Ecosystems:
Python
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:
- 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)
- Clone this project and run the
buildMakefile target from the root of the project (where this README.md file is located):
This should setup the project, build the docker image and download the OULAD dataset in a newly created OULAD directory.$ make build - Next, run JupyterLab with the
jupyterMakefile target:
This should start the JupyterLab server in the project's docker container and display the server's connection URL$ make jupyter
(e.g. http://127.0.0.1:8888/lab?token=52ccae28037a4012e1f4cefc46346f36ba29cea9e935fb14a) to which you can navigate.
Building with python virtual environment
- Clone this project and run the
build-venvMakefile target from the root of the project (where this README.md file is located):
This should setup the project and download the OULAD dataset in a newly created OULAD directory.$ make build-venv - Next, run JupyterLab with the
jupyter-venvMakefile target:
This should start the JupyterLab server and display the server's connection URL$ make jupyter-venv
(e.g. http://127.0.0.1:8888/lab?token=52ccae28037a4012e1f4cefc46346f36ba29cea9e935fb14a) to which you can navigate.