Technical task anomaly detection
=========== Fraud Transaction Detection
Fraud Transaction Detection provides a model developed by python sklearn library clustering and classification algorithm for finding the fraud transactions in a given dataset. You might find it most useful for tasks involving finding which transactions are fraudulent transactions in a given dataset. It supports both CSV and ods file types as of now and a single sample record can be provided as an array. Typical usage often looks like this::
#!/usr/bin/env python import frauddetection.use_model as fd fd.FraudDetectionPredict.predictSingleSample([1284b75c-ecae-4015-8e3d-359c0347ede8, 0, 1, 1, 1, 0, 188, 174, 0, 1, 3, 3, 8, 52, 1, 1, 1, 1]) fd.FraudDetectionPredict.predictDatasetCsv('data.csv') #path to csv file as argument fd.FraudDetectionPredict.predictDatasetOds('data.ods') #path to ods file as argument
When providing a single sample, the feature values should be provided as an array excluding the consumer id and gender column value.
Output look like this::
 [0 0 1 ... 0 0 0] [0 0 0 ... 0 1 0]
0 denotes normal transaction
1 denotes anomaly transaction (fraud transaction)
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