GC {RSADBE}

German Credit Screening Data

Description

Loans are an assest for the banks! However, not all the loans are promptly returned and it is thus important for a bank to build a classification model which can identify the loan defaulters from those who complete the loan tenure.

Usage

data(GC)

Format

A data frame with 1000 observations on the following 21 variables.

checking

Status of existing checking account

duration

Duration in month

history

Credit history

purpose

Purpose of loan

amount

Credit amount

savings

Savings account or bonds

employed

Present employment since

installp

Installment rate in percentage of disposable income

marital

Personal status and sex

coapp

Other debtors or guarantors

resident

Present residence since

property

Property

age

Age in years

other

Other installment plans

housing

Housing

existcr

Number of existing credits at this bank

job

Job

depends

Number of people being liable to provide maintenance for

telephon

Telephone

foreign

foreign worker

good_bad

Loan Defaulter

Source

http://www.stat.auckland.ac.nz/~reilly/credit-g.arff and http://archive.ics.uci.edu/ml/datasets/Statlog+(German+Credit+Data)

References

cran.r-project.org/doc/contrib/Sharma-CreditScoring.pdf

Examples

data(GC)