pushing files to the repo from the example!
Browse files- README.md +44 -16
- churn.pkl +2 -2
- config.json +1 -1
- confusion_matrix.png +0 -0
README.md
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@@ -5,7 +5,6 @@ tags:
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- sklearn
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- skops
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- tabular-classification
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model_file: churn.pkl
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widget:
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structuredData:
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Contract:
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@@ -106,18 +105,46 @@ The model is trained with below hyperparameters.
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| Hyperparameter | Value |
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|--------------------------------------------|-----------------------------------------------------------------------------------|
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| memory | |
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| steps | [('preprocessor', ColumnTransformer(transformers=[('num'
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| verbose | False |
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| preprocessor | ColumnTransformer(transformers=[('num'
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| classifier | LogisticRegression(class_weight='balanced', max_iter=300) |
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| preprocessor__n_jobs | |
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| preprocessor__remainder | drop |
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| preprocessor__sparse_threshold | 0.3 |
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| preprocessor__transformer_weights | |
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| preprocessor__transformers | [('num', Pipeline(steps=[('imputer', SimpleImputer(strategy='median'))
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| preprocessor__verbose | False |
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| preprocessor__verbose_feature_names_out | True |
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| preprocessor__num | Pipeline(steps=[('imputer', SimpleImputer(strategy='median'))
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| preprocessor__cat | OneHotEncoder(handle_unknown='ignore') |
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| preprocessor__num__memory | |
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| preprocessor__num__steps | [('imputer', SimpleImputer(strategy='median')), ('std_scaler', StandardScaler())] |
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| preprocessor__num__imputer__fill_value | |
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| preprocessor__num__imputer__missing_values | nan |
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| preprocessor__num__imputer__strategy | median |
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| preprocessor__num__imputer__verbose |
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| preprocessor__num__std_scaler__copy | True |
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| preprocessor__num__std_scaler__with_mean | True |
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| preprocessor__num__std_scaler__with_std | True |
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| preprocessor__cat__drop | |
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| preprocessor__cat__dtype | <class 'numpy.float64'> |
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| preprocessor__cat__handle_unknown | ignore |
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| preprocessor__cat__max_categories | |
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| preprocessor__cat__min_frequency | |
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| preprocessor__cat__sparse | True |
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| classifier__C | 1.0 |
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| classifier__class_weight | balanced |
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The model plot is below.
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<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 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-1 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 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-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 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-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 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-1 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-1" 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-1" type="checkbox" ><label for="sk-estimator-id-1" 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-2" type="checkbox" ><label for="sk-estimator-id-2" 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-3" type="checkbox" ><label for="sk-estimator-id-3" 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-4" type="checkbox" ><label for="sk-estimator-id-4" 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-5" type="checkbox" ><label for="sk-estimator-id-5" 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-6" type="checkbox" ><label for="sk-estimator-id-6" 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-7" type="checkbox" ><label for="sk-estimator-id-7" 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-8" type="checkbox" ><label for="sk-estimator-id-8" 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>
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## Evaluation Results
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Use the code below to get started with the model.
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```python
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clf = joblib.load(churn.pkl)
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with open("config.json") as f:
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config = json.load(f)
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clf.predict(pd.DataFrame.from_dict(config["sklearn"]["example_input"]))
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```
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# Model Card Authors
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- sklearn
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- skops
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- tabular-classification
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widget:
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structuredData:
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Contract:
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| Hyperparameter | Value |
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|--------------------------------------------|-----------------------------------------------------------------------------------|
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| memory | |
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Pipeline(steps=[('imputer',
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SimpleImputer(strategy='median')),
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('std_scaler',
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StandardScaler())]),
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['MonthlyCharges', 'TotalCharges', 'tenure']),
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('cat', OneHotEncoder(handle_unknown='ignore'),
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['SeniorCitizen', 'gender', 'Partner',
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'Dependents', 'PhoneService', 'MultipleLines',
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'InternetService', 'OnlineSecurity',
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'OnlineBackup', 'DeviceProtection',
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'TechSupport', 'StreamingTV',
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'StreamingMovies', 'Contract',
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'PaperlessBilling', 'PaymentMethod'])])), ('classifier', LogisticRegression(class_weight='balanced', max_iter=300))] |
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| verbose | False |
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| preprocessor | ColumnTransformer(transformers=[('num',
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Pipeline(steps=[('imputer',
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SimpleImputer(strategy='median')),
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('std_scaler',
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StandardScaler())]),
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['MonthlyCharges', 'TotalCharges', 'tenure']),
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('cat', OneHotEncoder(handle_unknown='ignore'),
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['SeniorCitizen', 'gender', 'Partner',
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'Dependents', 'PhoneService', 'MultipleLines',
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'InternetService', 'OnlineSecurity',
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'OnlineBackup', 'DeviceProtection',
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'TechSupport', 'StreamingTV',
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'StreamingMovies', 'Contract',
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'PaperlessBilling', 'PaymentMethod'])]) |
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| classifier | LogisticRegression(class_weight='balanced', max_iter=300) |
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| preprocessor__n_jobs | |
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| preprocessor__remainder | drop |
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| preprocessor__sparse_threshold | 0.3 |
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| preprocessor__transformer_weights | |
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| preprocessor__transformers | [('num', Pipeline(steps=[('imputer', SimpleImputer(strategy='median')),
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('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'])] |
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| preprocessor__verbose | False |
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| preprocessor__verbose_feature_names_out | True |
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| preprocessor__num | Pipeline(steps=[('imputer', SimpleImputer(strategy='median')),
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('std_scaler', StandardScaler())]) |
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| preprocessor__cat | OneHotEncoder(handle_unknown='ignore') |
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| preprocessor__num__memory | |
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| preprocessor__num__steps | [('imputer', SimpleImputer(strategy='median')), ('std_scaler', StandardScaler())] |
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| preprocessor__num__imputer__fill_value | |
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| preprocessor__num__imputer__missing_values | nan |
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| preprocessor__num__imputer__strategy | median |
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| preprocessor__num__imputer__verbose | 0 |
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| preprocessor__num__std_scaler__copy | True |
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| preprocessor__num__std_scaler__with_mean | True |
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| preprocessor__num__std_scaler__with_std | True |
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| preprocessor__cat__drop | |
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| preprocessor__cat__dtype | <class 'numpy.float64'> |
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| preprocessor__cat__handle_unknown | ignore |
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| preprocessor__cat__sparse | True |
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| classifier__C | 1.0 |
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| classifier__class_weight | balanced |
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The model plot is below.
