F1 Podium Predictor

A machine learning model and web application for predicting Formula 1 race podium finishers.

Project Overview

This project combines historical F1 data (2000-2024) from the Ergast API with recent telemetry data (2023-2025) from FastF1 to predict podium finishers for upcoming Grand Prix races. The system includes:

• Machine learning model for podium predictions
• Web application for viewing historical data and predictions
• MongoDB database for data storage
• RESTful API for data access

Features

Machine Learning Model

• Predicts top 3 finishers for upcoming races
• Considers driver performance history, track-specific performance, recent form, and team performance
• Uses neural networks for prediction

Web Application

• View historical race data
• View detailed driver statistics
• View upcoming race information
• Display circuit maps and race details

Setup Instructions

1. Clone the repository

2. Create a virtual environment:

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

3. Install dependencies:

   pip install -r requirements.txt

4. Set up environment variables:

   • Create a .env file
   • Add your MongoDB Atlas connection string
   • Add any API keys if needed

5. Initialize the database:

   python scripts/init_db.py

6. Run the web application:

   python app.py

Project Structure

f1-model/
├── app/                    # Web application
│   ├── static/            # Static files (CSS, JS, images)
│   ├── templates/         # HTML templates
│   └── routes/            # Flask routes
├── ml/                    # Machine learning code
│   ├── data/             # Data processing scripts
│   ├── models/           # ML model definitions
│   └── training/         # Training scripts
├── scripts/              # Utility scripts
├── tests/               # Test files
├── .env                 # Environment variables
├── requirements.txt     # Project dependencies
└── README.md           # This file

Data Sources

• Historical Data (2000-2024): Ergast API
• Recent Data (2023-2025): FastF1 library
• Circuit Maps: F1 official website
• Additional Driver Information: Ergast API

Contributing

Feel free to submit issues and enhancement requests!