This project restarts with a new ambition:

Explore various analytics approaches that has been applied to the OULAD dataset.

The previous version of this project is available at the old-master branch.

To run this project:

1. make sure you meet the requirements 2, build the project using docker-compose or python virtual environment

We use:

• GNU make to bootstrap this project.
• GNU wget to download the OULAD dataset from Open University.
• unzip to extract the .zip compressed OULAD dataset.
• docker compose or python -m venv for the development environment.

1. Clone this project and run the build Makefile target from the root of the project (where this README.md file is located):

   $ make build

   This should setup the project, build the docker image and download the OULAD dataset in a newly created OULAD directory.
2. Next, run JupyterLab with the jupyter Makefile target:

   $ make jupyter

   This should start the JupyterLab server in the project's docker container and display the server's connection URL (e.g. http://127.0.0.1:8888/lab?token=52ccae28037a4012e1f4cefc46346f36ba29cea9e935fb14a) to which you can navigate.

1. Clone this project and run the build-venv Makefile target from the root of the project (where this README.md file is located):

   $ make build-venv

   This should setup the project and download the OULAD dataset in a newly created OULAD directory.
2. Next, run JupyterLab with the jupyter-venv Makefile target:

   $ make jupyter-venv

   This should start the JupyterLab server and display the server's connection URL (e.g. http://127.0.0.1:8888/lab?token=52ccae28037a4012e1f4cefc46346f36ba29cea9e935fb14a) to which you can navigate.