21.12.2020

German Credit Data Set Arff Download

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Author: 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)
German Credit Data Set Arff Download

German Credit Data Set Arff Download Pdf

  1. Explore and run machine learning code with Kaggle Notebooks Using data from German Credit Risk - With Target.
  2. Credit classification.

German Credit Data Set Arff Download Free

A zip file containing a new, image-based version of the classic iris data, with 50 images for each of the three species of iris. The images have size 600x600. Please see the ARFF file for further information (irisreloaded.zip, 92,267,000 Bytes). After expanding into a directory using your jar utility (or an archive program that handles tar. The German credit dataset is a standard imbalanced classification dataset that has this property of differing costs to misclassification errors. Models evaluated on this dataset can be evaluated using the Fbeta-Measure that provides a way of both quantifying model performance generally, and captures the requirement that one type of.