We are conducting a data mining project focused on analyzing driver performance in Formula One racing, with the goal to correlate driver performance with track and weather conditions, and to predict future race results using these correlations. We will apply various data mining techniques learned throughout the course to extract meaningful insights from the dataset.

Getting Started

To run the project, you will need to set up a conda environment using the conda_env.yml file. To do this, run the following command in the terminal:

conda env create -f conda_env.yml

Then, activate the environment with:

conda activate csci349

Finally, open the Jupyter notebook of your choice and run the cells.

Important Dates

Data Selection Due: November 13, 2024
DataPrep_EDA.ipynb Due: November 22, 2024
Modeling.ipynb Due: December 4, 2024
Final Report PDF Due: December 10, 2024
Video Presentation Due: December 13, 2024

Package Structure

Directories:

data - Contains the dataset used for analysis.
notebooks - Contains Jupyter notebooks for data preparation, EDA, modeling, and the final report.

3rd Party Libraries

pandas
numpy
matplotlib
rapidfuzz
fastf1

Video Presentation

Our video presentation will be linked here.

Final Deliverables

DataPrep_EDA.ipynb - Notebook for data preparation and exploratory data analysis.
Modeling.ipynb - Notebook for developing and evaluating predictive models.
Final_Report.pdf - Comprehensive report summarizing our findings and methodologies, submitted to Gradescope.
Video Presentation - A recorded video summarizing our project, linked above.

README.md

CSCI 349 Final Project: Formula One Driver Performance Analysis

Team Members:

Project Summary