prikaz prve stranice dokumenta Unaprijedna neuronska mreža za procjenu kreditnog rizika
Embargo period 2026-03-25
master's thesis
Unaprijedna neuronska mreža za procjenu kreditnog rizika
Zagreb: University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture, 2021. urn:nbn:hr:235:451341
Kresnik, Iva
University of Zagreb
Faculty of Mechanical Engineering and Naval Architecture

Institutional repository: Repository of Faculty of Mechanical Engineering and Naval Architecture University of Zagreb

Cite this document

Kresnik, I. (2021). Unaprijedna neuronska mreža za procjenu kreditnog rizika (Master's thesis). Zagreb: University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture. Retrieved from https://urn.nsk.hr/urn:nbn:hr:235:451341

Kresnik, Iva. "Unaprijedna neuronska mreža za procjenu kreditnog rizika." Master's thesis, University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture, 2021. https://urn.nsk.hr/urn:nbn:hr:235:451341

Kresnik, Iva. "Unaprijedna neuronska mreža za procjenu kreditnog rizika." Master's thesis, University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture, 2021. https://urn.nsk.hr/urn:nbn:hr:235:451341

Kresnik, I. (2021). 'Unaprijedna neuronska mreža za procjenu kreditnog rizika', Master's thesis, University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture, accessed 02 November 2024, https://urn.nsk.hr/urn:nbn:hr:235:451341

Kresnik I. Unaprijedna neuronska mreža za procjenu kreditnog rizika [Master's thesis]. Zagreb: University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture; 2021 [cited 2024 November 02] Available at: https://urn.nsk.hr/urn:nbn:hr:235:451341

I. Kresnik, "Unaprijedna neuronska mreža za procjenu kreditnog rizika", Master's thesis, University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture, Zagreb, 2021. Available at: https://urn.nsk.hr/urn:nbn:hr:235:451341

Metadata
TitleUnaprijedna neuronska mreža za procjenu kreditnog rizika
Title (english)Feedforward neural network for credit risk assesment
AuthorIva Kresnik
MentorDubravko Majetić (mentor)
GranterUniversity of Zagreb
Faculty of Mechanical Engineering and Naval Architecture
Zagreb
Defense date and country2021-04-01, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Artificial Intelligence
Abstract
Cilj ovog diplomskog rada je primjena umjetnih neuronskih mreža u određivanju kreditnog rizika. U uvodu diplomskog rada objašnjena je problematika zadane teme, dana je definicija kredita te su navedeni svi programski paketi koji su korišteni u radu. U drugom poglavlju razrađena je teorija umjetnih neuronskih mreža. Zatim slijedi analiza dobivenih podataka o klijentima i kreditima od banke, kao i priprema istih za ulaz u unaprijednu neuronsku mrežu. U programskom jeziku Python 3 definirana je optimalna unaprijedna neuronska mreža pomoću paketa Keras (TensorFlow). Parametri mreže i njena struktura detaljno su opisani u četvrtom poglavlju rada, nakon čega slijedi analiza dobivenih izlaza mreže. U zaključku rada navedene su prednosti i mane korištene unaprijedne mreže, kao i moguća poboljšanja s ciljem dobivanja preciznijih predviđanja kreditnih rizika.
Abstract (english)
The aim of this thesis is to present the work of artificial neural networks application in credit score determining. In the introduction, the problems of the given assignment are explained, the definition of loan is given, and all software packages used in the paper are listed. In the second chapter, the theory of artificial neural networks is elaborated. This is followed by an analysis of the data obtained on clients and loans from the bank, as well as preprocessing the same data so it can be later used as input for the feedforward neural network. In the Python 3 programming language, the optimal feedforward neural network is defined using the Keras package (TensorFlow). The parameters of the neural network and its structure are described in detail in the fourth chapter of the paper, followed by the analysis of the obtained network outputs. The paper concludes with the advantages and disadvantages of the feedforward network that is used, as well as possible improvements in order toto obtain more accurate credit score forecasts.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:235:451341
Study programmeTitle: Mechanical Engineering
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka strojarstva (magistar/magistra inženjer/inženjerka strojarstva)
Type of resourceText
File originBorn digital
Access conditionsEmbargoed access
Embargo expiration date: 2026-03-25
Terms of useLicence In copyright icon
Created on2021-03-25 10:28:34