Data
Credit_Approval_Classification

Credit_Approval_Classification

active ARFF CC BY 4.0 Visibility: public Uploaded 19-11-2024 by Anna Wiewer
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A dataset for binary classification of credit approval status. Features include customer demographics, financial attributes, and credit history. The target variable `class` indicates whether the credit is good or bad.

21 features

class (target)nominal2 unique values
0 missing
checking_statusstring4 unique values
0 missing
durationnumeric33 unique values
0 missing
credit_historystring5 unique values
0 missing
purposestring10 unique values
0 missing
credit_amountnumeric921 unique values
0 missing
savings_statusstring5 unique values
0 missing
employmentstring5 unique values
0 missing
installment_commitmentnumeric4 unique values
0 missing
personal_statusstring4 unique values
0 missing
other_partiesstring3 unique values
0 missing
residence_sincenumeric4 unique values
0 missing
property_magnitudestring4 unique values
0 missing
agenumeric53 unique values
0 missing
other_payment_plansstring3 unique values
0 missing
housingstring3 unique values
0 missing
existing_creditsnumeric4 unique values
0 missing
jobstring4 unique values
0 missing
num_dependentsnumeric2 unique values
0 missing
own_telephonestring2 unique values
0 missing
foreign_workerstring2 unique values
0 missing
Show all 21 features

19 properties

NumberOfInstances
1000
Number of instances (rows) of the dataset.
NumberOfFeatures
21
Number of attributes (columns) of the dataset.
NumberOfClasses
2
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
0
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
0
Number of instances with at least one value missing.
NumberOfNumericFeatures
7
Number of numeric attributes.
NumberOfSymbolicFeatures
1
Number of nominal attributes.
PercentageOfBinaryFeatures
4.76
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfMissingValues
0
Percentage of missing values.
AutoCorrelation
0.57
Average class difference between consecutive instances.
PercentageOfNumericFeatures
33.33
Percentage of numeric attributes.
Dimensionality
0.02
Number of attributes divided by the number of instances.
PercentageOfSymbolicFeatures
4.76
Percentage of nominal attributes.
MajorityClassPercentage
70
Percentage of instances belonging to the most frequent class.
MajorityClassSize
700
Number of instances belonging to the most frequent class.
MinorityClassPercentage
30
Percentage of instances belonging to the least frequent class.
MinorityClassSize
300
Number of instances belonging to the least frequent class.
NumberOfBinaryFeatures
1
Number of binary attributes.
Show all 19 properties

1 tasks

Supervised Classification on Credit_Approval_Classification
0 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: class
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