Hybrid model of credit scoring
| dc.contributor | Graduate Program in Systems and Control Engineering. | |
| dc.contributor.advisor | Şıkoğlu, Tamer. | |
| dc.contributor.author | Akar, Serdar. | |
| dc.date.accessioned | 2023-03-16T11:34:43Z | |
| dc.date.available | 2023-03-16T11:34:43Z | |
| dc.date.issued | 2007. | |
| dc.description.abstract | One of the most important innovative concepts is the credit scoring. Today it can be interested in different sectors. Thus the improvement of credit scoring is increasing day by day. The credit scoring with the help of classification techniques provides to take easy and quick decisions in lending. However, no definite consensus has been reached with regard to the best method for credit scoring and in what conditions the meth- ods performs best. Although a huge range of classification techniques has been used in this area, the logistic regression has been seen an important tool and used very widely in studies. This study aims to examine accuracy and bias properties in parameter estimation of the logistic regression (binary logistic) , linear discriminant analysis , linear regression by using German Data which has different variables, data types, real basement and accurately results. Moreover, application of these significant statistical analyzes on German data is provided and the method accuracies are examine for new consumer elements by the software application. Finally, ratings on the results of best method is done by hybrid model by its most reliance comparing and completion all methods. | |
| dc.format.extent | 30cm. | |
| dc.format.pages | xiii, 70 leaves; | |
| dc.identifier.other | SCO 2007 A33 | |
| dc.identifier.uri | https://digitalarchive.library.bogazici.edu.tr/handle/123456789/15630 | |
| dc.publisher | Thesis (M.S.)-Bogazici University. Institute for Graduate Studies in Science and Engineering, 2007. | |
| dc.subject.lcsh | Credit scoring systems. | |
| dc.title | Hybrid model of credit scoring |
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