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data/tanveer/statlog-german-credit/german.doc
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data/tanveer/statlog-german-credit/german.doc
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Description of the German credit dataset.
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1. Title: German Credit data
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2. Source Information
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Professor Dr. Hans Hofmann
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Institut f"ur Statistik und "Okonometrie
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Universit"at Hamburg
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FB Wirtschaftswissenschaften
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Von-Melle-Park 5
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2000 Hamburg 13
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3. Number of Instances: 1000
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Two datasets are provided. the original dataset, in the form provided
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by Prof. Hofmann, contains categorical/symbolic attributes and
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is in the file "german.data".
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For algorithms that need numerical attributes, Strathclyde University
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produced the file "german.data-numeric". This file has been edited
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and several indicator variables added to make it suitable for
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algorithms which cannot cope with categorical variables. Several
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attributes that are ordered categorical (such as attribute 17) have
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been coded as integer. This was the form used by StatLog.
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6. Number of Attributes german: 20 (7 numerical, 13 categorical)
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Number of Attributes german.numer: 24 (24 numerical)
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7. Attribute description for german
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Attribute 1: (qualitative)
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Status of existing checking account
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A11 : ... < 0 DM
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A12 : 0 <= ... < 200 DM
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A13 : ... >= 200 DM /
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salary assignments for at least 1 year
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A14 : no checking account
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Attribute 2: (numerical)
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Duration in month
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Attribute 3: (qualitative)
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Credit history
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A30 : no credits taken/
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all credits paid back duly
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A31 : all credits at this bank paid back duly
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A32 : existing credits paid back duly till now
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A33 : delay in paying off in the past
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A34 : critical account/
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other credits existing (not at this bank)
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Attribute 4: (qualitative)
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Purpose
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A40 : car (new)
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A41 : car (used)
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A42 : furniture/equipment
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A43 : radio/television
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A44 : domestic appliances
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A45 : repairs
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A46 : education
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A47 : (vacation - does not exist?)
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A48 : retraining
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A49 : business
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A410 : others
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Attribute 5: (numerical)
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Credit amount
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Attibute 6: (qualitative)
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Savings account/bonds
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A61 : ... < 100 DM
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A62 : 100 <= ... < 500 DM
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A63 : 500 <= ... < 1000 DM
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A64 : .. >= 1000 DM
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A65 : unknown/ no savings account
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Attribute 7: (qualitative)
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Present employment since
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A71 : unemployed
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A72 : ... < 1 year
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A73 : 1 <= ... < 4 years
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A74 : 4 <= ... < 7 years
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A75 : .. >= 7 years
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Attribute 8: (numerical)
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Installment rate in percentage of disposable income
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Attribute 9: (qualitative)
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Personal status and sex
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A91 : male : divorced/separated
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A92 : female : divorced/separated/married
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A93 : male : single
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A94 : male : married/widowed
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A95 : female : single
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Attribute 10: (qualitative)
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Other debtors / guarantors
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A101 : none
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A102 : co-applicant
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A103 : guarantor
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Attribute 11: (numerical)
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Present residence since
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Attribute 12: (qualitative)
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Property
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A121 : real estate
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A122 : if not A121 : building society savings agreement/
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life insurance
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A123 : if not A121/A122 : car or other, not in attribute 6
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A124 : unknown / no property
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Attribute 13: (numerical)
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Age in years
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Attribute 14: (qualitative)
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Other installment plans
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A141 : bank
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A142 : stores
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A143 : none
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Attribute 15: (qualitative)
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Housing
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A151 : rent
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A152 : own
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A153 : for free
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Attribute 16: (numerical)
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Number of existing credits at this bank
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Attribute 17: (qualitative)
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Job
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A171 : unemployed/ unskilled - non-resident
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A172 : unskilled - resident
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A173 : skilled employee / official
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A174 : management/ self-employed/
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highly qualified employee/ officer
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Attribute 18: (numerical)
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Number of people being liable to provide maintenance for
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Attribute 19: (qualitative)
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Telephone
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A191 : none
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A192 : yes, registered under the customers name
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Attribute 20: (qualitative)
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foreign worker
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A201 : yes
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A202 : no
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8. Cost Matrix
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This dataset requires use of a cost matrix (see below)
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1 2
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----------------------------
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1 0 1
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-----------------------
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2 5 0
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(1 = Good, 2 = Bad)
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the rows represent the actual classification and the columns
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the predicted classification.
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It is worse to class a customer as good when they are bad (5),
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than it is to class a customer as bad when they are good (1).
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