A Machine Learning Approach to Predicting the Employability of a Graduate

For many credit-offering institutions, such as banks and retailers, credit scores play an important role in the decision-making process of credit applications. It becomes difficult to source the traditional information required to calculate these scores for applicants that do not have a credit history, such as recently graduated students. Thus, alternative credit scoring models are sought after to generate a score for these applicants. The aim for the dissertation is to build a machine learning classification model that can predict a students likelihood to become employed, based on their student data (for example, their GPA, degree/s held etc). The resulting model should be a feature that these institutions should use in their decision to approve a credit application from a recently graduated student.

Grantor

African Institute of Financial Markets and Risk Management

Year dc:date.issued

2019

Author dc:creator

• Modibane, Masego

Advisor dc:contributor.advisor

• Georg, Co-Pierre

• Data Science

Handle dc:identifier.uri

http://hdl.handle.net/11427/31082

OAI identifier oai:identifier

oai:open.uct.ac.za:11427/31082