March-Madness-2018
Applying machine learning to March Madness (Round 2)
Overview
In this project, I hope to use machine learning to create a model that can predict the winner of a game between two teams. This way, I can try to predict the winner of the NCAA Basketball Tournament (and hopefully get a perfect bracket LOL). I've separated this project into a couple of different components. Since I like to do this every year, I wanted to keep this code general enough so that it can work from year to year, you'll just have to add new data for the current year.
- Data: The Data folder contains different CSVs that show team stats, regular season game results, etc. It will contain data that I've scraped, data from Kaggle, and a folder that contains precomputed xTrain and yTrain matrices so that we don't have to keep recomputing the training set.
- DataPreprocessing.py: Script where we create our training matrices.
- MarchMadness2018.py: Script where we apply machine learning models to the training set. We can also create our Kaggle submissions here.
Requirements and Installation
In order to run these scripts, you'll need the following libraries.
What You Can Do
- Try to modify MarchMadness2018.py to include more ML models
- Modify DataPreprocessing.py to create different features to represent each game/team
- Perform data visualizations to see which features are the most important
- Decide what type of additional data preprocessing might be needed
Getting Started
- Download and unzip this entire repository from GitHub, either interactively, or by entering the following in your Terminal.
git clone https://github.com/fierysolid/March-Madness-2018.git
- Navigate into the top directory of the repo on your machine
cd March-Madness-2018 - First create your xTrain and yTrain matrices by running
This may take a while (Still trying to figure out ways to make this faster).
python DataPreprocessing.py
- Then run your machine learning model
python MarchMadness2018.py