pushing files to the repo from the example!
Browse files- README.md +219 -0
- churn.pkl +3 -0
- config.json +129 -0
- confusion_matrix.png +0 -0
README.md
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| 1 |
+
---
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| 2 |
+
license: mit
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| 3 |
+
library_name: sklearn
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| 4 |
+
tags:
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| 5 |
+
- sklearn
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| 6 |
+
- skops
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| 7 |
+
- tabular-classification
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| 8 |
+
model_file: churn.pkl
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+
widget:
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| 10 |
+
structuredData:
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| 11 |
+
Contract:
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| 12 |
+
- Two year
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| 13 |
+
- Month-to-month
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| 14 |
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- One year
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| 15 |
+
Dependents:
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| 16 |
+
- 'Yes'
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| 17 |
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- 'No'
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| 18 |
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- 'No'
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| 19 |
+
DeviceProtection:
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| 20 |
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- 'No'
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| 21 |
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- 'No'
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| 22 |
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- 'Yes'
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| 23 |
+
InternetService:
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| 24 |
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- Fiber optic
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| 25 |
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- Fiber optic
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| 26 |
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- DSL
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| 27 |
+
MonthlyCharges:
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| 28 |
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- 79.05
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| 29 |
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- 84.95
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| 30 |
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- 68.8
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| 31 |
+
MultipleLines:
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| 32 |
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- 'Yes'
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| 33 |
+
- 'Yes'
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| 34 |
+
- 'Yes'
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| 35 |
+
OnlineBackup:
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| 36 |
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- 'No'
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| 37 |
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- 'No'
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| 38 |
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- 'Yes'
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| 39 |
+
OnlineSecurity:
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| 40 |
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- 'Yes'
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| 41 |
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- 'No'
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| 42 |
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- 'Yes'
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| 43 |
+
PaperlessBilling:
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| 44 |
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- 'No'
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| 45 |
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- 'Yes'
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| 46 |
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- 'No'
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| 47 |
+
Partner:
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| 48 |
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- 'Yes'
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| 49 |
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- 'Yes'
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| 50 |
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- 'No'
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| 51 |
+
PaymentMethod:
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| 52 |
+
- Bank transfer (automatic)
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| 53 |
+
- Electronic check
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| 54 |
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- Bank transfer (automatic)
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| 55 |
+
PhoneService:
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| 56 |
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- 'Yes'
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| 57 |
+
- 'Yes'
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| 58 |
+
- 'Yes'
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| 59 |
+
SeniorCitizen:
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| 60 |
+
- 0
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| 61 |
+
- 0
|
| 62 |
+
- 0
|
| 63 |
+
StreamingMovies:
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| 64 |
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- 'No'
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| 65 |
+
- 'No'
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| 66 |
+
- 'No'
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| 67 |
+
StreamingTV:
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| 68 |
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- 'No'
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| 69 |
+
- 'Yes'
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| 70 |
+
- 'No'
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| 71 |
+
TechSupport:
|
| 72 |
+
- 'No'
|
| 73 |
+
- 'No'
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| 74 |
+
- 'Yes'
|
| 75 |
+
TotalCharges:
|
| 76 |
+
- 5730.7
|
| 77 |
+
- 1378.25
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| 78 |
+
- 4111.35
|
| 79 |
+
gender:
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| 80 |
+
- Female
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| 81 |
+
- Female
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| 82 |
+
- Male
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| 83 |
+
tenure:
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| 84 |
+
- 72
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| 85 |
+
- 16
|
| 86 |
+
- 63
|
| 87 |
+
---
|
| 88 |
+
|
| 89 |
+
# Model description
|
| 90 |
+
|
| 91 |
+
This is a Logistic Regression model trained on churn dataset.
|
| 92 |
+
|
| 93 |
+
## Intended uses & limitations
|
| 94 |
+
|
| 95 |
+
This model is not ready to be used in production.
|
| 96 |
+
|
| 97 |
+
## Training Procedure
|
| 98 |
+
|
| 99 |
+
### Hyperparameters
|
| 100 |
+
|
| 101 |
+
The model is trained with below hyperparameters.
