Data
Fraud_Detection_Dataset

Fraud_Detection_Dataset

active ARFF Public Domain Visibility: public Uploaded 12-11-2024 by Anna Wiewer
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A fraud detection dataset with date components and categorical encodings for OpenML upload.

29 features

bad_flag (target)numeric2 unique values
0 missing
dpd_5_cntnumeric8 unique values
3481 missing
dpd_15_cntnumeric6 unique values
3481 missing
dpd_30_cntnumeric4 unique values
3481 missing
close_loans_cntnumeric21 unique values
15 missing
federal_district_nmnumeric9 unique values
0 missing
payment_type_0numeric9 unique values
0 missing
payment_type_1numeric28 unique values
0 missing
payment_type_2numeric26 unique values
0 missing
payment_type_3numeric24 unique values
0 missing
payment_type_4numeric7 unique values
0 missing
payment_type_5numeric1 unique values
0 missing
past_billings_cntnumeric21 unique values
248 missing
score_1numeric1274 unique values
649 missing
score_2numeric47 unique values
3917 missing
agenumeric51 unique values
0 missing
gendernumeric2 unique values
0 missing
rep_loan_date_yearnumeric3 unique values
0 missing
rep_loan_date_monthnumeric12 unique values
0 missing
rep_loan_date_daynumeric31 unique values
0 missing
rep_loan_date_weekdaynumeric7 unique values
0 missing
first_loan_yearnumeric2 unique values
0 missing
first_loan_monthnumeric12 unique values
0 missing
first_loan_daynumeric31 unique values
0 missing
first_loan_weekdaynumeric7 unique values
0 missing
first_overdue_date_yearnumeric2 unique values
3481 missing
first_overdue_date_monthnumeric12 unique values
3481 missing
first_overdue_date_daynumeric19 unique values
3481 missing
first_overdue_date_weekdaynumeric8 unique values
0 missing
Show all 29 features

19 properties

NumberOfInstances
4156
Number of instances (rows) of the dataset.
NumberOfFeatures
29
Number of attributes (columns) of the dataset.
NumberOfClasses
0
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
25715
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
4156
Number of instances with at least one value missing.
NumberOfNumericFeatures
29
Number of numeric attributes.
NumberOfSymbolicFeatures
0
Number of nominal attributes.
PercentageOfBinaryFeatures
0
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
100
Percentage of instances having missing values.
AutoCorrelation
0.82
Average class difference between consecutive instances.
PercentageOfMissingValues
21.34
Percentage of missing values.
Dimensionality
0.01
Number of attributes divided by the number of instances.
PercentageOfNumericFeatures
100
Percentage of numeric attributes.
MajorityClassPercentage
Percentage of instances belonging to the most frequent class.
PercentageOfSymbolicFeatures
0
Percentage of nominal attributes.
MajorityClassSize
Number of instances belonging to the most frequent class.
MinorityClassPercentage
Percentage of instances belonging to the least frequent class.
MinorityClassSize
Number of instances belonging to the least frequent class.
NumberOfBinaryFeatures
0
Number of binary attributes.
Show all 19 properties

1 tasks

Supervised Classification on Fraud_Detection_Dataset
0 runs - estimation_procedure: 33% Holdout set - target_feature: class
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