German-Credit-Data
OpenML dataset with id 43808
Author name not available (Why is that?)
Full work available at URL: https://api.openml.org/data/v1/download/22102633/German-Credit-Data.arff
Upload date: 24 March 2022
Dataset Characteristics
Number of features: 21 (numeric: 21, symbolic: 0 and in total binary: 0 )
Number of instances: 1,000
Number of instances with missing values: 0
Number of missing values: 0
Context The original dataset contains 1000 entries with 20 categorial/symbolic attributes prepared by Prof. Hofmann. In this dataset, each entry represents a person who takes a credit by a bank. Each person is classified as good or bad credit risks according to the set of attributes. The link to the original dataset can be found below. Content It is almost impossible to understand the original dataset due to its complicated system of categories and symbols. Thus, I wrote a small Python script to convert it into a readable CSV file. The column names were also given in German originally. So, they are replaced by English names while processing. The attributes and their details in English are given below:
Status - Categorical (Ordinal) Duration - Numerical Credit History - Categorical (Nominal) Purpose - Categorical (Nominal) Amount - Numerical Savings - Categorical (Ordinal) Employment Duration - Categorical (Ordinal) Installment Rate - Categorical (Ordinal) Personal Status Sex - Categorical (Nominal) Other Debtors - Categorical (Nominal) Present Residence - Categorical (Ordinal) Property - Categorical (Nominal) Age - Numerical Other Installment Plans - Categorical (Nominal) Housing - Categorical (Nominal) Number Credits - Categorical (Ordinal) Job - Categorical (Nominal) People Liable - Categorical (Ordinal) Telephone - Categorical (Nominal) Foreign Worker - Categorical (Nominal) Credit Risk - Binary Target Variable
Acknowledgements
Source : UCI
This page was built for dataset: German-Credit-Data
