This project demonstrates the application of machine learning in motorsports analytics by predicting Formula 1 driver performance using a Random Forest regression model implemented in R. The objective is to analyze how race-related factors influence performance through data-driven modeling.
Formula 1 performance is influenced by multiple dynamic variables during a race. This project aims to model the relationship between key race conditions and driver performance using a supervised machine learning approach.
The model is trained on simulated Formula 1 telemetry data, incorporating essential performance-influencing factors such as:
- Tyre degradation
- Track temperature
- Fuel load
The project showcases a complete regression modeling workflow, from feature selection to performance prediction.
- Algorithm: Random Forest (Regression)
- Learning Type: Supervised Learning
- Data Type: Simulated telemetry data
- Objective: Predict driver performance metrics
- R Programming Language
- randomForest – Ensemble-based regression modeling
- dplyr – Data manipulation and preprocessing
- End-to-end machine learning pipeline in R
- Ensemble learning using Random Forest
- Clean and modular code structure
- Beginner-friendly implementation
- Introduction to sports analytics using ML
- Successfully models the impact of race conditions on driver performance
- Demonstrates the effectiveness of ensemble learning for regression tasks
- Provides a scalable foundation for real-world telemetry integration
- Integration of real-world Formula 1 telemetry data
- Inclusion of evaluation metrics (RMSE, R²)
- Feature importance visualization
- Comparison with alternative regression models
- Machine learning practice using R
- Sports analytics projects
- Academic and portfolio demonstrations
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