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	---
	license: mit
	library_name: sklearn
	tags:
	- sklearn
	- skops
	- tabular-classification
	widget:
	structuredData:
	Contract:
	- Two year
	- Month-to-month
	- One year
	Dependents:
	- 'Yes'
	- 'No'
	- 'No'
	DeviceProtection:
	- 'No'
	- 'No'
	- 'Yes'
	InternetService:
	- Fiber optic
	- Fiber optic
	- DSL
	MonthlyCharges:
	- 79.05
	- 84.95
	- 68.8
	MultipleLines:
	- 'Yes'
	- 'Yes'
	- 'Yes'
	OnlineBackup:
	- 'No'
	- 'No'
	- 'Yes'
	OnlineSecurity:
	- 'Yes'
	- 'No'
	- 'Yes'
	PaperlessBilling:
	- 'No'
	- 'Yes'
	- 'No'
	Partner:
	- 'Yes'
	- 'Yes'
	- 'No'
	PaymentMethod:
	- Bank transfer (automatic)
	- Electronic check
	- Bank transfer (automatic)
	PhoneService:
	- 'Yes'
	- 'Yes'
	- 'Yes'
	SeniorCitizen:
	- 0
	- 0
	- 0
	StreamingMovies:
	- 'No'
	- 'No'
	- 'No'
	StreamingTV:
	- 'No'
	- 'Yes'
	- 'No'
	TechSupport:
	- 'No'
	- 'No'
	- 'Yes'
	TotalCharges:
	- 5730.7
	- 1378.25
	- 4111.35
	gender:
	- Female
	- Female
	- Male
	tenure:
	- 72
	- 16
	- 63
	---

	# Model description

	This is a Logistic Regression model trained on churn dataset.

	## Intended uses & limitations

	This model is not ready to be used in production.

	## Training Procedure

	### Hyperparameters

	The model is trained with below hyperparameters.

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

	\| Hyperparameter \| Value \|
	\|--------------------------------------------\|-----------------------------------------------------------------------------------\|
	\| memory \| \|
	\| steps \| [('preprocessor', ColumnTransformer(transformers=[('num',
	Pipeline(steps=[('imputer',
	SimpleImputer(strategy='median')),
	('std_scaler',
	StandardScaler())]),
	['MonthlyCharges', 'TotalCharges', 'tenure']),
	('cat', OneHotEncoder(handle_unknown='ignore'),
	['SeniorCitizen', 'gender', 'Partner',
	'Dependents', 'PhoneService', 'MultipleLines',
	'InternetService', 'OnlineSecurity',
	'OnlineBackup', 'DeviceProtection',
	'TechSupport', 'StreamingTV',
	'StreamingMovies', 'Contract',
	'PaperlessBilling', 'PaymentMethod'])])), ('classifier', LogisticRegression(class_weight='balanced', max_iter=300))] \|
	\| verbose \| False \|
	\| preprocessor \| ColumnTransformer(transformers=[('num',
	Pipeline(steps=[('imputer',
	SimpleImputer(strategy='median')),
	('std_scaler',
	StandardScaler())]),
	['MonthlyCharges', 'TotalCharges', 'tenure']),
	('cat', OneHotEncoder(handle_unknown='ignore'),
	['SeniorCitizen', 'gender', 'Partner',
	'Dependents', 'PhoneService', 'MultipleLines',
	'InternetService', 'OnlineSecurity',
	'OnlineBackup', 'DeviceProtection',
	'TechSupport', 'StreamingTV',
	'StreamingMovies', 'Contract',
	'PaperlessBilling', 'PaymentMethod'])]) \|
	\| classifier \| LogisticRegression(class_weight='balanced', max_iter=300) \|
	\| preprocessor__n_jobs \| \|
	\| preprocessor__remainder \| drop \|
	\| preprocessor__sparse_threshold \| 0.3 \|
	\| preprocessor__transformer_weights \| \|
	\| preprocessor__transformers \| [('num', Pipeline(steps=[('imputer', SimpleImputer(strategy='median')),
	('std_scaler', StandardScaler())]), ['MonthlyCharges', 'TotalCharges', 'tenure']), ('cat', OneHotEncoder(handle_unknown='ignore'), ['SeniorCitizen', 'gender', 'Partner', 'Dependents', 'PhoneService', 'MultipleLines', 'InternetService', 'OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport', 'StreamingTV', 'StreamingMovies', 'Contract', 'PaperlessBilling', 'PaymentMethod'])] \|
	\| preprocessor__verbose \| False \|
	\| preprocessor__verbose_feature_names_out \| True \|
	\| preprocessor__num \| Pipeline(steps=[('imputer', SimpleImputer(strategy='median')),
	('std_scaler', StandardScaler())]) \|
	\| preprocessor__cat \| OneHotEncoder(handle_unknown='ignore') \|
	\| preprocessor__num__memory \| \|
	\| preprocessor__num__steps \| [('imputer', SimpleImputer(strategy='median')), ('std_scaler', StandardScaler())] \|
	\| preprocessor__num__verbose \| False \|
	\| preprocessor__num__imputer \| SimpleImputer(strategy='median') \|
	\| preprocessor__num__std_scaler \| StandardScaler() \|
	\| preprocessor__num__imputer__add_indicator \| False \|
	\| preprocessor__num__imputer__copy \| True \|
	\| preprocessor__num__imputer__fill_value \| \|
	\| preprocessor__num__imputer__missing_values \| nan \|
	\| preprocessor__num__imputer__strategy \| median \|
	\| preprocessor__num__imputer__verbose \| deprecated \|
	\| preprocessor__num__std_scaler__copy \| True \|
	\| preprocessor__num__std_scaler__with_mean \| True \|
	\| preprocessor__num__std_scaler__with_std \| True \|
	\| preprocessor__cat__categories \| auto \|
	\| preprocessor__cat__drop \| \|
	\| preprocessor__cat__dtype \| <class 'numpy.float64'> \|
	\| preprocessor__cat__handle_unknown \| ignore \|
	\| preprocessor__cat__max_categories \| \|
	\| preprocessor__cat__min_frequency \| \|
	\| preprocessor__cat__sparse \| True \|
	\| classifier__C \| 1.0 \|
	\| classifier__class_weight \| balanced \|
	\| classifier__dual \| False \|
	\| classifier__fit_intercept \| True \|
	\| classifier__intercept_scaling \| 1 \|
	\| classifier__l1_ratio \| \|
	\| classifier__max_iter \| 300 \|
	\| 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

