Applying machine learning to March Madness (Round 2). Check out my first repo here and my associated blog post.
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.
In order to run these scripts, you'll need the following libraries.
- 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
- Download and unzip this entire repository from GitHub, either interactively, or by entering the following in your Terminal.
git clone https://github.com/adeshpande3/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