--- library_name: sklearn tags: - sklearn - skops - tabular-classification model_format: pickle model_file: skops-89qohtne.pkl widget: - structuredData: AGE: - 32 - 45 - 25 GENDER: - m - f - f HOWPAID: - 'weekly ' - 'weekly ' - 'weekly ' INCOME: - 21772 - 27553 - 23477 LOANS: - 1 - 2 - 1 MARITAL: - 'married ' - divsepwid - 'single ' MORTGAGE: - y - y - n NUMCARDS: - 2 - 6 - 1 NUMKIDS: - 1 - 4 - 1 STORECAR: - 3 - 5 - 2 --- # Model description This is a logistic regression model trained on customers' credit card risk data in a bank using sklearn library. The model predicts whether a customer is worth issuing a credit card or not. The full dataset can be viewed at the following link: https://huggingface.co/datasets/saifhmb/CreditCardRisk ## Training Procedure The data preprocessing steps applied include the following: - Dropping high cardinality features, specifically ID - Transforming and Encoding categorical features namely: GENDER, MARITAL, HOWPAID, MORTGAGE and the target variable, RISK - Splitting the dataset into training/test set using 85/15 split ratio - Applying feature scaling on all features ### Hyperparameters

Click to expand

| Hyperparameter | Value | |-------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | memory | | | steps | [('preprocessor', ColumnTransformer(remainder='passthrough',
transformers=[('cat',
Pipeline(steps=[('onehot',
OneHotEncoder(handle_unknown='ignore'))]),
['GENDER', 'MARITAL', 'HOWPAID', 'MORTGAGE']),
('num',
Pipeline(steps=[('scale', StandardScaler())]),
Index(['AGE', 'INCOME', 'NUMKIDS', 'NUMCARDS', 'STORECAR', 'LOANS'], dtype='object'))])), ('classifier', LogisticRegression())] | | verbose | False | | preprocessor | ColumnTransformer(remainder='passthrough',
transformers=[('cat',
Pipeline(steps=[('onehot',
OneHotEncoder(handle_unknown='ignore'))]),
['GENDER', 'MARITAL', 'HOWPAID', 'MORTGAGE']),
('num',
Pipeline(steps=[('scale', StandardScaler())]),
Index(['AGE', 'INCOME', 'NUMKIDS', 'NUMCARDS', 'STORECAR', 'LOANS'], dtype='object'))]) | | classifier | LogisticRegression() | | preprocessor__n_jobs | | | preprocessor__remainder | passthrough | | preprocessor__sparse_threshold | 0.3 | | preprocessor__transformer_weights | | | preprocessor__transformers | [('cat', Pipeline(steps=[('onehot', OneHotEncoder(handle_unknown='ignore'))]), ['GENDER', 'MARITAL', 'HOWPAID', 'MORTGAGE']), ('num', Pipeline(steps=[('scale', StandardScaler())]), Index(['AGE', 'INCOME', 'NUMKIDS', 'NUMCARDS', 'STORECAR', 'LOANS'], dtype='object'))] | | preprocessor__verbose | False | | preprocessor__verbose_feature_names_out | True | | preprocessor__cat | Pipeline(steps=[('onehot', OneHotEncoder(handle_unknown='ignore'))]) | | preprocessor__num | Pipeline(steps=[('scale', StandardScaler())]) | | preprocessor__cat__memory | | | preprocessor__cat__steps | [('onehot', OneHotEncoder(handle_unknown='ignore'))] | | preprocessor__cat__verbose | False | | preprocessor__cat__onehot | OneHotEncoder(handle_unknown='ignore') | | preprocessor__cat__onehot__categories | auto | | preprocessor__cat__onehot__drop | | | preprocessor__cat__onehot__dtype | | | preprocessor__cat__onehot__handle_unknown | ignore | | preprocessor__cat__onehot__max_categories | | | preprocessor__cat__onehot__min_frequency | | | preprocessor__cat__onehot__sparse | deprecated | | preprocessor__cat__onehot__sparse_output | True | | preprocessor__num__memory | | | preprocessor__num__steps | [('scale', StandardScaler())] | | preprocessor__num__verbose | False | | preprocessor__num__scale | StandardScaler() | | preprocessor__num__scale__copy | True | | preprocessor__num__scale__with_mean | True | | preprocessor__num__scale__with_std | True | | classifier__C | 1.0 | | classifier__class_weight | | | classifier__dual | False | | classifier__fit_intercept | True | | classifier__intercept_scaling | 1 | | classifier__l1_ratio | | | classifier__max_iter | 100 | | classifier__multi_class | auto | | classifier__n_jobs | | | classifier__penalty | l2 | | classifier__random_state | | | classifier__solver | lbfgs | | classifier__tol | 0.0001 | | classifier__verbose | 0 | | classifier__warm_start | False |

### Model Plot

Pipeline(steps=[('preprocessor',ColumnTransformer(remainder='passthrough',transformers=[('cat',Pipeline(steps=[('onehot',OneHotEncoder(handle_unknown='ignore'))]),['GENDER', 'MARITAL','HOWPAID', 'MORTGAGE']),('num',Pipeline(steps=[('scale',StandardScaler())]),Index(['AGE', 'INCOME', 'NUMKIDS', 'NUMCARDS', 'STORECAR', 'LOANS'], dtype='object'))])),('classifier', LogisticRegression())])

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## Evaluation Results - The target variable, RISK is multiclass. In sklearn, precision and recall functions have a parameter called, average. This parameter is required for a multiclass/multilabel target. average = 'micro' was used to calculate the precision and recall metrics globally by counting the total true positives, false negatives and false positives | Metric | Value | |-----------|----------| | accuracy | 0.699187 | | precision | 0.699187 | | recall | 0.699187 | ### 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/OhZPMDUQi1N4R0XlMQm9A.png) ### Confusion Matrix ![Confusion Matrix](confusion_matrix.png) # Model Card Authors This model card is written by following authors: Seifullah Bello # Model Card Contact You can contact the model card authors through following channels: saifhmb@gmail.com