A Machine Learning Approach to Predicting the Employability of a Graduate

dc.contributor.advisorGeorg, Co-Pierre
dc.contributor.authorModibane, Masego
dc.date.accessioned2020-02-13T09:56:16Z
dc.date.available2020-02-13T09:56:16Z
dc.date.issued2019
dc.date.updated2020-02-12T10:46:56Z
dc.description.abstractFor 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.apacitationModibane, 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/11427/31082en_ZA
dc.identifier.chicagocitationModibane, 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/11427/31082en_ZA
dc.identifier.citationModibane, 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.urihttp://hdl.handle.net/11427/31082
dc.identifier.vancouvercitationModibane 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/11427/31082en_ZA
dc.language.rfc3066eng
dc.publisher.departmentAfrican Institute of Financial Markets and Risk Management
dc.publisher.facultyFaculty of Commerce
dc.subjectData Science
dc.titleA Machine Learning Approach to Predicting the Employability of a Graduate
dc.typeMaster Thesis
dc.type.qualificationlevelMasters
dc.type.qualificationnameMPhil
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