[
  {
    "path": "Churn Prediction Model.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Customer Churn Prediction\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import pandas as pd\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import seaborn as sns\\n\",\n    \"sns.set_style('darkgrid')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Data Preparation based on EDA\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def datapreparation(filepath):\\n\",\n    \"    \\n\",\n    \"    df = pd.read_csv(filepath)\\n\",\n    \"    df.drop([\\\"customerID\\\"], inplace = True, axis = 1)\\n\",\n    \"    \\n\",\n    \"    df.TotalCharges = df.TotalCharges.replace(\\\" \\\",np.nan)\\n\",\n    \"    df.TotalCharges.fillna(0, inplace = True)\\n\",\n    \"    df.TotalCharges = df.TotalCharges.astype(float)\\n\",\n    \"    \\n\",\n    \"    cols1 = ['Partner', 'Dependents', 'PaperlessBilling', 'Churn', 'PhoneService']\\n\",\n    \"    for col in cols1:\\n\",\n    \"        df[col] = df[col].apply(lambda x: 0 if x == \\\"No\\\" else 1)\\n\",\n    \"   \\n\",\n    \"    df.gender = df.gender.apply(lambda x: 0 if x == \\\"Male\\\" else 1)\\n\",\n    \"    df.MultipleLines = df.MultipleLines.map({'No phone service': 0, 'No': 0, 'Yes': 1})\\n\",\n    \"    \\n\",\n    \"    cols2 = ['OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport', 'StreamingTV', 'StreamingMovies']\\n\",\n    \"    for col in cols2:\\n\",\n    \"        df[col] = df[col].map({'No internet service': 0, 'No': 0, 'Yes': 1})\\n\",\n    \"    \\n\",\n    \"    df = pd.get_dummies(df, columns=['InternetService', 'Contract', 'PaymentMethod'], drop_first=True)\\n\",\n    \"    \\n\",\n    \"    return df\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>gender</th>\\n\",\n       \"      <th>SeniorCitizen</th>\\n\",\n       \"      <th>Partner</th>\\n\",\n       \"      <th>Dependents</th>\\n\",\n       \"      <th>tenure</th>\\n\",\n       \"      <th>PhoneService</th>\\n\",\n       \"      <th>MultipleLines</th>\\n\",\n       \"      <th>OnlineSecurity</th>\\n\",\n       \"      <th>OnlineBackup</th>\\n\",\n       \"      <th>DeviceProtection</th>\\n\",\n       \"      <th>...</th>\\n\",\n       \"      <th>MonthlyCharges</th>\\n\",\n       \"      <th>TotalCharges</th>\\n\",\n       \"      <th>Churn</th>\\n\",\n       \"      <th>InternetService_Fiber optic</th>\\n\",\n       \"      <th>InternetService_No</th>\\n\",\n       \"      <th>Contract_One year</th>\\n\",\n       \"      <th>Contract_Two year</th>\\n\",\n       \"      <th>PaymentMethod_Credit card (automatic)</th>\\n\",\n       \"      <th>PaymentMethod_Electronic check</th>\\n\",\n       \"      <th>PaymentMethod_Mailed check</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>29.85</td>\\n\",\n       \"      <td>29.85</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>34</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>56.95</td>\\n\",\n       \"      <td>1889.50</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>53.85</td>\\n\",\n       \"      <td>108.15</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>45</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>42.30</td>\\n\",\n       \"      <td>1840.75</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>70.70</td>\\n\",\n       \"      <td>151.65</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"<p>5 rows × 24 columns</p>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   gender  SeniorCitizen  Partner  Dependents  tenure  PhoneService  \\\\\\n\",\n       \"0       1              0        1           0       1             0   \\n\",\n       \"1       0              0        0           0      34             1   \\n\",\n       \"2       0              0        0           0       2             1   \\n\",\n       \"3       0              0        0           0      45             0   \\n\",\n       \"4       1              0        0           0       2             1   \\n\",\n       \"\\n\",\n       \"   MultipleLines  OnlineSecurity  OnlineBackup  DeviceProtection  ...  \\\\\\n\",\n       \"0              0               0             1                 0  ...   \\n\",\n       \"1              0               1             0                 1  ...   \\n\",\n       \"2              0               1             1                 0  ...   \\n\",\n       \"3              0               1             0                 1  ...   \\n\",\n       \"4              0               0             0                 0  ...   \\n\",\n       \"\\n\",\n       \"   MonthlyCharges  TotalCharges  Churn  InternetService_Fiber optic  \\\\\\n\",\n       \"0           29.85         29.85      0                            0   \\n\",\n       \"1           56.95       1889.50      0                            0   \\n\",\n       \"2           53.85        108.15      1                            0   \\n\",\n       \"3           42.30       1840.75      0                            0   \\n\",\n       \"4           70.70        151.65      1                            1   \\n\",\n       \"\\n\",\n       \"   InternetService_No  Contract_One year  Contract_Two year  \\\\\\n\",\n       \"0                   0                  0                  0   \\n\",\n       \"1                   0                  1                  0   \\n\",\n       \"2                   0                  0                  0   \\n\",\n       \"3                   0                  1                  0   \\n\",\n       \"4                   0                  0                  0   \\n\",\n       \"\\n\",\n       \"   PaymentMethod_Credit card (automatic)  PaymentMethod_Electronic check  \\\\\\n\",\n       \"0                                      0                               1   \\n\",\n       \"1                                      0                               0   \\n\",\n       \"2                                      0                               0   \\n\",\n       \"3                                      0                               0   \\n\",\n       \"4                                      0                               1   \\n\",\n       \"\\n\",\n       \"   PaymentMethod_Mailed check  \\n\",\n       \"0                           0  \\n\",\n       \"1                           1  \\n\",\n       \"2                           1  \\n\",\n       \"3                           0  \\n\",\n       \"4                           0  \\n\",\n       \"\\n\",\n       \"[5 rows x 24 columns]\"\n      ]\n     },\n     \"execution_count\": 3,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df = datapreparation(filepath = \\\"C:/Data/Telco-Customer-Churn.csv\\\")\\n\",\n    \"df.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"False\"\n      ]\n     },\n     \"execution_count\": 4,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df.isnull().any().any()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Model Building\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"I am going to build and tune random forest model because in this case tree based method would perform better. I am also interested in individual customer's churning probability and in understanding how the model calculates it using Shap values.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 84,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score\\n\",\n    \"from sklearn.metrics import confusion_matrix, accuracy_score, classification_report\\n\",\n    \"from sklearn.metrics import roc_auc_score, roc_curve, precision_score, recall_score, f1_score\\n\",\n    \"from imblearn.over_sampling import SMOTE\\n\",\n    \"from sklearn.ensemble import RandomForestClassifier\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"train, test = train_test_split(df, test_size=0.2, random_state=111, stratify = df.Churn)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"x = df.columns[df.columns!=\\\"Churn\\\"]\\n\",\n    \"y = \\\"Churn\\\"\\n\",\n    \"train_x = train[x]\\n\",\n    \"train_y = train[y]\\n\",\n    \"test_x = test[x]\\n\",\n    \"test_y = test[y]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"#function for model fitting\\n\",\n    \"def churn_prediction(algo, training_x, training_y, testing_x, testing_y, cols, cf = 'coefficients'):\\n\",\n    \"    algo.fit(training_x,training_y)\\n\",\n    \"    predictions = algo.predict(testing_x)\\n\",\n    \"    probabilities = algo.predict_proba(testing_x)[:,1]\\n\",\n    \"    \\n\",\n    \"    #coeffs\\n\",\n    \"    if cf == \\\"coefficients\\\":\\n\",\n    \"        coefficients = pd.DataFrame(algo.coef_.ravel())\\n\",\n    \"    elif cf == \\\"features\\\":\\n\",\n    \"        coefficients = pd.DataFrame(algo.feature_importances_)\\n\",\n    \"        \\n\",\n    \"    column_df = pd.DataFrame(cols)\\n\",\n    \"    coef_sumry = (pd.merge(coefficients,column_df,left_index= True,\\n\",\n    \"                              right_index= True, how = \\\"left\\\"))\\n\",\n    \"    coef_sumry.columns = [\\\"coefficients\\\",\\\"features\\\"]\\n\",\n    \"    coef_sumry = coef_sumry.sort_values(by = \\\"coefficients\\\",ascending = False)\\n\",\n    \"    \\n\",\n    \"    print (algo)\\n\",\n    \"    print (\\\"\\\\n Classification report : \\\\n\\\",classification_report(testing_y,predictions))\\n\",\n    \"    print (\\\"Accuracy   Score : \\\",accuracy_score(testing_y,predictions))\\n\",\n    \"    \\n\",\n    \"    #confusion matrix\\n\",\n    \"    conf_matrix = confusion_matrix(testing_y,predictions)\\n\",\n    \"    plt.figure(figsize=(12,12))\\n\",\n    \"    plt.subplot(221)\\n\",\n    \"    sns.heatmap(conf_matrix, fmt = \\\"d\\\",annot=True, cmap='Blues')\\n\",\n    \"    plt.title('Confuion Matrix')\\n\",\n    \"    plt.ylabel('True Values')\\n\",\n    \"    plt.xlabel('Predicted Values')\\n\",\n    \"    \\n\",\n    \"    #roc_auc_score\\n\",\n    \"    model_roc_auc = roc_auc_score(testing_y,probabilities) \\n\",\n    \"    print (\\\"Area under curve : \\\",model_roc_auc,\\\"\\\\n\\\")\\n\",\n    \"    fpr,tpr,thresholds = roc_curve(testing_y,probabilities)\\n\",\n    \"    \\n\",\n    \"    plt.subplot(222)\\n\",\n    \"    plt.plot(fpr, tpr, color='darkorange', lw=1, label = \\\"Auc : %.3f\\\" %model_roc_auc)\\n\",\n    \"    plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\\n\",\n    \"    plt.xlim([0.0, 1.0])\\n\",\n    \"    plt.ylim([0.0, 1.05])\\n\",\n    \"    plt.xlabel('False Positive Rate')\\n\",\n    \"    plt.ylabel('True Positive Rate')\\n\",\n    \"    plt.title('Receiver operating characteristic')\\n\",\n    \"    plt.legend(loc=\\\"lower right\\\")\\n\",\n    \"    \\n\",\n    \"    plt.subplot(212)\\n\",\n    \"    sns.barplot(x = coef_sumry[\\\"features\\\"] ,y = coef_sumry[\\\"coefficients\\\"])\\n\",\n    \"    plt.title('Feature Importances')\\n\",\n    \"    plt.xticks(rotation=\\\"vertical\\\")\\n\",\n    \"    \\n\",\n    \"    plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Hyperparameters Tuning\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Grid 1: Selecting class weight and estimators\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 32,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Fitting 3 folds for each of 24 candidates, totalling 72 fits\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\\n\",\n      \"[Parallel(n_jobs=-1)]: Done  34 tasks      | elapsed:   28.9s\\n\",\n      \"[Parallel(n_jobs=-1)]: Done  72 out of  72 | elapsed:  1.4min finished\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"GridSearchCV(cv=3, error_score=nan,\\n\",\n       \"             estimator=RandomForestClassifier(bootstrap=True, ccp_alpha=0.0,\\n\",\n       \"                                              class_weight=None,\\n\",\n       \"                                              criterion='gini', max_depth=None,\\n\",\n       \"                                              max_features='auto',\\n\",\n       \"                                              max_leaf_nodes=None,\\n\",\n       \"                                              max_samples=None,\\n\",\n       \"                                              min_impurity_decrease=0.0,\\n\",\n       \"                                              min_impurity_split=None,\\n\",\n       \"                                              min_samples_leaf=1,\\n\",\n       \"                                              min_samples_split=2,\\n\",\n       \"                                              min_weight_fraction_leaf=0.0,\\n\",\n       \"                                              n_estimators=100, n_jobs=None,\\n\",\n       \"                                              oob_score=False,\\n\",\n       \"                                              random_state=None, verbose=0,\\n\",\n       \"                                              warm_start=False),\\n\",\n       \"             iid='deprecated', n_jobs=-1,\\n\",\n       \"             param_grid={'max_features': ['auto', 'sqrt', 'log2', None],\\n\",\n       \"                         'n_estimators': [300, 500, 700, 900, 1100, 1300]},\\n\",\n       \"             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\\n\",\n       \"             scoring='f1', verbose=1)\"\n      ]\n     },\n     \"execution_count\": 32,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"param_grid1 = {'max_features':['auto', 'sqrt', 'log2', None],\\n\",\n    \"          'n_estimators':[300, 500, 700, 900, 1100, 1300]\\n\",\n    \"         }\\n\",\n    \"\\n\",\n    \"rf_model = RandomForestClassifier()\\n\",\n    \"grid1 = GridSearchCV(estimator=rf_model, param_grid=param_grid1, n_jobs=-1, cv=3, verbose=1, scoring = 'f1')\\n\",\n    \"grid1.fit(train_x, train_y)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 35,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\\n\",\n       \"                       criterion='gini', max_depth=None, max_features='auto',\\n\",\n       \"                       max_leaf_nodes=None, max_samples=None,\\n\",\n       \"                       min_impurity_decrease=0.0, min_impurity_split=None,\\n\",\n       \"                       min_samples_leaf=1, min_samples_split=2,\\n\",\n       \"                       min_weight_fraction_leaf=0.0, n_estimators=1100,\\n\",\n       \"                       n_jobs=None, oob_score=False, random_state=None,\\n\",\n       \"                       verbose=0, warm_start=False)\"\n      ]\n     },\n     \"execution_count\": 35,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"grid1.best_estimator_\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 37,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.axes._subplots.AxesSubplot at 0x24e31749898>\"\n      ]\n     },\n     \"execution_count\": 37,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"dt = pd.DataFrame(grid1.cv_results_)\\n\",\n    \"dt.param_max_features = dt.param_max_features.astype(str)\\n\",\n    \"dt.param_n_estimators = dt.param_n_estimators.astype(str)\\n\",\n    \"\\n\",\n    \"table = pd.pivot_table(dt, values='mean_test_score', index='param_n_estimators', \\n\",\n    \"                       columns='param_max_features')\\n\",\n    \"     \\n\",\n    \"sns.heatmap(table)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 38,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.5664538956289195\"\n      ]\n     },\n     \"execution_count\": 38,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"grid1.best_score_\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Grid 2: Selecting max depth and split criterion\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 39,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Fitting 3 folds for each of 36 candidates, totalling 108 fits\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\\n\",\n      \"[Parallel(n_jobs=-1)]: Done  34 tasks      | elapsed:   38.8s\\n\",\n      \"[Parallel(n_jobs=-1)]: Done 108 out of 108 | elapsed:  2.0min finished\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"GridSearchCV(cv=3, error_score=nan,\\n\",\n       \"             estimator=RandomForestClassifier(bootstrap=True, ccp_alpha=0.0,\\n\",\n       \"                                              class_weight=None,\\n\",\n       \"                                              criterion='gini', max_depth=None,\\n\",\n       \"                                              max_features='auto',\\n\",\n       \"                                              max_leaf_nodes=None,\\n\",\n       \"                                              max_samples=None,\\n\",\n       \"                                              min_impurity_decrease=0.0,\\n\",\n       \"                                              min_impurity_split=None,\\n\",\n       \"                                              min_samples_leaf=1,\\n\",\n       \"                                              min_samples_split=2,\\n\",\n       \"                                              min_weight_fraction_leaf=0.0,\\n\",\n       \"                                              n_estimators=100, n_jobs=None,\\n\",\n       \"                                              oob_score=False,\\n\",\n       \"                                              random_state=None, verbose=0,\\n\",\n       \"                                              warm_start=False),\\n\",\n       \"             iid='deprecated', n_jobs=-1,\\n\",\n       \"             param_grid={'criterion': ['entropy', 'gini'],\\n\",\n       \"                         'max_depth': [7, 9, 11, 13, 15, None],\\n\",\n       \"                         'max_features': ['auto'],\\n\",\n       \"                         'n_estimators': [1000, 1100, 1200]},\\n\",\n       \"             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\\n\",\n       \"             scoring='f1', verbose=1)\"\n      ]\n     },\n     \"execution_count\": 39,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"param_grid2 = {'max_features':['auto'],\\n\",\n    \"          'n_estimators':[1000, 1100, 1200],\\n\",\n    \"           'criterion': ['entropy', 'gini'],    \\n\",\n    \"          'max_depth': [7, 9, 11, 13, 15, None],\\n\",\n    \"         }\\n\",\n    \"\\n\",\n    \"rf_model = RandomForestClassifier()\\n\",\n    \"grid2 = GridSearchCV(estimator=rf_model, param_grid=param_grid2, n_jobs=-1, cv=3, verbose=1, scoring = 'f1')\\n\",\n    \"grid2.fit(train_x, train_y)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 40,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\\n\",\n       \"                       criterion='entropy', max_depth=11, max_features='auto',\\n\",\n       \"                       max_leaf_nodes=None, max_samples=None,\\n\",\n       \"                       min_impurity_decrease=0.0, min_impurity_split=None,\\n\",\n       \"                       min_samples_leaf=1, min_samples_split=2,\\n\",\n       \"                       min_weight_fraction_leaf=0.0, n_estimators=1000,\\n\",\n       \"                       n_jobs=None, oob_score=False, random_state=None,\\n\",\n       \"                       verbose=0, warm_start=False)\"\n      ]\n     },\n     \"execution_count\": 40,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"grid2.best_estimator_\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 41,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.axes._subplots.AxesSubplot at 0x24e26d720f0>\"\n      ]\n     },\n     \"execution_count\": 41,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"dt = pd.DataFrame(grid2.cv_results_)\\n\",\n    \"\\n\",\n    \"table = pd.pivot_table(dt, values='mean_test_score', index='param_max_depth', \\n\",\n    \"                       columns='param_criterion')\\n\",\n    \"     \\n\",\n    \"sns.heatmap(table)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 44,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.axes._subplots.AxesSubplot at 0x24e3e7c6550>\"\n      ]\n     },\n     \"execution_count\": 44,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"table = pd.pivot_table(dt, values='mean_test_score', index='param_max_depth', \\n\",\n    \"                       columns='param_n_estimators')\\n\",\n    \"     \\n\",\n    \"sns.heatmap(table)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 42,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.5754368499916416\"\n      ]\n     },\n     \"execution_count\": 42,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"grid2.best_score_\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Checking if other depth and estimator value results better\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 45,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Fitting 3 folds for each of 9 candidates, totalling 27 fits\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\\n\",\n      \"[Parallel(n_jobs=-1)]: Done  27 out of  27 | elapsed:   23.4s