- This project is an attempt at predicting the March Madness NCAA tournament perfectly
- At the very least this is supposed to be more accurate than a person's surface-level analysis and projection
- https://www.kaggle.com/datasets/nishaanamin/march-madness-data
- https://www.kaggle.com/code/nishaanamin/bracketology-part-1-upset-stats
- https://www.kaggle.com/code/nishaanamin/bracketology-part-2-confs-seeds-regions
- https://www.kaggle.com/code/nishaanamin/bracketology-part-3-heatmaps-archetypes
- https://www.kaggle.com/code/nishaanamin/bracketology-part-4-miscellaneous
- https://www.kaggle.com/code/nishaanamin/bracketology-part-5-seed-matchups
- https://www.kaggle.com/code/nishaanamin/bracketology-part-6-team-stats
- https://www.kaggle.com/code/nishaanamin/bracketology-part-7-machine-learning-model
- Houses all the data files that will be used for training the model
- Is essential to the success of this project
- Contains data files with stats from teams in the tournament from the current season
- Used to predict likelihood of winners based on season stats and favorites of the match
- Contains data files with stats from matchups in previous tournaments
- Used to predict likelihood of winners based on matchup trends (i.e. 6 seed vs 11 seed matchups usually lean toward the 11 seed)
- Uses python files to clean data
- Ensure data is ready to be used for training
- Contains files for prediction purposes
- Runs different model types, including XGBoost for one
- Runs the model's projections over previous tournaments
- Compares the projection accuracy and saves the results to compare which models perform the best