MarchMadness

This project was handled by the team of Leonard Armstrong, Tanbir Biryajh, Pierre Casco, Kyle Wojtaszek.

Purpose

The purpose of the project is to use machine learning to predict NCAA Tournament (AKA, "March Madness") team selections and team seedings.

Champ Polar Graph.ipynb: Jupyter notebook code for creating a polar graph of key statistics of tournament champions.
Data Transformation - Box Score to Team Stats.ipynb: Jupyter notebook for creating an aggregate data file that transforms detailed team game statistics into a simpler set of year-by-year aggregate statistics.
data: Subdirectory holding and the data files that the code uses. (See notes.)
exploratory_analysis.py: Exploratory data analysis creating additional graphs and tables on the NCAA March Madness data.
Data_Analysis_Who's_In.ipynb: Machine learning and prediction code to determine who would be selected to the NCAA tournament.
get_seeds.py: Creates a consolidated list of NCAA Tournament seeds, year-by-year from a larger data set of all NCAA games.
LICENSE: License to use this code. (MIT-based.)
seeding.ipynb: Machine learning and prediction code to determine what seed teams selected for the tournament will be given.
README.md: This file.

Some of the code was developed locally, some using Google Colaboratory. As such, how/where the code reads in the data may be inconsistent across scripts and you may need to update the code to point correctly to the data files for your instance.