finished\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"GridSearchCV(cv=3, error_score=nan,\\n\",\n       \"             estimator=RandomForestClassifier(bootstrap=True, ccp_alpha=0.0,\\n\",\n       \"                                              class_weight=None,\\n\",\n       \"                                              criterion='gini', max_depth=None,\\n\",\n       \"                                              max_features='auto',\\n\",\n       \"                                              max_leaf_nodes=None,\\n\",\n       \"                                              max_samples=None,\\n\",\n       \"                                              min_impurity_decrease=0.0,\\n\",\n       \"                                              min_impurity_split=None,\\n\",\n       \"                                              min_samples_leaf=1,\\n\",\n       \"                                              min_samples_split=2,\\n\",\n       \"                                              min_weight_fraction_leaf=0.0,\\n\",\n       \"                                              n_estimators=100, n_jobs=None,\\n\",\n       \"                                              oob_score=False,\\n\",\n       \"                                              random_state=None, verbose=0,\\n\",\n       \"                                              warm_start=False),\\n\",\n       \"             iid='deprecated', n_jobs=-1,\\n\",\n       \"             param_grid={'criterion': ['entropy'], 'max_depth': [10, 11, 12],\\n\",\n       \"                         'max_features': ['auto'],\\n\",\n       \"                         'n_estimators': [950, 1000, 1050]},\\n\",\n       \"             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\\n\",\n       \"             scoring='f1', verbose=1)\"\n      ]\n     },\n     \"execution_count\": 45,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"param_grid2_2 = {'max_features':['auto'],\\n\",\n    \"          'n_estimators':[950, 1000, 1050],\\n\",\n    \"           'criterion': ['entropy'],    \\n\",\n    \"          'max_depth': [10, 11, 12],\\n\",\n    \"         }\\n\",\n    \"\\n\",\n    \"rf_model = RandomForestClassifier()\\n\",\n    \"grid2_2 = GridSearchCV(estimator=rf_model, param_grid=param_grid2_2, n_jobs=-1, cv=3, verbose=1, scoring = 'f1')\\n\",\n    \"grid2_2.fit(train_x, train_y)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 46,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\\n\",\n       \"                       criterion='entropy', max_depth=10, max_features='auto',\\n\",\n       \"                       max_leaf_nodes=None, max_samples=None,\\n\",\n       \"                       min_impurity_decrease=0.0, min_impurity_split=None,\\n\",\n       \"                       min_samples_leaf=1, min_samples_split=2,\\n\",\n       \"                       min_weight_fraction_leaf=0.0, n_estimators=1000,\\n\",\n       \"                       n_jobs=None, oob_score=False, random_state=None,\\n\",\n       \"                       verbose=0, warm_start=False)\"\n      ]\n     },\n     \"execution_count\": 46,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"grid2_2.best_estimator_\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 47,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.57924338341986\"\n      ]\n     },\n     \"execution_count\": 47,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"grid2_2.best_score_\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Grid 3: Selecting minimum samples leaf and split\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 52,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Fitting 3 folds for each of 16 candidates, totalling 48 fits\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\\n\",\n      \"[Parallel(n_jobs=-1)]: Done  48 out of  48 | elapsed:   42.4s finished\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"GridSearchCV(cv=3, error_score=nan,\\n\",\n       \"             estimator=RandomForestClassifier(bootstrap=True, ccp_alpha=0.0,\\n\",\n       \"                                              class_weight=None,\\n\",\n       \"                                              criterion='gini', max_depth=None,\\n\",\n       \"                                              max_features='auto',\\n\",\n       \"                                              max_leaf_nodes=None,\\n\",\n       \"                                              max_samples=None,\\n\",\n       \"                                              min_impurity_decrease=0.0,\\n\",\n       \"                                              min_impurity_split=None,\\n\",\n       \"                                              min_samples_leaf=1,\\n\",\n       \"                                              min_samples_split=2,\\n\",\n       \"                                              min_weight_fraction_leaf=0.0,\\n\",\n       \"                                              n_estimators=100, n_jobs=None,\\n\",\n       \"                                              oob_score=False,\\n\",\n       \"                                              random_state=None, verbose=0,\\n\",\n       \"                                              warm_start=False),\\n\",\n       \"             iid='deprecated', n_jobs=-1,\\n\",\n       \"             param_grid={'criterion': ['entropy'], 'max_depth': [10],\\n\",\n       \"                         'max_features': ['auto'],\\n\",\n       \"                         'min_samples_leaf': [1, 3, 5, 7],\\n\",\n       \"                         'min_samples_split': [2, 4, 6, 8],\\n\",\n       \"                         'n_estimators': [1000]},\\n\",\n       \"             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\\n\",\n       \"             scoring='f1', verbose=1)\"\n      ]\n     },\n     \"execution_count\": 52,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"param_grid3 = {'max_features':['auto'],\\n\",\n    \"          'n_estimators':[1000],\\n\",\n    \"           'criterion': ['entropy'],    \\n\",\n    \"          'max_depth': [10],\\n\",\n    \"          'min_samples_leaf': [1, 3, 5, 7],\\n\",\n    \"          'min_samples_split': [2, 4, 6, 8]\\n\",\n    \"         }\\n\",\n    \"\\n\",\n    \"rf_model = RandomForestClassifier()\\n\",\n    \"grid3 = GridSearchCV(estimator=rf_model, param_grid=param_grid3, n_jobs=-1, cv=3, verbose=1, scoring = 'f1')\\n\",\n    \"grid3.fit(train_x, train_y)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 53,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\\n\",\n       \"                       criterion='entropy', max_depth=10, max_features='auto',\\n\",\n       \"                       max_leaf_nodes=None, max_samples=None,\\n\",\n       \"                       min_impurity_decrease=0.0, min_impurity_split=None,\\n\",\n       \"                       min_samples_leaf=1, min_samples_split=8,\\n\",\n       \"                       min_weight_fraction_leaf=0.0, n_estimators=1000,\\n\",\n       \"                       n_jobs=None, oob_score=False, random_state=None,\\n\",\n       \"                       verbose=0, warm_start=False)\"\n      ]\n     },\n     \"execution_count\": 53,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"grid3.best_estimator_\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 54,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.axes._subplots.AxesSubplot at 0x24e3e9f2470>\"\n      ]\n     },\n     \"execution_count\": 54,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"dt = pd.DataFrame(grid3.cv_results_)\\n\",\n    \"\\n\",\n    \"table = pd.pivot_table(dt, values='mean_test_score', index='param_min_samples_leaf', \\n\",\n    \"                       columns='param_min_samples_split')\\n\",\n    \"     \\n\",\n    \"sns.heatmap(table)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 55,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.5780629261081371\"\n      ]\n     },\n     \"execution_count\": 55,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"grid3.best_score_\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Grid 4: Selecting class weight\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 66,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Fitting 3 folds for each of 3 candidates, totalling 9 fits\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\\n\",\n      \"[Parallel(n_jobs=-1)]: Done   4 out of   9 | elapsed:    5.2s remaining:    6.6s\\n\",\n      \"[Parallel(n_jobs=-1)]: Done   9 out of   9 | elapsed:    8.3s finished\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"GridSearchCV(cv=3, error_score=nan,\\n\",\n       \"             estimator=RandomForestClassifier(bootstrap=True, ccp_alpha=0.0,\\n\",\n       \"                                              class_weight=None,\\n\",\n       \"                                              criterion='gini', max_depth=None,\\n\",\n       \"                                              max_features='auto',\\n\",\n       \"                                              max_leaf_nodes=None,\\n\",\n       \"                                              max_samples=None,\\n\",\n       \"                                              min_impurity_decrease=0.0,\\n\",\n       \"                                              min_impurity_split=None,\\n\",\n       \"                                              min_samples_leaf=1,\\n\",\n       \"                                              min_samples_split=2,\\n\",\n       \"                                              min_weight_fraction_leaf=0.0,\\n\",\n       \"                                              n_estimators=100, n_jobs=None,...\\n\",\n       \"                                              random_state=None, verbose=0,\\n\",\n       \"                                              warm_start=False),\\n\",\n       \"             iid='deprecated', n_jobs=-1,\\n\",\n       \"             param_grid={'class_weight': [{0: 1, 1: 1}, {0: 1, 1: 2},\\n\",\n       \"                                          {0: 1, 1: 3}],\\n\",\n       \"                         'criterion': ['entropy'], 'max_depth': [10],\\n\",\n       \"                         'max_features': ['auto'], 'min_samples_leaf': [1],\\n\",\n       \"                         'min_samples_split': [8], 'n_estimators': [1000]},\\n\",\n       \"             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\\n\",\n       \"             scoring='f1', verbose=1)\"\n      ]\n     },\n     \"execution_count\": 66,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"param_grid4 = {'class_weight':[{0:1, 1:1}, {0:1, 1:2}, {0:1, 1:3}],\\n\",\n    \"            'max_features':['auto'],\\n\",\n    \"          'n_estimators':[1000],\\n\",\n    \"           'criterion': ['entropy'],    \\n\",\n    \"          'max_depth': [10],\\n\",\n    \"          'min_samples_leaf': [1],\\n\",\n    \"          'min_samples_split': [8]\\n\",\n    \"         }\\n\",\n    \"\\n\",\n    \"rf_model = RandomForestClassifier()\\n\",\n    \"grid4 = GridSearchCV(estimator=rf_model, param_grid=param_grid4, n_jobs=-1, cv=3, verbose=1, scoring = 'f1')\\n\",\n    \"grid4.fit(train_x, train_y)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 67,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight={0: 1, 1: 3},\\n\",\n       \"                       criterion='entropy', max_depth=10, max_features='auto',\\n\",\n       \"                       max_leaf_nodes=None, max_samples=None,\\n\",\n       \"                       min_impurity_decrease=0.0, min_impurity_split=None,\\n\",\n       \"                       min_samples_leaf=1, min_samples_split=8,\\n\",\n       \"                       min_weight_fraction_leaf=0.0, n_estimators=1000,\\n\",\n       \"                       n_jobs=None, oob_score=False, random_state=None,\\n\",\n       \"                       verbose=0, warm_start=False)\"\n      ]\n     },\n     \"execution_count\": 67,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"grid4.best_estimator_\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 73,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.axes._subplots.AxesSubplot at 0x24e527cf748>\"\n      ]\n     },\n     \"execution_count\": 73,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"dt = pd.DataFrame(grid4.cv_results_)\\n\",\n    \"dt.param_class_weight = dt.param_class_weight.astype(str)\\n\",\n    \"table = pd.pivot_table(dt, values='mean_test_score', index='param_class_weight')\\n\",\n    \"     \\n\",\n    \"sns.heatmap(table)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 65,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.577445110448134\"\n      ]\n     },\n     \"execution_count\": 65,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"grid4.best_score_\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Final Model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 76,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"model = RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight={0: 1, 1: 2},\\n\",\n    \"                       criterion='entropy', max_depth=10, max_features='auto',\\n\",\n    \"                       max_leaf_nodes=None, max_samples=None,\\n\",\n    \"                       min_impurity_decrease=0.0, min_impurity_split=None,\\n\",\n    \"                       min_samples_leaf=1, min_samples_split=8,\\n\",\n    \"                       min_weight_fraction_leaf=0.0, n_estimators=1000,\\n\",\n    \"                       n_jobs=None, oob_score=False, random_state=None,\\n\",\n    \"                       verbose=0, warm_start=False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 77,\n   \"metadata\": {\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight={0: 1, 1: 2},\\n\",\n      \"                       criterion='entropy', max_depth=10, max_features='auto',\\n\",\n      \"                       max_leaf_nodes=None, max_samples=None,\\n\",\n      \"                       min_impurity_decrease=0.0, min_impurity_split=None,\\n\",\n      \"                       min_samples_leaf=1, min_samples_split=8,\\n\",\n      \"                       min_weight_fraction_leaf=0.0, n_estimators=1000,\\n\",\n      \"                       n_jobs=None, oob_score=False, random_state=None,\\n\",\n      \"                       verbose=0, warm_start=False)\\n\",\n      \"\\n\",\n      \" Classification report : \\n\",\n      \"               precision    recall  f1-score   support\\n\",\n      \"\\n\",\n      \"           0       0.87      0.83      0.85      1035\\n\",\n      \"           1       0.59      0.66      0.62       374\\n\",\n      \"\\n\",\n      \"    accuracy                           0.79      1409\\n\",\n      \"   macro avg       0.73      0.75      0.74      1409\\n\",\n      \"weighted avg       0.80      0.79      0.79      1409\\n\",\n      \"\\n\",\n      \"Accuracy   Score :  0.7877927608232789\\n\",\n      \"Area under curve :  0.8479681727763569 \\n\",\n      \"\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 864x864 with 4 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"churn_prediction(model, train_x, train_y, test_x, test_y, x,\\\"features\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Checking the model's performance on train data itself\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 87,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([0.60522273, 0.66346154, 0.62243286, 0.58139535, 0.63836478])\"\n      ]\n     },\n     \"execution_count\": 87,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"train_scores = cross_val_score(model, train_x, train_y, cv = 5, scoring='f1')\\n\",\n    \"train_scores\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 89,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.6221754521655275\"\n      ]\n     },\n     \"execution_count\": 89,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.mean(train_scores)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"As we can see that the performance of the model on test data is same as training data. So, we can conclude that there is no overfitting and underfitting.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Saving model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 78,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import pickle\\n\",\n    \"pickle.dump(model, open('model.pkl','wb'))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Explaining the model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 79,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"C:\\\\Users\\\\archd\\\\Anaconda3\\\\lib\\\\site-packages\\\\sklearn\\\\utils\\\\deprecation.py:144: FutureWarning: The sklearn.metrics.scorer module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.metrics. Anything that cannot be imported from sklearn.metrics is now part of the private API.\\n\",\n      \"  warnings.warn(message, FutureWarning)\\n\",\n      \"C:\\\\Users\\\\archd\\\\Anaconda3\\\\lib\\\\site-packages\\\\sklearn\\\\utils\\\\deprecation.py:144: FutureWarning: The sklearn.feature_selection.base module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.feature_selection. Anything that cannot be imported from sklearn.feature_selection is now part of the private API.\\n\",\n      \"  warnings.warn(message, FutureWarning)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import eli5\\n\",\n    \"from eli5.sklearn import PermutationImportance\\n\",\n    \"\\n\",\n    \"from pdpbox import pdp, info_plots\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 81,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"\\n\",\n       \"    <style>\\n\",\n       \"    table.eli5-weights tr:hover {\\n\",\n       \"        filter: brightness(85%);\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"    \\n\",\n       \"\\n\",\n       \"    \\n\",\n       \"\\n\",\n       \"    \\n\",\n       \"\\n\",\n       \"    \\n\",\n       \"\\n\",\n       \"    \\n\",\n       \"\\n\",\n       \"    \\n\",\n       \"\\n\",\n       \"\\n\",\n       \"    \\n\",\n       \"\\n\",\n       \"    \\n\",\n       \"\\n\",\n       \"    \\n\",\n       \"\\n\",\n       \"    \\n\",\n       \"\\n\",\n       \"    \\n\",\n       \"\\n\",\n       \"    \\n\",\n       \"\\n\",\n       \"\\n\",\n       \"    \\n\",\n       \"\\n\",\n       \"    \\n\",\n       \"\\n\",\n       \"    \\n\",\n       \"\\n\",\n       \"    \\n\",\n       \"\\n\",\n       \"    \\n\",\n       \"        <table class=\\\"eli5-weights eli5-feature-importances\\\" style=\\\"border-collapse: collapse; border: none; margin-top: 0em; table-layout: auto;\\\">\\n\",\n       \"    <thead>\\n\",\n       \"    <tr style=\\\"border: none;\\\">\\n\",\n       \"        <th style=\\\"padding: 0 1em 0 0.5em; text-align: right; border: none;\\\">Weight</th>\\n\",\n       \"        <th style=\\\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\\\">Feature</th>\\n\",\n       \"    </tr>\\n\",\n       \"    </thead>\\n\",\n       \"    <tbody>\\n\",\n       \"    \\n\",\n       \"        <tr style=\\\"background-color: hsl(120, 100.00%, 80.00%); border: none;\\\">\\n\",\n       \"            <td style=\\\"padding: 0 1em 0 0.5em; text-align: right; border: none;\\\">\\n\",\n       \"                0.0185\\n\",\n       \"                \\n\",\n       \"                    &plusmn; 0.0058\\n\",\n       \"                \\n\",\n       \"            </td>\\n\",\n       \"            <td style=\\\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\\\">\\n\",\n       \"                InternetService_Fiber optic\\n\",\n       \"            </td>\\n\",\n       \"        </tr>\\n\",\n       \"    \\n\",\n       \"        <tr style=\\\"background-color: hsl(120, 100.00%, 90.48%); border: none;\\\">\\n\",\n       \"            <td style=\\\"padding: 0 1em 0 0.5em; text-align: right; border: none;\\\">\\n\",\n       \"                0.0064\\n\",\n       \"                \\n\",\n       \"                    &plusmn; 0.0088\\n\",\n       \"                \\n\",\n       \"            </td>\\n\",\n       \"            <td style=\\\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\\\">\\n\",\n       \"                Contract_Two year\\n\",\n       \"            </td>\\n\",\n       \"        </tr>\\n\",\n       \"    \\n\",\n       \"        <tr style=\\\"background-color: hsl(120, 100.00%, 92.50%); border: none;\\\">\\n\",\n       \"            <td style=\\\"padding: 0 1em 0 0.5em; text-align: right; border: none;\\\">\\n\",\n       \"                0.0045\\n\",\n       \"                \\n\",\n       \"                    &plusmn; 0.0058\\n\",\n       \"                \\n\",\n       \"            </td>\\n\",\n       \"            <td style=\\\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\\\">\\n\",\n       \"                OnlineSecurity\\n\",\n       \"            </td>\\n\",\n       \"        </tr>\\n\",\n       \"    \\n\",\n       \"        <tr style=\\\"background-color: hsl(120, 100.00%, 93.00%); border: none;\\\">\\n\",\n       \"            <td style=\\\"padding: 0 1em 0 0.5em; text-align: right; border: none;\\\">\\n\",\n       \"                0.0041\\n\",\n       \"                \\n\",\n       \"                    &plusmn; 0.0134\\n\",\n       \"                \\n\",\n       \"            </td>\\n\",\n       \"            <td style=\\\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\\\">\\n\",\n       \"                Contract_One year\\n\",\n       \"            </td>\\n\",\n       \"        </tr>\\n\",\n       \"    \\n\",\n       \"        <tr style=\\\"background-color: hsl(120, 100.00%, 93.34%); border: none;\\\">\\n\",\n       \"            <td style=\\\"padding: 0 1em 0 0.5em; text-align: right; border: none;\\\">\\n\",\n       \"                0.0038\\n\",\n       \"                \\n\",\n       \"                    &plusmn; 0.0086\\n\",\n       \"                \\n\",\n       \"            </td>\\n\",\n       \"            <td style=\\\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\\\">\\n\",\n       \"                PaymentMethod_Electronic check\\n\",\n       \"            </td>\\n\",\n       \"        </tr>\\n\",\n       \"    \\n\",\n       \"        <tr style=\\\"background-color: hsl(120, 100.00%, 93.52%); border: none;\\\">\\n\",\n       \"            <td style=\\\"padding: 0 1em 0 0.5em; text-align: right; border: none;\\\">\\n\",\n       \"                0.0037\\n\",\n       \"                \\n\",\n       \"                    &plusmn; 0.0071\\n\",\n       \"                \\n\",\n       \"            </td>\\n\",\n       \"            <td style=\\\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\\\">\\n\",\n       \"                InternetService_No\\n\",\n       \"            </td>\\n\",\n       \"        </tr>\\n\",\n       \"    \\n\",\n       \"        <tr style=\\\"background-color: hsl(120, 100.00%, 94.61%); border: none;\\\">\\n\",\n       \"            <td style=\\\"padding: 0 1em 0 0.5em; text-align: right; border: none;\\\">\\n\",\n       \"                0.0028\\n\",\n       \"                \\n\",\n       \"                    &plusmn; 0.0094\\n\",\n       \"                \\n\",\n       \"            </td>\\n\",\n       \"            <td style=\\\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\\\">\\n\",\n       \"                tenure\\n\",\n       \"            </td>\\n\",\n       \"        </tr>\\n\",\n       \"    \\n\",\n       \"        <tr style=\\\"background-color: hsl(120, 100.00%, 94.99%); border: none;\\\">\\n\",\n       \"            <td style=\\\"padding: 0 1em 0 0.5em; text-align: right; border: none;\\\">\\n\",\n       \"                0.0026\\n\",\n       \"                \\n\",\n       \"                    &plusmn; 0.0011\\n\",\n       \"                \\n\",\n       \"            </td>\\n\",\n       \"            <td style=\\\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\\\">\\n\",\n       \"                OnlineBackup\\n\",\n       \"            </td>\\n\",\n       \"        </tr>\\n\",\n       \"    \\n\",\n       \"        <tr style=\\\"background-color: hsl(120, 100.00%, 95.80%); border: none;\\\">\\n\",\n       \"            <td style=\\\"padding: 0 1em 0 0.5em; text-align: right; border: none;\\\">\\n\",\n       \"                0.0020\\n\",\n       \"                \\n\",\n       \"                    &plusmn; 0.0078\\n\",\n       \"                \\n\",\n       \"            </td>\\n\",\n       \"            <td style=\\\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\\\">\\n\",\n       \"                MonthlyCharges\\n\",\n       \"            </td>\\n\",\n       \"        </tr>\\n\",\n       \"    \\n\",\n       \"        <tr style=\\\"background-color: hsl(120, 100.00%, 97.41%); border: none;\\\">\\n\",\n       \"            <td style=\\\"padding: 0 1em 0 0.5em; text-align: right; border: none;\\\">\\n\",\n       \"                0.0010\\n\",\n       \"                \\n\",\n       \"                    &plusmn; 0.0014\\n\",\n       \"                \\n\",\n       \"            </td>\\n\",\n       \"            <td style=\\\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\\\">\\n\",\n       \"                DeviceProtection\\n\",\n       \"            </td>\\n\",\n       \"        </tr>\\n\",\n       \"    \\n\",\n       \"        <tr style=\\\"background-color: hsl(120, 100.00%, 97.68%); border: none;\\\">\\n\",\n       \"            <td style=\\\"padding: 0 1em 0 0.5em; text-align: right; border: none;\\\">\\n\",\n       \"                0.0009\\n\",\n       \"                \\n\",\n       \"                    &plusmn; 0.0083\\n\",\n       \"                \\n\",\n       \"            </td>\\n\",\n       \"            <td style=\\\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\\\">\\n\",\n       \"                PaperlessBilling\\n\",\n       \"            </td>\\n\",\n       \"        </tr>\\n\",\n       \"    \\n\",\n       \"        <tr style=\\\"background-color: hsl(120, 100.00%, 97.96%); border: none;\\\">\\n\",\n       \"            <td style=\\\"padding: 0 1em 0 0.5em; text-align: right; border: none;\\\">\\n\",\n       \"                0.0007\\n\",\n       \"                \\n\",\n       \"                    &plusmn; 0.0030\\n\",\n       \"                \\n\",\n       \"            </td>\\n\",\n       \"            <td style=\\\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\\\">\\n\",\n       \"                TechSupport\\n\",\n       \"            </td>\\n\",\n       \"        </tr>\\n\",\n       \"    \\n\",\n       \"        <tr style=\\\"background-color: hsl(120, 100.00%, 98.57%); border: none;\\\">\\n\",\n       \"            <td style=\\\"padding: 0 1em 0 0.5em; text-align: right; border: none;\\\">\\n\",\n       \"                0.0004\\n\",\n       \"                \\n\",\n       \"                    &plusmn; 0.0032\\n\",\n       \"                \\n\",\n       \"            </td>\\n\",\n       \"            <td style=\\\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\\\">\\n\",\n       \"                StreamingMovies\\n\",\n       \"            </td>\\n\",\n       \"        </tr>\\n\",\n       \"    \\n\",\n       \"        <tr style=\\\"background-color: hsl(120, 100.00%, 98.92%); border: none;\\\">\\n\",\n       \"            <td style=\\\"padding: 0 1em 0 0.5em; text-align: right; border: none;\\\">\\n\",\n       \"                0.0003\\n\",\n       \"                \\n\",\n       \"                    &plusmn; 0.0017\\n\",\n       \"                \\n\",\n       \"            </td>\\n\",\n       \"            <td style=\\\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\\\">\\n\",\n       \"                gender\\n\",\n       \"            </td>\\n\",\n       \"        </tr>\\n\",\n       \"    \\n\",\n       \"        <tr style=\\\"background-color: hsl(120, 100.00%, 99.34%); border: none;\\\">\\n\",\n       \"            <td style=\\\"padding: 0 1em 0 0.5em; text-align: right; border: none;\\\">\\n\",\n       \"                0.0001\\n\",\n       \"                \\n\",\n       \"                    &plusmn; 0.0019\\n\",\n       \"                \\n\",\n       \"            </td>\\n\",\n       \"            <td style=\\\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\\\">\\n\",\n       \"                PhoneService\\n\",\n       \"            </td>\\n\",\n       \"        </tr>\\n\",\n       \"    \\n\",\n       \"        <tr style=\\\"background-color: hsl(0, 100.00%, 100.00%); border: none;\\\">\\n\",\n       \"            <td style=\\\"padding: 0 1em 0 0.5em; text-align: right; border: none;\\\">\\n\",\n       \"                -0.0000\\n\",\n       \"                \\n\",\n       \"                    &plusmn; 0.0009\\n\",\n       \"                \\n\",\n       \"            </td>\\n\",\n       \"            <td style=\\\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\\\">\\n\",\n       \"                MultipleLines\\n\",\n       \"            </td>\\n\",\n       \"        </tr>\\n\",\n       \"    \\n\",\n       \"        <tr style=\\\"background-color: hsl(0, 100.00%, 99.34%); border: none;\\\">\\n\",\n       \"            <td style=\\\"padding: 0 1em 0 0.5em; text-align: right; border: none;\\\">\\n\",\n       \"                -0.0001\\n\",\n       \"                \\n\",\n       \"                    &plusmn; 0.0006\\n\",\n       \"                \\n\",\n       \"            </td>\\n\",\n       \"            <td style=\\\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\\\">\\n\",\n       \"                StreamingTV\\n\",\n       \"            </td>\\n\",\n       \"        </tr>\\n\",\n       \"    \\n\",\n       \"        <tr style=\\\"background-color: hsl(0, 100.00%, 98.57%); border: none;\\\">\\n\",\n       \"            <td style=\\\"padding: 0 1em 0 0.5em; text-align: right; border: none;\\\">\\n\",\n       \"                -0.0004\\n\",\n       \"                \\n\",\n       \"                    &plusmn; 0.0044\\n\",\n       \"                \\n\",\n       \"            </td>\\n\",\n       \"            <td style=\\\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\\\">\\n\",\n       \"                SeniorCitizen\\n\",\n       \"            </td>\\n\",\n       \"        </tr>\\n\",\n       \"    \\n\",\n       \"        <tr style=\\\"background-color: hsl(0, 100.00%, 97.68%); border: none;\\\">\\n\",\n       \"            <td style=\\\"padding: 0 1em 0 0.5em; text-align: right; border: none;\\\">\\n\",\n       \"                -0.0009\\n\",\n       \"                \\n\",\n       \"                    &plusmn; 0.0033\\n\",\n       \"                \\n\",\n       \"            </td>\\n\",\n       \"            <td style=\\\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\\\">\\n\",\n       \"                Dependents\\n\",\n       \"            </td>\\n\",\n       \"        </tr>\\n\",\n       \"    \\n\",\n       \"        <tr style=\\\"background-color: hsl(0, 100.00%, 95.80%); border: none;\\\">\\n\",\n       \"            <td style=\\\"padding: 0 1em 0 0.5em; text-align: right; border: none;\\\">\\n\",\n       \"                -0.0020\\n\",\n       \"                \\n\",\n       \"                    &plusmn; 0.0026\\n\",\n       \"                \\n\",\n       \"            </td>\\n\",\n       \"            <td style=\\\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\\\">\\n\",\n       \"                PaymentMethod_Credit card (automatic)\\n\",\n       \"            </td>\\n\",\n       \"        </tr>\\n\",\n       \"    \\n\",\n       \"        <tr style=\\\"background-color: hsl(0, 100.00%, 93.17%); border: none;\\\">\\n\",\n       \"            <td style=\\\"padding: 0 1em 0 0.5em; text-align: right; border: none;\\\">\\n\",\n       \"                -0.0040\\n\",\n       \"                \\n\",\n       \"                    &plusmn; 0.0064\\n\",\n       \"                \\n\",\n       \"            </td>\\n\",\n       \"            <td style=\\\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\\\">\\n\",\n       \"                TotalCharges\\n\",\n       \"            </td>\\n\",\n       \"        </tr>\\n\",\n       \"    \\n\",\n       \"        <tr style=\\\"background-color: hsl(0, 100.00%, 93.17%); border: none;\\\">\\n\",\n       \"            <td style=\\\"padding: 0 1em 0 0.5em; text-align: right; border: none;\\\">\\n\",\n       \"                -0.0040\\n\",\n       \"                \\n\",\n       \"                    &plusmn; 0.0039\\n\",\n       \"                \\n\",\n       \"            </td>\\n\",\n       \"            <td style=\\\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\\\">\\n\",\n       \"                Partner\\n\",\n       \"            </td>\\n\",\n       \"        </tr>\\n\",\n       \"    \\n\",\n       \"        <tr style=\\\"background-color: hsl(0, 100.00%, 89.33%); border: none;\\\">\\n\",\n       \"            <td style=\\\"padding: 0 1em 0 0.5em; text-align: right; border: none;\\\">\\n\",\n       \"                -0.0075\\n\",\n       \"                \\n\",\n       \"                    &plusmn; 0.0033\\n\",\n       \"                \\n\",\n       \"            </td>\\n\",\n       \"            <td style=\\\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\\\">\\n\",\n       \"                PaymentMethod_Mailed check\\n\",\n       \"            </td>\\n\",\n       \"        </tr>\\n\",\n       \"    \\n\",\n       \"    \\n\",\n       \"    </tbody>\\n\",\n       \"</table>\\n\",\n       \"    \\n\",\n       \"\\n\",\n       \"    \\n\",\n       \"\\n\",\n       \"\\n\",\n       \"    \\n\",\n       \"\\n\",\n       \"    \\n\",\n       \"\\n\",\n       \"    \\n\",\n       \"\\n\",\n       \"    \\n\",\n       \"\\n\",\n       \"    \\n\",\n       \"\\n\",\n       \"    \\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"execution_count\": 81,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"perm = PermutationImportance(model, random_state=1).fit(test_x, test_y)\\n\",\n    \"eli5.show_weights(perm, feature_names = test_x.columns.tolist())\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Visualizing how the partial dependance plots look for top features\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Internet Service: Fiber Optic\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 95,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1080x684 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"pdp_p = pdp.pdp_isolate(model=model, dataset=test_x, model_features=test_x.columns.values, \\n\",\n    \"                        feature='InternetService_Fiber optic')\\n\",\n    \"pdp.pdp_plot(pdp_p, 'InternetService_Fiber optic')\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Payment Method: Mailed Check\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 97,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1080x684 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"pdp_p = pdp.pdp_isolate(model=model, dataset=test_x, model_features=test_x.columns.values, \\n\",\n    \"                        feature='PaymentMethod_Mailed check')\\n\",\n    \"pdp.pdp_plot(pdp_p, 'PaymentMethod_Mailed check')\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Monthly Charges\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 100,\n   \"metadata\": {\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1080x684 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"pdp_p = pdp.pdp_isolate(model=model, dataset=test_x, model_features=test_x.columns.values, feature='MonthlyCharges')\\n\",\n    \"pdp.pdp_plot(pdp_p, 'MonthlyCharges')\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Total Charges\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 96,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1080x684 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"pdp_p = pdp.pdp_isolate(model=model, dataset=test_x, model_features=test_x.columns.values, feature='TotalCharges')\\n\",\n    \"pdp.pdp_plot(pdp_p, 'TotalCharges')\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Contract - Two years\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 98,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1080x684 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"pdp_p = pdp.pdp_isolate(model=model, dataset=test_x, model_features=test_x.columns.values, \\n\",\n    \"                        feature='Contract_Two year')\\n\",\n    \"pdp.pdp_plot(pdp_p, 'Contract_Two year')\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Tenure\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 99,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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     \"text/plain\": [\n       \"<Figure size 1080x684 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"pdp_p = pdp.pdp_isolate(model=model, dataset=test_x, model_features=test_x.columns.values, \\n\",\n    \"                        feature='tenure')\\n\",\n    \"pdp.pdp_plot(pdp_p, 'tenure')\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Shap Values\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 102,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div align='center'><img 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r={topAbort:null,topAnimationEnd:null,topAnimationIteration:null,topAnimationStart:null,topBlur:null,topCanPlay:null,topCanPlayThrough:null,topChange:null,topClick:null,topCompositionEnd:null,topCompositionStart:null,topCompositionUpdate:null,topContextMenu:null,topCopy:null,topCut:null,topDoubleClick:null,topDrag:null,topDragEnd:null,topDragEnter:null,topDragExit:null,topDragLeave:null,topDragOver:null,topDragStart:null,topDrop:null,topDurationChange:null,topEmptied:null,topEncrypted:null,topEnded:null,topError:null,topFocus:null,topInput:null,topInvalid:null,topKeyDown:null,topKeyPress:null,topKeyUp:null,topLoad:null,topLoadedData:null,topLoadedMetadata:null,topLoadStart:null,topMouseDown:null,topMouseMove:null,topMouseOut:null,topMouseOver:null,topMouseUp:null,topPaste:null,topPause:null,topPlay:null,topPlaying:null,topProgress:null,topRateChange:null,topReset:null,topScroll:null,topSeeked:null,topSeeking:null,topSelectionChange:null,topStalled:null,topSubmit:null,topSuspend:null,topTextInput:null,topTimeUpdate:null,topTouchCancel:null,topTouchEnd:null,topTouchMove:null,topTouchStart:null,topTransitionEnd:null,topVolumeChange:null,topWaiting:null,topWheel:null},i={topLevelTypes:r};t.exports=i},function(t,e,n){\\\"use strict\\\";function r(t){this._root=t,this._startText=this.getText(),this._fallbackText=null}var i=n(3),o=n(18),a=n(172);i(r.prototype,{destructor:function(){this._root=null,this._startText=null,this._fallbackText=null},getText:function(){return\\\"value\\\"in this._root?this._root.value:this._root[a()]},getData:function(){if(this._fallbackText)return this._fallbackText;var t,e,n=this._startText,r=n.length,i=this.getText(),o=i.length;for(t=0;t<r&&n[t]===i[t];t++);var a=r-t;for(e=1;e<=a&&n[r-e]===i[o-e];e++);var u=e>1?1-e:void 0;return this._fallbackText=i.slice(t,u),this._fallbackText}}),o.addPoolingTo(r),t.exports=r},function(t,e,n){\\\"use strict\\\";var r=n(21),i=r.injection.MUST_USE_PROPERTY,o=r.injection.HAS_BOOLEAN_VALUE,a=r.injection.HAS_NUMERIC_VALUE,u=r.injection.HAS_POSITIVE_NUMERIC_VALUE,c=r.injection.HAS_OVERLOADED_BOOLEAN_VALUE,s={isCustomAttribute:RegExp.prototype.test.bind(new RegExp(\\\"^(data|aria)-[\\\"+r.ATTRIBUTE_NAME_CHAR+\\\"]*$\\\")),Properties:{accept:0,acceptCharset:0,accessKey:0,action:0,allowFullScreen:o,allowTransparency:0,alt:0,as:0,async:o,autoComplete:0,autoPlay:o,capture:o,cellPadding:0,cellSpacing:0,charSet:0,challenge:0,checked:i|o,cite:0,classID:0,className:0,cols:u,colSpan:0,content:0,contentEditable:0,contextMenu:0,controls:o,controlsList:0,coords:0,crossOrigin:0,data:0,dateTime:0,default:o,defer:o,dir:0,disabled:o,download:c,draggable:0,encType:0,form:0,formAction:0,formEncType:0,formMethod:0,formNoValidate:o,formTarget:0,frameBorder:0,headers:0,height:0,hidden:o,high:0,href:0,hrefLang:0,htmlFor:0,httpEquiv:0,icon:0,id:0,inputMode:0,integrity:0,is:0,keyParams:0,keyType:0,kind:0,label:0,lang:0,list:0,loop:o,low:0,manifest:0,marginHeight:0,marginWidth:0,max:0,maxLength:0,media:0,mediaGroup:0,method:0,min:0,minLength:0,multiple:i|o,muted:i|o,name:0,nonce:0,noValidate:o,open:o,optimum:0,pattern:0,placeholder:0,playsInline:o,poster:0,preload:0,profile:0,radioGroup:0,readOnly:o,referrerPolicy:0,rel:0,required:o,reversed:o,role:0,rows:u,rowSpan:a,sandbox:0,scope:0,scoped:o,scrolling:0,seamless:o,selected:i|o,shape:0,size:u,sizes:0,span:u,spellCheck:0,src:0,srcDoc:0,srcLang:0,srcSet:0,start:a,step:0,style:0,summary:0,tabIndex:0,target:0,title:0,type:0,useMap:0,value:0,width:0,wmode:0,wrap:0,about:0,datatype:0,inlist:0,prefix:0,property:0,resource:0,typeof:0,vocab:0,autoCapitalize:0,autoCorrect:0,autoSave:0,color:0,itemProp:0,itemScope:o,itemType:0,itemID:0,itemRef:0,results:0,security:0,unselectable:0},DOMAttributeNames:{acceptCharset:\\\"accept-charset\\\",className:\\\"class\\\",htmlFor:\\\"for\\\",httpEquiv:\\\"http-equiv\\\"},DOMPropertyNames:{},DOMMutationMethods:{value:function(t,e){if(null==e)return t.removeAttribute(\\\"value\\\");\\\"number\\\"!==t.type||!1===t.hasAttribute(\\\"value\\\")?t.setAttribute(\\\"value\\\",\\\"\\\"+e):t.validity&&!t.validity.badInput&&t.ownerDocument.activeElement!==t&&t.setAttribute(\\\"value\\\",\\\"\\\"+e)}}};t.exports=s},function(t,e,n){\\\"use strict\\\";(function(e){function r(t,e,n,r){var i=void 0===t[n];null!=e&&i&&(t[n]=o(e,!0))}var i=n(24),o=n(174),a=(n(85),n(96)),u=n(177);n(2);void 0!