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93 lines
2.4 KiB
Markdown
93 lines
2.4 KiB
Markdown
# Formula One Lap Time Prediction
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## Project Overview
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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.
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## Video Presentation
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Our video presentation can be found [here](https://youtu.be/6TfnljHx6Os).
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## Key Findings
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- LightGBM model achieved the best overall performance (R² = 0.806, RMSE = 3.663)
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- Weather conditions explain up to 80% of lap time variation
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- Track-specific characteristics significantly impact model performance
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- Best performance on Mexico City GP (R² = 0.899) and Belgian GP (R² = 0.845)
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## Data Source
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Data was obtained from the FastF1 API, which provides:
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- Detailed lap timing data
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- Weather conditions
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- Track information
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- Tire compound data
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Link: [FastF1 Documentation](https://docs.fastf1.dev/)
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## Required Packages
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```yaml
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dependencies:
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- python=3.10
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- pandas
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- numpy
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- matplotlib
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- seaborn
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- scikit-learn
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- xgboost
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- lightgbm
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- fastf1
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- jupyter
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```
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## Project Structure
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- `DataPrep_EDA.ipynb`: Data preparation and exploratory analysis
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- `Modeling.ipynb`: Model development and evaluation
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- `Final_Report.ipynb`: Comprehensive analysis and findings
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- `data/`: Directory containing cached race data
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- `conda_env.yml`: Conda environment specification
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## Models Used
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1. LightGBM (Best performer)
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2. XGBoost
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3. Random Forest
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4. Gradient Boosting
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## Key Features
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- Track temperature and air temperature interaction
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- Weather complexity score
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- Tire degradation metrics
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- Track evolution throughout races
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## Performance Metrics
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Average metrics across all tracks:
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```
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Model RMSE R² MAE
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Gradient Boosting 4.259 0.726 2.083
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LightGBM 3.663 0.806 1.839
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Random Forest 4.915 0.644 2.396
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XGBoost 4.333 0.717 2.122
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```
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## Setup Instructions
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1. Clone the repository
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2. Create conda environment:
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```bash
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conda env create -f conda_env.yml
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```
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3. Activate environment:
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```bash
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conda activate csci349
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```
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4. Launch Jupyter:
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```bash
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jupyter notebook
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```
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## Authors
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- Sean O'Connor
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- Connor Coles
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## Course Information
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- Course: CSCI 349 - Introduction to Data Mining
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- Semester: Fall 2024
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- Instructor: Brian King
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## License
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This project is part of academic coursework and is not licensed for commercial use. |