f1-race-prediction/README.md

Formula One Lap Time Prediction

Project Overview

This project analyzes Formula One race data to predict lap times based on weather conditions, track characteristics, and other race-specific variables. Using machine learning models, we achieved significant success in predicting lap times across different tracks and conditions.

Video Presentation

Our video presentation can be found here.

Key Findings

• LightGBM model achieved the best overall performance (R² = 0.806, RMSE = 3.663)
• Weather conditions explain up to 80% of lap time variation
• Track-specific characteristics significantly impact model performance
• Best performance on Mexico City GP (R² = 0.899) and Belgian GP (R² = 0.845)

Data Source

Data was obtained from the FastF1 API, which provides:

• Detailed lap timing data
• Weather conditions
• Track information
• Tire compound data Link: FastF1 Documentation

Required Packages

dependencies:
  - python=3.10
  - pandas
  - numpy
  - matplotlib
  - seaborn
  - scikit-learn
  - xgboost
  - lightgbm
  - fastf1
  - jupyter

Project Structure

• DataPrep_EDA.ipynb: Data preparation and exploratory analysis
• Modeling.ipynb: Model development and evaluation
• Final_Report.ipynb: Comprehensive analysis and findings
• data/: Directory containing cached race data
• conda_env.yml: Conda environment specification

Models Used

1. LightGBM (Best performer)
2. XGBoost
3. Random Forest
4. Gradient Boosting

Key Features

• Track temperature and air temperature interaction
• Weather complexity score
• Tire degradation metrics
• Track evolution throughout races

Performance Metrics

Average metrics across all tracks:

Model               RMSE     R²    MAE
Gradient Boosting  4.259  0.726  2.083
LightGBM           3.663  0.806  1.839
Random Forest      4.915  0.644  2.396
XGBoost            4.333  0.717  2.122

Setup Instructions

1. Clone the repository
2. Create conda environment:

   conda env create -f conda_env.yml
   

3. Activate environment:

   conda activate csci349
   

4. Launch Jupyter:

   jupyter notebook
   

Authors

• Sean O'Connor
• Connor Coles

Course Information

• Course: CSCI 349 - Introduction to Data Mining
• Semester: Fall 2024
• Instructor: Brian King

License

This project is part of academic coursework and is not licensed for commercial use.