==e&&e.env;var c={instantiateChildren:function(t,e,n,i){if(null==t)return null;var o={};return u(t,r,o),o},updateChildren:function(t,e,n,r,u,c,s,l,f){if(e||t){var p,h;for(p in e)if(e.hasOwnProperty(p)){h=t&&t[p];var d=h&&h._currentElement,v=e[p];if(null!=h&&a(d,v))i.receiveComponent(h,v,u,l),e[p]=h;else{h&&(r[p]=i.getHostNode(h),i.unmountComponent(h,!1));var g=o(v,!0);e[p]=g;var m=i.mountComponent(g,u,c,s,l,f);n.push(m)}}for(p in t)!t.hasOwnProperty(p)||e&&e.hasOwnProperty(p)||(h=t[p],r[p]=i.getHostNode(h),i.unmountComponent(h,!1))}},unmountChildren:function(t,e){for(var n in t)if(t.hasOwnProperty(n)){var r=t[n];i.unmountComponent(r,e)}}};t.exports=c}).call(e,n(156))},function(t,e,n){\\\"use strict\\\";var r=n(82),i=n(364),o={processChildrenUpdates:i.dangerouslyProcessChildrenUpdates,replaceNodeWithMarkup:r.dangerouslyReplaceNodeWithMarkup};t.exports=o},function(t,e,n){\\\"use strict\\\";function r(t){}function i(t){return!(!t.prototype||!t.prototype.isReactComponent)}function o(t){return!(!t.prototype||!t.prototype.isPureReactComponent)}var a=n(1),u=n(3),c=n(26),s=n(87),l=n(15),f=n(88),p=n(39),h=(n(9),n(168)),d=n(24),v=n(51),g=(n(0),n(81)),m=n(96),y=(n(2),{ImpureClass:0,PureClass:1,StatelessFunctional:2});r.prototype.render=function(){var t=p.get(this)._currentElement.type,e=t(this.props,this.context,this.updater);return e};var _=1,b={construct:function(t){this._currentElement=t,this._rootNodeID=0,this._compositeType=null,this._instance=null,this._hostParent=null,this._hostContainerInfo=null,this._updateBatchNumber=null,this._pendingElement=null,this._pendingStateQueue=null,this._pendingReplaceState=!1,this._pendingForceUpdate=!1,this._renderedNodeType=null,this._renderedComponent=null,this._context=null,this._mountOrder=0,this._topLevelWrapper=null,this._pendingCallbacks=null,this._calledComponentWillUnmount=!1},mountComponent:function(t,e,n,u){this._context=u,this._mountOrder=_++,this._hostParent=e,this._hostContainerInfo=n;var s,l=this._currentElement.props,f=this._processContext(u),h=this._currentElement.type,d=t.getUpdateQueue(),g=i(h),m=this._constructComponent(g,l,f,d);g||null!=m&&null!=m.render?o(h)?this._compositeType=y.PureClass:this._compositeType=y.ImpureClass:(s=m,null===m||!1===m||c.isValidElement(m)||a(\\\"105\\\",h.displayName||h.name||\\\"Component\\\"),m=new r(h),this._compositeType=y.StatelessFunctional);m.props=l,m.context=f,m.refs=v,m.updater=d,this._instance=m,p.set(m,this);var b=m.state;void 0===b&&(m.state=b=null),(\\\"object\\\"!=typeof b||Array.isArray(b))&&a(\\\"106\\\",this.getName()||\\\"ReactCompositeComponent\\\"),this._pendingStateQueue=null,this._pendingReplaceState=!1,this._pendingForceUpdate=!1;var x;return x=m.unstable_handleError?this.performInitialMountWithErrorHandling(s,e,n,t,u):this.performInitialMount(s,e,n,t,u),m.componentDidMount&&t.getReactMountReady().enqueue(m.componentDidMount,m),x},_constructComponent:function(t,e,n,r){return this._constructComponentWithoutOwner(t,e,n,r)},_constructComponentWithoutOwner:function(t,e,n,r){var i=this._currentElement.type;return t?new i(e,n,r):i(e,n,r)},performInitialMountWithErrorHandling:function(t,e,n,r,i){var o,a=r.checkpoint();try{o=this.performInitialMount(t,e,n,r,i)}catch(u){r.rollback(a),this._instance.unstable_handleError(u),this._pendingStateQueue&&(this._instance.state=this._processPendingState(this._instance.props,this._instance.context)),a=r.checkpoint(),this._renderedComponent.unmountComponent(!0),r.rollback(a),o=this.performInitialMount(t,e,n,r,i)}return o},performInitialMount:function(t,e,n,r,i){var o=this._instance,a=0;o.componentWillMount&&(o.componentWillMount(),this._pendingStateQueue&&(o.state=this._processPendingState(o.props,o.context))),void 0===t&&(t=this._renderValidatedComponent());var u=h.getType(t);this._renderedNodeType=u;var c=this._instantiateReactComponent(t,u!==h.EMPTY);this._renderedComponent=c;var s=d.mountComponent(c,r,e,n,this._processChildContext(i),a);return s},getHostNode:function(){return d.getHostNode(this._renderedComponent)},unmountComponent:function(t){if(this._renderedComponent){var e=this._instance;if(e.componentWillUnmount&&!e._calledComponentWillUnmount)if(e._calledComponentWillUnmount=!0,t){var n=this.getName()+\\\".componentWillUnmount()\\\";f.invokeGuardedCallback(n,e.componentWillUnmount.bind(e))}else e.componentWillUnmount();this._renderedComponent&&(d.unmountComponent(this._renderedComponent,t),this._renderedNodeType=null,this._renderedComponent=null,this._instance=null),this._pendingStateQueue=null,this._pendingReplaceState=!1,this._pendingForceUpdate=!1,this._pendingCallbacks=null,this._pendingElement=null,this._context=null,this._rootNodeID=0,this._topLevelWrapper=null,p.remove(e)}},_maskContext:function(t){var e=this._currentElement.type,n=e.contextTypes;if(!n)return v;var r={};for(var i in n)r[i]=t[i];return r},_processContext:function(t){var e=this._maskContext(t);return e},_processChildContext:function(t){var e,n=this._currentElement.type,r=this._instance;if(r.getChildContext&&(e=r.getChildContext()),e){\\\"object\\\"!=typeof n.childContextTypes&&a(\\\"107\\\",this.getName()||\\\"ReactCompositeComponent\\\");for(var i in e)i in n.childContextTypes||a(\\\"108\\\",this.getName()||\\\"ReactCompositeComponent\\\",i);return u({},t,e)}return t},_checkContextTypes:function(t,e,n){},receiveComponent:function(t,e,n){var r=this._currentElement,i=this._context;this._pendingElement=null,this.updateComponent(e,r,t,i,n)},performUpdateIfNecessary:function(t){null!=this._pendingElement?d.receiveComponent(this,this._pendingElement,t,this._context):null!==this._pendingStateQueue||this._pendingForceUpdate?this.updateComponent(t,this._currentElement,this._currentElement,this._context,this._context):this._updateBatchNumber=null},updateComponent:function(t,e,n,r,i){var o=this._instance;null==o&&a(\\\"136\\\",this.getName()||\\\"ReactCompositeComponent\\\");var u,c=!1;this._context===i?u=o.context:(u=this._processContext(i),c=!0);var s=e.props,l=n.props;e!==n&&(c=!0),c&&o.componentWillReceiveProps&&o.componentWillReceiveProps(l,u);var f=this._processPendingState(l,u),p=!0;this._pendingForceUpdate||(o.shouldComponentUpdate?p=o.shouldComponentUpdate(l,f,u):this._compositeType===y.PureClass&&(p=!g(s,l)||!g(o.state,f))),this._updateBatchNumber=null,p?(this._pendingForceUpdate=!1,this._performComponentUpdate(n,l,f,u,t,i)):(this._currentElement=n,this._context=i,o.props=l,o.state=f,o.context=u)},_processPendingState:function(t,e){var n=this._instance,r=this._pendingStateQueue,i=this._pendingReplaceState;if(this._pendingReplaceState=!1,this._pendingStateQueue=null,!r)return n.state;if(i&&1===r.length)return r[0];for(var o=u({},i?r[0]:n.state),a=i?1:0;a<r.length;a++){var c=r[a];u(o,\\\"function\\\"==typeof c?c.call(n,o,t,e):c)}return o},_performComponentUpdate:function(t,e,n,r,i,o){var a,u,c,s=this._instance,l=Boolean(s.componentDidUpdate);l&&(a=s.props,u=s.state,c=s.context),s.componentWillUpdate&&s.componentWillUpdate(e,n,r),this._currentElement=t,this._context=o,s.props=e,s.state=n,s.context=r,this._updateRenderedComponent(i,o),l&&i.getReactMountReady().enqueue(s.componentDidUpdate.bind(s,a,u,c),s)},_updateRenderedComponent:function(t,e){var n=this._renderedComponent,r=n._currentElement,i=this._renderValidatedComponent(),o=0;if(m(r,i))d.receiveComponent(n,i,t,this._processChildContext(e));else{var a=d.getHostNode(n);d.unmountComponent(n,!1);var u=h.getType(i);this._renderedNodeType=u;var c=this._instantiateReactComponent(i,u!==h.EMPTY);this._renderedComponent=c;var s=d.mountComponent(c,t,this._hostParent,this._hostContainerInfo,this._processChildContext(e),o);this._replaceNodeWithMarkup(a,s,n)}},_replaceNodeWithMarkup:function(t,e,n){s.replaceNodeWithMarkup(t,e,n)},_renderValidatedComponentWithoutOwnerOrContext:function(){var t=this._instance;return t.render()},_renderValidatedComponent:function(){var t;if(this._compositeType!==y.StatelessFunctional){l.current=this;try{t=this._renderValidatedComponentWithoutOwnerOrContext()}finally{l.current=null}}else t=this._renderValidatedComponentWithoutOwnerOrContext();return null===t||!1===t||c.isValidElement(t)||a(\\\"109\\\",this.getName()||\\\"ReactCompositeComponent\\\"),t},attachRef:function(t,e){var n=this.getPublicInstance();null==n&&a(\\\"110\\\");var r=e.getPublicInstance();(n.refs===v?n.refs={}:n.refs)[t]=r},detachRef:function(t){delete this.getPublicInstance().refs[t]},getName:function(){var t=this._currentElement.type,e=this._instance&&this._instance.constructor;return t.displayName||e&&e.displayName||t.name||e&&e.name||null},getPublicInstance:function(){var t=this._instance;return this._compositeType===y.StatelessFunctional?null:t},_instantiateReactComponent:null};t.exports=b},function(t,e,n){\\\"use strict\\\";var r=n(4),i=n(372),o=n(167),a=n(24),u=n(12),c=n(385),s=n(401),l=n(171),f=n(408);n(2);i.inject();var p={findDOMNode:s,render:o.render,unmountComponentAtNode:o.unmountComponentAtNode,version:c,unstable_batchedUpdates:u.batchedUpdates,unstable_renderSubtreeIntoContainer:f};\\\"undefined\\\"!=typeof __REACT_DEVTOOLS_GLOBAL_HOOK__&&\\\"function\\\"==typeof __REACT_DEVTOOLS_GLOBAL_HOOK__.inject&&__REACT_DEVTOOLS_GLOBAL_HOOK__.inject({ComponentTree:{getClosestInstanceFromNode:r.getClosestInstanceFromNode,getNodeFromInstance:function(t){return t._renderedComponent&&(t=l(t)),t?r.getNodeFromInstance(t):null}},Mount:o,Reconciler:a});t.exports=p},function(t,e,n){\\\"use strict\\\";function r(t){if(t){var e=t._currentElement._owner||null;if(e){var n=e.getName();if(n)return\\\" This DOM node was rendered by `\\\"+n+\\\"`.\\\"}}return\\\"\\\"}function i(t,e){e&&($[t._tag]&&(null!=e.children||null!=e.dangerouslySetInnerHTML)&&g(\\\"137\\\",t._tag,t._currentElement._owner?\\\" Check the render method of 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e=j(t);switch(e||g(\\\"64\\\"),t._tag){case\\\"iframe\\\":case\\\"object\\\":t._wrapperState.listeners=[M.trapBubbledEvent(\\\"topLoad\\\",\\\"load\\\",e)];break;case\\\"video\\\":case\\\"audio\\\":t._wrapperState.listeners=[];for(var n in Y)Y.hasOwnProperty(n)&&t._wrapperState.listeners.push(M.trapBubbledEvent(n,Y[n],e));break;case\\\"source\\\":t._wrapperState.listeners=[M.trapBubbledEvent(\\\"topError\\\",\\\"error\\\",e)];break;case\\\"img\\\":t._wrapperState.listeners=[M.trapBubbledEvent(\\\"topError\\\",\\\"error\\\",e),M.trapBubbledEvent(\\\"topLoad\\\",\\\"load\\\",e)];break;case\\\"form\\\":t._wrapperState.listeners=[M.trapBubbledEvent(\\\"topReset\\\",\\\"reset\\\",e),M.trapBubbledEvent(\\\"topSubmit\\\",\\\"submit\\\",e)];break;case\\\"input\\\":case\\\"select\\\":case\\\"textarea\\\":t._wrapperState.listeners=[M.trapBubbledEvent(\\\"topInvalid\\\",\\\"invalid\\\",e)]}}function p(){P.postUpdateWrapper(this)}function h(t){Z.call(Q,t)||(X.test(t)||g(\\\"65\\\",t),Q[t]=!0)}function d(t,e){return t.indexOf(\\\"-\\\")>=0||null!=e.is}function v(t){var e=t.type;h(e),this._currentElement=t,this._tag=e.toLowerCase(),this._namespaceURI=null,this._renderedChildren=null,this._previousStyle=null,this._previousStyleCopy=null,this._hostNode=null,this._hostParent=null,this._rootNodeID=0,this._domID=0,this._hostContainerInfo=null,this._wrapperState=null,this._topLevelWrapper=null,this._flags=0}var g=n(1),m=n(3),y=n(346),_=n(348),b=n(20),x=n(83),w=n(21),C=n(160),k=n(22),E=n(84),M=n(53),T=n(161),S=n(4),N=n(365),A=n(366),P=n(162),O=n(369),I=(n(9),n(378)),D=n(383),R=(n(11),n(56)),L=(n(0),n(95),n(81),n(173)),U=(n(97),n(2),T),F=k.deleteListener,j=S.getNodeFromInstance,B=M.listenTo,V=E.registrationNameModules,W={string:!0,number:!0},z=\\\"__html\\\",H={children:null,dangerouslySetInnerHTML:null,suppressContentEditableWarning:null},q=11,Y={topAbort:\\\"abort\\\",topCanPlay:\\\"canplay\\\",topCanPlayThrough:\\\"canplaythrough\\\",topDurationChange:\\\"durationchange\\\",topEmptied:\\\"emptied\\\",topEncrypted:\\\"encrypted\\\",topEnded:\\\"ended\\\",topError:\\\"error\\\",topLoadedData:\\\"loadeddata\\\",topLoadedMetadata:\\\"loadedmetadata\\\",topLoadStart:\\\"loadstart\\\",topPause:\\\"pause\\\",topPlay:\\\"play\\\",topPlaying:\\\"playing\\\",topProgress:\\\"progress\\\",topRateChange:\\\"ratechange\\\",topSeeked:\\\"seeked\\\",topSeeking:\\\"seeking\\\",topStalled:\\\"stalled\\\",topSuspend:\\\"suspend\\\",topTimeUpdate:\\\"timeupdate\\\",topVolumeChange:\\\"volumechange\\\",topWaiting:\\\"waiting\\\"},K={area:!0,base:!0,br:!0,col:!0,embed:!0,hr:!0,img:!0,input:!0,keygen:!0,link:!0,meta:!0,param:!0,source:!0,track:!0,wbr:!0},G={listing:!0,pre:!0,textarea:!0},$=m({menuitem:!0},K),X=/^[a-zA-Z][a-zA-Z:_\\\\.\\\\-\\\\d]*$/,Q={},Z={}.hasOwnProperty,J=1;v.displayName=\\\"ReactDOMComponent\\\",v.Mixin={mountComponent:function(t,e,n,r){this._rootNodeID=J++,this._domID=n._idCounter++,this._hostParent=e,this._hostContainerInfo=n;var o=this._currentElement.props;switch(this._tag){case\\\"audio\\\":case\\\"form\\\":case\\\"iframe\\\":case\\\"img\\\":case\\\"link\\\":case\\\"object\\\":case\\\"source\\\":case\\\"video\\\":this._wrapperState={listeners:null},t.getReactMountReady().enqueue(f,this);break;case\\\"input\\\":N.mountWrapper(this,o,e),o=N.getHostProps(this,o),t.getReactMountReady().enqueue(l,this),t.getReactMountReady().enqueue(f,this);break;case\\\"option\\\":A.mountWrapper(this,o,e),o=A.getHostProps(this,o);break;case\\\"select\\\":P.mountWrapper(this,o,e),o=P.getHostProps(this,o),t.getReactMountReady().enqueue(f,this);break;case\\\"textarea\\\":O.mountWrapper(this,o,e),o=O.getHostProps(this,o),t.getReactMountReady().enqueue(l,this),t.getReactMountReady().enqueue(f,this)}i(this,o);var a,p;null!=e?(a=e._namespaceURI,p=e._tag):n._tag&&(a=n._namespaceURI,p=n._tag),(null==a||a===x.svg&&\\\"foreignobject\\\"===p)&&(a=x.html),a===x.html&&(\\\"svg\\\"===this._tag?a=x.svg:\\\"math\\\"===this._tag&&(a=x.mathml)),this._namespaceURI=a;var h;if(t.useCreateElement){var d,v=n._ownerDocument;if(a===x.html)if(\\\"script\\\"===this._tag){var g=v.createElement(\\\"div\\\"),m=this._currentElement.type;g.innerHTML=\\\"<\\\"+m+\\\"></\\\"+m+\\\">\\\",d=g.removeChild(g.firstChild)}else d=o.is?v.createElement(this._currentElement.type,o.is):v.createElement(this._currentElement.type);else d=v.createElementNS(a,this._currentElement.type);S.precacheNode(this,d),this._flags|=U.hasCachedChildNodes,this._hostParent||C.setAttributeForRoot(d),this._updateDOMProperties(null,o,t);var _=b(d);this._createInitialChildren(t,o,r,_),h=_}else{var w=this._createOpenTagMarkupAndPutListeners(t,o),k=this._createContentMarkup(t,o,r);h=!k&&K[this._tag]?w+\\\"/>\\\":w+\\\">\\\"+k+\\\"</\\\"+this._currentElement.type+\\\">\\\"}switch(this._tag){case\\\"input\\\":t.getReactMountReady().enqueue(u,this),o.autoFocus&&t.getReactMountReady().enqueue(y.focusDOMComponent,this);break;case\\\"textarea\\\":t.getReactMountReady().enqueue(c,this),o.autoFocus&&t.getReactMountReady().enqueue(y.focusDOMComponent,this);break;case\\\"select\\\":case\\\"button\\\":o.autoFocus&&t.getReactMountReady().enqueue(y.focusDOMComponent,this);break;case\\\"option\\\":t.getReactMountReady().enqueue(s,this)}return h},_createOpenTagMarkupAndPutListeners:function(t,e){var n=\\\"<\\\"+this._currentElement.type;for(var r in e)if(e.hasOwnProperty(r)){var i=e[r];if(null!=i)if(V.hasOwnProperty(r))i&&o(this,r,i,t);else{\\\"style\\\"===r&&(i&&(i=this._previousStyleCopy=m({},e.style)),i=_.createMarkupForStyles(i,this));var a=null;null!=this._tag&&d(this._tag,e)?H.hasOwnProperty(r)||(a=C.createMarkupForCustomAttribute(r,i)):a=C.createMarkupForProperty(r,i),a&&(n+=\\\" \\\"+a)}}return t.renderToStaticMarkup?n:(this._hostParent||(n+=\\\" \\\"+C.createMarkupForRoot()),n+=\\\" \\\"+C.createMarkupForID(this._domID))},_createContentMarkup:function(t,e,n){var r=\\\"\\\",i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&(r=i.__html);else{var o=W[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)r=R(o);else if(null!=a){var u=this.mountChildren(a,t,n);r=u.join(\\\"\\\")}}return G[this._tag]&&\\\"\\\\n\\\"===r.charAt(0)?\\\"\\\\n\\\"+r:r},_createInitialChildren:function(t,e,n,r){var i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&b.queueHTML(r,i.__html);else{var o=W[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)\\\"\\\"!==o&&b.queueText(r,o);else if(null!=a)for(var u=this.mountChildren(a,t,n),c=0;c<u.length;c++)b.queueChild(r,u[c])}},receiveComponent:function(t,e,n){var r=this._currentElement;this._currentElement=t,this.updateComponent(e,r,t,n)},updateComponent:function(t,e,n,r){var o=e.props,a=this._currentElement.props;switch(this._tag){case\\\"input\\\":o=N.getHostProps(this,o),a=N.getHostProps(this,a);break;case\\\"option\\\":o=A.getHostProps(this,o),a=A.getHostProps(this,a);break;case\\\"select\\\":o=P.getHostProps(this,o),a=P.getHostProps(this,a);break;case\\\"textarea\\\":o=O.getHostProps(this,o),a=O.getHostProps(this,a)}switch(i(this,a),this._updateDOMProperties(o,a,t),this._updateDOMChildren(o,a,t,r),this._tag){case\\\"input\\\":N.updateWrapper(this),L.updateValueIfChanged(this);break;case\\\"textarea\\\":O.updateWrapper(this);break;case\\\"select\\\":t.getReactMountReady().enqueue(p,this)}},_updateDOMProperties:function(t,e,n){var r,i,a;for(r in t)if(!e.hasOwnProperty(r)&&t.hasOwnProperty(r)&&null!=t[r])if(\\\"style\\\"===r){var u=this._previousStyleCopy;for(i in u)u.hasOwnProperty(i)&&(a=a||{},a[i]=\\\"\\\");this._previousStyleCopy=null}else V.hasOwnProperty(r)?t[r]&&F(this,r):d(this._tag,t)?H.hasOwnProperty(r)||C.deleteValueForAttribute(j(this),r):(w.properties[r]||w.isCustomAttribute(r))&&C.deleteValueForProperty(j(this),r);for(r in e){var c=e[r],s=\\\"style\\\"===r?this._previousStyleCopy:null!=t?t[r]:void 0;if(e.hasOwnProperty(r)&&c!==s&&(null!=c||null!=s))if(\\\"style\\\"===r)if(c?c=this._previousStyleCopy=m({},c):this._previousStyleCopy=null,s){for(i in s)!s.hasOwnProperty(i)||c&&c.hasOwnProperty(i)||(a=a||{},a[i]=\\\"\\\");for(i in c)c.hasOwnProperty(i)&&s[i]!==c[i]&&(a=a||{},a[i]=c[i])}else a=c;else if(V.hasOwnProperty(r))c?o(this,r,c,n):s&&F(this,r);else if(d(this._tag,e))H.hasOwnProperty(r)||C.setValueForAttribute(j(this),r,c);else if(w.properties[r]||w.isCustomAttribute(r)){var l=j(this);null!=c?C.setValueForProperty(l,r,c):C.deleteValueForProperty(l,r)}}a&&_.setValueForStyles(j(this),a,this)},_updateDOMChildren:function(t,e,n,r){var i=W[typeof t.children]?t.children:null,o=W[typeof e.children]?e.children:null,a=t.dangerouslySetInnerHTML&&t.dangerouslySetInnerHTML.__html,u=e.dangerouslySetInnerHTML&&e.dangerouslySetInnerHTML.__html,c=null!=i?null:t.children,s=null!=o?null:e.children,l=null!=i||null!=a,f=null!=o||null!=u;null!=c&&null==s?this.updateChildren(null,n,r):l&&!f&&this.updateTextContent(\\\"\\\"),null!=o?i!==o&&this.updateTextContent(\\\"\\\"+o):null!=u?a!==u&&this.updateMarkup(\\\"\\\"+u):null!=s&&this.updateChildren(s,n,r)},getHostNode:function(){return j(this)},unmountComponent:function(t){switch(this._tag){case\\\"audio\\\":case\\\"form\\\":case\\\"iframe\\\":case\\\"img\\\":case\\\"link\\\":case\\\"object\\\":case\\\"source\\\":case\\\"video\\\":var e=this._wrapperState.listeners;if(e)for(var n=0;n<e.length;n++)e[n].remove();break;case\\\"input\\\":case\\\"textarea\\\":L.stopTracking(this);break;case\\\"html\\\":case\\\"head\\\":case\\\"body\\\":g(\\\"66\\\",this._tag)}this.unmountChildren(t),S.uncacheNode(this),k.deleteAllListeners(this),this._rootNodeID=0,this._domID=0,this._wrapperState=null},getPublicInstance:function(){return j(this)}},m(v.prototype,v.Mixin,I.Mixin),t.exports=v},function(t,e,n){\\\"use strict\\\";function r(t,e){var n={_topLevelWrapper:t,_idCounter:1,_ownerDocument:e?e.nodeType===i?e:e.ownerDocument:null,_node:e,_tag:e?e.nodeName.toLowerCase():null,_namespaceURI:e?e.namespaceURI:null};return n}var i=(n(97),9);t.exports=r},function(t,e,n){\\\"use strict\\\";var r=n(3),i=n(20),o=n(4),a=function(t){this._currentElement=null,this._hostNode=null,this._hostParent=null,this._hostContainerInfo=null,this._domID=0};r(a.prototype,{mountComponent:function(t,e,n,r){var a=n._idCounter++;this._domID=a,this._hostParent=e,this._hostContainerInfo=n;var u=\\\" react-empty: \\\"+this._domID+\\\" \\\";if(t.useCreateElement){var c=n._ownerDocument,s=c.createComment(u);return o.precacheNode(this,s),i(s)}return t.renderToStaticMarkup?\\\"\\\":\\\"\\\\x3c!--\\\"+u+\\\"--\\\\x3e\\\"},receiveComponent:function(){},getHostNode:function(){return o.getNodeFromInstance(this)},unmountComponent:function(){o.uncacheNode(this)}}),t.exports=a},function(t,e,n){\\\"use strict\\\";var r={useCreateElement:!0,useFiber:!1};t.exports=r},function(t,e,n){\\\"use strict\\\";var r=n(82),i=n(4),o={dangerouslyProcessChildrenUpdates:function(t,e){var n=i.getNodeFromInstance(t);r.processUpdates(n,e)}};t.exports=o},function(t,e,n){\\\"use strict\\\";function r(){this._rootNodeID&&p.updateWrapper(this)}function i(t){return\\\"checkbox\\\"===t.type||\\\"radio\\\"===t.type?null!=t.checked:null!