The goal of our project is to develop a predictive model for the FIFA World Cup outcomes using a large dataset of international football matches. This project aims to leverage data science techniques learned in class such as single/multiple linear regression, classification, and bagging to analyze historical match data and predict the outcomes of future matches.
| Bryan Quintero | Scott Willard | Jayden McKenna | Ernesto Rendon |
|---|---|---|---|
| 48344993 | 44868436 | 80364767 | 94109996 |
gh repo clone ernestorendon/cap4770-finalproject
cd cap4770-finalproject
./setup.sh
This will create and activate a Python virtual environment (venv), and then install the dependencies into it.
Additionally, the provided .env file contains a guest account with access to the dataset.
The notebooks will pull in these variables from the .env file automatically.
After the script finishes activating the virtual environment and installing the dependencies, you'll be able to use the MongoDB and query it as normal.
Some example code is included there for testing; simply run the code in the cell to try it out.