From Clustering to Forecasting: A Full-Season Data-Driven Look at the 2023 F1 Season

1. 1Collect and preprocess data from the 2023 F1 season, including lap times and driver information
2. 2Apply clustering algorithms to identify distinct driving styles among the 22 drivers
3. 3Use machine learning models to predict lap times based on driver style and other factors
4. 4Visualize and compare the results across different races and drivers to identify trends and patterns
5. 5Refine the models using techniques such as feature engineering and hyperparameter tuning to improve forecasting accuracy

Data scientists and analysts on a team can benefit from this approach to gain insights into complex racing data, while F1 teams can use these methods to inform their strategy and improve performance

💡 Data-driven approaches can reveal hidden patterns in F1 racing data, enabling more accurate predictions and informed decision-making

Decode driving styles & predict lap times in F1 with data science!