metadata

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.

Click to expand

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))]                                                                                   |

Model Plot

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(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))])

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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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.

Click to expand

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

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}}

rajistics
/

churn-model