How satyamkumar457/f1-2026-prediction works
This is a notebook-era F1 race-outcome prediction project, organized as per-Grand-Prix Python scripts. Its documented workflow uses historical race, qualifying, and telemetry information to engineer features, predict finishing standings, rank drivers, and evaluate predictions. The sampled EDA code confirms cleaning, feature engineering, and writing training data; other stages and integrations are represented from the README and file tree, so their links are dashed where implementation flow was not shown.
Main entry points: Race Race EDA
Components 14
Race data
- Historical race data
- Race ETL
Race - Race data files
Race
Feature analysis
- Race EDA
Race - Training data
Race - Feature visualizations
Race
Prediction and evaluation
- Model training
Race - Trained model
Race - Race prediction
Race - Model evaluation
Race
Race outputs
- Predicted results
Race - Prediction plots
Race
Other components
- Analyst
- FastF1 API
Connections 13
- FastF1 API → Race ETL: supplies sessions
README.md - Historical race data → Race ETL: supplies results
README.md - Race ETL → Race data files: writes data
- Race data files → Race EDA: reads data
Race/20.BrazilGP/EDA.py - Race EDA → Training data: writes features
Race/16.BahrainGP/EDA.py - Race EDA → Feature visualizations: analyzes features
README.md - Training data → Model training: trains from
README.md - Model training → Trained model: fits model
README.md - Trained model → Race prediction: predicts standings
README.md - Race prediction → Predicted results: ranks drivers
README.md - Predicted results → Model evaluation: compares outcomes
README.md - Model evaluation → Prediction plots: visualizes results
README.md - Analyst → Race prediction: requests prediction