• N. Varghese, A. Vitek, Z. Zhao

The training data is large (~10GB uncompressed). It can be downloaded with the following instructions. Alternatively, the iPython notebooks contain saved states with the results.

1. kaggle competitions download -c petfinder-adoption-prediction
2. Ensure the training data is extracted in ./all
3. Download https://github.com/git-lfs/git-lfs/releases/download/v2.7.1/git-lfs-darwin-amd64-v2.7.1.tar.gz
4. git lfs install
5. git lfs pull

• all contains a single txt file, which will be populated once git lfs pull is performed. Also, it should contain the downloaded training data
• attempts contains the approaches described in the project report. Three are iPython notebooks, one is a regular script
• checkpoints contains the best models we obtained
• data_exploration contains iPython notebooks containing the data analytics we performed, the the Decision Tree discovery in the traditional machine learning pre-trials we made for the project