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))]
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	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

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))])

Please rerun this cell to show the HTML repr or trust the notebook.

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.

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}}

rajistics
/

churn-model1