--- library_name: sklearn tags: - sklearn - skops - tabular-classification - finance model_format: pickle model_file: skops-ise057qg.pkl widget: - structuredData: Bene_Country: - COMOROS - CANADA - MOROCCO Sender_Country: - SRI-LANKA - USA - USA Transaction_Type: - MOVE-FUNDS - PAY-CHECK - MAKE-PAYMENT USD_amount: - 598.31 - 398.72 - 87.03 --- # Model description This is a Gaussian Naive Bayes model trained on a synthetic dataset, containining a large variety of transaction types representing normal activities as well as abnormal/fraudulent activities generated by J.P. Morgan AI Research. The model predicts whether a transaction is normal or fraudulent. ## Intended uses & limitations For educational purposes ## Training Procedure The data preprocessing steps applied include the following: - Dropping high cardinality features. This includes Transaction ID, Sender ID, Sender Account, Beneficiary ID, Beneficiary Account, Sender Sector - Dropping no variance features. This includes Sender LOB - Dropping Time and date feature since the model is not time-series based - Transforming and Encoding categorical features namely: Sender Country, Beneficiary Country, Transaction Type, and the target variable, Label - Applying feature scaling on all features - Splitting the dataset into training/test set using 85/15 split ratio - Handling imbalanced dataset using imblearn framework and applying RandomUnderSampler method to eliminate noise which led to a 2.5% improvement in accuracy ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6662300a0ad8c45a1ce59190/BEi0CfOfJ2ytxD5VoN4IM.png) ### Hyperparameters

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| Hyperparameter | Value | |----------------------------------------------|---------------------------------------------------------------------------| | memory | | | steps | [('preprocessorAll', ColumnTransformer(remainder='passthrough',
transformers=[('cat',
Pipeline(steps=[('onehot',
OneHotEncoder(handle_unknown='ignore',
sparse_output=False))]),
['Sender_Country', 'Bene_Country',
'Transaction_Type']),
('num',
Pipeline(steps=[('scale', StandardScaler())]),
Index(['USD_amount'], dtype='object'))])), ('classifier', GaussianNB())] | | verbose | False | | preprocessorAll | ColumnTransformer(remainder='passthrough',
transformers=[('cat',
Pipeline(steps=[('onehot',
OneHotEncoder(handle_unknown='ignore',
sparse_output=False))]),
['Sender_Country', 'Bene_Country',
'Transaction_Type']),
('num',
Pipeline(steps=[('scale', StandardScaler())]),
Index(['USD_amount'], dtype='object'))]) | | classifier | GaussianNB() | | preprocessorAll__n_jobs | | | preprocessorAll__remainder | passthrough | | preprocessorAll__sparse_threshold | 0.3 | | preprocessorAll__transformer_weights | | | preprocessorAll__transformers | [('cat', Pipeline(steps=[('onehot',
OneHotEncoder(handle_unknown='ignore', sparse_output=False))]), ['Sender_Country', 'Bene_Country', 'Transaction_Type']), ('num', Pipeline(steps=[('scale', StandardScaler())]), Index(['USD_amount'], dtype='object'))] | | preprocessorAll__verbose | False | | preprocessorAll__verbose_feature_names_out | True | | preprocessorAll__cat | Pipeline(steps=[('onehot',
OneHotEncoder(handle_unknown='ignore', sparse_output=False))]) | | preprocessorAll__num | Pipeline(steps=[('scale', StandardScaler())]) | | preprocessorAll__cat__memory | | | preprocessorAll__cat__steps | [('onehot', OneHotEncoder(handle_unknown='ignore', sparse_output=False))] | | preprocessorAll__cat__verbose | False | | preprocessorAll__cat__onehot | OneHotEncoder(handle_unknown='ignore', sparse_output=False) | | preprocessorAll__cat__onehot__categories | auto | | preprocessorAll__cat__onehot__drop | | | preprocessorAll__cat__onehot__dtype | | | preprocessorAll__cat__onehot__handle_unknown | ignore | | preprocessorAll__cat__onehot__max_categories | | | preprocessorAll__cat__onehot__min_frequency | | | preprocessorAll__cat__onehot__sparse | deprecated | | preprocessorAll__cat__onehot__sparse_output | False | | preprocessorAll__num__memory | | | preprocessorAll__num__steps | [('scale', StandardScaler())] | | preprocessorAll__num__verbose | False | | preprocessorAll__num__scale | StandardScaler() | | preprocessorAll__num__scale__copy | True | | preprocessorAll__num__scale__with_mean | True | | preprocessorAll__num__scale__with_std | True | | classifier__priors | | | classifier__var_smoothing | 1e-09 |

### Model Plot

Pipeline(steps=[('preprocessorAll',ColumnTransformer(remainder='passthrough',transformers=[('cat',Pipeline(steps=[('onehot',OneHotEncoder(handle_unknown='ignore',sparse_output=False))]),['Sender_Country','Bene_Country','Transaction_Type']),('num',Pipeline(steps=[('scale',StandardScaler())]),Index(['USD_amount'], dtype='object'))])),('classifier', GaussianNB())])

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## Evaluation Results | Metric | Value | |----------|----------| | accuracy | 0.794582 | ## Model Explainability SHAP was used to determine the important features that helps the model make decisions ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6662300a0ad8c45a1ce59190/rQYxJoz86TtdkSSSGnCOr.png) ### Confusion Matrix ![Confusion Matrix](confusion_matrix.png) # Model Card Authors This model card is written by following authors: Seifullah Bello