f1podiumpredictor

pipeline_tag: tabular-classification

F1 Race Prediction & Visualization System

A full-stack machine learning project that predicts Formula 1 podium finishers and race winners using real telemetry data, with a live race replay simulation and an autonomous AI driver.

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Screenshots

Pre-Race Predictions & Track Map

Full Driver Rankings

Model Accuracy Dashboard

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What It Does

• Predicts podium finishers and race winners using two separate Random Forest models trained on 3+ seasons of real F1 data
• Serves predictions through a Streamlit web app with qualifying order input, race condition settings, and a model accuracy dashboard
• Replays real races in a live Arcade simulation with actual GPS telemetry from FastF1 — cars move around the real circuit lap by lap
• Updates predictions live during the replay as race positions evolve
• Autonomous AI driver — a Deep Q-Network (DQN) ported from C++ drives around real F1 circuits using LIDAR sensors

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Results

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Tech Stack

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Project Structure

f1podiumpredictor/
├── app.py                  # Streamlit web app
├── main.py                 # Full pipeline runner
├── replay.py               # Arcade race replay entry point
├── multiclass.py           # Optional P1-P20 position predictor
│
├── f1predictor/            # Core ML package
│   ├── collect.py          # FastF1 data collection
│   ├── features.py         # Feature engineering
│   ├── train.py            # Model training (podium + winner)
│   └── predict.py          # Prediction logic
│
├── src/                    # Replay + AI modules
│   ├── arcade_window.py    # Main Arcade window
│   ├── replay_data.py      # Lap position loader
│   ├── live_predict.py     # In-race prediction updates
│   ├── ai_driver.py        # DQN agent (PyTorch)
│   └── ai_track.py         # F1 circuit surface + LIDAR
│
├── models/
│   └── best_time.pt        # Trained DQN model
├── data/                   # Generated after running pipeline
└── model/                  # Generated after training

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Setup

1. Clone the repo

git clone https://github.com/sarthak-codes11/f1podiumpredictor.git
cd f1podiumpredictor

2. Install dependencies

pip install -r requirements.txt
pip install torch --index-url https://download.pytorch.org/whl/cpu

3. Collect data and train models

python main.py

This collects 3 seasons of F1 data from the FastF1 API and trains both models. Takes 15–30 minutes due to API rate limits.

4. If you already have data, skip collection

python main.py --skip-collect

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Usage

Streamlit Predictions App

streamlit run app.py

• Enter qualifying order and race conditions
• Get win % and podium % for each driver
• Check model accuracy, confusion matrices, and feature importances

Race Replay Simulation

python replay.py --year 2024 --round 12

Replay Controls

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Features Used by the Models

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How the Models Work

Two separate Random Forest classifiers are trained:

Podium Model — predicts whether a driver finishes in the top 3. Class imbalance is handled with class_weight={0:1, 1:6} since only 3 of 20 drivers podium per race. Decision threshold set to 0.4.

Winner Model — predicts whether a driver wins the race. Far rarer event (1 in 20), so uses class_weight={0:1, 1:55} and a lower decision threshold of 0.3 to avoid always predicting "no win".

Both models use a time-ordered train/test split — trained on older seasons and tested on newer ones — to prevent data leakage.

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AI Driver

The AI driver is a Deep Q-Network trained in C++ using Raylib and ported to Python for inference. It uses a 23-dimensional state space:

• Speed, heading (sin/cos), normalized position (5 dims)
• 13 short-range LIDAR rays for wall danger detection
• 5 long-range LIDAR rays for corner anticipation

The agent drives around real F1 circuit centerlines extracted from FastF1 telemetry, with wall detection based on distance to the circuit edge rather than pixel colors.

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Disclaimer

Data is sourced from the FastF1 library which accesses publicly available F1 timing data. This project is for educational purposes only. Formula 1 and related trademarks are property of their respective owners.