Performance Analysis of Credit Scoring Models on Lending Club Data
| Author: | Mgr. Michal Polena |
|---|---|
| Year: | 2017 - summer |
| Leaders: | prof. PhDr. Petr Teplý Ph.D. |
| Consultants: | |
| Work type: | Finance, Financial Markets and Banking Masters |
| Language: | English |
| Pages: | 100 |
| Awards and prizes: | |
| Link: | https://is.cuni.cz/webapps/zzp/detail/185577/ |
| Abstract: | In our master thesis, we compare ten classification algorithms for credit scoring. Their prediction performances are measured by six different classification performance measurements. We use a unique P2P lending data set with more than 200,000 records and 23 variables for our classifiers comparison. This data set comes from Lending Club, the biggest P2P lending platform in the United States. Logistic regression, Artificial neural network, and Linear discriminant analysis are the best three classifiers according to our results. Random forest ranks as the fifth best classifier. On the other hand, Classification and regression tree and k-Nearest neighbors are ranked as the worse classifiers in our ranking. |
