This is a Logistic Regression model trained on churn dataset.
This model is not ready to be used in production.
The model is trained with below hyperparameters.
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Hyperparameter Value memory steps [('preprocessor', ColumnTransformer(transformers=[('num',
Pipeline(steps=[('imputer',
SimpleImputer(strategy='median')),
('std_scaler',
StandardScaler())]),
['MonthlyCharges', 'TotalCharges', 'tenure']),
('cat', OneHotEncoder(),
['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(),
['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(), ['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() |
| 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 | error |
| 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 |
The model plot is below.
Pipeline(steps=[('preprocessor',ColumnTransformer(transformers=[('num',Pipeline(steps=[('imputer',SimpleImputer(strategy='median')),('std_scaler',StandardScaler())]),['MonthlyCharges','TotalCharges', 'tenure']),('cat', OneHotEncoder(),['SeniorCitizen', 'gender','Partner', 'Dependents','PhoneService','MultipleLines','InternetService','OnlineSecurity','OnlineBackup','DeviceProtection','TechSupport', 'StreamingTV','StreamingMovies','Contract','PaperlessBilling','PaymentMethod'])])),('classifier',LogisticRegression(class_weight='balanced', max_iter=300))])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. Pipeline Pipeline(steps=[('preprocessor',ColumnTransformer(transformers=[('num',Pipeline(steps=[('imputer',SimpleImputer(strategy='median')),('std_scaler',StandardScaler())]),['MonthlyCharges','TotalCharges', 'tenure']),('cat', OneHotEncoder(),['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: ColumnTransformer ColumnTransformer(transformers=[('num',Pipeline(steps=[('imputer',SimpleImputer(strategy='median')),('std_scaler',StandardScaler())]),['MonthlyCharges', 'TotalCharges', 'tenure']),('cat', OneHotEncoder(),['SeniorCitizen', 'gender', 'Partner','Dependents', 'PhoneService', 'MultipleLines','InternetService', 'OnlineSecurity','OnlineBackup', 'DeviceProtection','TechSupport', 'StreamingTV','StreamingMovies', 'Contract','PaperlessBilling', 'PaymentMethod'])])
cat ['SeniorCitizen', 'gender', 'Partner', 'Dependents', 'PhoneService', 'MultipleLines', 'InternetService', 'OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport', 'StreamingTV', 'StreamingMovies', 'Contract', 'PaperlessBilling', 'PaymentMethod']
You can find the details about evaluation process and the evaluation results.
Use the code below to get started with the model.
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Below you can find information related to citation.
bibtex
@inproceedings{...,year={2020}}
