Implementation
Reference implementation, transaction fraud detection
- Identifier
AYENI-2026-0014 - Version1
- Published2026-06-24
- AccessTier 2: verified institutions
Description #
The working code behind the transaction fraud detection methodology paper: the shared DataPreProcessing notebook (column selection, categorical encoding, the train test split, and feature scaling) that every classifier notebook imports from, an exploratory data analysis notebook, and all four trained classifiers, Decision Tree, K Nearest Neighbors, Logistic Regression, and Random Forest, each with training, an overfitting check, a classification report, ROC and precision recall curves, a confusion matrix, and a calibration curve. Random Forest is the paper's recommended model for deployment.
Files #
| Path | Size | SHA-256 |
|---|---|---|
files/data-pre-processing.ipynb |
15,543 bytes | ad77aabc9e117229544ac5645c4e54d0a39eb86d403f5ba1c0eccbcf3028b74a |
files/data-visualization.ipynb |
3,769,898 bytes | 83b40fd55623b5b86148d8c4a371831cf6e5886a581faf8ecbcafcf6e2eb1827 |
files/decision-tree.ipynb |
92,594 bytes | 3174eada35120e8dbb6731215b2422624408bd1582e34f63bb29febb3d730a80 |
files/k-nearest-neighbours.ipynb |
97,591 bytes | 11779b62174c7f18b50a189d6a493444db472a443046c6f89af024cb1f22012a |
files/logistic-regression.ipynb |
103,598 bytes | 9074d7fd19b670c2181db91aa7cd90bb72eb8b502dc1a1616d85622c676e24f2 |
files/random-forest.ipynb |
92,055 bytes | 30a70244a638af1a5f6e340767e49da03a358a8dff5bf0df425fc9a895fae4d7 |
Each file's SHA-256 is listed above. To confirm a download is unmodified: shasum -a 256 filename
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Licence: Available to verified institutions