German Credit Data Set Arff Download
2021年11月12日Download here: http://gg.gg/wun23
German Credit Data Set Arff Downloads
Download gaana app for mac. Explore and run machine learning code with Kaggle Notebooks Using data from German Credit Risk - With Target. Nike+ connect software download for mac. Burn software for mac free download.German Credit Data Set Arff Download Free
*German Credit Data Set Arff Download. The following formats are available: DST, EXP, HUS, JEF, PES, PCS, VIP, VP3, SEW, SHV and XXX. Comes in sizes- 4×4, 5×7, 6×10 If you are in need of a different format, feel free to email and see if it is available. This is a instant download! THIS IS NOT A PATCH. Item not refundable, No fabric needed!!
*Another older available one is ’German Credit fraud data’, which is in ARFF format as used by Weka machine learning. Or at least a synthetic generated data set?
*German Credit data This dataset classifies people described by a set of attributes as good or bad credit risks. This dataset comes with a cost matrix: Good Bad (predicted) Good 0 1 (actual) Bad 5 0 It is worse to class a customer as good when they are bad (5), than it.German Credit Data Set Arff Download PdfGerman Credit Data Set Arff Download SoftwareAuthor: Dr. Hans Hofmann Source: [UCI](https://archive.ics.uci.edu/ml/datasets/statlog+(german+credit+data)) - 1994 Please cite: [UCI](https://archive.ics.uci.edu/ml/citation_policy.html)German Credit dataset This dataset classifies people described by a set of attributes as good or bad credit risks.This dataset comes with a cost matrix: ``` Good Bad (predicted) Good 0 1 (actual) Bad 5 0 ```It is worse to class a customer as good when they are bad (5), than it is to class a customer as bad when they are good (1). ### Attribute description 1. Status of existing checking account, in Deutsche Mark. 2. Duration in months 3. Credit history (credits taken, paid back duly, delays, critical accounts) 4. Purpose of the credit (car, television,..) 5. Credit amount 6. Status of savings account/bonds, in Deutsche Mark. 7. Present employment, in number of years. 8. Installment rate in percentage of disposable income 9. Personal status (married, single,..) and sex 10. Other debtors / guarantors 11. Present residence since X years 12. Property (e.g. real estate) 13. Age in years 14. Other installment plans (banks, stores) 15. Housing (rent, own,..) 16. Number of existing credits at this bank 17. Job 18. Number of people being liable to provide maintenance for 19. Telephone (yes,no) 20. Foreign worker (yes,no)
Download here: http://gg.gg/wun23
https://diarynote.indered.space
German Credit Data Set Arff Downloads
Download gaana app for mac. Explore and run machine learning code with Kaggle Notebooks Using data from German Credit Risk - With Target. Nike+ connect software download for mac. Burn software for mac free download.German Credit Data Set Arff Download Free
*German Credit Data Set Arff Download. The following formats are available: DST, EXP, HUS, JEF, PES, PCS, VIP, VP3, SEW, SHV and XXX. Comes in sizes- 4×4, 5×7, 6×10 If you are in need of a different format, feel free to email and see if it is available. This is a instant download! THIS IS NOT A PATCH. Item not refundable, No fabric needed!!
*Another older available one is ’German Credit fraud data’, which is in ARFF format as used by Weka machine learning. Or at least a synthetic generated data set?
*German Credit data This dataset classifies people described by a set of attributes as good or bad credit risks. This dataset comes with a cost matrix: Good Bad (predicted) Good 0 1 (actual) Bad 5 0 It is worse to class a customer as good when they are bad (5), than it.German Credit Data Set Arff Download PdfGerman Credit Data Set Arff Download SoftwareAuthor: Dr. Hans Hofmann Source: [UCI](https://archive.ics.uci.edu/ml/datasets/statlog+(german+credit+data)) - 1994 Please cite: [UCI](https://archive.ics.uci.edu/ml/citation_policy.html)German Credit dataset This dataset classifies people described by a set of attributes as good or bad credit risks.This dataset comes with a cost matrix: ``` Good Bad (predicted) Good 0 1 (actual) Bad 5 0 ```It is worse to class a customer as good when they are bad (5), than it is to class a customer as bad when they are good (1). ### Attribute description 1. Status of existing checking account, in Deutsche Mark. 2. Duration in months 3. Credit history (credits taken, paid back duly, delays, critical accounts) 4. Purpose of the credit (car, television,..) 5. Credit amount 6. Status of savings account/bonds, in Deutsche Mark. 7. Present employment, in number of years. 8. Installment rate in percentage of disposable income 9. Personal status (married, single,..) and sex 10. Other debtors / guarantors 11. Present residence since X years 12. Property (e.g. real estate) 13. Age in years 14. Other installment plans (banks, stores) 15. Housing (rent, own,..) 16. Number of existing credits at this bank 17. Job 18. Number of people being liable to provide maintenance for 19. Telephone (yes,no) 20. Foreign worker (yes,no)
Download here: http://gg.gg/wun23
https://diarynote.indered.space
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