A Machine Learning Approach to Predicting the Employability of a Graduate
| dc.contributor.advisor | Georg, Co-Pierre | |
| dc.contributor.author | Modibane, Masego | |
| dc.date.accessioned | 2020-02-13T09:56:16Z | |
| dc.date.available | 2020-02-13T09:56:16Z | |
| dc.date.issued | 2019 | |
| dc.date.updated | 2020-02-12T10:46:56Z | |
| dc.description.abstract | 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. | |
| dc.identifier.apacitation | Modibane, M. (2019). <i>A Machine Learning Approach to Predicting the Employability of a Graduate</i>. (). ,Faculty of Commerce ,African Institute of Financial Markets and Risk Management. Retrieved from http://hdl.handle.net/11427/31082 | en_ZA |
| dc.identifier.chicagocitation | Modibane, Masego. <i>"A Machine Learning Approach to Predicting the Employability of a Graduate."</i> ., ,Faculty of Commerce ,African Institute of Financial Markets and Risk Management, 2019. http://hdl.handle.net/11427/31082 | en_ZA |
| dc.identifier.citation | Modibane, M. 2019. A Machine Learning Approach to Predicting the Employability of a Graduate. | en_ZA |
| dc.identifier.ris | TY - Thesis / Dissertation AU - Modibane, Masego AB - 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. DA - 2019 DB - OpenUCT DP - University of Cape Town KW - Data Science LK - https://open.uct.ac.za PY - 2019 T1 - A Machine Learning Approach to Predicting the Employability of a Graduate TI - A Machine Learning Approach to Predicting the Employability of a Graduate UR - http://hdl.handle.net/11427/31082 ER - | en_ZA |
| dc.identifier.uri | http://hdl.handle.net/11427/31082 | |
| dc.identifier.vancouvercitation | Modibane M. A Machine Learning Approach to Predicting the Employability of a Graduate. []. ,Faculty of Commerce ,African Institute of Financial Markets and Risk Management, 2019 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/31082 | en_ZA |
| dc.language.rfc3066 | eng | |
| dc.publisher.department | African Institute of Financial Markets and Risk Management | |
| dc.publisher.faculty | Faculty of Commerce | |
| dc.subject | Data Science | |
| dc.title | A Machine Learning Approach to Predicting the Employability of a Graduate | |
| dc.type | Master Thesis | |
| dc.type.qualificationlevel | Masters | |
| dc.type.qualificationname | MPhil |