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	---
	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

	<details>
	<summary> Click to expand </summary>

	\| Hyperparameter \| Value \|
	\|-------------------------------------------\|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| memory \| \|
	\| steps \| [('preprocessor', ColumnTransformer(remainder='passthrough',<br /> transformers=[('cat',<br /> Pipeline(steps=[('onehot',<br /> OneHotEncoder(handle_unknown='ignore'))]),<br /> ['GENDER', 'MARITAL', 'HOWPAID', 'MORTGAGE']),<br /> ('num',<br /> Pipeline(steps=[('scale', StandardScaler())]),<br /> Index(['AGE', 'INCOME', 'NUMKIDS', 'NUMCARDS', 'STORECAR', 'LOANS'], dtype='object'))])), ('classifier', LogisticRegression())] \|
	\| verbose \| False \|
	\| preprocessor \| ColumnTransformer(remainder='passthrough',<br /> transformers=[('cat',<br /> Pipeline(steps=[('onehot',<br /> OneHotEncoder(handle_unknown='ignore'))]),<br /> ['GENDER', 'MARITAL', 'HOWPAID', 'MORTGAGE']),<br /> ('num',<br /> Pipeline(steps=[('scale', StandardScaler())]),<br /> 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 \| <class 'numpy.float64'> \|
	\| 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 \|

	</details>

	### Model Plot

	<style>#sk-container-id-13 {color: black;background-color: white;}#sk-container-id-13 pre{padding: 0;}#sk-container-id-13 div.sk-toggleable {background-color: white;}#sk-container-id-13 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-13 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-13 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-13 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-13 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-13 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-13 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-13 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-13 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-13 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-13 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-13 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-13 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-13 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-13 div.sk-item {position: relative;z-index: 1;}#sk-container-id-13 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-13 div.sk-item::before, #sk-container-id-13 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-13 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-13 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-13 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-13 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-13 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-13 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-13 div.sk-label-container {text-align: center;}#sk-container-id-13 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-13 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-13" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>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())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-97" type="checkbox" ><label for="sk-estimator-id-97" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>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())])</pre></div></div></div><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-98" type="checkbox" ><label for="sk-estimator-id-98" class="sk-toggleable__label sk-toggleable__label-arrow">preprocessor: ColumnTransformer</label><div class="sk-toggleable__content"><pre>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'))])</pre></div></div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-99" type="checkbox" ><label for="sk-estimator-id-99" class="sk-toggleable__label sk-toggleable__label-arrow">cat</label><div class="sk-toggleable__content"><pre>['GENDER', 'MARITAL', 'HOWPAID', 'MORTGAGE']</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-100" type="checkbox" ><label for="sk-estimator-id-100" class="sk-toggleable__label sk-toggleable__label-arrow">OneHotEncoder</label><div class="sk-toggleable__content"><pre>OneHotEncoder(handle_unknown='ignore')</pre></div></div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-101" type="checkbox" ><label for="sk-estimator-id-101" class="sk-toggleable__label sk-toggleable__label-arrow">num</label><div class="sk-toggleable__content"><pre>Index(['AGE', 'INCOME', 'NUMKIDS', 'NUMCARDS', 'STORECAR', 'LOANS'], dtype='object')</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-102" type="checkbox" ><label for="sk-estimator-id-102" class="sk-toggleable__label sk-toggleable__label-arrow">StandardScaler</label><div class="sk-toggleable__content"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-103" type="checkbox" ><label for="sk-estimator-id-103" class="sk-toggleable__label sk-toggleable__label-arrow">remainder</label><div class="sk-toggleable__content"><pre>[]</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-104" type="checkbox" ><label for="sk-estimator-id-104" class="sk-toggleable__label sk-toggleable__label-arrow">passthrough</label><div class="sk-toggleable__content"><pre>passthrough</pre></div></div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-105" type="checkbox" ><label for="sk-estimator-id-105" class="sk-toggleable__label sk-toggleable__label-arrow">LogisticRegression</label><div class="sk-toggleable__content"><pre>LogisticRegression()</pre></div></div></div></div></div></div></div>

	## 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