	The model plot is below.

	<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 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-5 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 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-5 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-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 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-5 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-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 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-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 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-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 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-5 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-5" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[('preprocessor',ColumnTransformer(transformers=[('num',Pipeline(steps=[('imputer',SimpleImputer(strategy='median')),('std_scaler',StandardScaler())]),['MonthlyCharges','TotalCharges', 'tenure']),('cat',OneHotEncoder(handle_unknown='ignore'),['SeniorCitizen', 'gender','Partner', 'Dependents','PhoneService','MultipleLines','InternetService','OnlineSecurity','OnlineBackup','DeviceProtection','TechSupport', 'StreamingTV','StreamingMovies','Contract','PaperlessBilling','PaymentMethod'])])),('classifier',LogisticRegression(class_weight='balanced', max_iter=300))])</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-26" type="checkbox" ><label for="sk-estimator-id-26" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[('preprocessor',ColumnTransformer(transformers=[('num',Pipeline(steps=[('imputer',SimpleImputer(strategy='median')),('std_scaler',StandardScaler())]),['MonthlyCharges','TotalCharges', 'tenure']),('cat',OneHotEncoder(handle_unknown='ignore'),['SeniorCitizen', 'gender','Partner', 'Dependents','PhoneService','MultipleLines','InternetService','OnlineSecurity','OnlineBackup','DeviceProtection','TechSupport', 'StreamingTV','StreamingMovies','Contract','PaperlessBilling','PaymentMethod'])])),('classifier',LogisticRegression(class_weight='balanced', max_iter=300))])</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-27" type="checkbox" ><label for="sk-estimator-id-27" class="sk-toggleable__label sk-toggleable__label-arrow">preprocessor: ColumnTransformer</label><div class="sk-toggleable__content"><pre>ColumnTransformer(transformers=[('num',Pipeline(steps=[('imputer',SimpleImputer(strategy='median')),('std_scaler',StandardScaler())]),['MonthlyCharges', 'TotalCharges', 'tenure']),('cat', OneHotEncoder(handle_unknown='ignore'),['SeniorCitizen', 'gender', 'Partner','Dependents', 'PhoneService', 'MultipleLines','InternetService', 'OnlineSecurity','OnlineBackup', 'DeviceProtection','TechSupport', 'StreamingTV','StreamingMovies', 'Contract','PaperlessBilling', 'PaymentMethod'])])</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-28" type="checkbox" ><label for="sk-estimator-id-28" class="sk-toggleable__label sk-toggleable__label-arrow">num</label><div class="sk-toggleable__content"><pre>['MonthlyCharges', 'TotalCharges', 'tenure']</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-29" type="checkbox" ><label for="sk-estimator-id-29" class="sk-toggleable__label sk-toggleable__label-arrow">SimpleImputer</label><div class="sk-toggleable__content"><pre>SimpleImputer(strategy='median')</pre></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-30" type="checkbox" ><label for="sk-estimator-id-30" 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-31" type="checkbox" ><label for="sk-estimator-id-31" class="sk-toggleable__label sk-toggleable__label-arrow">cat</label><div class="sk-toggleable__content"><pre>['SeniorCitizen', 'gender', 'Partner', 'Dependents', 'PhoneService', 'MultipleLines', 'InternetService', 'OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport', 'StreamingTV', 'StreamingMovies', 'Contract', 'PaperlessBilling', 'PaymentMethod']</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-32" type="checkbox" ><label for="sk-estimator-id-32" 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-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-33" type="checkbox" ><label for="sk-estimator-id-33" class="sk-toggleable__label sk-toggleable__label-arrow">LogisticRegression</label><div class="sk-toggleable__content"><pre>LogisticRegression(class_weight='balanced', max_iter=300)</pre></div></div></div></div></div></div></div>

	## Evaluation Results

	You can find the details about evaluation process and the evaluation results.



	\| Metric \| Value \|
	\|----------\|----------\|
	\| accuracy \| 0.730305 \|
	\| f1 score \| 0.730305 \|

	# How to Get Started with the Model

	Use the code below to get started with the model.

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

	```python
	import pickle
	with open(dtc_pkl_filename, 'rb') as file:
	clf = pickle.load(file)
	```

	</details>




	# Model Card Authors

	This model card is written by following authors:

	skops_user

	# Model Card Contact

	You can contact the model card authors through following channels:
	[More Information Needed]

	# Citation

	Below you can find information related to citation.

	BibTeX:
	```
	bibtex
	@inproceedings{...,year={2020}}
	```


	# Additional Content

	## confusion_matrix

	![confusion_matrix](confusion_matrix.png)