sergiosim / oulad

Analysis of the OULAD dataset

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.

Running this project

To run this project:

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

Requirements

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.

Building with docker compose (default)

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.

Building with python virtual environment

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.