Technical task anomaly detection
Project 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)
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
File details
Details for the file fraudtransaction_task-0.0.4.tar.gz
.
File metadata
- Download URL: fraudtransaction_task-0.0.4.tar.gz
- Upload date:
- Size: 1.2 MB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.23.0 setuptools/46.1.3 requests-toolbelt/0.9.1 tqdm/4.45.0 CPython/3.8.2
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 |
4192f17f26735b93db4802da6b22e4dff8e20f4ab81d14a774804825003ba280
|
|
MD5 |
13649e0fd970135bc038edbd7b027c14
|
|
BLAKE2b-256 |
26a09e4f58474e0ed66e140f7f4eefcd07bf0c116a53f7f21a8a892ff22304d2
|