prikaz prve stranice dokumenta Data science modelling of the credit rating : case study of the German credit data
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master's thesis
Data science modelling of the credit rating : case study of the German credit data
Zagreb: University of Zagreb, Faculty of Economics and Business, 2023. urn:nbn:hr:148:272581
Mandić, Adam
University of Zagreb
Faculty of Economics and Business

Institutional repository: REPEFZG

Cite this document

Mandić, A. (2023). Data science modelling of the credit rating : case study of the German credit data (Master's thesis). Zagreb: University of Zagreb, Faculty of Economics and Business. Retrieved from https://urn.nsk.hr/urn:nbn:hr:148:272581

Mandić, Adam. "Data science modelling of the credit rating : case study of the German credit data." Master's thesis, University of Zagreb, Faculty of Economics and Business, 2023. https://urn.nsk.hr/urn:nbn:hr:148:272581

Mandić, Adam. "Data science modelling of the credit rating : case study of the German credit data." Master's thesis, University of Zagreb, Faculty of Economics and Business, 2023. https://urn.nsk.hr/urn:nbn:hr:148:272581

Mandić, A. (2023). 'Data science modelling of the credit rating : case study of the German credit data', Master's thesis, University of Zagreb, Faculty of Economics and Business, accessed 25 December 2024, https://urn.nsk.hr/urn:nbn:hr:148:272581

Mandić A. Data science modelling of the credit rating : case study of the German credit data [Master's thesis]. Zagreb: University of Zagreb, Faculty of Economics and Business; 2023 [cited 2024 December 25] Available at: https://urn.nsk.hr/urn:nbn:hr:148:272581

A. Mandić, "Data science modelling of the credit rating : case study of the German credit data", Master's thesis, University of Zagreb, Faculty of Economics and Business, Zagreb, 2023. Available at: https://urn.nsk.hr/urn:nbn:hr:148:272581

Metadata
TitleData science modelling of the credit rating : case study of the German credit data
AuthorAdam Mandić
MentorMirjana Pejić Bach (mentor)
Committee memberMario Spremić (član povjerenstva)
Committee memberBožidar Jaković (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Economics and Business
Zagreb
Defense date and country2023-09-29, Croatia
Scientific / art field,
discipline and subdiscipline		SOCIAL SCIENCES
Economics
Business Informatics
Abstract
In recent years there has been an increased adoption of data science methods in numerous fields and industries. With usage of statistical analysis, machine learning, programming, but also domain expertise, it finds and identifies different patterns that allow experts to make certain predictions. It also allows experts to derive valuable insights from numerous datasets with high level of intricacy, complexity and size. In this paper we focus on how financial institutions utilize data science methods for various tasks. One of the main ways financial institutions make use of such methods is for determining credit score, which highlights good and bad candidates for loan. Credit scoring would be a numerical evaluation of an individual with goal to calculate the probability of concluding a contract, making a purchase decision, or in order to be able to measure the creditworthiness of a person that needs a loan. The primary aim of this paper is demonstrating the main characteristic of (German Credit Data) data set. By applying our analysis, we were able to determine whether the individual would be considered a good or a bad candidate for receiving credit. After gathering the number of correctly classified instances, an experiment was conducted to compare three different decision trees, and how reducing its size and the number of leaves would affect further correctly classified instances.
Keywords
Languageenglish
URN:NBNurn:nbn:hr:148:272581
Study programmeTitle: Master Degree in Business; specializations in: Marketing in english, Managerial informatics, Management, Marketing, Trade and international business, Leadership
Course: Managerial informatics
Study programme type: university
Study level: graduate
Academic / professional title: sveučilišni magistar ekonomije/sveučilišna magistra ekonomije (sveučilišni magistar ekonomije/sveučilišna magistra ekonomije)
Type of resourceText
File originBorn digital
Access conditionsAccess restricted to students and staff of home institution
Terms of useLicence In copyright icon
Created on2023-10-04 10:31:04