Submitted as part of the degree of Msci Natural Sciences (2nd year) to the Board of Examiners in the Department of Computer Sciences, Durham University. This summative assignment was assessed and marked by the professor of the module in question:

A brief project and report on using the OULAD data set to predict and return a CSV of students final grades, from a variety of features, using a Random Forest or an SVC.

See classifier.py for full readme and instructions.

On default parameters, this script will first train and test a Random Forest, then print statistics of the test before exporting all predicted results to a file called "PREDICTED_GRADES.csv". This script is also capable of running with an SVC instead (in order to compare their performance), and has a variety of options in terms of using binary vs non binary classifications on the dataset.