F1 Race Prediction Simulator

Link to the Frontend Repository:

https://github.com/HiraethSerra/ProjectF1.git

Overview

This project is a Formula 1 Race Prediction Simulator. It allows users to:

• View all F1 drivers with profile details and photos
• Filter drivers by year
• Select a race and simulate race predictions using a trained machine learning model
• Explore race results and historical data

The system is built with a Django REST API backend and ML model integration.

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Inspiration

The inspiration for this project came from the fascination with Formula 1 racing and the challenge of predicting race outcomes using real-world data. F1 fans are eager to analyze drivers’ performances, and combining historical data with machine learning allows creating an interactive simulator.

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Project Goal

• Provide accurate race predictions based on historical race results and driver performance
• Return driver statistics, teams, flags, and race results
• Fetch any race by year and name

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Technologies Used

• Backend: Django, Django REST Framework, PostgreSQL
  • Provides robust APIs for drivers, races, and predictions
• Machine Learning: Python, Scikit-learn / Pandas / NumPy
  • Random Forest classifier for race prediction
• Data Sources: FastF1 API
• Docker: Containerized backend and frontend for easy deployment

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Why this Project is the Best

• Interactive, real-time F1 simulation
• Combines data analysis, ML, and full-stack development
• Unique driver cards with team gradients, flags, and headshots
• Predicts upcoming races using historical data, not just static results

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Lessons Learned

• Working with real-world F1 datasets requires careful preprocessing
• Learned dynamic filtering in Django and relationship handling in models
• Understood ML feature building based on historical sequences

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Project Setup / Installation

Clone Repository

git clone https://github.com/lyes0000/ProjectF1-API.git
cd ProjectF1-API

# Build Docker images
docker compose build

# Start containers
docker compose up

# Make migrations
docker compose exec web python manage.py makemigrations

# Apply migrations
docker compose exec web python manage.py migrate

# Fetch seasons to train the model:
docker compose exec web python manage.py fetch_all_seasons # this is set to fetch from 2022 to 2024 by default

# Fetch a specific season by year:
docker compose exec web python manage.py fetch_season --year 2022

# Fetch one specific race in a given year: 
docker compose exec web python manage.py fetch_f1_data

# Run ML model training
docker compose exec web python manage.py train_ml_model

# (Optional) Create superuser
python manage.py createsuperuser

# Run backend server
docker compose exec web python manage.py runserver 0.0.0.0:8000