|
| 102 |
+
|
| 103 |
+
<details>
|
| 104 |
+
<summary> Click to expand </summary>
|
| 105 |
+
|
| 106 |
+
| Hyperparameter | Value |
|
| 107 |
+
|--------------------------------------------|-----------------------------------------------------------------------------------|
|
| 108 |
+
| memory | |
|
| 109 |
+
| 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))] |
|
| 110 |
+
| verbose | False |
|
| 111 |
+
| 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'])]) |
|
| 112 |
+
| classifier | LogisticRegression(class_weight='balanced', max_iter=300) |
|
| 113 |
+
| preprocessor__n_jobs | |
|
| 114 |
+
| preprocessor__remainder | drop |
|
| 115 |
+
| preprocessor__sparse_threshold | 0.3 |
|
| 116 |
+
| preprocessor__transformer_weights | |
|
| 117 |
+
| 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'])] |
|
| 118 |
+
| preprocessor__verbose | False |
|
| 119 |
+
| preprocessor__verbose_feature_names_out | True |
|
| 120 |
+
| preprocessor__num | Pipeline(steps=[('imputer', SimpleImputer(strategy='median')),<br /> ('std_scaler', StandardScaler())]) |
|
| 121 |
+
| preprocessor__cat | OneHotEncoder(handle_unknown='ignore') |
|
| 122 |
+
| preprocessor__num__memory | |
|
| 123 |
+
| preprocessor__num__steps | [('imputer', SimpleImputer(strategy='median')), ('std_scaler', StandardScaler())] |
|
| 124 |
+
| preprocessor__num__verbose | False |
|
| 125 |
+
| preprocessor__num__imputer | SimpleImputer(strategy='median') |
|
| 126 |
+
| preprocessor__num__std_scaler | StandardScaler() |
|
| 127 |
+
| preprocessor__num__imputer__add_indicator | False |
|
| 128 |
+
| preprocessor__num__imputer__copy | True |
|
| 129 |
+
| preprocessor__num__imputer__fill_value | |
|
| 130 |
+
| preprocessor__num__imputer__missing_values | nan |
|
| 131 |
+
| preprocessor__num__imputer__strategy | median |
|
| 132 |
+
| preprocessor__num__imputer__verbose | deprecated |
|
| 133 |
+
| preprocessor__num__std_scaler__copy | True |
|
| 134 |
+
| preprocessor__num__std_scaler__with_mean | True |
|
| 135 |
+
| preprocessor__num__std_scaler__with_std | True |
|
| 136 |
+
| preprocessor__cat__categories | auto |
|
| 137 |
+
| preprocessor__cat__drop | |
|
| 138 |
+
| preprocessor__cat__dtype | <class 'numpy.float64'> |
|
| 139 |
+
| preprocessor__cat__handle_unknown | ignore |
|
| 140 |
+
| preprocessor__cat__max_categories | |
|
| 141 |
+
| preprocessor__cat__min_frequency | |
|
| 142 |
+
| preprocessor__cat__sparse | True |
|
| 143 |
+
| classifier__C | 1.0 |
|
| 144 |
+
| classifier__class_weight | balanced |
|
| 145 |
+
| classifier__dual | False |
|
| 146 |
+
| classifier__fit_intercept | True |
|
| 147 |
+
| classifier__intercept_scaling | 1 |
|
| 148 |
+
| classifier__l1_ratio | |
|
| 149 |
+
| classifier__max_iter | 300 |
|
| 150 |
+
| classifier__multi_class | auto |
|
| 151 |
+
| classifier__n_jobs | |
|
| 152 |
+
| classifier__penalty | l2 |
|
| 153 |
+
| classifier__random_state | |
|
| 154 |
+
| classifier__solver | lbfgs |
|
| 155 |
+
| classifier__tol | 0.0001 |
|
| 156 |
+
| classifier__verbose | 0 |
|
| 157 |
+
| classifier__warm_start | False |
|
| 158 |
+
|
| 159 |
+
</details>
|
| 160 |
+
|
| 161 |
+
### Model Plot
|
| 162 |
+
|
| 163 |
+
The model plot is below.
|
| 164 |
+
|
| 165 |
+
<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>
|
| 166 |
+
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+
## Evaluation Results
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| 168 |
+
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| 169 |
+
You can find the details about evaluation process and the evaluation results.
