  In this article, a large data set containing every course taken by every
undergraduate student in a major university in Canada over 10 years is
analysed. Modern machine learning algorithms can use large data sets to build
useful tools for the data provider, in this case, the university. In this
article, two classifiers are constructed using random forests. To begin, the
first two semesters of courses completed by a student are used to predict if
they will obtain an undergraduate degree. Secondly, for the students that
completed a program, their major is predicted using once again the first few
courses they have registered to. A classification tree is an intuitive and
powerful classifier and building a random forest of trees improves this
classifier. Random forests also allow for reliable variable importance
measurements. These measures explain what variables are useful to the
classifiers and can be used to better understand what is statistically related
to the students' situation. The results are two accurate classifiers and a
variable importance analysis that provides useful information to university
administrations.
