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Credit_Card_Fraud_Classification

Credit_Card_Fraud_Classification

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# Credit Card Fraud Detection Dataset It is important that credit card companies are able to recognize fraudulent credit card transactions so that customers are not charged for items that they did not purchase. The original complete dataset contains transactions made by credit cards in September 2013 by European cardholders. This dataset presents transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions. The dataset is highly imbalanced, the positive class (frauds) account for 0.172% of all transactionsThis dataset takes a 10% stratified sample.

31 features

class (target)nominal2 unique values
0 missing
timenumeric25763 unique values
0 missing
v1numeric28271 unique values
0 missing
v2numeric28271 unique values
0 missing
v3numeric28271 unique values
0 missing
v4numeric28271 unique values
0 missing
v5numeric28271 unique values
0 missing
v6numeric28271 unique values
0 missing
v7numeric28271 unique values
0 missing
v8numeric28271 unique values
0 missing
v9numeric28271 unique values
0 missing
v10numeric28271 unique values
0 missing
v11numeric28271 unique values
0 missing
v12numeric28271 unique values
0 missing
v13numeric28271 unique values
0 missing
v14numeric28271 unique values
0 missing
v15numeric28271 unique values
0 missing
v16numeric28271 unique values
0 missing
v17numeric28271 unique values
0 missing
v18numeric28271 unique values
0 missing
v19numeric28271 unique values
0 missing
v20numeric28271 unique values
0 missing
v21numeric28271 unique values
0 missing
v22numeric28271 unique values
0 missing
v23numeric28271 unique values
0 missing
v24numeric28271 unique values
0 missing
v25numeric28271 unique values
0 missing
v26numeric28271 unique values
0 missing
v27numeric28271 unique values
0 missing
v28numeric28271 unique values
0 missing
amountnumeric8807 unique values
0 missing
Show all 31 features

19 properties

NumberOfInstances
28480
Number of instances (rows) of the dataset.
NumberOfFeatures
31
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
30
Number of numeric attributes.
NumberOfSymbolicFeatures
1
Number of nominal attributes.
PercentageOfBinaryFeatures
3.23
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfMissingValues
0
Percentage of missing values.
AutoCorrelation
1
Average class difference between consecutive instances.
PercentageOfNumericFeatures
96.77
Percentage of numeric attributes.
Dimensionality
0
Number of attributes divided by the number of instances.
PercentageOfSymbolicFeatures
3.23
Percentage of nominal attributes.
MajorityClassPercentage
99.83
Percentage of instances belonging to the most frequent class.
MajorityClassSize
28431
Number of instances belonging to the most frequent class.
MinorityClassPercentage
0.17
Percentage of instances belonging to the least frequent class.
MinorityClassSize
49
Number of instances belonging to the least frequent class.
NumberOfBinaryFeatures
1
Number of binary attributes.
Show all 19 properties

2 tasks

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