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| 170 |
+
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| 171 |
+
|
| 172 |
+
|
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+
| Metric | Value |
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| 174 |
+
|----------|----------|
|
| 175 |
+
| accuracy | 0.730305 |
|
| 176 |
+
| f1 score | 0.730305 |
|
| 177 |
+
|
| 178 |
+
# How to Get Started with the Model
|
| 179 |
+
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+
Use the code below to get started with the model.
|
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+
|
| 182 |
+
```python
|
| 183 |
+
import joblib
|
| 184 |
+
import json
|
| 185 |
+
import pandas as pd
|
| 186 |
+
clf = joblib.load(churn.pkl)
|
| 187 |
+
with open("config.json") as f:
|
| 188 |
+
config = json.load(f)
|
| 189 |
+
clf.predict(pd.DataFrame.from_dict(config["sklearn"]["example_input"]))
|
| 190 |
+
```
|
| 191 |
+
|
| 192 |
+
|
| 193 |
+
# Model Card Authors
|
| 194 |
+
|
| 195 |
+
This model card is written by following authors:
|
| 196 |
+
|
| 197 |
+
skops_user
|
| 198 |
+
|
| 199 |
+
# Model Card Contact
|
| 200 |
+
|
| 201 |
+
You can contact the model card authors through following channels:
|
| 202 |
+
[More Information Needed]
|
| 203 |
+
|
| 204 |
+
# Citation
|
| 205 |
+
|
| 206 |
+
Below you can find information related to citation.
|
| 207 |
+
|
| 208 |
+
**BibTeX:**
|
| 209 |
+
```
|
| 210 |
+
bibtex
|
| 211 |
+
@inproceedings{...,year={2020}}
|
| 212 |
+
```
|
| 213 |
+
|
| 214 |
+
|
| 215 |
+
# Additional Content
|
| 216 |
+
|
| 217 |
+
## confusion_matrix
|
| 218 |
+
|
| 219 |
+
|
churn.pkl
ADDED
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@@ -0,0 +1,3 @@
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+
version https://git-lfs.github.com/spec/v1
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| 2 |
+
oid sha256:8b11dada9e639d7770537daf493675f1c499516c362a9e233bfee765ab3a5bed
|
| 3 |
+
size 4439
|
config.json
ADDED
|
@@ -0,0 +1,129 @@
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|
|
| 1 |
+
{
|
| 2 |
+
"sklearn": {
|
| 3 |
+
"columns": [
|
| 4 |
+
"gender",
|
| 5 |
+
"SeniorCitizen",
|
| 6 |
+
"Partner",
|
| 7 |
+
"Dependents",
|
| 8 |
+
"tenure",
|
| 9 |
+
"PhoneService",
|
| 10 |
+
"MultipleLines",
|
| 11 |
+
"InternetService",
|
| 12 |
+
"OnlineSecurity",
|
| 13 |
+
"OnlineBackup",
|
| 14 |
+
"DeviceProtection",
|
| 15 |
+
