This repository contains:

• Definitions of new features derived from the raw input data
• Scripts to process the input data, split it into cross validation set, add new features, and save the results
• Scripts to read the processed data, fit a model, run that model on the test data, and write the results of the test data to a directory
• A jupyter notebook which walks through the model design and building process, showing different iterations and why the final version was chosen
• Docker files so that the analysis can be run in an environment that has all the necessary dependencies

To view the analysis, see notebooks/modeling.ipynb

To run a notebook server from within a Docker container (that can be accessed from outside the container), do:

> run_notebook.sh

To process features, build a model, and make predictions on the test set (from within a Docker container), do:

> ./run_predictions.sh