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<style>#sk-459e3303-a36f-477f-9be8-a83d3277a824 {color: black;background-color: white;}#sk-459e3303-a36f-477f-9be8-a83d3277a824 pre{padding: 0;}#sk-459e3303-a36f-477f-9be8-a83d3277a824 div.sk-toggleable {background-color: white;}#sk-459e3303-a36f-477f-9be8-a83d3277a824 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-459e3303-a36f-477f-9be8-a83d3277a824 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-459e3303-a36f-477f-9be8-a83d3277a824 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-459e3303-a36f-477f-9be8-a83d3277a824 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-459e3303-a36f-477f-9be8-a83d3277a824 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-459e3303-a36f-477f-9be8-a83d3277a824 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-459e3303-a36f-477f-9be8-a83d3277a824 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-459e3303-a36f-477f-9be8-a83d3277a824 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-459e3303-a36f-477f-9be8-a83d3277a824 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-459e3303-a36f-477f-9be8-a83d3277a824 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-459e3303-a36f-477f-9be8-a83d3277a824 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-459e3303-a36f-477f-9be8-a83d3277a824 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-459e3303-a36f-477f-9be8-a83d3277a824 div.sk-estimator:hover {background-color: #d4ebff;}#sk-459e3303-a36f-477f-9be8-a83d3277a824 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-459e3303-a36f-477f-9be8-a83d3277a824 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-459e3303-a36f-477f-9be8-a83d3277a824 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-459e3303-a36f-477f-9be8-a83d3277a824 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-459e3303-a36f-477f-9be8-a83d3277a824 div.sk-item {z-index: 1;}#sk-459e3303-a36f-477f-9be8-a83d3277a824 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-459e3303-a36f-477f-9be8-a83d3277a824 div.sk-parallel::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-459e3303-a36f-477f-9be8-a83d3277a824 div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-459e3303-a36f-477f-9be8-a83d3277a824 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-459e3303-a36f-477f-9be8-a83d3277a824 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-459e3303-a36f-477f-9be8-a83d3277a824 div.sk-parallel-item:only-child::after {width: 0;}#sk-459e3303-a36f-477f-9be8-a83d3277a824 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;position: relative;}#sk-459e3303-a36f-477f-9be8-a83d3277a824 div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-459e3303-a36f-477f-9be8-a83d3277a824 div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-459e3303-a36f-477f-9be8-a83d3277a824 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-459e3303-a36f-477f-9be8-a83d3277a824 div.sk-text-repr-fallback {display: none;}</style><div id="sk-459e3303-a36f-477f-9be8-a83d3277a824" 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>Please rerun this cell to show the HTML repr or trust the notebook.</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="566c17ff-b86a-4477-87fa-7072c594f96a" type="checkbox" ><label for="566c17ff-b86a-4477-87fa-7072c594f96a" 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="ea3ce53d-6a62-45ee-a94f-65a5fd9a0c91" type="checkbox" ><label for="ea3ce53d-6a62-45ee-a94f-65a5fd9a0c91" 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="a20cd11e-3fb6-49ad-9a0e-a753e490fcd5" type="checkbox" ><label for="a20cd11e-3fb6-49ad-9a0e-a753e490fcd5" 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="c47d1b33-32bd-4fb4-ae4f-ff45d68c33bf" type="checkbox" ><label for="c47d1b33-32bd-4fb4-ae4f-ff45d68c33bf" 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="a5419c35-03e3-48c1-9250-5eedc50f5221" type="checkbox" ><label for="a5419c35-03e3-48c1-9250-5eedc50f5221" 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="fede319e-13b1-4e7c-aef1-e079c4f70e29" type="checkbox" ><label for="fede319e-13b1-4e7c-aef1-e079c4f70e29" 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="861a3701-0fb9-4735-916c-a7566a2a2de7" type="checkbox" ><label for="861a3701-0fb9-4735-916c-a7566a2a2de7" 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="e1ec4529-26c8-413b-b53c-0e06543d5ad5" type="checkbox" ><label for="e1ec4529-26c8-413b-b53c-0e06543d5ad5" 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>
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## Evaluation Results
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Use the code below to get started with the model.
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<details>
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<summary> Click to expand </summary>
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```python
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import pickle
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with open(dtc_pkl_filename, 'rb') as file:
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clf = pickle.load(file)
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```
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</details>
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# Model Card Authors
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churn.pkl
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@@ -1,3 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size
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version https://git-lfs.github.com/spec/v1
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oid sha256:91d55154bc5efbad522284cc8aca6fd455155b5b909f0d2c57486246bfc58db5
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size 5574
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config.json
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"TotalCharges"
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],
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"environment": [
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"scikit-learn=1.
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],
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"example_input": {
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"Contract": [
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"TotalCharges"
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],
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"environment": [
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"scikit-learn=1.0.2"
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],
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"example_input": {
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"Contract": [
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confusion_matrix.png
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