Implementation of a component based prediction model. This implementation make use of machine learning techniques in order to manage the uncertainty, in an academic enviroment, for the decision process of courses selection by a student.

Be sure to have installed R. And for linux have installed liblzma, here the instructions for debian based systems:

apt-get update
apt-get install r-base r-base-dev liblzma5 liblzma-dev

Them clone the git repository and install the python dependences:

git clone https://github.com/rxgranda/uncertaintyServerComponents.git
cd uncertaintyServerComponents
pip install -r requirements.txt

Run the script to install the R required packages:

./r_requirements_install.py

Finally import the data from the remote database:

./query2csv.py

If there exits a problem, a zip file is alocated in this link, and needs to be uncompresed in the data/ folder.

Documentation can be find in the doc/ folder.

For an example code of the prediction model instantiation can be found in the test_script/ folder, simply run:

./test_scripts/classifier_test.py

A short snippet describing the license (MIT)