Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11779/1157
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dc.contributor.advisorÇakar, Tuna-
dc.contributor.authorAkman, Özkan-
dc.date.accessioned2019-11-12T13:41:59Z
dc.date.available2019-11-12T13:41:59Z
dc.date.issued2018-
dc.identifier.citationAkman, Ö. (2018). Credit risk models using machine learning models, MEF Üniversitesi Fen Bilimleri Enstitüsü, İstanbul, Türkiyeen_US
dc.identifier.urihttps://hdl.handle.net/20.500.11779/1157-
dc.description.abstractCredit scoring is an important subject in financial institutions, mainly in banks. I want to examine some machine learning techniques to find out a model that performs good in predicting or classifying the loaner person a good credit or a bad one by evaluating his/her demographic features as marital status, wealth, job seniority, monthly income and expenses.en_US
dc.language.isoenen_US
dc.publisherMEF Üniversitesi, Fen Bilimleri Enstitüsüen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectCredit Rankingen_US
dc.subjectCredit Scoring Modelsen_US
dc.subjectMachine Learningen_US
dc.subjectSupport Vector Machineen_US
dc.subjectDecision Tree Modelen_US
dc.subjectLinear Discrimant Analysisen_US
dc.subjectLoan-to-Value Rationen_US
dc.subjectSaving Capacityen_US
dc.subjectLogistic Regression Modelen_US
dc.titleCredit risk models using machine learning modelsen_US
dc.title.alternativeMakine öğrenmesi uygulamaları ile kredi risk modellemeen_US
dc.typeMaster's Degree Projecten_US
dc.relation.publicationcategoryYL-Bitirme Projesien_US
dc.departmentBüyük Veri Analitigi Yüksek Lisans Programıen_US
dc.institutionauthorAkman, Özkan-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.grantfulltextopen-
item.languageiso639-1en-
item.cerifentitytypePublications-
item.fulltextWith Fulltext-
item.openairetypeMaster's Degree Project-
Appears in Collections:FBE, Yüksek Lisans, Proje Koleksiyonu
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