"TechSupport",
|
| 16 |
+
"StreamingTV",
|
| 17 |
+
"StreamingMovies",
|
| 18 |
+
"Contract",
|
| 19 |
+
"PaperlessBilling",
|
| 20 |
+
"PaymentMethod",
|
| 21 |
+
"MonthlyCharges",
|
| 22 |
+
"TotalCharges"
|
| 23 |
+
],
|
| 24 |
+
"environment": [
|
| 25 |
+
"scikit-learn=1.1.1"
|
| 26 |
+
],
|
| 27 |
+
"example_input": {
|
| 28 |
+
"Contract": [
|
| 29 |
+
"Two year",
|
| 30 |
+
"Month-to-month",
|
| 31 |
+
"One year"
|
| 32 |
+
],
|
| 33 |
+
"Dependents": [
|
| 34 |
+
"Yes",
|
| 35 |
+
"No",
|
| 36 |
+
"No"
|
| 37 |
+
],
|
| 38 |
+
"DeviceProtection": [
|
| 39 |
+
"No",
|
| 40 |
+
"No",
|
| 41 |
+
"Yes"
|
| 42 |
+
],
|
| 43 |
+
"InternetService": [
|
| 44 |
+
"Fiber optic",
|
| 45 |
+
"Fiber optic",
|
| 46 |
+
"DSL"
|
| 47 |
+
],
|
| 48 |
+
"MonthlyCharges": [
|
| 49 |
+
79.05,
|
| 50 |
+
84.95,
|
| 51 |
+
68.8
|
| 52 |
+
],
|
| 53 |
+
"MultipleLines": [
|
| 54 |
+
"Yes",
|
| 55 |
+
"Yes",
|
| 56 |
+
"Yes"
|
| 57 |
+
],
|
| 58 |
+
"OnlineBackup": [
|
| 59 |
+
"No",
|
| 60 |
+
"No",
|
| 61 |
+
"Yes"
|
| 62 |
+
],
|
| 63 |
+
"OnlineSecurity": [
|
| 64 |
+
"Yes",
|
| 65 |
+
"No",
|
| 66 |
+
"Yes"
|
| 67 |
+
],
|
| 68 |
+
"PaperlessBilling": [
|
| 69 |
+
"No",
|
| 70 |
+
"Yes",
|
| 71 |
+
"No"
|
| 72 |
+
],
|
| 73 |
+
"Partner": [
|
| 74 |
+
"Yes",
|
| 75 |
+
"Yes",
|
| 76 |
+
"No"
|
| 77 |
+
],
|
| 78 |
+
"PaymentMethod": [
|
| 79 |
+
"Bank transfer (automatic)",
|
| 80 |
+
"Electronic check",
|
| 81 |
+
"Bank transfer (automatic)"
|
| 82 |
+
],
|
| 83 |
+
"PhoneService": [
|
| 84 |
+
"Yes",
|
| 85 |
+
"Yes",
|
| 86 |
+
"Yes"
|
| 87 |
+
],
|
| 88 |
+
"SeniorCitizen": [
|
| 89 |
+
0,
|
| 90 |
+
0,
|
| 91 |
+
0
|
| 92 |
+
],
|
| 93 |
+
"StreamingMovies": [
|
| 94 |
+
"No",
|
| 95 |
+
"No",
|
| 96 |
+
"No"
|
| 97 |
+
],
|
| 98 |
+
"StreamingTV": [
|
| 99 |
+
"No",
|
| 100 |
+
"Yes",
|
| 101 |
+
"No"
|
| 102 |
+
],
|
| 103 |
+
"TechSupport": [
|
| 104 |
+
"No",
|
| 105 |
+
"No",
|
| 106 |
+
"Yes"
|
| 107 |
+
],
|
| 108 |
+
"TotalCharges": [
|
| 109 |
+
5730.7,
|
| 110 |
+
1378.25,
|
| 111 |
+
4111.35
|
| 112 |
+
],
|
| 113 |
+
"gender": [
|
| 114 |
+
"Female",
|
| 115 |
+
"Female",
|
| 116 |
+
"Male"
|
| 117 |
+
],
|
| 118 |
+
"tenure": [
|
| 119 |
+
72,
|
| 120 |
+
16,
|
| 121 |
+
63
|
| 122 |
+
]
|
| 123 |
+
},
|
| 124 |
+
"model": {
|
| 125 |
+
"file": "churn.pkl"
|
| 126 |
+
},
|
| 127 |
+
"task": "tabular-classification"
|
| 128 |
+
}
|
| 129 |
+
}
|
confusion_matrix.png
ADDED
|