CapstoneProject1_Springboard
German Credit Rating
Source: UCI - 1994
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)
Data Wrangling
Extracting the numerical values from the Categorical Columns for further Analysis.