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dc.contributor.authorManyasi, Eunice Engefu
dc.date.accessioned2017-11-21T10:00:05Z
dc.date.available2017-11-21T10:00:05Z
dc.date.issued2017
dc.identifier.urihttp://hdl.handle.net/11071/5631
dc.descriptionThesis submitted in partial fulfillment of the requirements for the Degree of Master of Science in Information Technology (MSIT) at Strathmore Universityen_US
dc.description.abstractFinancial aid has been used worldwide to assist students at higher learning institutions finance their education. The aid has majorly been offered by the government, private companies and non-governmental institutions in form of loans, grants, scholarships and work study programs. It has made great progress in increasing the enrolment rate of students to higher learning institutions. The aid is usually given to applicants who have been selected after applying for the aid, and a committee ensuring that they have meet the set criteria to be awarded. Currently the number of applicants applying for financial aid has increased leading to challenges of errors and bias in the selection and award process due to overwhelming data which becomes too complex for the committees to analyse. This has led to some more deserving students not receiving the financial aid due to inaccuracies. Artificial intelligence has been applied in various fields for the analysis and classification of huge amounts of data. It has been applied in finance to predict the credit rating of customers which uses a similar concept in classification of applicants. The research sought to apply machine learning to in the selection and award process of needy students. Historical financial aid data which was labelled as awarded and not awarded, was used to train the feed forward neural network learning model. The inputs used included parents occupation and income, family income and family spending. The research employed experimental research to determine the variables that best identified the needy students and qualitative research to get the ideas and opinions of participants with regards to the study. The model accurately classified 2955 instances as true positives and 18 instances as true negative out of 3043 instances, giving it a 97.6% accuracy.en_US
dc.language.isoenen_US
dc.publisherStrathmore Universityen_US
dc.subjectArtificial Neural Networksen_US
dc.subjectFinancial Aid Allocationen_US
dc.subjectSupervised learningen_US
dc.titleA Model for the classification of student neediness using artificial neural networksen_US
dc.typeThesisen_US


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