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---
license: mit
library_name: sklearn
tags:
- sklearn
- skops
- tabular-classification
model_file: churn.pkl
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',<br /> Pipeline(steps=[('imputer',<br /> SimpleImputer(strategy='median')),<br /> ('std_scaler',<br /> StandardScaler())]),<br /> ['MonthlyCharges', 'TotalCharges', 'tenure']),<br /> ('cat', OneHotEncoder(handle_unknown='ignore'),<br /> ['SeniorCitizen', 'gender', 'Partner',<br /> 'Dependents', 'PhoneService', 'MultipleLines',<br /> 'InternetService', 'OnlineSecurity',<br /> 'OnlineBackup', 'DeviceProtection',<br /> 'TechSupport', 'StreamingTV',<br /> 'StreamingMovies', 'Contract',<br /> 'PaperlessBilling', 'PaymentMethod'])])), ('classifier', LogisticRegression(class_weight='balanced', max_iter=300))] |
| verbose | False |
| preprocessor | ColumnTransformer(transformers=[('num',<br /> Pipeline(steps=[('imputer',<br /> SimpleImputer(strategy='median')),<br /> ('std_scaler',<br /> StandardScaler())]),<br /> ['MonthlyCharges', 'TotalCharges', 'tenure']),<br /> ('cat', OneHotEncoder(handle_unknown='ignore'),<br /> ['SeniorCitizen', 'gender', 'Partner',<br /> 'Dependents', 'PhoneService', 'MultipleLines',<br /> 'InternetService', 'OnlineSecurity',<br /> 'OnlineBackup', 'DeviceProtection',<br /> 'TechSupport', 'StreamingTV',<br /> 'StreamingMovies', 'Contract',<br /> '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')),<br /> ('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')),<br /> ('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 | 0 |
| 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__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-f0122ce0-64cb-41b3-8d66-0b116516efc3 {color: black;background-color: white;}#sk-f0122ce0-64cb-41b3-8d66-0b116516efc3 pre{padding: 0;}#sk-f0122ce0-64cb-41b3-8d66-0b116516efc3 div.sk-toggleable {background-color: white;}#sk-f0122ce0-64cb-41b3-8d66-0b116516efc3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-f0122ce0-64cb-41b3-8d66-0b116516efc3 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-f0122ce0-64cb-41b3-8d66-0b116516efc3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-f0122ce0-64cb-41b3-8d66-0b116516efc3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-f0122ce0-64cb-41b3-8d66-0b116516efc3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-f0122ce0-64cb-41b3-8d66-0b116516efc3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-f0122ce0-64cb-41b3-8d66-0b116516efc3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-f0122ce0-64cb-41b3-8d66-0b116516efc3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-f0122ce0-64cb-41b3-8d66-0b116516efc3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-f0122ce0-64cb-41b3-8d66-0b116516efc3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-f0122ce0-64cb-41b3-8d66-0b116516efc3 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-f0122ce0-64cb-41b3-8d66-0b116516efc3 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-f0122ce0-64cb-41b3-8d66-0b116516efc3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-f0122ce0-64cb-41b3-8d66-0b116516efc3 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-f0122ce0-64cb-41b3-8d66-0b116516efc3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-f0122ce0-64cb-41b3-8d66-0b116516efc3 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-f0122ce0-64cb-41b3-8d66-0b116516efc3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-f0122ce0-64cb-41b3-8d66-0b116516efc3 div.sk-item {z-index: 1;}#sk-f0122ce0-64cb-41b3-8d66-0b116516efc3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-f0122ce0-64cb-41b3-8d66-0b116516efc3 div.sk-parallel::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-f0122ce0-64cb-41b3-8d66-0b116516efc3 div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-f0122ce0-64cb-41b3-8d66-0b116516efc3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-f0122ce0-64cb-41b3-8d66-0b116516efc3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-f0122ce0-64cb-41b3-8d66-0b116516efc3 div.sk-parallel-item:only-child::after {width: 0;}#sk-f0122ce0-64cb-41b3-8d66-0b116516efc3 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-f0122ce0-64cb-41b3-8d66-0b116516efc3 div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-f0122ce0-64cb-41b3-8d66-0b116516efc3 div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-f0122ce0-64cb-41b3-8d66-0b116516efc3 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-f0122ce0-64cb-41b3-8d66-0b116516efc3 div.sk-text-repr-fallback {display: none;}</style><div id="sk-f0122ce0-64cb-41b3-8d66-0b116516efc3" class="sk-top-container" style="overflow: auto;"><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="193bb424-11e4-4240-a49c-2b9ff9c16021" type="checkbox" ><label for="193bb424-11e4-4240-a49c-2b9ff9c16021" 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="54004708-11cd-4f85-bff3-744af144ae72" type="checkbox" ><label for="54004708-11cd-4f85-bff3-744af144ae72" 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="53cbe948-0bd7-4512-874e-7c0e8287ebf2" type="checkbox" ><label for="53cbe948-0bd7-4512-874e-7c0e8287ebf2" 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="9748155a-6575-4ba1-b5a2-9171c6ac1a11" type="checkbox" ><label for="9748155a-6575-4ba1-b5a2-9171c6ac1a11" 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="27303a89-9235-4743-862c-fa1959656bb7" type="checkbox" ><label for="27303a89-9235-4743-862c-fa1959656bb7" 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="0a07f5b9-db03-4bf5-bc2c-9b3f60e6ab16" type="checkbox" ><label for="0a07f5b9-db03-4bf5-bc2c-9b3f60e6ab16" 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="d985852a-65b0-4b77-897a-82c0ef3fa365" type="checkbox" ><label for="d985852a-65b0-4b77-897a-82c0ef3fa365" 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="050e23d8-6e98-4cfa-9ff8-cc01091c6a1f" type="checkbox" ><label for="050e23d8-6e98-4cfa-9ff8-cc01091c6a1f" 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.
```python
import joblib
import json
import pandas as pd
clf = joblib.load(churn.pkl)
with open("config.json") as f:
config = json.load(f)
clf.predict(pd.DataFrame.from_dict(config["sklearn"]["example_input"]))
```
# 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
 |