F1RaceAnalysis
Project Overview
A data science and machine learning project focused on predicting Formula 1 lap times using historical race data and environmental factors. The system analyzes driver, car, track, tyre, fuel, and weather data to generate accurate lap predictions, estimate race outcomes, and provide data-driven insights into race strategies.
Tech Stack
Additional Tools
Key Features
- 1
Dynamic Lap Time Prediction
Uses a Random Forest Regressor to predict individual lap times based on driver, car, track, tyre, fuel load, and weather variables.
- 2
Comprehensive Race Time Estimation
Aggregates predicted lap times to estimate total race duration and support pit-stop and tyre strategy planning.
- 3
Probability-Based Outcome Analysis
Calculates win probabilities and comparative performance metrics for drivers using predicted race times.
- 4
Advanced Feature Engineering
Models tyre degradation, fuel load impact, and evolving track and weather conditions to refine prediction accuracy.
- 5
Interactive Visualization Dashboard
Provides a Streamlit-based interface to visualize lap predictions, race progression, correlations, and model performance metrics.
Did You Know?
"I have always been fascinated by the enormous amount of data captured in Formula 1, and this project gave me the perfect excuse to finally work with it."