| dc.contributor | 統計系 | |
| dc.creator (作者) | 陳立榜 | |
| dc.creator (作者) | Chen, Li-Pang | |
| dc.date (日期) | 2021-04 | |
| dc.date.accessioned | 21-Sep-2022 11:45:59 (UTC+8) | - |
| dc.date.available | 21-Sep-2022 11:45:59 (UTC+8) | - |
| dc.date.issued (上傳時間) | 21-Sep-2022 11:45:59 (UTC+8) | - |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/142024 | - |
| dc.description.abstract (摘要) | In the financial sector, credit risk and financial modeling have been widely explored in practice, establishing particular scale characterization through pre-existing models and now the introduction of machine learning approaches. Our investigation is to generate a prediction model on a "Give Me Some Credit" dataset from Kaggle to help understand credit scoring and potential patterns of delinquency. Using various analytical models based on machine learning methods, risk levels of future credit loans are identified by accurately predicting the probability of an individual experiencing future financial distress. The results of data analysis in terms of the accuracy and the quality of the classifier....... | |
| dc.format.extent | 145 bytes | - |
| dc.format.mimetype | text/html | - |
| dc.relation (關聯) | IOSR Journal of Mathematics, Volume 17, Issue 2, Series 4, pp.45-51 | |
| dc.subject (關鍵詞) | Credit scoring; data analysis; financial distress; machine learning | |
| dc.title (題名) | Statistical learning for analysis of credit risk data | |
| dc.type (資料類型) | article | |
| dc.identifier.doi (DOI) | 10.9790/5728-1702044551 | |
