机器学习_Statlog (German Credit Data) Data Set(Statlog(德国

This dataset classifies people described by a set of attributes as good or bad credit risks. Comes in two formats (one all numeric). Also comes with a cost matrix.

Statlog (German Credit Data) Data Set(Statlog(德国

信用数据)数据集)

数据摘要：

This dataset classifies people described by a set of attributes as good

or bad credit risks. Comes in two formats (one all numeric). Also comes with a cost matrix.

中文关键词：

Statlog,德国信用数据,多变量,分类,UCI,

英文关键词：

Statlog,German Credit Data,Multivariate,Classification,UCI,

数据格式：

TEXT

数据用途：

This data set is used for classification.

数据详细介绍：

Statlog (German Credit Data) Data Set

This dataset classifies people described by a set of attributes as good or bad credit risks. Comes in two formats (one all numeric). Also comes with a cost matrix.

Abstract: This dataset classifies people described by a set of attributes as good or bad credit

Source:

Professor Dr. Hans Hofmann

Institut f"ur Statistik und "Okonometrie Universit"at Hamburg FB Wirtschaftswissenschaften Von-Melle-Park 5 2000 Hamburg 13

Data Set Information:

Two datasets are provided. the original dataset, in the form provided by Prof. Hofmann, contains categorical/symbolic attributes and is in the file "german.data".

For algorithms that need numerical attributes, Strathclyde University produced the file "german.data-numeric". This file has been edited and several indicator variables added to make it suitable for algorithms which cannot cope with categorical variables. Several attributes that are ordered categorical (such as attribute 17) have been coded as integer. This was the form used by StatLog.

This dataset requires use of a cost matrix (see below) ..... 1 2

---------------------------- 1 0 1

----------------------- 2 5 0

(1 = Good, 2 = Bad)

This dataset classifies people described by a set of attributes as good or bad credit risks. Comes in two formats (one all numeric). Also comes with a cost matrix.

The rows represent the actual classification and the columns the predicted classification.

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 Information:

Attribute 1: (qualitative)

Status of existing checking account A11 : ... < 0 DM A12 : 0 <= ... < 200 DM

A13 : ... >= 200 DM / salary assignments for at least 1 year A14 : no checking account

Attribute 2: (numerical) Duration in month

Attribute 3: (qualitative) Credit history

A30 : no credits taken/ all credits paid back duly A31 : all credits at this bank paid back duly A32 : existing credits paid back duly till now A33 : delay in paying off in the past

A34 : critical account/ other credits existing (not at this bank)

Attribute 4: (qualitative) Purpose A40 : car (new) A41 : car (used)

A42 : furniture/equipment A43 : radio/television A44 : domestic appliances A45 : repairs A46 : education

A47 : (vacation - does not exist?) A48 : retraining A49 : business A410 : others

Attribute 5: (numerical) Credit amount

Attibute 6: (qualitative)

This dataset classifies people described by a set of attributes as good or bad credit risks. Comes in two formats (one all numeric). Also comes with a cost matrix.

Savings account/bonds A61 : ... < 100 DM A62 : 100 <= ... < 500 DM A63 : 500 <= ... < 1000 DM A64 : .. >= 1000 DM

A65 : unknown/ no savings account

Attribute 7: (qualitative) Present employment since A71 : unemployed A72 : ... < 1 year A73 : 1 <= ... < 4 years A74 : 4 <= ... < 7 years A75 : .. >= 7 years

Attribute 8: (numerical)

Installment rate in percentage of disposable income

Attribute 9: (qualitative) Personal status and sex A91 : male : porced/separated

A92 : female : porced/separated/married A93 : male : single

A94 : male : married/widowed A95 : female : single

Attribute 10: (qualitative) Other debtors / guarantors A101 : none A102 : co-applicant A103 : guarantor

Attribute 11: (numerical) Present residence since

Attribute 12: (qualitative) Property

A121 : real estate

A122 : if not A121 : building society savings agreement/ life insurance A123 : if not A121/A122 : car or other, not in attribute 6 A124 : unknown / no property

Attribute 13: (numerical) Age in years

This dataset classifies people described by a set of attributes as good or bad credit risks. Comes in two formats (one all numeric). Also comes with a cost matrix.

Attribute 14: (qualitative) Other installment plans A141 : bank A142 : stores A143 : none

Attribute 15: (qualitative) Housing A151 : rent A152 : own A153 : for free

Attribute 16: (numerical)

Number of existing credits at this bank

Attribute 17: (qualitative) Job

A171 : unemployed/ unskilled - non-resident A172 : unskilled - resident A173 : skilled employee / official A174 : management/ self-employed/ highly qualified employee/ officer

Attribute 18: (numerical)

Number of people being liable to provide maintenance for

Attribute 19: (qualitative) Telephone A191 : none

A192 : yes, registered under the customers name

Attribute 20: (qualitative) foreign worker A201 : yes A202 : no

数据预览：

This dataset classifies people described by a set of attributes as good or bad credit risks. Comes in two formats (one all numeric). Also comes with a cost matrix.

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