Metadata-Version: 2.1
Name: fraudtransaction_task
Version: 0.0.4
Summary: Technical task anomaly detection
Home-page: https://bitbucket.org/srijithm7_bb/fraudtransactiondetection/
Author: Srijith M
Author-email: srijithm7@gmail.com
License: UNKNOWN
Description: ===========
        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
        
        Note
        -------------
        
        When providing a single sample, the feature values should be provided as an array excluding the consumer id and gender column value.
        
        Output
        =========
        
        Output look like this::
        
            [1]
            [0 0 1 ... 0 0 0]
            [0 0 0 ... 0 1 0]
        
        * 0 denotes normal transaction
        
        * 1 denotes anomaly transaction (fraud transaction)
        
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
