I am enjoying beautiful sunny spring morning. Couple days ago I was looking for well-known dataset – german credit. It is a good starter for practicing credit risk scoring. Unfourtuanetly I have found only original file in.data format without column names. I have prepared CSV and R file to quick use and I decided to share it with you and hopefully save you couple minutes of your time. Here is a link to original file in UCI Machine Learning Repository (). And here you can find ready to use: • CSV (), • R object ().
If you would like to take a look at code I’ve used to prepare those files here is a. You can also download it directly to your R data frame. Canopus Xplode Pro 4.60 For Adobe Premiere.
German Credit Data. Well-known data set from source. We have copied the data set and their description of the 20 predictor variables. The last column of the data is coded 1 (bad loans) and 2 (good loans). Sas code to read in the variables and create numerical variables from the ordered categorical variables (proc print. View Lab Report - credit-g.arff from DATA AND W Data minin at Shanghai Maritime University. The UCI German Dataset. The source: this data set is a public benchmark from the UCI Machine. The dataset in ARFF format. German phone rates are.