Formula 1 Grid Prediction

This repository provides a Python-based pipeline for fetching current Formula 1 data, storing it in CSV files, and using machine learning techniques to predict the outcomes of the Abu Dhabi Grand Prix 2024. The data is fetched from the Ergast API and includes information about drivers, driver standings, and circuits. The repository also contains a Jupyter Notebook where various machine learning models are applied to predict driver performance at the Abu Dhabi GP.

Results

The model predicts the grid well including the podiums. One of the drawbacks being that it does not account for any car crashes or physical interuptions, i.e., it assumes a no casualty race; contradictory to what happened that day, potentially being a variable in the model to be accounted for. Future works include introducing such variables.

Repository Structure

• data.py: A Python script for fetching data from the Ergast API and saving it to CSV files. The script gathers the following data:

  • Current F1 driver information
  • Current F1 driver standings
  • Abu Dhabi circuit information
  • Placeholder for Abu Dhabi GP predictions

• Modeling Notebook: A Jupyter Notebook that applies several machine learning techniques to the data collected by the data.py script. The models used in the notebook include:

  • Random Forest
  • Gradient Boosting
  • Support Vector Regression (SVR)
  • Neural Network
  • K-Nearest Neighbors
  • Linear Regression

• CSV Files: CSV files containing the data fetched by data.py and used for modeling:

  • current_drivers.csv: Contains information about current F1 drivers.
  • driver_standings.csv: Contains current driver standings.
  • abu_dhabi_circuit_info.csv: Contains information about the Abu Dhabi circuit.
  • abu_dhabi_predictions_placeholder.csv: A placeholder CSV file where machine learning model predictions for the Abu Dhabi GP are stored.

Installation & Setup

To use this repository, you will need Python installed on your machine. You can also use a virtual environment to manage dependencies.

Step 1: Clone the Repository

First, clone this repository to your local machine:

git clone https://github.com/quarktetra23/F1-Predictor

Step 2: Dependencies Required

pandas==1.3.3 requests==2.26.0 scikit-learn==0.24.2 tensorflow==2.6.0

Step 3: Run data.py to Fetch Data

Run the data.py script to fetch the data from the Ergast API and store it in CSV files. This will generate the data files for further use in machine learning models.

python data.py

Step 4: Run the Jupyter Notebook for Modeling

Run the models in the jupyter notebook, to view the results stores in csv files.