Data Sources

UCIR machine learning repository

http://archive.ics.edu/ml/
http://archive.ics.edu/ml/machine-learning-databases/car/

car data, csv file with header
http://win-vector.com/dfiles/car.data.csv
https://github.com/WinVector/ZmPDSwR/tree/master/UCICar
–reading data for the card
examples <- read.csv("http://win-vector.com/dfiles/car.data.csv", header = TRUE, quote="\"", skip=0) German Bank Load data http://mng.bz/mZbu d <- read.table(paste('http://archive.ics.uci.edu/ml','machine-learning-databases/statlog/german/german.data',sep=''),stringAsFactors=F,header=F) print(d[1:3]) --corrected d <- read.table(paste('http://archive.ics.uci.edu/ml/','machine-learning-databases/statlog/german/german.data',sep=''),stringsAsFactors=F,header=F) print(d[1:3,]) --adding columns colnames(d) <-c('Status.of.existing.checing.account','Duration.in.month','Credit.history','Purpose','Credit.Amount','Savings account/bonds','Present.employment.since','Installment.rate.in.percentage.of.disposable.income','Personal.status.and.sex','Other.debtors/guarantors','Present.resident.since','Property','Age.in.years','Other.installment.plans','Housing','Number.of.existing.credits.at.this.bank','Job','Number.of.people.being.liable.to.provide.maintenance.for','Telephone','foreign.worker','Good.Loan') d$Good.Loan <- as.factor(ifelse(d$Good.Loan==1,'GoodLoan','BadLoan')) print (d[1:3,])