Site URL: https://889884m.github.io/DSC180_Project/
Most of the project code was actually built using Jupyter Notebooks, so the latest working code would be found in the Notebooks folder. Here the latest figures and hyperparameter tuning can be found. Demonstrations of the Neural Net, Support Vector Machine, and Random Forest are here.
The code is run via the command python run.py test. This runs the baseline model on the test data, which is simply the normal data but randomized.
Project code with working models can be found in src folder. Here the code for data generation and the test data can be found. This is also where the model code can be found which is in the prediction folder. Here, the code for the feed-forward Neural Net and the SVM can be found.
To build the docker build -t <tag_name> . which gives a local docker container with the libraries scipy, numpy, pandas, pytorch, and sklearn.