

Our advanced machine learning model analyzes multiple factors to predict race outcomes:

• Grid position impact analysis
• Constructor performance metrics
• Track-specific predictions
• Weather condition considerations
• Tire strategy optimization

📊 Interactive Visualizations

• Position change predictions
• Historical performance heatmaps
• Real-time confidence metrics
• Feature importance analysis
• Performance trend tracking

👨‍🚀 Driver Insights Hub

Comprehensive driver analytics including:

• Head-to-head comparisons
• Career statistics
• Driving style analysis
• Performance metrics tracking
• Historical achievements

📚 Historical Encyclopedia

Explore F1's rich history through:

• Interactive timeline of significant events
• Championship records and statistics
• Iconic circuit information
• Technical evolution tracking
• Memorable moments archive

🚀 Getting Started

Prerequisites

python 3.8+
pip
virtualenv (recommended)

Installation

1. Clone the repository:

git clone https://github.com/yourusername/f1-prediction-hub.git
cd f1-prediction-hub

2. Create and activate virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Launch the dashboard:

streamlit run app.py

🌐 Hosting the Dashboard

Local Hosting

The dashboard can be run locally using Streamlit:

streamlit run app.py

Access at http://localhost:8501

Cloud Deployment

Streamlit Cloud

1. Push your code to GitHub
2. Visit Streamlit Cloud
3. Connect your repository
4. Deploy with one click

Docker Deployment

1. Build the image:

docker build -t f1-prediction-hub .

2. Run the container:

docker run -p 8501:8501 f1-prediction-hub

🔧 Technical Architecture

Components

• app.py: Main dashboard application
• api_module.py: F1 data retrieval and processing
• ml_module.py: Machine learning model implementation
• driver_insights.py: Driver analytics processing
• historical_wiki.py: Historical data management

Data Sources

• Ergast F1 API
• Historical race data
• Driver statistics
• Circuit information

📈 Model Features

The prediction model considers:

• Grid position
• Constructor performance
• Circuit characteristics
• Weather conditions
• Track temperature
• Tire strategies
• Historical performance

🎨 UI Features

• Dark mode design
• Responsive layout
• Interactive charts
• Real-time updates
• Custom animations
• Mobile-friendly interface

📝 License

• This project is released under an open-source license. It is completely free for anyone to use, modify, and distribute. You are welcome to:

• Use the code for any purpose

• Study how it works and adapt it

• Redistribute it to anyone

• Make improvements and share them with everyone

🙏 Acknowledgments

• Formula 1 for the inspiration
• Ergast API for the data
• Streamlit team for the amazing framework
• The F1 community for their support

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Made with ❤️ and ☕