This repository organises projects that use machine learning to solve motorsport-related challenges. Each project leverages ML models to predict, classify, or optimise various aspects of racing performance.
Predicts lap times based on car setup, track conditions, and driver inputs using regression models.
Uses historical race data to predict the remaining life of tyres during a race.
Classifies the likelihood of successful overtakes based on track layout and car performance.
Classifies race strategies as "successful" or "unsuccessful" based on historical data.
Predicts track conditions (e.g., dry, wet, damp) using telemetry and weather data.
Visit each project's repository for specific instructions on how to use the machine learning models.