https://github.com/tmalik1116/f1_qualifying_predictor_ml

Estimate Formula 1 qualifying results using ML
https://github.com/tmalik1116/f1_qualifying_predictor_ml

ai ergast-api f1 fastf1 formula1 machine-learning machine-learning-algorithms ml motorsport pandas python racing react reactjs sports xgboost xgboost-regression

Last synced: 8 months ago
JSON representation

Estimate Formula 1 qualifying results using ML

• Host: GitHub
• URL: https://github.com/tmalik1116/f1_qualifying_predictor_ml
• Owner: tmalik1116
• Created: 2024-08-12T17:05:07.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2025-02-05T21:45:36.000Z (8 months ago)
• Last Synced: 2025-02-05T22:29:50.706Z (8 months ago)
• Topics: ai, ergast-api, f1, fastf1, formula1, machine-learning, machine-learning-algorithms, ml, motorsport, pandas, python, racing, react, reactjs, sports, xgboost, xgboost-regression
• Language: Python
• Homepage: https://f1-qualifying-predictor-ml.vercel.app
• Size: 162 MB
• Stars: 4
• Watchers: 1
• Forks: 0
• Open Issues: 2
• Metadata Files:
  • Readme: docs/README.md

Awesome Lists containing this project

README

          
# Formula 1 Qualif-AI

#### V1.0

## Estimate future Formula 1 qualifying results using ML (XGBoost Regression)

![F1_UI](https://github.com/user-attachments/assets/fb577ccd-3e13-45aa-bdf7-cf0aa4185ab6)

## Usage

The user is given a choice between predicting results for a single driver or for all participants in a session. Shown below are the menus for both and example inputs. 

### Driver Prediction

https://github.com/user-attachments/assets/10a2d042-d3bd-470a-aa2a-3d0270f36414

### Session Prediction

https://github.com/user-attachments/assets/84aaae1c-0758-46e2-9646-06f82d8068d2

## Deep-Dive

The entire frontend UI is built using React. GET and POST requests are made to the backend server which runs a custom Python API using the trained model. The model used was an XGBoost Regressor from the open-source xgboost library in Python. I trained an instance of this model on a custom dataset which includes data from the past decade of the sport. 

The relevant data was obtained programatically from theOehrly's fastf1 Python library (https://github.com/theOehrly/Fast-F1).