=t.value}function o(t){var e=this._currentElement.props,n=s.executeOnChange(e,t);f.asap(r,this);var i=e.name;if(\\\"radio\\\"===e.type&&null!=i){for(var o=l.getNodeFromInstance(this),u=o;u.parentNode;)u=u.parentNode;for(var c=u.querySelectorAll(\\\"input[name=\\\"+JSON.stringify(\\\"\\\"+i)+'][type=\\\"radio\\\"]'),p=0;p<c.length;p++){var h=c[p];if(h!==o&&h.form===o.form){var d=l.getInstanceFromNode(h);d||a(\\\"90\\\"),f.asap(r,d)}}}return n}var a=n(1),u=n(3),c=n(160),s=n(86),l=n(4),f=n(12),p=(n(0),n(2),{getHostProps:function(t,e){var n=s.getValue(e),r=s.getChecked(e);return u({type:void 0,step:void 0,min:void 0,max:void 0},e,{defaultChecked:void 0,defaultValue:void 0,value:null!=n?n:t._wrapperState.initialValue,checked:null!=r?r:t._wrapperState.initialChecked,onChange:t._wrapperState.onChange})},mountWrapper:function(t,e){var n=e.defaultValue;t._wrapperState={initialChecked:null!=e.checked?e.checked:e.defaultChecked,initialValue:null!=e.value?e.value:n,listeners:null,onChange:o.bind(t),controlled:i(e)}},updateWrapper:function(t){var e=t._currentElement.props,n=e.checked;null!=n&&c.setValueForProperty(l.getNodeFromInstance(t),\\\"checked\\\",n||!1);var r=l.getNodeFromInstance(t),i=s.getValue(e);if(null!=i)if(0===i&&\\\"\\\"===r.value)r.value=\\\"0\\\";else if(\\\"number\\\"===e.type){var o=parseFloat(r.value,10)||0;(i!=o||i==o&&r.value!=i)&&(r.value=\\\"\\\"+i)}else r.value!==\\\"\\\"+i&&(r.value=\\\"\\\"+i);else null==e.value&&null!=e.defaultValue&&r.defaultValue!==\\\"\\\"+e.defaultValue&&(r.defaultValue=\\\"\\\"+e.defaultValue),null==e.checked&&null!=e.defaultChecked&&(r.defaultChecked=!!e.defaultChecked)},postMountWrapper:function(t){var e=t._currentElement.props,n=l.getNodeFromInstance(t);switch(e.type){case\\\"submit\\\":case\\\"reset\\\":break;case\\\"color\\\":case\\\"date\\\":case\\\"datetime\\\":case\\\"datetime-local\\\":case\\\"month\\\":case\\\"time\\\":case\\\"week\\\":n.value=\\\"\\\",n.value=n.defaultValue;break;default:n.value=n.value}var r=n.name;\\\"\\\"!==r&&(n.name=\\\"\\\"),n.defaultChecked=!n.defaultChecked,n.defaultChecked=!n.defaultChecked,\\\"\\\"!==r&&(n.name=r)}});t.exports=p},function(t,e,n){\\\"use strict\\\";function r(t){var e=\\\"\\\";return o.Children.forEach(t,function(t){null!=t&&(\\\"string\\\"==typeof t||\\\"number\\\"==typeof t?e+=t:c||(c=!0))}),e}var i=n(3),o=n(26),a=n(4),u=n(162),c=(n(2),!1),s={mountWrapper:function(t,e,n){var i=null;if(null!=n){var o=n;\\\"optgroup\\\"===o._tag&&(o=o._hostParent),null!=o&&\\\"select\\\"===o._tag&&(i=u.getSelectValueContext(o))}var a=null;if(null!=i){var c;if(c=null!=e.value?e.value+\\\"\\\":r(e.children),a=!1,Array.isArray(i)){for(var s=0;s<i.length;s++)if(\\\"\\\"+i[s]===c){a=!0;break}}else a=\\\"\\\"+i===c}t._wrapperState={selected:a}},postMountWrapper:function(t){var e=t._currentElement.props;if(null!=e.value){a.getNodeFromInstance(t).setAttribute(\\\"value\\\",e.value)}},getHostProps:function(t,e){var n=i({selected:void 0,children:void 0},e);null!=t._wrapperState.selected&&(n.selected=t._wrapperState.selected);var o=r(e.children);return o&&(n.children=o),n}};t.exports=s},function(t,e,n){\\\"use strict\\\";function r(t,e,n,r){return t===n&&e===r}function i(t){var e=document.selection,n=e.createRange(),r=n.text.length,i=n.duplicate();i.moveToElementText(t),i.setEndPoint(\\\"EndToStart\\\",n);var o=i.text.length;return{start:o,end:o+r}}function o(t){var e=window.getSelection&&window.getSelection();if(!e||0===e.rangeCount)return null;var n=e.anchorNode,i=e.anchorOffset,o=e.focusNode,a=e.focusOffset,u=e.getRangeAt(0);try{u.startContainer.nodeType,u.endContainer.nodeType}catch(t){return null}var c=r(e.anchorNode,e.anchorOffset,e.focusNode,e.focusOffset),s=c?0:u.toString().length,l=u.cloneRange();l.selectNodeContents(t),l.setEnd(u.startContainer,u.startOffset);var f=r(l.startContainer,l.startOffset,l.endContainer,l.endOffset),p=f?0:l.toString().length,h=p+s,d=document.createRange();d.setStart(n,i),d.setEnd(o,a);var v=d.collapsed;return{start:v?h:p,end:v?p:h}}function a(t,e){var n,r,i=document.selection.createRange().duplicate();void 0===e.end?(n=e.start,r=n):e.start>e.end?(n=e.end,r=e.start):(n=e.start,r=e.end),i.moveToElementText(t),i.moveStart(\\\"character\\\",n),i.setEndPoint(\\\"EndToStart\\\",i),i.moveEnd(\\\"character\\\",r-n),i.select()}function u(t,e){if(window.getSelection){var n=window.getSelection(),r=t[l()].length,i=Math.min(e.start,r),o=void 0===e.end?i:Math.min(e.end,r);if(!n.extend&&i>o){var a=o;o=i,i=a}var u=s(t,i),c=s(t,o);if(u&&c){var f=document.createRange();f.setStart(u.node,u.offset),n.removeAllRanges(),i>o?(n.addRange(f),n.extend(c.node,c.offset)):(f.setEnd(c.node,c.offset),n.addRange(f))}}}var c=n(6),s=n(405),l=n(172),f=c.canUseDOM&&\\\"selection\\\"in document&&!(\\\"getSelection\\\"in window),p={getOffsets:f?i:o,setOffsets:f?a:u};t.exports=p},function(t,e,n){\\\"use strict\\\";var r=n(1),i=n(3),o=n(82),a=n(20),u=n(4),c=n(56),s=(n(0),n(97),function(t){this._currentElement=t,this._stringText=\\\"\\\"+t,this._hostNode=null,this._hostParent=null,this._domID=0,this._mountIndex=0,this._closingComment=null,this._commentNodes=null});i(s.prototype,{mountComponent:function(t,e,n,r){var i=n._idCounter++,o=\\\" react-text: \\\"+i+\\\" \\\";if(this._domID=i,this._hostParent=e,t.useCreateElement){var s=n._ownerDocument,l=s.createComment(o),f=s.createComment(\\\" /react-text \\\"),p=a(s.createDocumentFragment());return a.queueChild(p,a(l)),this._stringText&&a.queueChild(p,a(s.createTextNode(this._stringText))),a.queueChild(p,a(f)),u.precacheNode(this,l),this._closingComment=f,p}var h=c(this._stringText);return t.renderToStaticMarkup?h:\\\"\\\\x3c!--\\\"+o+\\\"--\\\\x3e\\\"+h+\\\"\\\\x3c!-- /react-text --\\\\x3e\\\"},receiveComponent:function(t,e){if(t!==this._currentElement){this._currentElement=t;var n=\\\"\\\"+t;if(n!==this._stringText){this._stringText=n;var r=this.getHostNode();o.replaceDelimitedText(r[0],r[1],n)}}},getHostNode:function(){var t=this._commentNodes;if(t)return t;if(!this._closingComment)for(var e=u.getNodeFromInstance(this),n=e.nextSibling;;){if(null==n&&r(\\\"67\\\",this._domID),8===n.nodeType&&\\\" /react-text \\\"===n.nodeValue){this._closingComment=n;break}n=n.nextSibling}return t=[this._hostNode,this._closingComment],this._commentNodes=t,t},unmountComponent:function(){this._closingComment=null,this._commentNodes=null,u.uncacheNode(this)}}),t.exports=s},function(t,e,n){\\\"use strict\\\";function r(){this._rootNodeID&&l.updateWrapper(this)}function i(t){var e=this._currentElement.props,n=u.executeOnChange(e,t);return s.asap(r,this),n}var o=n(1),a=n(3),u=n(86),c=n(4),s=n(12),l=(n(0),n(2),{getHostProps:function(t,e){return null!=e.dangerouslySetInnerHTML&&o(\\\"91\\\"),a({},e,{value:void 0,defaultValue:void 0,children:\\\"\\\"+t._wrapperState.initialValue,onChange:t._wrapperState.onChange})},mountWrapper:function(t,e){var n=u.getValue(e),r=n;if(null==n){var a=e.defaultValue,c=e.children;null!=c&&(null!=a&&o(\\\"92\\\"),Array.isArray(c)&&(c.length<=1||o(\\\"93\\\"),c=c[0]),a=\\\"\\\"+c),null==a&&(a=\\\"\\\"),r=a}t._wrapperState={initialValue:\\\"\\\"+r,listeners:null,onChange:i.bind(t)}},updateWrapper:function(t){var e=t._currentElement.props,n=c.getNodeFromInstance(t),r=u.getValue(e);if(null!=r){var i=\\\"\\\"+r;i!==n.value&&(n.value=i),null==e.defaultValue&&(n.defaultValue=i)}null!=e.defaultValue&&(n.defaultValue=e.defaultValue)},postMountWrapper:function(t){var e=c.getNodeFromInstance(t),n=e.textContent;n===t._wrapperState.initialValue&&(e.value=n)}});t.exports=l},function(t,e,n){\\\"use strict\\\";function r(t,e){\\\"_hostNode\\\"in t||c(\\\"33\\\"),\\\"_hostNode\\\"in e||c(\\\"33\\\");for(var n=0,r=t;r;r=r._hostParent)n++;for(var i=0,o=e;o;o=o._hostParent)i++;for(;n-i>0;)t=t._hostParent,n--;for(;i-n>0;)e=e._hostParent,i--;for(var a=n;a--;){if(t===e)return t;t=t._hostParent,e=e._hostParent}return null}function i(t,e){\\\"_hostNode\\\"in t||c(\\\"35\\\"),\\\"_hostNode\\\"in e||c(\\\"35\\\");for(;e;){if(e===t)return!0;e=e._hostParent}return!1}function o(t){return\\\"_hostNode\\\"in t||c(\\\"36\\\"),t._hostParent}function a(t,e,n){for(var r=[];t;)r.push(t),t=t._hostParent;var i;for(i=r.length;i-- >0;)e(r[i],\\\"captured\\\",n);for(i=0;i<r.length;i++)e(r[i],\\\"bubbled\\\",n)}function u(t,e,n,i,o){for(var a=t&&e?r(t,e):null,u=[];t&&t!==a;)u.push(t),t=t._hostParent;for(var c=[];e&&e!==a;)c.push(e),e=e._hostParent;var s;for(s=0;s<u.length;s++)n(u[s],\\\"bubbled\\\",i);for(s=c.length;s-- >0;)n(c[s],\\\"captured\\\",o)}var c=n(1);n(0);t.exports={isAncestor:i,getLowestCommonAncestor:r,getParentInstance:o,traverseTwoPhase:a,traverseEnterLeave:u}},function(t,e,n){\\\"use strict\\\";function r(){this.reinitializeTransaction()}var i=n(3),o=n(12),a=n(55),u=n(11),c={initialize:u,close:function(){p.isBatchingUpdates=!1}},s={initialize:u,close:o.flushBatchedUpdates.bind(o)},l=[s,c];i(r.prototype,a,{getTransactionWrappers:function(){return l}});var f=new r,p={isBatchingUpdates:!1,batchedUpdates:function(t,e,n,r,i,o){var a=p.isBatchingUpdates;return p.isBatchingUpdates=!0,a?t(e,n,r,i,o):f.perform(t,null,e,n,r,i,o)}};t.exports=p},function(t,e,n){\\\"use strict\\\";function r(){C||(C=!0,y.EventEmitter.injectReactEventListener(m),y.EventPluginHub.injectEventPluginOrder(u),y.EventPluginUtils.injectComponentTree(p),y.EventPluginUtils.injectTreeTraversal(d),y.EventPluginHub.injectEventPluginsByName({SimpleEventPlugin:w,EnterLeaveEventPlugin:c,ChangeEventPlugin:a,SelectEventPlugin:x,BeforeInputEventPlugin:o}),y.HostComponent.injectGenericComponentClass(f),y.HostComponent.injectTextComponentClass(v),y.DOMProperty.injectDOMPropertyConfig(i),y.DOMProperty.injectDOMPropertyConfig(s),y.DOMProperty.injectDOMPropertyConfig(b),y.EmptyComponent.injectEmptyComponentFactory(function(t){return new h(t)}),y.Updates.injectReconcileTransaction(_),y.Updates.injectBatchingStrategy(g),y.Component.injectEnvironment(l))}var i=n(345),o=n(347),a=n(349),u=n(351),c=n(352),s=n(355),l=n(357),f=n(360),p=n(4),h=n(362),d=n(370),v=n(368),g=n(371),m=n(375),y=n(376),_=n(381),b=n(386),x=n(387),w=n(388),C=!1;t.exports={inject:r}},function(t,e,n){\\\"use strict\\\";var r=\\\"function\\\"==typeof Symbol&&Symbol.for&&Symbol.for(\\\"react.element\\\")||60103;t.exports=r},function(t,e,n){\\\"use strict\\\";function r(t){i.enqueueEvents(t),i.processEventQueue(!1)}var i=n(22),o={handleTopLevel:function(t,e,n,o){r(i.extractEvents(t,e,n,o))}};t.exports=o},function(t,e,n){\\\"use strict\\\";function r(t){for(;t._hostParent;)t=t._hostParent;var e=f.getNodeFromInstance(t),n=e.parentNode;return f.getClosestInstanceFromNode(n)}function i(t,e){this.topLevelType=t,this.nativeEvent=e,this.ancestors=[]}function o(t){var e=h(t.nativeEvent),n=f.getClosestInstanceFromNode(e),i=n;do{t.ancestors.push(i),i=i&&r(i)}while(i);for(var o=0;o<t.ancestors.length;o++)n=t.ancestors[o],v._handleTopLevel(t.topLevelType,n,t.nativeEvent,h(t.nativeEvent))}function a(t){t(d(window))}var 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r=n(21),i=n(22),o=n(52),a=n(87),u=n(163),c=n(53),s=n(165),l=n(12),f={Component:a.injection,DOMProperty:r.injection,EmptyComponent:u.injection,EventPluginHub:i.injection,EventPluginUtils:o.injection,EventEmitter:c.injection,HostComponent:s.injection,Updates:l.injection};t.exports=f},function(t,e,n){\\\"use strict\\\";var r=n(399),i=/\\\\/?>/,o=/^<\\\\!\\\\-\\\\-/,a={CHECKSUM_ATTR_NAME:\\\"data-react-checksum\\\",addChecksumToMarkup:function(t){var e=r(t);return o.test(t)?t:t.replace(i,\\\" \\\"+a.CHECKSUM_ATTR_NAME+'=\\\"'+e+'\\\"$&')},canReuseMarkup:function(t,e){var n=e.getAttribute(a.CHECKSUM_ATTR_NAME);return n=n&&parseInt(n,10),r(t)===n}};t.exports=a},function(t,e,n){\\\"use strict\\\";function r(t,e,n){return{type:\\\"INSERT_MARKUP\\\",content:t,fromIndex:null,fromNode:null,toIndex:n,afterNode:e}}function i(t,e,n){return{type:\\\"MOVE_EXISTING\\\",content:null,fromIndex:t._mountIndex,fromNode:p.getHostNode(t),toIndex:n,afterNode:e}}function 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\"cell_type\": \"code\",\n   \"execution_count\": 106,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1440x216 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"explainer = joblib.load(filename=\\\"explainer.bz2\\\")\\n\",\n    \"shap_values = explainer.shap_values(np.array(test_x.iloc[0]))\\n\",\n    \"shap.force_plot(explainer.expected_value[1], shap_values[1], list(test_x.columns), matplotlib = True, show = False).savefig('static/images/shap.png', bbox_inches=\\\"tight\\\")    \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Gauge Chart \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 107,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from matplotlib.patches import Circle, Wedge, Rectangle\\n\",\n    \"\\n\",\n    \"def degree_range(n): \\n\",\n    \"    start = np.linspace(0,180,n+1, endpoint=True)[0:-1]\\n\",\n    \"    end = np.linspace(0,180,n+1, endpoint=True)[1::]\\n\",\n    \"    mid_points = start + ((end-start)/2.)\\n\",\n    \"    return np.c_[start, end], mid_points\\n\",\n    \"\\n\",\n    \"def rot_text(ang): \\n\",\n    \"    rotation = np.degrees(np.radians(ang) * np.pi / np.pi - np.radians(90))\\n\",\n    \"    return rotation\\n\",\n    \"\\n\",\n    \"def gauge(labels=['LOW','MEDIUM','HIGH','EXTREME'], \\\\\\n\",\n    \"          colors=['#007A00','#0063BF','#FFCC00','#ED1C24'], Probability=1, fname=False): \\n\",\n    \"    \\n\",\n    \"    N = len(labels)\\n\",\n    \"    colors = colors[::-1]\\n\",\n    \"\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    begins the plotting\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    \\n\",\n    \"    fig, ax = plt.subplots()\\n\",\n    \"\\n\",\n    \"    ang_range, mid_points = degree_range(4)\\n\",\n    \"\\n\",\n    \"    labels = labels[::-1]\\n\",\n    \"    \\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    plots the sectors and the arcs\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    patches = []\\n\",\n    \"    for ang, c in zip(ang_range, colors): \\n\",\n    \"        # sectors\\n\",\n    \"        patches.append(Wedge((0.,0.), .4, *ang, facecolor='w', lw=2))\\n\",\n    \"        # arcs\\n\",\n    \"        patches.append(Wedge((0.,0.), .4, *ang, width=0.10, facecolor=c, lw=2, alpha=0.5))\\n\",\n    \"    \\n\",\n    \"    [ax.add_patch(p) for p in patches]\\n\",\n    \"\\n\",\n    \"    \\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    set the labels (e.g. 'LOW','MEDIUM',...)\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"\\n\",\n    \"    for mid, lab in zip(mid_points, labels): \\n\",\n    \"\\n\",\n    \"        ax.text(0.35 * np.cos(np.radians(mid)), 0.35 * np.sin(np.radians(mid)), lab, \\\\\\n\",\n    \"            horizontalalignment='center', verticalalignment='center', fontsize=14, \\\\\\n\",\n    \"            fontweight='bold', rotation = rot_text(mid))\\n\",\n    \"\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    set the bottom banner and the title\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    r = Rectangle((-0.4,-0.1),0.8,0.1, facecolor='w', lw=2)\\n\",\n    \"    ax.add_patch(r)\\n\",\n    \"    \\n\",\n    \"    ax.text(0, -0.05, 'Churn Probability ' + np.round(Probability,2).astype(str), horizontalalignment='center', \\\\\\n\",\n    \"         verticalalignment='center', fontsize=22, fontweight='bold')\\n\",\n    \"\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    plots the arrow now\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    \\n\",\n    \"    pos = (1-Probability)*180\\n\",\n    \"    ax.arrow(0, 0, 0.225 * np.cos(np.radians(pos)), 0.225 * np.sin(np.radians(pos)), \\\\\\n\",\n    \"                 width=0.04, head_width=0.09, head_length=0.1, fc='k', ec='k')\\n\",\n    \"    \\n\",\n    \"    ax.add_patch(Circle((0, 0), radius=0.02, facecolor='k'))\\n\",\n    \"    ax.add_patch(Circle((0, 0), radius=0.01, facecolor='w', zorder=11))\\n\",\n    \"\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    removes frame and ticks, and makes axis equal and tight\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    \\n\",\n    \"    ax.set_frame_on(False)\\n\",\n    \"    ax.axes.set_xticks([])\\n\",\n    \"    ax.axes.set_yticks([])\\n\",\n    \"    ax.axis('equal')\\n\",\n    \"    plt.tight_layout()\\n\",\n    \"    if fname:\\n\",\n    \"        fig.savefig(fname, dpi=200)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 109,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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d59rYv8dJvvkV73WZsziSsdhe9Tft47Y8/oHXv64DpNQE07nwegEWnXwHAke3PAqCoVrRgAEdSOhklS/G7R+gLJz5MfO+ep/8AwKorv8Q1P/gLlWdfO3sLXsD8vzvMvcKPXVYGgNcXorQwkTNOywLgnHU5ZKU76Oh289Kb7aiKzFUXFuH1hfjxr9/m8RebKStM5F/+sYYvf7qGJRUpPPzkEX75uz14fVEsezAC4K07tjflb16Boe0Gvo/wpmIS4UHFJqcDDw2MB/MOdXto7PPS1OclWtG8sb4O4tNykCSJ3U/9DoD8mtMpX385aYWVaKEgh958lLqX7mf/podJza+gcMW57HvxPjoObGe0t42k7GLSi2roa66jedcmilZuRLFYCXjG0ENBFNWK1ZkAQG7lWsrWXEz24tUAWB2u2OpmMI9p7RijpWOM5VVplBcnousG9jjzNnHnA/sB+MxHzS2cJ19qZtwT5KoLislKd3DXgweobxikMM/Fn27bSEpSHABfuKGKL938Bi++2c5Lb7Zz+XlFALy1t5d4p4WK0uTZXeSkNzUMoX47cRU/QYm/BjMlvW12jRF8EMKDii0k4JaQpr9ysMuTtyOcBHGkNzri1Fr7Oi/+7zd46hc3TaaCV280Ex5sjgQyy5ahWKxY7U5qPvIJUnLLGOpqpHn3JmwOF0UrzgGYTJaoOPNqAOpeup+jO5+n6e2X2P7X2+hvPUBO5RqSs0sm5z7to18hr2YDgBCnWSTBZeXaS0r5/CeqAAgEzczQbbt7aG4foyAnnvWrTE/q6U1muO+jF5fQ2e3mhdfNe/tNH6skJSmOYLjfo9WicMGZeVhUma27etA0nWBI53s/385V//Qc2/f0zPYyw97UvvDe1HYItK7E0OuAj82+MYL3Q3hQsUMq8He3XzurvtNNY6+Xwz2eWRcm7+gAh7c8yeFtzxDwjhHnTCK3Yg22+CQAyjdcRv2mB2l86wWWXfxZrA4XuhZCVlSKVn6EwY4jdB16m0UbLqd41fk0bHmS5t2bqDn/BvJqNlB62kUc3fkcO/72X5NzZpYtp+rcj6FYrLO7WMF7SEqw8ZNvrZn8f6vFzLi8489m+PWTVy8C4IXX2zjcPEJFaRKnLctgx55eDhwZIjfLyXln5AEwMurHYlFIdFm5/rIySgsTURQJRZHZsaub1s5xFhUnsaI6DTC7VMiyxNCIn+RE2+wsONQd9qbGwTacQNziB5FtFwBfAGKzDcsCQghUbPAR4MHOYX96Q7eHg10eukdnN3Vc1zQOvPoX9r7wJwCSsksoXXMROZVrSC+smjwo0O5KIa9qPe37t9K8exOLTr8SwzCflHOr1lG/6SEG2xvwDPeRkldORslSehv30r5vM0UrN7Ls4s+SU7mGvuZ6JFmmcNnZJOeUzupaBSePLEuENJ2cDCeqKnPNRaaX+8RLzQBce4n5szvUaHYKWVqRSnKijUBQ49Vtndz8ix2cvTaHL36ymlVLjmVdPvZ8EwCXnFuA1aIQDOlYVJlDjcNcedOzXLqxkP/87jpUZRaCPIYfvHvMkJ8+LhFXeSNq8jrgSsQpvlFFhPiiiwT8PKjpz+/vdKfvahljR9PorIlTKODjyLanObLtaWRFQbFYkZBIzVvEuuu/wfJLbiSjuAZJlhkf6GKkxwzplG+4HIDDW58CzIQHXdOIT8kiKasILRRgtK8dgJJwwsPB1x8BwOZMIK96Pcsv+RzLL75RiNMcQFVk/t931rH7meuId1poahvlpTfNn+8l55q1UFaLjCRJkz37DAPSkuNYvzKTN3d28Q//50X+6TuvAtDWOc7zr7fhdFi4+qJiACyqeSt6Jhw2tFrk2RGnSQyzbsq90wz7+ZuqMPQ9wGc+9FsFM4YQqOiRCWwb84W+vatlXNnTOsaulrFZO3a9rW4zj/zgWnY+ejvNuzbhGx8mp3ItCZkF+MaHMHTTju7Du9n91F28fu8P2ffifQBkla/AlZrDaF/7ZHcIOVx8a3OYiQ5Wu5lOnL/kDOzxydjiEwn6PJPzi32luYdFlTEMg+L8BH7/n+fwo2+cNpkIsWpJOoZhsPmtbkbGAtisCuesz+WeX2xkWZUZwjt9tbl39cRLzfj8Gqoi8cBjh9m2u4dQuLj372HPaiKUOOtoQ+DZCZ494N3rRPf/EbgfsETHoIWNCPFFh9OBx9qH/GkN3R4OdLnpG5v5Q0GDPg+WOAcA3pEBZEVF1zXcw330HNlD4fJzyF68moOvP8Le5/+Eaouj+/Bugj43VruLsrUXEwr6US02Fp1+JW8/8RuObn+WrPIV+MaHadnzKu31W0jMLCQpuwgA1RrHpd/+/eS8grnNxIPFGadlT35N1w3KihL5ymeX8Ot79vGP39zEJ64sp6IsiYbGEXbV9WG3qXz88jKCIX0yPKgoEnc+sJ/7HjvMWWuycTkt9A14OX1VFtWLUqKxPBMjYBb3asOgucFe+QnUlJWYNYkiy28WEQI1+3xe0/VfN/R4bUd6vOzvdOMNzpzX5PeMUffifTRseYLC5eew4rJ/wu5KISV/EaGgnzhnEt6RfjoPvUX+kjPJqTiNlj2v0tVgHmmRUbyERadfQf6SM94xbslpF7Drid/Suu8N4p/7I31N9fQ112Gx2Vly/iexORMnPyvEaX4z0avvmotK6O338tCTR/i3X+54x2euurCYOJvKs6+20tI+xtrlmdz7q428srWDux8+yLOvtk6K31UXFs/6Gt6LAYGW8L6UG+wVFVgLdgPXAK9H27qFghCo2UMC7vAFtS/s7/RIR3rNZIiZLroN+T2TPfC6Du4kMbOQ6o0fJyW3HJszEVd6LorFSu/RWgY7DpOSt4iM4iW01L5K9caPv6Prw+SYQT+qNY6S0y7g6M7n2f/Kw8iySulpF1Fx9kdJSM+b2UUJYpKsdAe3fP00/vG6Ch57oYnDTSMkxFsIaQafudasnZpIjjj/TPPfyLnrczl3fS4vvN7GV2558x0tk2ICbcQ8wdfwg+ZOJW7Ri0jK14E7om3aQkAI1OzgAp4e9YbOrOtwc6jbQ8uAb1YmdiZnkphZyGhv22RSRFJWEblV60jMyCfgHad49fnUvXQ/nQd2kFZQQfbiVbTte4Ph7mZ848PEhVPMxwe7aa/bgmekn2UXf5aydZfSeWAH1efdQMlp56OoIk1cAEV5Lr5241IMwyAY1LFazf3JukODvLa9k9wsJ5dsLATAH9CwWZXJWqjLzyua/HzMYPjBsxt0D+geK/aa/0W2rQA+DzF17Nq8QwjUzLMYeL5nJFC4v8tNfYeb/vGZ3286nhWX3sSrd/8biVklhPxeap+9h5T8RcS5UuhrrmfF5V/AnpBK58EdlK69mMzSZSTnltHfvJ/exr04k7NorX2NzkM7Ge01Q/CFy88mNX8xV/3bA7O6FsHcQZKkd4hNc/soAGuWZZCSaEPXDWxWhb5BL39/rgmrReHKC4qiZO2HoYPvgBnuM/xgr7kJJaESuAQYjbZ18xUhUDPLZbph3N/U50to6PFQ1zGO2z/7RxFklq9AQiLoHaNg2TkceuNRjm57FgwDSVbwjQ6SX3M6DVueoPvwLkpPu5DcyrXse/HP1D57D37PGEGfmzhnEkvO+yRl6y+d9KoEgpPlso8UcdbaHPwBszuFFi7MfWZTK15fiIvPKaAwN0rNZE+WQKuZOKH7wF51OpasfcDFwP5omzYfEWnmM8eXgpr+aH2HO2Ff+zi7WsaiIk4AsqxQc94NuIf7sMQ5KFt3CYc2P4Z7uBdZVvF7RsmuOA0Jia6DOwkF/WSWr0C12hkf7CY5p5QzPnUzV//7g9Scf4MQJ8GUSYi3kp5iB8y0dV03uO9R8+ypK88viqJlp4A2AJ63zLCf/2gBhr4VODvaZs1HhAc1M/zIH9Jv3tfulhq63Rzu8UY9UF2+4XL2vXQfPUf2cPonv0/zrpcZbGvAwEALBUnOLiatqIr2+i30Ht1LVvkK1nz0qyTnluFKy4my9bOPKkvYVAmrKmNV5ePeS1gUGUkyn+4kSUKSzAwYmyX8vCcngmM15vZE+DImXgPmpfvf+57YOBF5NpFlibt/cS4vv9nOOetzo23OyaN7wP22+bPTfQnEVTyHpHwK+Fu0TZtPCIGKLBLwO29Au2lvu5sDnW6aZykZ4sOwORPIKF5Cz9Fagj43Ky7/AvuevxfP6ABD7YepOuc6shevJuAZw+ZIQFZUCpadFW2zZwRZAqdNwWlVsKoStrAIvfu9VZWxKu8UJ5sqY1GkdwiT+Wr+v02VQMoHaybvFSj9OFEKmHsZ7xCpoPk1Pfy1iT/X/aCPg+5lPu7J52fH84/XVUTbjCkQMuuljCAYoTjiqh5EtmQC/xtty+YLkmHMv3/wUUIFHh33hS6rbR+nvsNN53BsHcU+2H6Y5//nKyw+42pWXv55dj1xJ4c2P4bdlcIV3/sThq6jqPOrYH5CjFxxCq44lXibQnycYr7aFGyWsCApE+JkipEiR6HThRF6H+8qLFBa+NLHQBsLi5aH+ShacwsJbOUQtwjsSwxk238A/xZtq+YDQqAigwN4acgdXL+vw82+9vFZ6QxxqhiGwV9vvgotFODqf3sQSZLoPLiT3Kr1WO3OaJs3bWSJSQFyxam4JoQoTiHepoZFSsFplVFmtc9bhDBCYWEaO06o3McEa+JrQrSig7UQ7FVgXwqy/W7gJsQPYlqIEN/0SQNe6xsLVO1rd7O3bZxhb2x26ZckiRWXfZ6jO55FCwZwJmdQvOq8aJs1ZRQJkp0WUuMtx4nRMSGKtyk4bTKKPAfF6ERIKqjJwHEH/Bmhd3lVx4vWKIQGzGJTcZ+ceQItZrhPD4Fj2Y0o8RnA1YhjO6aM8KCmRx6wuWPIX1DfaYrTuD+2N7oNw5jTjVptqkRqvClKqU7LpEAl2hUc1nkkRtNhQrS0EQj1mw1QQ4Pm+9Ag4n45w6hpYF9ielJq8mbM43T80TZrLiIEauoUAptbB3y59Z1uatvG8c1gT72FjCtOITUsRClOCynxKqlOCylOdZaPZJij6AEzNXpCoEKDpmcVGgDDG23r5idKkilQjmWgpm4HzgFiI2NqDiEEamqUAa+3DPiy6zvG2dM2TiAk/h4jhSxBkkMlLewpJTksk+8T4pTJ5qSCKWBoYY8qLFChiff9ZlhQhAIjh+wCx/KwSKW9hSlS7ihbNacQAnXqnAu81D8WkLc3jVLbOk5gts9ln4fIEqTFW8hIsJLitJDsPCZQjljrzTZfMAxTlCYEKjRkelrBXlPEBNNnQqTsS8GS7gEKgIFomzVXEEkSp0YR8FTnaKec5kgn3ioLcZomDqtMdqKNrCQrGS4rmQlWUp3q5AmrghlEkkBJMC9bsdm+J9QPwW4I9UGwy3xvxFa5xJxCHzP/DhUXhpLqkGT5LaASEe47KYQHdfLkA9uah5pzjgwcIdmezJLMpbzZMMbrh0eibducQpYg3WUlJ8lKustKVqKV7EQrduEpxQ7aaFigesJXV9irEveLU8JaAPZqjLhlaO29yK545JSUHcBZiMSJD0V4UCdHDrClZbglZ3/vfvZ07aEgqQCAMxYtBRAidRLEqTK5yTayk2xkJFjISTTDeWJPKQaZ8KyspRDqNQUq1AeBDvO9yAT8cI4Tp9C+BoLbd2CproLqyjVycvKrmCIVewWTMYQQqA8nCXizbbgtr76nnj1dewhoAY4MHJn8wFwXKb9nDC0UxJEwM8dsJ9pV8pJtZCVayUmykZtsJc4ivKU5gayCNce8tDEItEOoGwKdEOwIFwUL3sO7xWnTqwCE9h8wQ6tVlevk5KTngPMQbun7otxyyy3RtiGWsQFbusa6Kvb17JsUpwkGvYPoho5u6JxWWICERMvg3PPa/3brV3juDz8mJauQjIJFERlTliAzwUpFtoPKHCfVOU4qsh2kuawiNXyuItvAkg6WXFCTzKJhOT7ci06kq0/yPuIEQCCIMTKKJMvISYnFks22GHgkWqbGOkKg3h8ZeKnf3b92X/c+dnfuxqe9d19zPohUx+FaWup3cPjtV2k98BYly07HGje11kcSkJVgpTrXSVVYmEoz7CTYVRHKmy9IiilOllxQU0FJBMVlJlkYC3zv/4PEaYJZ/+WjAAAgAElEQVRAAGN8HGQZOSW5RrJYkoDnZ9vUuYAQqPfn4RHfyCW13bXs6dqDO/j+5QtzXaRG+7to3LuFkN/LcG8nbz93P4npOWQULj6lrhOp8Raqc5xU5jhZmhdPYVocdqsypztXCD4ASQYlHizZpmAp8SA7zbDfQsz8OxlxmsDnx/B4za74qSnrJFX1Aptny9S5ghCoE/M/7oD7s3u69rCnaw8jvg/fW5rLIuX3jLN/8zNowQCGoaOFAhzZ/Tot9dspXXbGhzaSTbQrVOU4qcx2siQvnpJ0O3EWEcZbMEgSKE6w5JgdFBQXSDaz3dJCSaY4FXGawOPBCATMo1pSUzZKitIM1M6wpXMKIVDv5Xu+kO+7tV211HbVMuA5+Zq6uSpSsqKw7cl70LVjNxNdCzHS18lbz91PQmoWmUUV7/GEnDaZiiwHVTlOanLjWZRpx2ETyQ8LFkkyxcmScywLULKEO1TEdo/KaTEVcQpjjI9j6DoSyFJq6qXhOqkjH/qNCwRRB/VOPhcIBX63p2uPvLd7L+2j7VMapCy1jMr0yjlTJ2XoOj+5ruIdAnU8FpudvEUruPrrv8SVkkmcKlOUHkd+so3C1Dhykmxif0nwXvQgBFsh0Ar+Vgi0Me88qmmI0/EoZaUoi8pQFpW7JVk+F9gZUTvnKMKDOsbZmq49tK97n7q/dz8tIy1THmiueVKSJLHrxYfwe8ZP+Oe6FmKk39ybKi3M5eJz1lITToJIclrEHpPgxEgKqClhjyre9KgMwzxocT5kVkdInACMoWFQFSRFscopyVcB9wEn/oVcQAiBMskHNh3sO+g60HeAwwOHpz3gXBOphh0vM9z7AR6jYaCFghx4+w0Ovf061115MUlJibNnoGDuIqnmERRqprlXpSSEz02aw31TIyhOExiDQ0hWC8TFOWWX60Lg98CCPiJBCJRZ67S5dbi14EDvAep66zAi9HQ3l0Sq62gdHYc/fH82FAzS2dnBb3/7W9LT01m5cqXwoAQnh2wFS4Z5yXFmXZU2zJzbn5oBcQLAMNCHh5GtFqTEhAwpLq4a+EtkBp+bCIGCxwc8Axv2de+jtquWkB7ZGPlcEamRvg6a9m59332o49F1nWAwyKZNm3j22Wc577zzSEwU3pTgJJFtYMkyU9JlB+j+ueNNzZQ4TaBpGGNjSKqKnJpSKVksAK9FdpK5w0IXqJ+6A+4b93TtobarFk9wZtq2zAWR8rlHObD1ObTgydevBINBOjs7ufPOO0lOTmb16tXCmxKcHJIU7kaRYQrWXPCmZlqcJggEMPx+JEVGSk09U1KUvcDBmZkstlnIxSofC2iBf63rqWN/735G/aMzOtmRgSMc6DvAvp69nLHIxVnlseVxpGQVnZT39G40TcPtdvPtb3+bDRs20NzcHHnjBPMXJR4cK8GxCpxrzX2qWGS2xCmM0dtHqLEZ7chRxTCMPwFVMzphjLJQBWqZrut3H+g9IDf0N9Az3jMrk8aySOXk5qGFpl7973a72blzJ9XV1dx+++3o+oLe2xWcCpIMtiJToJyrwV4DkjXaVh1jlsVpAr25Ba25Fb2l1QU8CyTMysQxxEIUKCfw1NHBo46jg0dpHGyc1cljTaQkoCg1jvXlyWRkTO/pVdM0PB4P3/nOd1i3bh2NjbP7dyuY4yjxpiflWAXONbHhTUVJnCbQDh9Ga2pG7+srAJ7A/JVdMCzEPahHe8d7V9f31LO3ey+6MftP+rGyJxVvU1iaH09FjpNlefE8/dQTtLa2TnvcYDBId3c3d911F06nkzVr1oi9KcHJIUlmXz813cz6i+beVJTFCQDdQB8ZRbJYkVNTiiSLxWABJU0sNA/qa96g99KDfQep762PeMbeqRBtTyo/xcbakgRWF7lYmhePzSJTU1MTsfF1Xcfj8XDzzTezZs0ajhwR3VsEp4Dieqc3paTO7vyxIE4TeL2EjhxFaziCoWnfB06PnjGzy0LyoJZruvbQvp59al1P3Sn12JspouFJyRJUZDuoyolneYGLJIdl8s+am5vZtGkToVDkhDsYDNLT08Ndd92FzWZj7dq1yPJCey4STIlJbyrNLPY1gqDNQtuwWBKnCTweUGQki1WRU5Ivwizija1U4Blgodwp4oBHm4aabE1DTXSMdkTbnklm05OyqRIrClwsyYtneYHzPR3Hy8rKsNlsEZ9X13W8Xi+33HILq1at4tChQxGfQzCPURJMT8q+DOKqmNHbViyKUxitsRmttRW9rz+HBVLAu1A8qAf73f0b6nrqorbv9EHMhieVEKewvMBFTa6TskwH8gn2hCRJ4o477iAYDEZ07gmCwSC9vb384Q9/QFVV1q9fL7wpwckhqWDJNF9lG4QGifi+VAyLE2B2mhgdRbJakdNSyySLZd6fIbUQupl/3hfy3bmzfSc723fOeL3TdJipLuiZCdbwkRhOUuMt7/u5UChEXFwcmjbzG9JOp5OSkhIefvhhKisrZ3w+wTwi0Areg+CtAz1Cv8+xLk7HIWdmoNZUo9RUBSRFOYN53Pl8vj++LtZ1/bb9vftp6G+IaXGCyIf7JKA03c7KQheri1wfKE4AqqqSlpY2rTlPFrfbTV1dHatWreInP/lJRPe9BPMcawE4V5k1U2pWZMabI+IEoPf0orW2obe0WoFHAEe0bZop5rNAScBfWkZaHM1DzbSNtEXbnpMiUiKlyhJL8pwszY9nZUE8zpM8SLCkpGRK800FwzDwer387Gc/Y9myZdTX18/a3II5jppqCpRzBdhKmXJ50BwTpwm0xia0llb0wcF84M5o2zNTzOc9qB+O+ceu39e9j9ruWjQjhnt8vYvp7knZLTLLC+KpzHFSkeVAVU7+OWTbtm3s2rVrKmZPmWAwSH9/P/fccw+hUIgzzjhD7E0JPhzJYhbzSrL5PjTAKZ1OMUfFCQDDwHC7kWw25PT0GklRdjAPT+Kdr3eBKk3X/vVg30Ea+hsIaFNv4RMtpupJpThVVhcnsKLARXmm45RPul2yZMmMZPJ9GBPe1M9//nNqamrYu3fvrNsgmIPIFrAvAftScK40u6OfDHNZnMIYY+PhUF+LDNzNPAz1zUcPSgJebh5qzjnYd5CmoaZo2zNlTtWTykmysiw/nuX5LtJcU+tlNjAwwOOPP47fH50Si2AwyMDAAPfeey8+n48zzzwTRTm58KRggSJJ5sm9SrzZw08bAcP3/p+fB+I0gTE2hmSPQ0pIcEkOezHw92jbFEnmowf1ozH/2JLGwUYa+huibcu0OVlPKi/ZxtK8eFYWukh0qFOer6ysbFay+D4Mr9fLr371K6qrq9mzZ0+0zRHMBSzZ4FhhXkryiT8zj8QJAF1HO9qI1tiEEQh+HLgo2iZFkvkmUNWarn17Lof2TsSHiVRBii1cfOvCbp2et1FUVITX653WGJHC4/Fw+PBhNmzYwHe/+10Cgfnx8xTMIGoyOJaHRepd7ZHmmziFmQz1NTfPu1DffBIoCXioZbjF1jzUTK+7N9r2RJT3E6mitDhq8uJZnv/ezhBTwWq1kpKSMu1xIonX6+XXv/41lZWVs57AIZiDKIlhkVpuNp2FeStOE+ht7WjtHegDg9nAXdG2J1LMpz2oH436Rq+r66ljX8++OZW1d7K8e08q0a6S7rKyPD8emyVy+zR//etf6ezsjNh4kSAYDDI0NMSf//xnEhMTWbNmTbRNEsQyss1MRQeQ4yCufN6KE2Bm9Y27keJsyBnpNZKibGceZPVNfbMitsjXdf1bh/oPzavQ3ok4MnDs39zS/KXIknJKaeQnQ3V1NTt3xl5xuqIoSJKEy+WKtimCuYASb4b6/E4MW+X8FacwxrgZ6pOTkiSlvPQ3QCmnlHcfe8yXEN9dnWOd9vaR9nkX2jsRmq5hYJjJSxEWJ4Camhqs1hg60RSzNdKGDRuoq6vjM5/5TLTNEcwVFCeGfTla1yCh2vlfuqC3taN3dqKPjhUBP4y2PdNlPgjUpf6Q/4LGwUYODxyOti0zTmFSIVUZVSzNXIpF+eDWRVOlvLycuLi4GRn7VLFarSQmJnLnnXfy2muvUVxcHG2TBHMMSZJQkpNQKyuQkt8nu2++oOuEmlvQm5owdP0bQF60TZoOc12gVOB/GwcbpebhZjxBT7TtmVHyE/OpzqhmWdYyrOrMeThlZWXEQhNhh8PBtddeS2NjIzfccIM4lVcwZSSnA7WqErWqAilpdg8HnW2MgUG0rm6Mnl4H8Lto2zMd5voe1C0jvpHC1uFWWoZaom3LjJKbkEtNZg3LsmdWnMDsx+fxRE/sHQ4HycnJ3H///Zx99tlRs0Mwv5DinahVFaaXsf8AxkhsN4+eDlpTC3JyMlJqykWS1XoR8Fy0bZoKc9mDytN1/esN/Q0cGTwyL7P2JsiKzzLFKWsZNnXm2xDFxcWRlJQ04/O8G0mSsNvtfOUrX+HIkSNCnAQRR3K5UKorUSsrkeKd0TbnpOgcG2XE9wGdMU6E14vW1o7e0iYBdzBH7/Vz0ugwd3WOdTraR9rpc/dF25YZI9GWSFVGFcuylhFnmb19ocLCwlmb63hefvllfvazn8XMHphg/iEnJKBULEJdvBgsM7OPO100XWd7exvX/fVBLnvgz/zmrR2nPIbe0TmRMFEM3BJxI2eBuSpQF/lD/gubhprmdWKETbFRk1lDdWY1DuvsFodXVVXN6nxgJkT87ndzOmQumCPIKcnIpcUoFYsghjrnD/t83Pn2Ttb8/rf889NPcHRwEJuq8vzRKdznNC2cMNGMoevfBLIjbvAMEzs/mZNHAn7VPNQsNQ/N38QIWZKpyaphcfpiUh2pH/4NEWbJkiWo6uxuUfr9fh5++GE2b57Xp1gLYgQlLxe1sAClJLqZoYZhcKCvjy88+Rgb7/0Dd4a9pSsWV/L4xz/J51asYsTn46mGQ6c+9sAgWlcXRl+/A/ivCJs+48zFJIlPewKeyvbRdlqG529iREV6BWUpZRQkFURl/vLychwOB6Ojs7uR7PV6ueGGG2hoaIi5WizB/EMuLUHx+TA8HvTOrlmd2zAM/rq/ntu2bcYfPlG6LCUV3TDoHh/j+uoa8hMTWZ2TS7Ldzv379nDZosWnPI/e0oaWloaUlnqNpCgVwMEIL2XGmGselAz8uGmoieahZkL6/DwmvDCpkLKUMhalLYqaDdFMNe/r6+OnP/1pVOYWLCwkRUFZvAiltGTW088lSeKphoP4QyFOy8nlj1d9lL9c93FuOWcjnmCQJxtMHVmelc3p+YUc6u/nYP+p77cbbjd6Vzd6d48K/HeElzGjzDWB+saobzS/faSdjtGOaNsyI6Q50qhIr6A6sxpFjt45SCUlJVHrau7xePj5z39OQ8PcPy5FEPtINls4aWIR2O2zOvc/rzZ7StZkZLI0M2vyfYYznq1tbTQODQKwPj8fgAf2Ta0bhtbWjt7egREMng+sm77ls8NcEqg44F8bhxppHm5GN+Z0i6kT4rQ6qc6spiazZlbSyT/QFqeT+Pj4qM3v9/v55Cc/GRMFw4L5j+xyoZSXoVYsAnX2HgzX5xeQGR/PS01HORoWI4CP1yyhdWSYV5oaAdiQV8Bnlq1gUWrq1H4nfD7Ti+rskphDXtRcEqgfDnoG0zpHO+ke6462LRHHIluoyayhKqMKly02mqFGK9UcQNd19u/fzz333BM1GwQLCzkjHaW4CKW8fFbnvb56CW0jI2xvb5v82jWVZhZtyDAI6TqJcXH806rVXF+9hK7xsSnNo7W1o3d0Yfj9a4BLI2H7TDNXBCpZN/R/bhxqpHGwEYP59VQtIVGdWc3itMVkxmdG25xJKioqojq/2+3ma1/7Gn1987fOTRBbyAX5KEUFyEWz93B2bWU1AJuaGhnz+wFIsMXx8qdv5AurTkMNp8E7LFb+XLubTzzyl6lNFAyidXait3cA3IqZER3TzBWB+nm/u9/VOdpJv6c/2rZEnLLUMkpTSilOjq1GqMuWLZv1VPN34/f7+eIXvxhVGwQLB0mWUcrKUIuLkNLTZ2VOl83GeSWlHB0a5EA4CULTdZLtdvSwBwXwVmcHTx0+xKjfT8vI8JTm0js60bu6MLzeSuAfI7SEGWMuCFSqrus3NA010TjYGG1bIk5mfCblaeVUplfGXDPUiVTzk8HhcMzInlUgEOC5557jxRdfjPjYAsGJkCyqmTSxqAycs1Mg/+U167i2spolGWYERQl7TbIkocoy44EAd+zcTsvwMJeULyLNPkW7NA2tvRO9rR3gZmLci5oLAnVLv6ff3j3WzbBvak8NsYpNsbEobRFV6VWoSuyVpJWVlX3oZyb65339618nJydnRuzweDx86lOfimoDW8HCQrLbUUqKUEtLYRYeHAsTkyhNSeX+fbVsaWtlf18vve5xNF2nZ3ycn73xGnu6uzivpJSbzzoX5zRqBPXuHvSeXgyvtwT4WORWEXli7674ThzAp1uHW+dlUe6itEWUppQSb4tettwHUVpa+oGi4HQ6qaio4L777qOiooKrr76aM888c0bS00dHR/n+97/PbbfdFvGxBYITIaenI+flIA8NTXgcM8pYwM8dO7djU1X8oRAFiUkEdY0VWTlsbmvBoigsTktHliSGfT6SptqvUtfRurqRO7pQykq+BzwU0YVEECnG03h/MuAZ+P7W1q281fFWtG2JKFnxWazOW83K7JXIMdQL7N0kJia+p5uExWLBZrPxq1/9is997nPvsP/LX/4yd999N75T7b58EtjtdrZt28bSpUsjPrZAcCKMQIDQnlqCu2vBPbMevG4Y/Pj1V+gcG6N7fAzdMOgcG5tMK5ckiQynE4usYFMVKtMy+NG5H5naZKqCZdVK1NUrkWy2i4DnI7eSyBHLAmUBOnd37k7b2rp1Xh3lblNsrM1fy5r8NcRbY9N7mmDJkiXU1dVN/r/D4eD888/nzjvvJDPzvRmH4+PjFBcX098/M8ks1dXV1NbWoijRK2IWLCz0vn5CtfsI7auDWbhfeoIB4lQLr7c086utb9I+Osp5JaX0eTyEdI1D/f04rVZGfD5+fO55XDqF9kcAcmEBlpUrUIoLtwHrI7uKyBDLIb6vjvhG0rrHuufdcRqL0xdTklIS8+IEZqp5XV0ddrsdl8vFH//4Ry6++OL3/Xx8fDz33HMPH/vYx2Zkz6i5uZnbb7+dr371qxEfWyA4EXJ62qyG+hwWc3+pcWiQPo+H3IQEblq5mvKUVEK6Tq/bjYHZYHZRWtqU59E7u9Bzc5DzctZKFstaYHuElhAxYjW2JAFfaxluoXWkNWJ1T7HgLWbFZ1GQVEBBYnSawJ4qy5YtQ5IkbrrpJhobGz9QnCa47LLL2LhxI5YZOGvH7Xbzve99j/b2mb9RCAQTKCXFKIX5SM7ZOeTwqYZD3L5jG/5QiO+ecTaLUtOQJAlFlslNSCAvIZGipGS+9/KLHBkcmNokwaCZMNHdIwH/EdEFRIhYFaibxv3juV1jXfSM90x7MD1cRxDtNO6JrL2K9IqY3nc6nu985zscPXqUX//61zhP4ZfzrrvuwmabmXZNgUCAG2+8cUbGFghOhGS1ohQXo5SVzHhWX9fYGLdueQOLovDN9WewIb9g8uFalqTJ9/t6uznQ18tDdVPrzweYhbtd3Rih0LnA7B8C9yHE6l3ym60jrbSPtEek596R145Q92wdQW8QMAUrGt7U4vTFZtbeHAjtTaCqKsXFp15AnJWVxa233npKonayhEIhNm/ezN///veIjy0QvB9yehpybi5yXu6MzpPtcuENBlmcmsYVi81uLsc/XE8U7l5ctog1uXk82XCIoKZNbTKfH72nF723TwZ+PF3bI00sCtRGf8i/uGusi87RzogMePDlg9Q/U0/dM3UEvAFkWZ51byrblU1hUiH5ifmzOm80+fznP095efmM/F17PB5uuukmRkZGIj62QPB+KCVFsxLq+8/zLuTbG87AFY5CGMd1lLCEE4R0w0CSJIKaNnk0x1TQOjsxuroxdP0SIHnaxkcQ5ZZbbom2De/mt63DraUH+g4w4JlibPU4Qv4QilVhvH+cjroOWra3YHVYiU+LR7EoGOEf8kxiU2wszVrK8uzlUe9SPptIksRZZ53F3XffTSg0M2d3dXV1ccUVV8zI2ALBu5EUBclqM/dvemcueasoKYnMeBdBTcPA7Cwhh+9Tb3V2cOuWN/nBqy/TES4BaR8d5frqJVObLBgEVzxycrIqOew68HJkVjF9Yk2gcjRd+++DfQeV+p76iBxIKKsyqUWppBSkEPQEGWgZoPdQL4ZhkJSbhGozExkNfeaEqjqzmurMatKds9PbK5ZIT09nbGyM3bt3EwwGIzp2KBTi4MGDbNy4kfz8heOZCqKL5HRgBIIYPh/GDJ04PXEvmhAmbzDI3w7Uc/OmF7l/by0tw8OossxVFZVkuxLY1dXJurwCsqbabkzTkBMSkDPSy4BfRW4l0yPW6qD+p3us+1+2tG5hb/fUN/4mCPlDeEe8uDKOHV/Rvredgy8dpP9oP6nFqSy7ahkZZRnTnuv9SHemszZvLStzYrsgdybx+XyUlZXR0TEzh0wWFxdz6NChGckaFAhOhBEIENpdS/Dt3TADRekTHOjr45EDdTx68MDkvnlRUjLXVddwafkiEmxxvN3ZyReeeowV2TncdflVU5tIklBXLsdy2iokh+MTwIORW8XUiSUPSgbubehvcNT31uMNTr9dTt2zdRx44QAtb7Uw3DFM0BtEVmQcyQ4scRa693fTXttOyBfCmerE6ph6f6sTMXGMxpKsJTiss9N0MhZRVZWVK1fy17/+NeJeFJhZfQBnn312xMcWCE6ENFEo7vFiDA5+8IenwfV/e4g93V0AbCwu5TtnnMU31p/OkoxMbOGTBuJUhQP9feQlJHJmYdHUI0GqipzgQk5OzgbujtASpkUseVCfHveP3/tmy5tsb9s+7donLajxl68dOzdFVmR0TUe1qljiLBiGgX/cj2EYqDaVsjPKWHHNiumu4R1ku7JZl7+OZdnLIjruXOUTn/gEjzzyyKSgRBK73c7evXtPqsGtQBAJDE0jtLuW0Nu7MdzuGZnjiUMHaRjo59PLlpPhPBa+C2gaEscSJgAGvR66x8epSp9iRMhqxXLaKtRVK3RJVcuApulZP31iqZPElzvHOuka64pIYW7IHyJ/eT49h3oI+oLkVOeQXpaOb8xHz8EeAt4AFrsF34iPoN/0rCKJLMkUJxdTklIS0XHnMrfffjvPPPPMjAiU3+/nU5/6FFu2bIl6vZtgYSApCkpeLnpfP9qBqWfRfRATaeZgnhGlGQYWWcYaFqagprGtvY1Xm5t4pbmJOFXlmRs+PbXJAgH0/n6MgUFZysz4v8A/R2AJ0yJWQnzlIS300wP9B6T9vfvRjCnm9B+HalVJKUxBkiSG2ocY7x8nMTuRxRsXU/GRCnJqcsgsz6RobREZZRmUnlGKokauv1t+Yj6VGZXkJeZFbMy5jt1uJy8vjxdeeCHioT7DMBgaGiIvL4/ly5dHdGyB4H1xOMDtRh8agvBpuJFG03U0XUdVFJRwiUzn2ChPNhzkN2/t4E9791Df14skSQz7vFSmZ1CYlDTFyTTkxETkzIxi4BcQ3ePLYyXE95vO0c4vbm7ZTH1v/bQHM3QDSTafog3DoGNvB7WP1zLaM0re0jxWXrcSZ8o76xgimW6uyirrC9azvmA9DsvC3Xs6EYZhcNZZZ7F161a0qRYXfgAJCQkcPXqUtGn0KBMITgW9f4Dg7j1o+6Z/7/owdnd18nJTI1vaWunzuHEHAlSmZ3BRWTmJNhu3bnmTirT0qSdLAOqKcLJEvPNTwH2Rs/7UiQUPSgJ+d2TgiGt/3/6IJEfUP1tP665WJEVClmUSsxPJqclBURXa9rRx5I0jyBYZV7oL1WpGOSMZFipKLqIqvYpsV3bExpwvSJLEueeey5133jkjtVGGYdDY2Mh1110X8bEFghNit4Pbgz4yAjNwFhrA5tYW/uON13igbi9vdXbgDgYJ6jqFiUncf811LM/KpiwllcMDA7zZ2sKlixaTYJvieVEWFTk5CTkpKRH4U0QXcorEgkBt9AV9Xz7Uf4iG/oZpD+Z3+3ntjtcYbBmkeUczzTua6azrZLBlkFAghCzLuAfdjHaNEvKHSMxOxBIXufRkm2KjJrOGJVlLUOVY2uKLHZKTk5Flma1bt0Y81KdpGk1NTaxbt46SErH/J5h5JEkCmw28XvSemTkW6GB/H3+q3U1WfDzXVFTxpdPW0ufxsK+nmysXV5IQF2fWS4WCvNrchMNiYU3u1LYXjEAAOTkZOSszV5Kk/wYiv2l8ksRCYc4Xetw99Iz3RCw5Ys0Na1hy2RIyyjOIc8XhGfTQtqeN1rdbGWgZwDAM3ENu9r+wn8GWyKaIFiUXUZBUsKA6RkyFb33rWzN+RPxMnOwrEJwIOSkROTMDOWNmivEvKC3nx+eex/9cfBk3rljFiuwcSpNTAHjwuGaxmc54LIrC3t5pNNn2+dCHhjBGR23ATdM0fVpE+xFfBS7sGe+hdzwyTx7OFCelG0oBqLm4ZrJY1zfmo+9oH4Otg+hBHc+wh9HuUbKrIxeGc1gc5CfmL6h+e1PFYrHwwAMPcPbZZ8+IkAwPD/Pv//7v3HrrrREfWyA4EUpBPkbfAHpfP+jTb3L9bo4/mNAXCrG3pxuARw7U8//bu+/4qKq0D+C/O30yyWTSQwohBJIAAUykhGIERAMYlSJtJRERG1ZeRc3qvi8Lgiy7LggqsBQpAoLKgkpxBamGIkLohIRICQTSy/TJzH3/GHJ3BhLS7k1mkufrJ5/P1HPvxDDPPec85zn+Hh4wWCzYmZMNi9WKLv4BsLEsVx6podiiYrCFxYBG8wyABXycf2O0dIAaV2mq9C7Rl6Dc1PSinyzLouxGGXIP58JUaYJZb4ZfpB869O6AgMrvqiQAACAASURBVKgABETZr270ZXqutBGf6eWRPpFor2kPqZgqGtRH7969kZaWhjVr1vC+RbzBYMDnn3+OtLQ0dO/eyBplhDQA4+kJUbsgiG4Fw3aDn0LXtfns2BFk3spHUkQHFOn1WHT0MABALpHgwZBQjIrt2ujgBNgTP2wlJRBZO8QzYnEYgBbZgK2ls/h+zinOGfrr1V/xR2nT14RlH8hG1i9ZqCysvOe50O6h6PpYV/h3FCa7Sy1Xo1/7fugT1gdiEW1HXl+VlZWIjIxEcXHTCwPfjWEYxMXFITMzs82WmSLNizUYUPX7SVhOnASq+M9SBYDMW/l49+efUGzQY2HyCLTz8sKmc2fAsizC1N4Y3KEjIjQaVJpMEDEMVLLGVcgRd4mBtHcviPz9PgbwZ34/RT3PoQWTJFQ21vZ5VlGW5ELBBVhsTZss1xZpcehfh1BlqkK34d0Q2TcSYT3CIFPKoC3Uojy/HLpiHQI7B/Je0ggAYgNiERcUB7VCzXvbrZlcLkdsbCy+//57Qcog6XQ6qNVq9O3bl/e2CbkbI5UCVVVgtTqw5fwXkq0wGTH7wD5cLi1Bv7D2eKlXH/h5eCApogOSIiLxQHA7WG027M69jGXHf8PVinIkhjV+ykGk0UAU4B8MYDF/n6IBx2+Jg97xQrmxXF6sL4beom9yY5f2XUKVuQo9n+yJuOFx6JjYER37dUTfSX2R9HISgmOCcfvSbZz6/hS3wy5fVDIVgryCEOQZxGu7bcWTTz6JpKQkSCT8jzjrdDqkp6cLVqiWkLuJQtpBHBwECNBrV8sV+KOsFKFqNf7y8CBuGM9iteL4zRtYdvwYpv+0Ax8f2o/DedewJvME9JbGJeGxJaVgS0rAWiydAbRIvbaWnIMaW6At4CU5gmVZ3Lp4CzKlDOEJ9quF6sW6jIhBUEwQWLAovlqMkqslMOvMUHg1co1ADcLUYQj1CqVhpCZYuXIloqOjBVkbZTKZ8Pzzz2PXrl28t03I3RiFAoy/P0QB/oKknU9N6AWJSIRgTy8U6LQ4desWfr1+DecLC3C9ohwihkH/8PYQMwx2517Gt+fPIa1nI+qM2mywlZSALS4BExw0FcDrvH+YOrTUN6rCxtoeLNYXo1DX9E2/zDozpAopGBHDlaR3vA0AwTHBCIgKgL5Mj8qCe+eoGksqkqKdVztalNtEISEhmDdvnmBbxB88eBBbt27lvW1CaiJqFwxRcLAgbU+M64HeIaFYe+okZu77BbMO7MX3WRdwvaIcpqoqjO/WHX9/dBiej38Q4d7e+PrsmUYfy1ZSai/jBDzK2wdogJYKUE9rTVp5qaEUxqqmZ2/JPeXwCfOBSWfCuZ3nYCi3py07VoewWW0wVtqPpQ7mb56ondoenGQS/ue12ppp06YhKipK0C3iKyv5uzghpDYibzVE/n5gvL0FaX9XTjYWHsnAyVv56OIfgHcHPIT0gfbtZor1eogYBjH+Aegf3h63tJU4kne9Ucdhy8rtP1VV0QCavbBoiwWoIn0RL1u6V2v/YHuIxCLkHMrByS0ncePMDa6nxNpYXNp3CSXXShDSLQRyFT+LaBkwCPUKRah3KC/ttXUikQjr16+HQsHf8KsjnU6Hd955R5C2CbmbqF0QRO2E6UU9HBEJAIgLDMJfkgZjdJdueDImFp39/JCRd41bI/VQ+w5QSCRYdyqzcQeyWmErKwdbXsEAaGSZ9MZriSw+BsDnOcU5youFF2Gy8lMBWOWngnewN0rzSlGQXYCCSwUo+qMIV45dQW5GLi5nXIZcJUevib3goeGngGugKhBdArugvaY9L+0RIDAwEOXl5Th58iTv81FVVVU4f/48Hn30UYSG0kUFEZhCAbaiAraCQoDnv+UAlQp7r/yBfG0lHo3qhKA7e0VJRSLsysmGv4cKvUPDEKBS4XxRIY7n38DTXeOgaEQiEiMRQ+TnC5GvrwrASl4/SB1aogeVZLAYfEsNpag08TvcEhIXggdGPoDwB8LBsixKrpbgdtZtFF4uhF+EH/qm9oVfhB9/x1OHIFRNX3R8mzVrFnx8fARp22Aw4E9/+pMgKe2EOGLEYnv5o6BGbiBYh4lxPVCs12PfH7ncYyNjuwIAfs+/iWK9HgqJBM/HP4hlKU9B08iRCVtpKWxlZWBZNh5As27P0BI9qD/f1t5+8GLhRRTpi3htWCQWQR2sRnhCOAKiAuDb3heRiZGI6BWBLo91gW+4L2/H8pB6oEtAF0T7RdMGeTyTSCSIj4/Ht99+K0ggMZlMEIvFeOihh3hvmxBHjFwOlJfDln+L97Zj/QOw7nQmKkwmPNS+A7cgt52XGlE+voj284dcIkGQpyd8lUqcLyxAkKdnHa3WwGqFyFsDUaC/hJHLswGc4veT1K4lelBDi/XFvM4/VWNZlith5N/RH1EDohAcEwxjhRHGCn5L6bTzaodgr2BKLRfIkCFD8MQTT0DWyFXw96PX6/HRRx8hNze37hcT0gSMQgHGzxeMrzAjAiNju+J8YQH2XvlvJZ4nY2Ixpms3eMnlyK+sxK6cbHz4y248u/U77MjOatRxbKWlYEtKAeBpfs68fpr72zXSYrVElhhKUGoobVJDFbcqUHLt3krk1RsVWu+UGbmccRm/ff0bsg9kN+l4jkSMCMGewZRaLrDPP/9csIQJk8mEtLQ0uMiGnaQVEwUKN8w3tlscADj9HestZmQVFWLJb0fx2s4f8Oc9/8HPuTkAgE3nzjbqOGxpKdjSMgAYAHseQbNo7gA1ocxYxpQaSpu8rfuvq37FT3/7Cfu/2I/rJ6+jylzFDbWxNpbbvv3GaXsFgU4PdWramTvw9/BHoGcglFIlb22Se/n5+WHx4sWCrI2y2WzIzMzEV1+16IahpA1gfH0g8vcDFPxvwRPhrcHh51/C+LjuKDcaceDqFcw9eAAv/rgNqzJPQCmRIqFdCFdxotJkQmUjtqZntTrYKirAGk0+AJqtblhzV5IYXGYoa3LviWVZxAyJwdkfz+LmuZu4ee4mPP080bFfR0T0joCnv32cNf9CPgpyCuAX4QefMP662LQwt/mkpqZi2bJlOHLkCO8lqnQ6HV577TWMGDECfn78Jc8Q4ogRiSDy94coMBC2a41bj1SX5SeO4/D1azh1+xZEDIPugUGI8fdHsV6PQ9euwlfpgYnde2Byz/hGz5mz5RVgKyrAKAKeBHCE309Qs+YOUPFlxjKUGcqa1AjDMOiY2BGRfSNRdqMMZ3ecRd6pPJz+8TTObD+D8IRwxAyKwdXfrgIAogdH83HuAACFRIFAz0D4ewhTFZ04YxgGa9euRffu3QXZN8poNOK1117Dxo0beW+bkGqioEB76SMBApRcIsHXZ0+j0mTCoA6ReDgiEt5yOY7eyMPBa1cR4OGBqQm98Ehk0xbBsxX2AIXAgCQeT/++mjOLr6vZap6RXZSNnJIcXhpkGAZKtRIRD0YgelA0FF4KFP1RhNLrpcg9nIuyG2WQKWXo/1x/Xo4HAMGewegS0AUBnsLsnEnu5evrC5vNhqNHjwqyRXxubi769++PyMhIXtsmpBojlYItr4CtqAgw87+DeqyfP0Z0jsaU+AchYhisOXUSO7KzkBzVGXMeeQwJ7UJQZbOCYRicK7iNUqMB/h4NGzpnrVaIA/whatfOB8A83j9EDZpzP6j0Ql3h3AN/HMCZ242vDVUXm9WGwsuFOLv9LApyChA7JBbxYxpRKLEWPYN74qEOD8FfRT2o5mSxWBAbGytY5l1ISAguX74sWFIGIdbrebAc/U2wYb4Sgx7rTmVizamTAIChHaMwJDIKuaUlyKsoR4XJhMulJbit1SLWPwAbxoxr8DEkD8ZD2rcPGIW8H5phmK85h/gGlxnKUGZs2vBeXURiEYKigxAUHQRdsQ5SD/52t5WIJPD18IWPUpiUUVK76i3iBw8eLNgW8TNnzsS8ec1yYUjaIJGPBiJfH8EC1Lasi1hz6iTEIhG3J9Tu3MsQMQwYhoGnTAa9xQKxSISLRYU4kX8TCe1CGnQMtqKyWeehmjNAJfAx/9QQKj9+s798lb7wVfrSjrktpG/fvnjmmWfw1Vdf8b5FvF6vx6JFi5CWloauXbvy2jYhAACVCoxaDSgUAM9/vwDQq10IZGIxfJRKJASHoGtAIKpsVugtFjAMg1O3b+Fo3nUopVIEe3ujWN/wffgc5qEe5v0D1KC5hvi6ma3mswf/OIhDVw+BhXuuPeka2BUDIwYiRN2wqw7Cn4qKCkRGRqKk5N41cE3FMAx69OiBEydO0AJsIgjr5VxYjh0XpLIEAOSWlqC9twZWmw1H8q4j8/YtnLqVj4tFhRAxDPqGhiMpogMeDAlBmLoRldYVCsgSe0OSEK8FoAaE/TJvrh7UU9XDe+4anBgw8FX6UvZeC1Or1VixYgVSU1Oh0+l4bZtlWeTk5GDp0qWYNm0ar20TAtjXRDE+PoBAAaqdpxe2XDiP4zfz8Hv+TZQaDAhVq/FUbBcMiohE96AgeEhlKNDpsOnsGfh7eOCRjlH1P4DRCFulFqzR6MkoFP0AZAjyQe5orgA1sNxYLvj8k5C8Fd7wUfrQvk8uYNSoURgwYAB++eUX3iue63Q6vPvuuxg1ahTataO1boRfjFoNkcYbVokYqGpasYKayMRiLDqaAZPVioTgdng4PhKJYeHo6OMLG8viQlEhfr12FUfyruNCUSE8ZbKGBSjcmYeqrASjUAxDKwlQXSvNlbxXL29O/h7+1HtyIatWrUJMTIxgW8S/8MIL+PHHH3lvm7RtjFgMRqMBo9GALeK/HqlYJEL6wIchEYmQGBYOb4UCFSYjfvkjFweuXsHp27dwpawUKpkM/h4euFFRgb1XcjG4Q8d6H4PVasFqdUBAQC/eP8BdmmOgXWFjbWFakxZas7YZDicMP5Uf/FRUbcBVhIaGYu7cuYJtEb9371788MMPvLdNiMhXA5FA28kAwOPRMUju1BnFBj02nDmFd3/+CX/Zuxs/XrqIK2WleLhDJJY8/iTe6NMPnjJ5gzczZHV6sPbh9VhBPoCD5ghQDxksBrHOrEOVjf+r3eagkqrgo/CBp6wRpeqJYF599VXBFtfq9XpMmTIFWq37XlQR18T4+EDkowEE3KZnw5lTeGvXdiw+dgQnb+UjLjAIyVGdAQChXmrEBQbh0ahO6Bcejsxb+bhaXv/pF1answcplg0HwH+BQQfNEqAqTZWoNLvv8J6fyg9+HtR7cjVisRjr16+HUilM0V6tVosZM2YI0jZpuxipFIy3Nxi1l2DHaOfphbyKCoSp1Zg75FH8JWkQZg1+BN4KBX67mYcbFRUAgAHhEQCAjWdO179xqxWsXg8YDBLYq5sLpjkCVC+tWev2808UoFxTjx49MG3aNEGClNFoxJo1a3D8+HHe2yZtG+OjsWfzCWRwZEf4KJWwsSxi/QMQpvaGWCTC6NiuyC4uRkbeNQBAYlg4ugUGYlvWBdgasOSI1dp7UQAErcvXHAEqttLkvgkSMrEMPkofaBSalj4VUovZs2fD27sRazrqoXqLeCGSMUjbJfL1EXQeCgBGxXbFH6WlOJL338oVE+J6AAD25F6GwWJBoEqFlM6xSI7qjDJj/Su0sDqdPVECEDRRQugAJbextnCt2X0TJDQKDXyUPrRw04UplUqsW7cOHh4egrR/48YNfPLJJ4K0TdomxsPDPsQnF24KZ3y37gCAA1evwHjnAitApcIT0bEI9vREuclezWJM126YOWgIfJX1//djn4fSAUAXvs/bkdDfugMMFoNEa9K6bYKEp9wTXjLhxooJP4YOHYrhw4cLtkX8rFmzcOXKFd7bJm0Xo1KB8eQ/C7VagEqFhyI64MStm/j95g3u8en9BuDFB3sj2NP+vSa5c/HdkKpC1QGKZdn2APgreHoXwQOU1qx16wQJL5kXPOWUvecOlixZArlAV6S0RTzhG6PyACPAMglHE+N6QGc24+C1q9xjGoUCIV7qe8+nIVmFVXcSJYxGCYA+PJxqjYQOULF6ix56S8OLEroKL7kXvOTUg3IHAQEB+PTTTwVZG2W1WnHixAl8/fXXvLdN2ibG01PwAJUYFo4Pkwbjzb79eG+bNRjBGowA8ADvjd8hdIDqaLAYYLDwvz1Cc1BKlFDJVJCJqbyRu5g8eTLi4uIEmTPU6XR45ZVXUFpaynvbpO1hVB6CDvFVG92lK5RSKe+9f9ZoBGuvyi5Y+X+hA1S4O/egPOWe1HtyMwzDYN26dYIN9RmNRrz++uuCtE3aFkYmA+PhIWiihNPx+F4YbDDYf4DO/Db8X0IGKBGAQHfuQXnJvKh6hBvq3Lkz3nvvPUGy+kwmE7Zs2YIDBw7w3jZpe5pjHkooDj2oCKGOIWSAijVVmaQGi8FtM/ho/sl9paenIygoSJC2DQYDnnnmGZhMJkHaJ22H0Jl8QnKYgxKs7L+QASrBnXtPAKWYuzOZTCZoGaTi4mL89a9/FaRt0nYwniq37UHBbLb3oqxWLwCBQhxCyAAV587zTwqJAp4yT9r/yY3169cPEyZMgEKh4L1tg8GAhQsX4sKFC7y3TdoOd+5BAQCMJsDei0oQonkhA1Rnd+5Beclp/qk1WLBggWC9KKPRiGeeeQY2m02Q9knrx8hkYJRKQO6eF8IO81A9hWhfyAAVaagyuG0PyktG80+tgbe3N5YvXy7I2iiWZXHp0iWsWLGC97ZJ28GoVGBU7nkxzBoMYO2ZfIKUPBIyQPkbq4wwVbnnRDKlmLceo0ePRmJiIiQS/jeQ1ul0ePvtt3Hr1i3e2yZtg30eSpg6koIzm+0/AiVKCBmgNGarGWarWcBDCIdSzFsPhmHw5ZdfClKnD7Cnnr/00kuCtE1aP/s8lHt+17BmC1iLBQAChGhfqAAltdlsnuYq9wxQEpEECqkCcknzLKAjwgsPD8ecOXMEGeqzWCzYvXs3duzYwXvbpPVjlAowCjf9rrGYAbMFAATZME+oANXRYrMwZqsZLNyvuKZMLINc7KZ/MKRWr7/+OiIihFlTqNfr8dxzz0Fn34KAkPqTyQCpYAXBBcWaLWDtQ3yCbG4lVICKdOfhPblYTunlrZBYLMaGDRsEy+qrrKzE+++/L0jbpPVixGIwUikgFrf0qTScxQJYLGBZ1hMA75O8ggYod02QkElkVCC2lerZsydefPFFQYKUwWDAypUrceLECd7bJq0b4669KJa1z0FZLAyADnw3L1SACjdVmdy2B0VDfK3b3LlzoVbfux8OH2iLeNIoMqk9SLkjMzcP1ZHvpoUKUCFuP8RHPahWy8PDA2vXrhVsi/i8vDwsWLBAkLZJ68RIpYDMDXtQAFgLl8nXge+2hQpQwW4/xEdzUK3aY489huTkZEgFGFbR6XSYOXMmrl69WveLCQEAmcwepNyR2VK9Fiqc76aFClAai9UCi80iUPPCoiG+tmHp0qWC1OkD7GujJk+eTFvEk/qRyew/boi1WMDah7T9+W5bqACltLJWWG1WgZoXFg3xtQ2BgYFYsGCBYFvEHzt2DN988w3vbZPWh5FKwbjpEB9sNsBqBQDeVxsLFaA8rDb3DVAyMQ3xtRVTpkxBly5dBNkiXq/X46WXXkJZWRnvbZNWRiYFpG76nWO1AlYb4EYBSmm1WWFl3S9AiRkxZBIZJCL+67YR18MwDNavXy/YFvEGgwFvvvmmIG2T1oORydy2B8VarYDNvXpQiiq2yi17UHIJDe+1NdHR0ZgxY4ZgW8R/8803OHToEO9tk1ZE6u49KCsA8D5WLliActcelExMi3Tbog8++AABAYLUu6Qt4kmdGInE3oMSYKhZcFabvRcF8H6FJ9RvQ261WVFlc7/FijKxjIrEtkFCbxFfVFSEOXPmCNI2aR0YmZuuhfrvHJRbBCgpy7ISK2uFjXW/nUYlIgnEjBvWxCJNNmDAAIwdO1aQ+Si9Xo+///3vyMrK4r1t0kqIxW5Zj4+1cUN8vF/dCRGgfN05g49hGDAM09KnQVrIp59+2uheVGRkJBISEhAcHFzj89Vrowipkbt+91ht1UkSvC8qFCJAqd219wQAzJ3/SNuk0WiwbNmyBq2NGjNmDC5cuIAzZ85gz549yM3Nxf79+9G3b1+n17Esi/79+/N9yqS1YBjAHb97rFaw9iE+3ocehBniA+u2K+ipB0XGjh2LPn36QFyP4Zb09HSsWbMGsbGxUKlU0Gg0UCqVSEpKwp49ezBs2DCn11utVtozitSCAUTu+t3DAgJEVyEClBgs3HKjQuBOD4oCVJvGMAxWr15d51xUXFwcPvjgg1p7WyqVCps3b3ZKX1+9erUgi4JJK8Awd3pRboZl78Qn9whQIncNTsCdHpQ7drMJr9q3b4+//vWv9x3q+5//+R/I6qifxjAMJk6cyN0vLy/H0aNHeTtP0oq4a4ACIFQPihFgKC4BwO98N0pIc7NarYiLi8PFixdrfP7atWsID6+7gPO3336LsWPHcvc3btyICRMm8HaehLgQXoOUMEN8hLQCdW0RX9+h4Lvnstx1fpaQ5iZEgKLeE2k14uPjMXXq1BqD1OHDh2G13n85hVarxb59+5weE6piBSGtjRBDfADceBKKkLvodDp07NgRBQUFTo/37dsXe/bsue88lV6vR0hICMrLywHYd/MtKCgQZIsPQlyAyw/xEdKqqFQqfPnll/cUkz169CjWrFkDrVZb4/t0Oh1eeeUVLjgBwMSJE2GzuecaQUKaG/WgCKmnJ554Ajt37rxnWO+tt97CBx98AKlUCpZlIRaLUVBQgOnTp+OHH35weu2lS5fQuXPn5jxtQpoTrz0oClCE1FNBQQE+//xz7Nq1C8eOHXN6TiwWo1+/ftBoNMjLy0NmZuY97582bRrmz59Pw3ukNaMARUhLqqiowKBBg3Dy5Ml6v2fcuHE1DhMS0srQHBQhLcnLywsHDx5EamoqpNL7b4+gVqsxc+ZMrFq1ioITIQ1EPShCGqmiogI2mw1Lly7FkiVLcO3aNQD29VHdu3fH9OnTMX78eFitVnh68r4bNiGuiIb4CHElRqMRACCVSmE2myGXy2EwGCCXyyGRSFr47AhpVhSgCCGEuCSagyKEENL6UYAihBDikihAEUIIcUkUoAgh91VVVdXSp0DaKEoxciHl5eXYsGED9u3bhytXrkCn00Gj0aBbt2546qmnMGzYsHt2Y128eDE+++wzAMD48eMxa9asljh1QeTl5eGRRx6553GRSASxWAyVSoXw8HCMGzcOTz/9dLPsVNtcv+8hQ4bgxo0bAIBNmzbhgQceqPM9R48eRVpaGgAgMjISu3bt4p6LiYnhbh86dIirqJ6amspVxfjnP/+Jxx9/nHud1WrFhg0bkJmZiU8++aTpH6oWer0ey5Ytw/bt23Hr1i1oNBo89NBDeOONN9CuXbt6t2MymbB69Wps374d165dg0gkQpcuXTBp0iQMHz68xvdkZGRg7dq1yMzMRGVlJXx8fNCrVy+89NJL6NKlC18fkTQSBSgXsX//fsyYMcOpsCgAFBYWYt++fdi3bx82btyIzz77DN7e3i10lq7BZrPBZrOhrKwMZWVlOHPmDI4fP4758+e39Km1Gjk5OZg+fTouXbqEgQMHCnYcs9mMyZMn49SpU9xjhYWF2LJlCw4ePIiNGzfWa1NIvV6PtLQ0nDlzxunx48eP4/jx4zh9+jTee+89p+eWLFmChQsXOj1WWFiInTt3Yvfu3Vi0aBGGDBnShE9HmooClAs4fPgwXnnlFa4IaWBgIJKSkiCTyXDixAluR9djx47hnXfewfLly1vydFvMCy+8AMB+ZV9eXo6DBw9yW2Bs27YNycnJNfa42oqQkBDud+Tn51ev96SkpKBnz54AgKioKO7x06dP49KlS/yf5F1WrlzJBaeAgAAkJyfjt99+Q1ZWFgoLCzFnzhwsXbq0znZWr17NBSeNRoPhw4cjPz+f24tr1apVeOSRR9CrVy8AwMWLF7Fo0SLu/f3790dkZCQOHTqEq1evwmKxID09HXv27KFF1i2IAlQLM5lMmDFjBhecUlJSMGfOHCgUCgD23VeXLVuGBQsWAAAOHDiAw4cPo1+/fi12zi3lnXfecbpfWVmJcePGITc3FwCwdevWNh2gwsPD7/kd1WX8+PECnU3dWJbF119/zd1fsWIFYmNjodVq8cgjj6CsrAz79u1Dfn5+nUN927ZtA2Av2rtx40Z07NgRAPDGG2/gp59+AgDs3buXC1Dbtm3jtj0ZPXo0Pv74YwD2LVKGDx+O27dvo6ysDAcPHqx1eJAIj5IkWti2bdtQWFgIwH4FPHfuXC44AfayOS+//DLi4+Ph5eWFpKQkrnJBTW7fvo0ZM2agb9++iI+PR2pqKg4fPnzP62JiYrif6uNXe/7557nntmzZwj3+/vvvc48fOnQI//d//4f4+HgkJCRg9uzZAOxzJ9WvAYBdu3Zh3Lhx6NmzJxITE/H+++/j9u3bjf+FOfDy8sLYsWO5+9Wlhup7rtX27t2Ll19+GQMHDkRcXBwGDx6M9PR0XL58uc5zuHTpEqZOnYr4+Hj06dMHr776aq09j61bt+KZZ55B79690aVLF8THx2PkyJH48ssv60xE2Lx5M0aMGIHu3btj6NChWLBgAQwGg9Nrjh49yn3mYcOG1XnugH0Oqvo927dv5x5LT0/nXnPo0CHExMQgNTUVGzdu5F5fU3DLycnhnk9MTITFYqn12Dk5Obh16xYA+5xZbGwsAMDT0xODBw8GYA9iv/76a52fY+3atVi9ejX+8Y9/cMEJAHx9fbnbjnUTtVotfH19wTCM0zCeSqVymu/j62+VNA71oFrY/v37uduPP/445HJ5ja9bunQp1Gr1fRMBrl+/jtGjR6OoqIh77NixY/j999+xatUqJCYm8nbe8+fPR1ZWFne/pnmCzz77DIsXL+buG41G/Pvf/8bx48fx/fffAvOWCwAADMRJREFU8148tbaqKLWdK8uy+Mtf/oJvvvnG6fU3b97Eli1b8MMPP2Du3Ll48skna2z34sWLGD9+PPR6PffY7t27kZGRgRUrVuDBBx/kHl++fDn+8Y9/OL1fr9fjwoUL3E9tc2gLFy50usi4fv06li5diiNHjmDt2rW1/s0IISUlBfPmzYPRaERmZiZu3LiB0NBQ7nnHxIzHH3/8vsV0s7OzudsdOnRwei4yMpK7XZ8LhaCgIAQFBXH3KysrsX//fvz73/8GYO9ZjRgxgnt+9uzZmD17NsxmMxjGufhBdXIKYB8uJC2HelAt7Pz589ztrl271vo6jUZTZ5ZaRkYG9Ho9Ro8ejTFjxnBfXFarFStWrODnhO/IyspCVFQUJk2ahOjo6BqHQRYvXowOHTogNTUV8fHx3OPXr1/Hf/7znyafg1ardQoujlfO9TnXFStWOL0/MTEREydORHR0NABw8xC1batx6tQpWCwWpKSkYNSoUdw+T3q9HjNmzOB6RUVFRfj0008BABKJBCkpKXj22WfRu3dvrq3vv/8eJSUlNR7n8OHDCAsLw4QJE5yGdjMzM/HFF1/c/5fUCCkpKXj44Ye5+2FhYXjhhReQkpICLy8vPProo9xzO3fudHqvY4B66qmn7nscx5773YFArVZzt6vnGeurpKQEvXr1wttvvw2j0QilUon58+dz/18dyWQypyCakZGBs2fPArBni/J5UUcajnpQLay0tJS73dTsPIlEgvXr13OBrlu3blwatGMPgg8qlQobN2687zk/8MADWLduHWQyGcxmM8aMGcMNf+Xk5DT4mNU9EKvVipKSEvz6669OX3JPP/10vc/VYDBgyZIl3P0PP/wQqampAOzrft555x3s3LkTVVVV+OSTT/DVV1/V2Pby5cu5oDF16lQ8/fTTMBgMuHHjBg4dOoRBgwahvLwcEyZMQFZWFoYOHYpnn30WgL0HN2zYMFy5cgUsyyIvL89pSKpap06dsGnTJm6y3jHVffPmzXjrrbfu6QU0xfjx4yGVSrnefYcOHZzmtsaMGcPtFLxjxw5MnToVgL2nU90r6tixI3r06HHf4zgOUcpkMqfnHIOGYw+1PvLz853ux8bGIjg4uM73ZWdnY/r06dz9lJSUer2PCId6UC3Mce6hetK2seLj4516YX369OFua7XaJrV9twEDBtQZUCdOnMh98chkMqerf51O1+BjLl++HMuXL8eqVauwdetWp+A0ZcqUWtOhazrXQ4cOcecQGRmJSZMmcc9JJBKnOZjjx487XUhUe+CBB5w+U6dOnbi5EwBcdlpUVBQ+/PBDrFu3Ds8++yyqqqpw7tw5rFq1ChUVFdzrTSZTjeefmprqlEk2ZcoUrkp6SUkJ8vLyanyfUBITE7lhvXPnzuHq1asAnHtPI0eOrLMdgQpVo6qqCqNHj8bIkSMhlUpx8uRJpKWlYceOHbW+JysrC88++yzKysoA2LMg33//fUHOj9Qf9aBamEaj4b5oq/9xNJbjXAAAp2SL6izB+qjPF8fdx6rJ3ZlXjludN+R8aiKXy6HRaBATE4MJEybcN3uvpnN1TKiIjY29pwcSFBQEPz8/FBcXg2VZXL9+HT4+Pk6viYiIuKddx2HG4uJi7rZWq8W3336LPXv24PTp0zUmutR2gXL3cVQqFYKCgri5kqKionqtFeILwzAYPXo0N7+4c+dOvPzyy1yAEolEtc7bOVIqldztu5MpHO83dK6yZ8+eXOr8iBEj8OKLL8JqtWLOnDkYOnToPb218+fP47nnnuP+/Xl4eGDp0qX1TtUnwqEA1cIcs+jOnz+PJ554osbX/etf/8KVK1eQnJyMfv363fOPDMA9k+WOX7r3Czp3fzHeL/OqWn3Whtx9Pk2t9NDYYcqaztVxn6bahsccf2c1vaam35Njj7j685aUlGDChAlcTyM2Nhb9+/dHQkICli1bds/i0oYeRywW3/f9Qhg1ahQ+++wzsCyL7du3Izk5mRu+7dOnT70qQFRXswDg1JO8+35gYGCjz/Phhx/mLjSKiopw5coVp7moixcvYvLkydwCeU9PTyxfvrzO4UnSPGiIr4UlJSVxt3fu3FnjMI/ZbMbXX3+N7777Di+++CI3/9AUjsHi7qv5moaz7lbXVueupKZzdfwCvXDhwj0B/Pbt21zSgkgkqrGH4piFVs2xZ1b9xbpy5UouOKWlpWHbtm147733nJIN7ufu4+j1eqdMzaZ8gTdWaGgol0Bw6dIlp7/JupIjqjn2Nh1/b3ff79Sp033bKSkpwaJFi5Ceno6ZM2fe87xjMHcM9jdv3sTzzz/PBSeNRoPVq1cjISGhXudPhEcBqoWNGTOGy2DKz8/Hn//8Z5jNZu75qqoqzJ49mxvOEYvF9Rrfr4tjltS5c+e429nZ2dzC1/vhc1JeaDWda//+/bkh0D/++AMbNmzgnquqqsLf/vY37n7v3r1rTDfOzs52ykbMzc3F7t27ufvVaeaO66Ichwmzs7O5KiFA7UN869evdxr+XblyJTdEGhoaKshEvuMFjOPfo6MxY8Zwt3/88UcA9mG75OTkeh0jJiaGSwq5dOkS97vQ6XTYu3cvAPv/uwEDBty3HblcjuXLl2PLli3YvHmzU490x44dXABSKpVctYyqqiq8/vrrXKBXKpX48ssv0b1793qdO2keNMTXwjw9PfHxxx9j2rRpYFkWP/74I37//XeuZ3X06FFcuXKFe31aWlqt6dQNERsbiyNHjgAAPvroIxQUFMBkMmHNmjVNnh9yB56enkhNTeXKRs2aNQu7d+9Ghw4dcPz4cS6oSKVSvP3227W2M336dDz22GNQqVT46aefuC/zTp06cUkqYWFh3Ou/+OILXLt2DSzL4qeffnK6oq8tSeLGjRt48sknMXToUFy/fh0HDhzgnps4cWIjfwP35zgsevLkSfzv//4vlEqlU/LIY489BrVa7TQcN3ToUKe5xvsRiUQYN24cV8rohRdeQHJyMo4dO8bN3w0aNMgpAO/cuZO7oEpKSkKfPn2gUqkwcuRIbN68GVarFWlpaRg+fDiMRiNXRQIAJk2axF2UbNu2jUsnB+yJMjt27LgnkaL6GKRlUIByAUOGDMHChQuRnp4OvV6P/Px8bNq06Z7XjRo1qsGlbGrz3HPP4ejRo2BZFsXFxVypl+o6Znevb2mN3nrrLeTn53NX/xkZGcjIyOCel0qlmDdvHjfhfrexY8fi+++/v+dLzcvLC/Pnz+d6bpMmTcJ3330Hk8kEi8XCLR6tfm1lZSUAcMOAd0tNTcW6deuwfv16p8cTExPx3HPPNfBT10/Pnj0hlUphsVhgsViwadMmhISEOAUouVyOESNGOJUramjv/qWXXsKRI0eQmZmJgoICrFu3jnsuICAAH374odPrHRffqtVqLni8++67uHDhAs6cOQO9Xo/vvvvO6X2DBw/Gm2++yd2/e9nA+fPnndYkVnM8Bml+NMTnIoYNG4aff/4ZL7/8MmJjY6FSqSCVShEcHIxhw4Zh1apVmDdvntPkflMMGjQIS5YsQc+ePaFQKODv74/Ro0dj69atTkVDWzOJRIJPPvkES5YswZAhQxAQEMD9zkePHo1t27YhJSWl1vcnJCRg8+bNGDx4MFQqFdRqNZKTk/HNN9+gW7du3OuioqKwfv16DBw4EBqNBmq1GnFxcUhPT3eqpu04POjoww8/xOzZsxEdHQ2ZTIawsDC8/vrrWL58OW9/D3cLCAjAP//5T3Tu3BlSqRQ+Pj5On6ma4wLswMBA9O/fv0HH8fDwwJo1a/Dqq68iIiICUqkU/v7+GDVqFDZv3uzU+7wfLy8vbNiwAW+//Taio6Mhl8uhVCrRs2dPfPTRR/jiiy+4uUitVus0tEpcFyPQWgRhFjgQQlyGzWbDn/70J67SxpQpU+7Z0oK0ObxOTtMQHyGkQZYsWQKj0cgNzQH25J0JEya08JmR1oYCFCGkQTIyMrhdeKtNnjy5xoXLhDQFBShCSIN0794dFy9eRFVVFcLCwjB27FiujiEhfKI5KEIIIXzhdQ6KsvgIIYS4JApQhBBCXBIFKEIIIS6JAhQhhBCXRAGKEEKIS6IARQghxCVRgCKEEOKSKEARQghxSRSgCCGEuCQKUIQQQlwSBShCCCEuiQIUIYQQl0QBihBCiEsSarsNXivaEkIIaXuoB0UIIcQlUYAihBDikihAEUIIcUkUoAghhLgkClCEEEJcEgUoQgghLokCFCGEEJdEAYoQQohLogBFCCHEJVGAIoQQ4pIoQBFCCHFJFKAIIYS4JApQhBBCXBIFKEIIIS6JAhQhhBCXRAGKEEKIS6IARQghxCVRgCKEEOKSKEARQghxSf8PFpjxFGct4ogAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"gauge(Probability=model.predict_proba(test_x.iloc[0:1])[0,1])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Final Features\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 103,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Index(['gender', 'SeniorCitizen', 'Partner', 'Dependents', 'tenure',\\n\",\n       \"       'PhoneService', 'MultipleLines', 'OnlineSecurity', 'OnlineBackup',\\n\",\n       \"       'DeviceProtection', 'TechSupport', 'StreamingTV', 'StreamingMovies',\\n\",\n       \"       'PaperlessBilling', 'MonthlyCharges', 'TotalCharges',\\n\",\n       \"       'InternetService_Fiber optic', 'InternetService_No',\\n\",\n       \"       'Contract_One year', 'Contract_Two year',\\n\",\n       \"       'PaymentMethod_Credit card (automatic)',\\n\",\n       \"       'PaymentMethod_Electronic check', 'PaymentMethod_Mailed check'],\\n\",\n       \"      dtype='object')\"\n      ]\n     },\n     \"execution_count\": 103,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"test_x.columns\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.3\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "Customers Survival Analysis.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Customer Survival Analysis\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Theory\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"If time to event has the probability density function $f(t)$ and cumulative distribution function $F(t)$, then the probability of surviving at least to time $t$ is: $Pr(T>t)=S(t)=1-F(t)$. \\n\",\n    \"\\n\",\n    \"Cumulative hazard at time t is defined as $H(t)=-ln(S(t))$ and instantaneous hazard at time $t$ is $h(t)=\\\\frac{dH(t)}{dt}$. The instantateous hazard can also be written as $h(t)=\\\\frac{f(t)}{S(t)}$\\n\",\n    \"\\n\",\n    \"The likelihood function for survival analysis is described as:\\n\",\n    \"\\n\",\n    \"$$ l(\\\\beta) = \\\\prod_{n=1}^{n} h(t_{i})^{d_{i}} S(t_{i}) $$\\n\",\n    \"where $d_i$ is the censoring variable that equals to 1 if the event is observed for individual $i$ and 0 if the event is not observed (censored) for individual $i$, $h(t_i)$ is the hazard for individual $i$ at time $t$, $H(t_i)$ is the cumulative hazard for individual $i$ at time $t$, and $S(t_i)$ is the survival probability for individual $i$ at time $t$. Note that when $d_i=0$, the contribution of the $i$'th individual to the likelihood function is just its survival probability until time $t$: S(t). If the individual has the event, the contribution to the likelihood function is given by the density function $f(t)=h(t)S(t)$.\\n\",\n    \"\\n\",\n    \"The log of likelihood is:\\n\",\n    \"\\n\",\n    \"$$ logl(\\\\beta) = \\\\sum_{i=1}^n d_i log(h(t_i)) - H(t_i) $$\\n\",\n    \"where $log$ is the natural logarithm.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Importing Libraries\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import pandas as pd\\n\",\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import seaborn as sns\\n\",\n    \"from scipy.stats import norm\\n\",\n    \"import statsmodels.api as st\\n\",\n    \"from sklearn.preprocessing import LabelEncoder\\n\",\n    \"labelencoder = LabelEncoder()\\n\",\n    \"\\n\",\n    \"#Lifelines is a survival analysis package\\n\",\n    \"from lifelines import KaplanMeierFitter\\n\",\n    \"from lifelines.statistics import multivariate_logrank_test   \\n\",\n    \"from lifelines.statistics import logrank_test\\n\",\n    \"from lifelines import CoxPHFitter\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Data Preparation\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>customerID</th>\\n\",\n       \"      <th>gender</th>\\n\",\n       \"      <th>SeniorCitizen</th>\\n\",\n       \"      <th>Partner</th>\\n\",\n       \"      <th>Dependents</th>\\n\",\n       \"      <th>tenure</th>\\n\",\n       \"      <th>PhoneService</th>\\n\",\n       \"      <th>MultipleLines</th>\\n\",\n       \"      <th>InternetService</th>\\n\",\n       \"      <th>OnlineSecurity</th>\\n\",\n       \"      <th>...</th>\\n\",\n       \"      <th>DeviceProtection</th>\\n\",\n       \"      <th>TechSupport</th>\\n\",\n       \"      <th>StreamingTV</th>\\n\",\n       \"      <th>StreamingMovies</th>\\n\",\n       \"      <th>Contract</th>\\n\",\n       \"      <th>PaperlessBilling</th>\\n\",\n       \"      <th>PaymentMethod</th>\\n\",\n       \"      <th>MonthlyCharges</th>\\n\",\n       \"      <th>TotalCharges</th>\\n\",\n       \"      <th>Churn</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>7590-VHVEG</td>\\n\",\n       \"      <td>Female</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>No phone service</td>\\n\",\n       \"      <td>DSL</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>Month-to-month</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"      <td>Electronic check</td>\\n\",\n       \"      <td>29.85</td>\\n\",\n       \"      <td>29.85</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>5575-GNVDE</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>34</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>DSL</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>One year</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>Mailed check</td>\\n\",\n       \"      <td>56.95</td>\\n\",\n       \"      <td>1889.5</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>3668-QPYBK</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>DSL</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>Month-to-month</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"      <td>Mailed check</td>\\n\",\n       \"      <td>53.85</td>\\n\",\n       \"      <td>108.15</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>7795-CFOCW</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>45</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>No phone service</td>\\n\",\n       \"      <td>DSL</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>One year</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>Bank transfer (automatic)</td>\\n\",\n       \"      <td>42.30</td>\\n\",\n       \"      <td>1840.75</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>9237-HQITU</td>\\n\",\n       \"      <td>Female</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>Fiber optic</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>Month-to-month</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"      <td>Electronic check</td>\\n\",\n       \"      <td>70.70</td>\\n\",\n       \"      <td>151.65</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"<p>5 rows × 21 columns</p>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   customerID  gender  SeniorCitizen Partner Dependents  tenure PhoneService  \\\\\\n\",\n       \"0  7590-VHVEG  Female              0     Yes         No       1           No   \\n\",\n       \"1  5575-GNVDE    Male              0      No         No      34          Yes   \\n\",\n       \"2  3668-QPYBK    Male              0      No         No       2          Yes   \\n\",\n       \"3  7795-CFOCW    Male              0      No         No      45           No   \\n\",\n       \"4  9237-HQITU  Female              0      No         No       2          Yes   \\n\",\n       \"\\n\",\n       \"      MultipleLines InternetService OnlineSecurity  ... DeviceProtection  \\\\\\n\",\n       \"0  No phone service             DSL             No  ...               No   \\n\",\n       \"1                No             DSL            Yes  ...              Yes   \\n\",\n       \"2                No             DSL            Yes  ...               No   \\n\",\n       \"3  No phone service             DSL            Yes  ...              Yes   \\n\",\n       \"4                No     Fiber optic             No  ...               No   \\n\",\n       \"\\n\",\n       \"  TechSupport StreamingTV StreamingMovies        Contract PaperlessBilling  \\\\\\n\",\n       \"0          No          No              No  Month-to-month              Yes   \\n\",\n       \"1          No          No              No        One year               No   \\n\",\n       \"2          No          No              No  Month-to-month              Yes   \\n\",\n       \"3         Yes          No              No        One year               No   \\n\",\n       \"4          No          No              No  Month-to-month              Yes   \\n\",\n       \"\\n\",\n       \"               PaymentMethod MonthlyCharges  TotalCharges Churn  \\n\",\n       \"0           Electronic check          29.85         29.85    No  \\n\",\n       \"1               Mailed check          56.95        1889.5    No  \\n\",\n       \"2               Mailed check          53.85        108.15   Yes  \\n\",\n       \"3  Bank transfer (automatic)          42.30       1840.75    No  \\n\",\n       \"4           Electronic check          70.70        151.65   Yes  \\n\",\n       \"\\n\",\n       \"[5 rows x 21 columns]\"\n      ]\n     },\n     \"execution_count\": 2,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df = pd.read_csv(\\\"C:/Data/Telco-Customer-Churn.csv\\\")\\n\",\n    \"df.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"<class 'pandas.core.frame.DataFrame'>\\n\",\n      \"RangeIndex: 7043 entries, 0 to 7042\\n\",\n      \"Data columns (total 21 columns):\\n\",\n      \"customerID          7043 non-null object\\n\",\n      \"gender              7043 non-null object\\n\",\n      \"SeniorCitizen       7043 non-null int64\\n\",\n      \"Partner             7043 non-null object\\n\",\n      \"Dependents          7043 non-null object\\n\",\n      \"tenure              7043 non-null int64\\n\",\n      \"PhoneService        7043 non-null object\\n\",\n      \"MultipleLines       7043 non-null object\\n\",\n      \"InternetService     7043 non-null object\\n\",\n      \"OnlineSecurity      7043 non-null object\\n\",\n      \"OnlineBackup        7043 non-null object\\n\",\n      \"DeviceProtection    7043 non-null object\\n\",\n      \"TechSupport         7043 non-null object\\n\",\n      \"StreamingTV         7043 non-null object\\n\",\n      \"StreamingMovies     7043 non-null object\\n\",\n      \"Contract            7043 non-null object\\n\",\n      \"PaperlessBilling    7043 non-null object\\n\",\n      \"PaymentMethod       7043 non-null object\\n\",\n      \"MonthlyCharges      7043 non-null float64\\n\",\n      \"TotalCharges        7043 non-null object\\n\",\n      \"Churn               7043 non-null object\\n\",\n      \"dtypes: float64(1), int64(2), object(18)\\n\",\n      \"memory usage: 1.1+ MB\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"df.info()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Here, Churn is an event which indicates whether customer exited or not. Tenure shows how long customer remained in our service. Both of these variables are very important for customer survival analysis.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0    5174\\n\",\n       \"1    1869\\n\",\n       \"Name: Churn, dtype: int64\"\n      ]\n     },\n     \"execution_count\": 4,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df.Churn = labelencoder.fit_transform(df.Churn)\\n\",\n    \"df.Churn.value_counts()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"eventvar = df['Churn']\\n\",\n    \"timevar = df['tenure']\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"For the analysis, I will need to create dummy variables for all categorical variables.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>customerID</th>\\n\",\n       \"      <th>tenure</th>\\n\",\n       \"      <th>MonthlyCharges</th>\\n\",\n       \"      <th>TotalCharges</th>\\n\",\n       \"      <th>Churn</th>\\n\",\n       \"      <th>gender_Male</th>\\n\",\n       \"      <th>SeniorCitizen_1</th>\\n\",\n       \"      <th>Partner_Yes</th>\\n\",\n       \"      <th>Dependents_Yes</th>\\n\",\n       \"      <th>PhoneService_Yes</th>\\n\",\n       \"      <th>...</th>\\n\",\n       \"      <th>StreamingTV_No internet service</th>\\n\",\n       \"      <th>StreamingTV_Yes</th>\\n\",\n       \"      <th>StreamingMovies_No internet service</th>\\n\",\n       \"      <th>StreamingMovies_Yes</th>\\n\",\n       \"      <th>Contract_One year</th>\\n\",\n       \"      <th>Contract_Two year</th>\\n\",\n       \"      <th>PaperlessBilling_Yes</th>\\n\",\n       \"      <th>PaymentMethod_Credit card (automatic)</th>\\n\",\n       \"      <th>PaymentMethod_Electronic check</th>\\n\",\n       \"      <th>PaymentMethod_Mailed check</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>7590-VHVEG</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>29.85</td>\\n\",\n       \"      <td>29.85</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>5575-GNVDE</td>\\n\",\n       \"      <td>34</td>\\n\",\n       \"      <td>56.95</td>\\n\",\n       \"      <td>1889.5</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>3668-QPYBK</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>53.85</td>\\n\",\n       \"      <td>108.15</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>7795-CFOCW</td>\\n\",\n       \"      <td>45</td>\\n\",\n       \"      <td>42.30</td>\\n\",\n       \"      <td>1840.75</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>9237-HQITU</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>70.70</td>\\n\",\n       \"      <td>151.65</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"<p>5 rows × 32 columns</p>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   customerID  tenure  MonthlyCharges TotalCharges  Churn  gender_Male  \\\\\\n\",\n       \"0  7590-VHVEG       1           29.85        29.85      0            0   \\n\",\n       \"1  5575-GNVDE      34           56.95       1889.5      0            1   \\n\",\n       \"2  3668-QPYBK       2           53.85       108.15      1            1   \\n\",\n       \"3  7795-CFOCW      45           42.30      1840.75      0            1   \\n\",\n       \"4  9237-HQITU       2           70.70       151.65      1            0   \\n\",\n       \"\\n\",\n       \"   SeniorCitizen_1  Partner_Yes  Dependents_Yes  PhoneService_Yes  ...  \\\\\\n\",\n       \"0                0            1               0                 0  ...   \\n\",\n       \"1                0            0               0                 1  ...   \\n\",\n       \"2                0            0               0                 1  ...   \\n\",\n       \"3                0            0               0                 0  ...   \\n\",\n       \"4                0            0               0                 1  ...   \\n\",\n       \"\\n\",\n       \"   StreamingTV_No internet service  StreamingTV_Yes  \\\\\\n\",\n       \"0                                0                0   \\n\",\n       \"1                                0                0   \\n\",\n       \"2                                0                0   \\n\",\n       \"3                                0                0   \\n\",\n       \"4                                0                0   \\n\",\n       \"\\n\",\n       \"   StreamingMovies_No internet service  StreamingMovies_Yes  \\\\\\n\",\n       \"0                                    0                    0   \\n\",\n       \"1                                    0                    0   \\n\",\n       \"2                                    0                    0   \\n\",\n       \"3                                    0                    0   \\n\",\n       \"4                                    0                    0   \\n\",\n       \"\\n\",\n       \"   Contract_One year  Contract_Two year  PaperlessBilling_Yes  \\\\\\n\",\n       \"0                  0                  0                     1   \\n\",\n       \"1                  1                  0                     0   \\n\",\n       \"2                  0                  0                     1   \\n\",\n       \"3                  1                  0                     0   \\n\",\n       \"4                  0                  0                     1   \\n\",\n       \"\\n\",\n       \"   PaymentMethod_Credit card (automatic)  PaymentMethod_Electronic check  \\\\\\n\",\n       \"0                                      0                               1   \\n\",\n       \"1                                      0                               0   \\n\",\n       \"2                                      0                               0   \\n\",\n       \"3                                      0                               0   \\n\",\n       \"4                                      0                               1   \\n\",\n       \"\\n\",\n       \"   PaymentMethod_Mailed check  \\n\",\n       \"0                           0  \\n\",\n       \"1                           1  \\n\",\n       \"2                           1  \\n\",\n       \"3                           0  \\n\",\n       \"4                           0  \\n\",\n       \"\\n\",\n       \"[5 rows x 32 columns]\"\n      ]\n     },\n     \"execution_count\": 6,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"categorical = ['gender', 'SeniorCitizen', 'Partner', 'Dependents', 'PhoneService', 'MultipleLines',\\n\",\n    \"       'InternetService', 'OnlineSecurity', 'OnlineBackup', 'DeviceProtection',\\n\",\n    \"       'TechSupport', 'StreamingTV', 'StreamingMovies', 'Contract',\\n\",\n    \"       'PaperlessBilling', 'PaymentMethod']\\n\",\n    \"\\n\",\n    \"survivaldata = pd.get_dummies(df, columns = categorical, drop_first= True)\\n\",\n    \"survivaldata.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We need to drop variables such as customerID, tenure, Churn as they are not needed in survival data. Also, we need to add constant for survival analysis.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>MonthlyCharges</th>\\n\",\n       \"      <th>TotalCharges</th>\\n\",\n       \"      <th>gender_Male</th>\\n\",\n       \"      <th>SeniorCitizen_1</th>\\n\",\n       \"      <th>Partner_Yes</th>\\n\",\n       \"      <th>Dependents_Yes</th>\\n\",\n       \"      <th>PhoneService_Yes</th>\\n\",\n       \"      <th>MultipleLines_No phone service</th>\\n\",\n       \"      <th>MultipleLines_Yes</th>\\n\",\n       \"      <th>InternetService_Fiber optic</th>\\n\",\n       \"      <th>...</th>\\n\",\n       \"      <th>StreamingTV_Yes</th>\\n\",\n       \"      <th>StreamingMovies_No internet service</th>\\n\",\n       \"      <th>StreamingMovies_Yes</th>\\n\",\n       \"      <th>Contract_One year</th>\\n\",\n       \"      <th>Contract_Two year</th>\\n\",\n       \"      <th>PaperlessBilling_Yes</th>\\n\",\n       \"      <th>PaymentMethod_Credit card (automatic)</th>\\n\",\n       \"      <th>PaymentMethod_Electronic check</th>\\n\",\n       \"      <th>PaymentMethod_Mailed check</th>\\n\",\n       \"      <th>const</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>29.85</td>\\n\",\n       \"      <td>29.85</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>56.95</td>\\n\",\n       \"      <td>1889.5</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>53.85</td>\\n\",\n       \"      <td>108.15</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>42.30</td>\\n\",\n       \"      <td>1840.75</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>70.70</td>\\n\",\n       \"      <td>151.65</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"<p>5 rows × 30 columns</p>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   MonthlyCharges TotalCharges  gender_Male  SeniorCitizen_1  Partner_Yes  \\\\\\n\",\n       \"0           29.85        29.85            0                0            1   \\n\",\n       \"1           56.95       1889.5            1                0            0   \\n\",\n       \"2           53.85       108.15            1                0            0   \\n\",\n       \"3           42.30      1840.75            1                0            0   \\n\",\n       \"4           70.70       151.65            0                0            0   \\n\",\n       \"\\n\",\n       \"   Dependents_Yes  PhoneService_Yes  MultipleLines_No phone service  \\\\\\n\",\n       \"0               0                 0                               1   \\n\",\n       \"1               0                 1                               0   \\n\",\n       \"2               0                 1                               0   \\n\",\n       \"3               0                 0                               1   \\n\",\n       \"4               0                 1                               0   \\n\",\n       \"\\n\",\n       \"   MultipleLines_Yes  InternetService_Fiber optic  ...  StreamingTV_Yes  \\\\\\n\",\n       \"0                  0                            0  ...                0   \\n\",\n       \"1                  0                            0  ...                0   \\n\",\n       \"2                  0                            0  ...                0   \\n\",\n       \"3                  0                            0  ...                0   \\n\",\n       \"4                  0                            1  ...                0   \\n\",\n       \"\\n\",\n       \"   StreamingMovies_No internet service  StreamingMovies_Yes  \\\\\\n\",\n       \"0                                    0                    0   \\n\",\n       \"1                                    0                    0   \\n\",\n       \"2                                    0                    0   \\n\",\n       \"3                                    0                    0   \\n\",\n       \"4                                    0                    0   \\n\",\n       \"\\n\",\n       \"   Contract_One year  Contract_Two year  PaperlessBilling_Yes  \\\\\\n\",\n       \"0                  0                  0                     1   \\n\",\n       \"1                  1                  0                     0   \\n\",\n       \"2                  0                  0                     1   \\n\",\n       \"3                  1                  0                     0   \\n\",\n       \"4                  0                  0                     1   \\n\",\n       \"\\n\",\n       \"   PaymentMethod_Credit card (automatic)  PaymentMethod_Electronic check  \\\\\\n\",\n       \"0                                      0                               1   \\n\",\n       \"1                                      0                               0   \\n\",\n       \"2                                      0                               0   \\n\",\n       \"3                                      0                               0   \\n\",\n       \"4                                      0                               1   \\n\",\n       \"\\n\",\n       \"   PaymentMethod_Mailed check  const  \\n\",\n       \"0                           0    1.0  \\n\",\n       \"1                           1    1.0  \\n\",\n       \"2                           1    1.0  \\n\",\n       \"3                           0    1.0  \\n\",\n       \"4                           0    1.0  \\n\",\n       \"\\n\",\n       \"[5 rows x 30 columns]\"\n      ]\n     },\n     \"execution_count\": 7,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"survivaldata.drop(['customerID', 'tenure', 'Churn'], axis = 1, inplace= True)\\n\",\n    \"survivaldata = st.add_constant(survivaldata, prepend=False)\\n\",\n    \"survivaldata.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Survival Analysis\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Kaplan-Meier Curve\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The Kaplan-Meier method calculates the probability of survival at time 𝑡  as:\\n\",\n    \"\\n\",\n    \"$$ S(t) = \\\\prod_{i=1}^{t-1} (1 - \\\\frac{d_i}{n_i}) $$\\n\",\n    \"\\n\",\n    \"where,\\n\",\n    \"- 𝑆(𝑡) is the probability of survival until time 𝑡, \\n\",\n    \"- $𝑑_𝑖$  is the number of units that experienced the event at time 𝑡,  \\n\",\n    \"- $𝑛_𝑖$  is the number of units at risk of experiencing the event at time 𝑡.  \\n\",\n    \"\\n\",\n    \"$𝑛_𝑖$ decreases with time, as units experience the event or are censored. $\\\\frac{d_i}{n_i}$ is the probability of experiencing the event at time 𝑖 and $(1− \\\\frac{d_i}{n_i})$ is the probability of surviving at time 𝑖. \\n\",\n    \"\\n\",\n    \"Note that this method does not use any parameters, it only depends on the data on time and censoring.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 89,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"#Create a KaplanMeier object, imported from lifelines\\n\",\n    \"kmf = KaplanMeierFitter()\\n\",\n    \"#Calculate the K-M curve for all groups\\n\",\n    \"kmf.fit(timevar,event_observed = eventvar,label = \\\"All Customers\\\")\\n\",\n    \"#Plot the curve and assign labels\\n\",\n    \"kmf.plot()\\n\",\n    \"plt.ylabel('Probability of Customer Survival')\\n\",\n    \"plt.xlabel('Tenure')\\n\",\n    \"plt.title('Kaplan-Meier Curve');\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"As we can see, there is a sudden drop in the starting which says that after one tenure only customers starts churning rapidly and after that churning rate decreases. To deal with that we can consider giving more discounts on long-term plans and make more customers to subscribe for long term plans.  \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Log-Rank Test\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We can use non-parametric method log-rank test to compare survival curves between different groups. The log-rank test assumes that the hazards of the groups are proportional. Under the null hypothesis, the probability of event across the groups is the same for all time points. \\n\",\n    \"\\n\",\n    \"To test the null hypothesis, the log-rank test calculates the difference between the observed number of events and the number of events expected in each group proportional to the size of the groups at each time point an event is observed. The log-rank test statistic for group $j$ $(k_{j})$ follows a $\\\\chi^2$ distribution and is calculated as:\\n\",\n    \"\\n\",\n    \"$$k_{j} = \\\\frac{(O_{j}-E_{j})^{2}}{var(O_{j}-E_{j})}$$\\n\",\n    \"\\n\",\n    \"$O_{j}-E_{j}$ is calculated as:\\n\",\n    \"\\n\",\n    \"$$O_{j}-E_{j} = \\\\sum_{i}(o_{ij}-e_{ij})$$ \\n\",\n    \"\\n\",\n    \"and $var(O_{j}-E_{j})$ is:\\n\",\n    \"\\n\",\n    \"$$var(O_{j}-E_{j}) = o_{i}\\\\frac{n_{ij}}{n_{i}}\\\\Big(1-\\\\frac{n_{ij}}{n_{i}}\\\\Big)\\\\frac{(n_{i}-o_{i})}{(n_{i}-1)}$$\\n\",\n    \"\\n\",\n    \"$o_{ij}$ is the observed number of events in group $j$ at time $i$ and $e_{ij}$ is the expected number of events in group $j$ at time $i$, which is calculated as $e_{ij} = \\\\frac{n_{ij}}{n_i}{o_{i}}$. Note that $\\\\frac{n_{ij}}{n_i}$ is the proportion of units in group $j$ at risk of event at time $i$ ($n_{ij}$) to the number of units in all groups at risk of event at time $i$ ($n_{i}$) and ${o_{i}}$ is the observed number of events in all groups at time $i$. \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"When comparing multiple groups, we first calculate the pairwise log-rank test scores between each of the $k-1$ groups, and write them as a vector of log-rank statistics, $\\\\bf{Z}$ which has $k - 1$ elements. We can leave any of one of the statistics out, because the $k$ covariances are linearly dependent on each other (the sum of log-rank statistics is 0, $\\\\sum k_{j}=0$.\\n\",\n    \"\\n\",\n    \"The test statistic for the hypothesis that there is no difference in survival times of $k$ groups is calculated as: \\n\",\n    \"\\n\",\n    \"$$logrankstatistic = \\\\bf{Z} {\\\\sum}^{-1} \\\\bf{Z}'$$ \\n\",\n    \"\\n\",\n    \"which has a $\\\\chi^2$ distribution, where ${\\\\sum}^{-1}$ is the inverse of the $k-1$ by $k-1$ variance-covariance matrix of $\\\\bf{Z}$, which has variance of $k_{j}$ on its diagonal elements and $covar(k_{jg})$ on its off-diagonal elements.\\n\",\n    \"\\n\",\n    \"The variance of observed number of events in group $j$ is calculated as $var(O_{j}-E_{j})$ as demonstrated above. The covariance between the observed number of events in group $j$ and $g$ is calculated as:\\n\",\n    \"\\n\",\n    \"$$covar(k_{jg})=o_{i}\\\\frac{(n_{ij}n_{ig})}{(n_{i}n_{i})}\\\\frac{(n_{i}-o_{i})}{(n_{i}-1)}$$\\n\",\n    \"\\n\",\n    \"Note that rejecting the null hypothesis means that the survival times of groups do not come from the same distribution, it does not specify which group's survival time is different. The following plots and test statistics compare the groups in the dataset in terms of the different explanatory variables. Astatistically significant log-rank test statistic indicates that we can reject the null hypothesis that time to survival in all groups come from the same distribution.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Gender\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 90,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"<lifelines.StatisticalResult>\\n\",\n      \"               t_0 = -1\\n\",\n      \" null_distribution = chi squared\\n\",\n      \"degrees_of_freedom = 1\\n\",\n      \"\\n\",\n      \"---\\n\",\n      \" test_statistic    p  -log2(p)\\n\",\n      \"           0.53 0.47      1.09\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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r39StyXy8tX80Li5cysG+vgq/iJGFm1tTUxJe//GXGjx/PmDFj+MlPfgIkC+scf/zxfPCDH+Sd73wnU6dO5Ze//CUTJkxg9OjRLFyYNGH//ve/5+ijj2bs2LGccsopvP7661vdY/ny5ZxzzjmMHz+e8ePH8+ijj3b466hkTaEBGJq3PQR4Jb9ARLwCfABAUh/gnIhYzY4w7pP07tkX+rwj2b5vKoe8sTBXc4C09rDm3XzjzsI8ddTARmBibts1B7Mq+fNUeO25jr3mvqPhjOvaLLJ+/XqOOOIIAEaOHMnvfvc7fvazn9GvXz9mzpzJxo0bOfbYYznttNMAePbZZ6mvr2fvvfdm//3356KLLuKpp57i+uuv54YbbuD73/8+xx13HE888QSSuPnmm/nWt77Fd77znYL7XnrppXz+85/nuOOO46WXXuL000+nvr6+Q19+JZPCTGCUpJEkNYDzgQ/nF5A0AFgZEVuAy0hGIu04vfrB2jQb73ck69euLag6jWIJ53SHWV1Pye17bgXMWdWDJ9a6g9psV1Wq+eiBBx5gzpw53HXXXUAyKd4LL7xAjx49GD9+PIMGDQLggAMOyCWL0aNH88gjjwDQ0NDAhz70IV599VU2bdrEyJEjt7rvQw89xLx5Ld2yb731FmvWrKFv374d9toqlhQiolHSJcD9JENSb4mIuZKuBuoiYjpwAvANSQH8Fbi4UvGUlD9KafS5vHT/LQWd0Yc99w3etW4xd/S4Jrdv5V4wffN47l9/EuAOarOqKvOJfkeKCG644QZOP/30gv0zZsygZ8+eue0uXbrktrt06UJjY9IS8ZnPfIYvfOELTJ48mRkzZnDVVVdtdY8tW7bw+OOPs9tuu1XsdVT0OYWI+BPwp6J9V+Z9fxdwVyVj2BZbdUa/fTTrFmyisWlLbtfQ9fO5mHo+0Xcm0NJBffN9WwquddTARtasn1iwz4nCbOd1+umn86Mf/YiTTjqJ7t27849//GOb1m3On277tttuK1nmtNNO48Ybb+TLX/4yALNnz841Y3WUne+J5u1R9HwDo8+l93N3tfQ7APzjPlbPfTD3g8vaxARuZjLbmV100UUsXryYI488kohg4MCB3HNP9lH2V111Feeddx6DBw/mmGOO4cUXX9yqzA9+8AMuvvhixowZQ2NjI+9973v58Y9/3JEvY+eYOruiXniwsPYA1DcsY2W3pH3wsOe+we7rXmLTHiNyx1duSJuYup2U27dwbTeG9t7C/zmjZeZa1xzMtp2nzi5vl586u6KKaw/AwdwFfdI+hC2nsqb+IbbkNTEN2vQSH959CyccMym3b+pjsGh14fxLbmIys87GSaE9CkYtjaXvwINhr+Etx++bSt83FjKirqWD+odb4N7dJ3J/15OBliamWcsLJ+lzojCzanJSaI/i2sMLD7YkCYD9jmTj2+u26qC+hHo+2fsxIBnFdG9TS5KA1vsiip+qdpKwXV1EIJV6Pta2t0vASaEjtKODeuj6+oIkAVsPd4WkL6LbxtWcd1TLc4BL31zntSFsl9WrVy9WrFhB//79nRiKRAQrVqygV69e7b6Gk0Kl5DcxAew3lle2DM51UL/jtRn0X/Y4PfJOGbq+vmC4K5R+qjq6dKOxZ7+C27k2YbuKIUOG0NDQwPLly6sdSqfUq1cvhgwZ0u7znRQqpVQHda+8kUz7vof6hoNySQJKJ4pSQ17VtCE3uyu4NmG7lu7du5d82tc6hoekVlNxExPwzNJVvL2xpVZQasjr6n0n8uaQlr6IZGTTFoYNLKw9FDt40B6c8M7CCQWdKMx2DR6SWguKm5iAsX02wNC8kUxbTi3oi+j9Zj2936yn32stfRE/3ALTexX2RRQ3MdW/uib9eqvgfm52MrN8TgrVVKKJaeuRTFv3RQxc/jiUeS6i59qlbOna0sT0x369+J9lvQvmJS+VKEqtH1GKk4fZzsnNR7WgRDNTgfum0rhiEet2b+lTeLX/0TQdOKmgWJIoWkYl/LEhTRRpjaL+1TUAHDyocMbFYw8YwMkHF95/6Zvr6NWta27bScKsc+sUzUeSJpGsqtYVuDkiris6Pgy4DdgzLTM1nUTP8hU3M23eUPiw3P4n0A3INRa9/jz93prP6hVPFlymOFGcNgreP6ilRtFWbeLRhW8UXKs4Ubhj22znULGagqSuwD+AU0kW3JkJXBAR8/LKTAOeiYgfSToE+FNEjGjrurtkTaFYifmYChLFP+6DRTMKj7/+PACr9zioYHdxoihVm3jktZ4F58xZlYyPyq9RZKlNgBOFWbV0hprCBGBBRCxKA7oDOBuYl1cmgD3S7/tRtDKbtaJUX8RzeTOQv3NS8pUvTRQF45NK1ChK1SZOG1V4qT8vgb++tAHWbwKSJJGlNgFb1yicJMw6l0rWFM4FJkXERen2R4GjI+KSvDKDgAeAvYDewCkRMaut67qm0Iri2kNxE1MpxTWKVmoTWwJ6dCtczjt/WGwuSeStW9dcmxiz56aC844fIia++z25bdcmzHaMrDWFSiaF84DTi5LChIj4TF6ZL6QxfEfSu4GfAYely3PmX2sKMAVg2LBhRy1ZsqQiMe9UyjUxlVKq2QlYs7GRLVta/k76vTUfgHV7tUzNW/zsxJ+XwF8Kuxh4Lh0MlZ8oipMElE4UxZw4zLZNZ0gK7wauiojT0+3LACLiG3ll5pLUJpam24uAYyJiWWvXdU1hO7QnUZTyj/uS6cLTRFEqSZSyckM6CWD3lplioXxtopQsiQOcPMyadYak0I2ko/lk4GWSjuYPR8TcvDJ/Bn4dEbdKOhh4GBgcbQTlpNDB2tPsVKwoSTQrbnbq/WY90JI8ipMEtJ4oimVJHOBah1mzqieFNIgzge+TDDe9JSKulXQ1UBcR09MRRz8F+pB0On8lIh5o65pOChVWqjZRSobkUTxlR25upzRRFCcJKJ0oirWWOEpNFFiqs7uY+zVsV9ApkkIlOCl0Eu2sYeQniuIkAaUTRbHWEoeaSnd2Fz+MVyzrcNpiThxWS5wUbMdqZw2jXG2ilNYSR6nO7uJRUcW1iSUr3mZ4/9258qxDy8dexE91Wy1xUrDOKUMNozhRFNuWGkZxoih+OO9Ldf1YuLYbB/QpvF+ppqhipZ7qdue3dVZOClYb2jkiKksNo1SiyDJ0FrZuiio16yyUf6q7FNcwrBqcFKx2dWCiGLj88dx21qGzUL6GsT2TCRZzDcN2BCcF27lk6bPI8HBee4bOwtZJAgoTRdY5okppbw2jFCcOa42Tgu16KtQUlbW/olipjm4obIranhpGKR5ea61xUjCDDhs6m98MBdmbolzDsM7CScGslApN9VFKa4nDNQyrBicFs6w6KlEUK5E4skwm2JriGkZ+Rze0niiKuYaxa+qwpCDpsIh4vsMi205OCrZDdMScUKW0MplgucWPSik3IqoU1zB2XR2ZFP4X6AHcCvwqIlZ1SITt5KRgVdERo59K2YZV8opHSZXrryilGn0YThKdQ4c2H0kaBVwInAc8BfxXRDy43VG2g5OCdVod2F9Rbl2L7alhFKt0H4ZrE51Dh/cppGsu/xPwA+AtQMDlEfHb7Ql0WzkpWE2pYH9Fe9fhzqqSfRhOFDteRzYfjQE+CbwPeBD4WUQ8LWk/4PGIaPWvW9Ik4HqSqbNvjojrio5/Dzgx3dwd2Cci9mwrHicFq3mdoCmqWHv7MIqboqD9NQx3bFdWRyaFv5KseXBXRKwvOvbRiPh5K+d1JVlk51SggWSRnQsiYl4r5T8DjI2IC9uKx0nBdgkVbooqsB19GKW09zmM9jRFOUlk15FJ4XMR8f2ifZdGxPVlziu7HGdR+ceAr5brq3BSsF1WBUdEVbsPo3mm2m+Pa3l9jT32YPXg49u8lpuhsuvIpPB0RBxZtO+ZiBhb5rxzSdZfvijd/ihwdERcUqLscOAJYEhENJU4PgWYAjBs2LCjlixZ0varMtsVVKq/opQ2mqa29eG8UqY+BotWw/55o2nVtInNu/UvKOeO7fbb7qQg6QLgw8BxwN/yDvUFmiLilDIBnAecXpQUJkTEZ0qU/XeShLDVsWKuKZi1oVL9FaW08XBefo2iuBmqlFKr6bW2kl7+MqztXat7V0wSHZEUhgMjgW8AU/MOrQHmRETrq6Cwbc1Hkp4BLo6Ix8oF7KRgtp06cB3urZSoUeQ3QzVrz8y0xWtftLZWd5ZEsStOV171aS4kdSPpaD4ZeJmko/nDETG3qNxBwP3AyMgQjJOC2Q5SqT4Mys9M22vNEjb0Hc7icVe0eo1SCyRtT6IoZWeqYXRETeF/I+I4SWuA/EICIiL2yBDEmcD3SYak3hIR10q6GqiLiOlpmauAXhExtfUrtXBSMKuSCvdhPLrgDfbcPWkiGlF3TS4xtCXLSnqlEsWJ+27kfUM2lI2puLM7aw2jWGdIJlWvKVSKk4JZJ9KBiaLcdOVQ2OyUdZ2L1pqdRhf2YXP8YDijKOxy04ZAtlFSy9dsYPIRg9ssU2kdUVPYu60TI2JlO2PbLk4KZp1c1j6LYtu4IFLWdbmhMFG0VZsoThTFsiaOLDWMHV176Iik8CJJs5FKHI6I2H/7QmwfJwWznVQ7+jDK9U1A64ki38oNsGrj1vv/0q1lRFTWxAFbj5wq1Vy1orEn7550QfmLdRA3H5lZbXvuLuiz7dN5Z0kUWRQnk1LDZrNofv7iuomF+9e9voAu3Xcv2Ne9z54cfuJ523T9rDqipvCuiPi7pCNLHY+Ip7czxnZxUjDbRXTgMxfFiWJT0xb26dt2X8FeDQ/T77WWUfJZahzN8purSj2YB6WboooTRUcmiY5ICtMiYoqkR0ocjog4aXuDbA8nBTPLaWdHd3GSgPKJojhJtCZLDaO12kOxTcsXMfaAQYU7e/WDUaeWjaOYm4/MbNe0AxNFKVlqGItWw92bJzJrj8KmqOLaw7I1G+jRtbDZa+/GVzl4SFEHdYZEkTUpdCtXQFIv4P+QTHcRJFNe/Dgiyg/yNTPb0Uq9Ob7wIKx9vc3TxvbZAEMLE8czS1ex6u1NrZyRKE4cbw45uWBIbKkaxugt9YzuWs9z61v2r2+CpxZPhOEt55ZKSCvfHgR9BhTuLPPatkWWCfF+QzK1xS/SXRcAe0VEZXpDynBNwcwqogNrGMWKE0epRNFco3iuS0uN4tUBExl8eGFtInPtocjeE857YeX6eGebhchQUwAOiojD87YfkfRshvPMzGpHB9YwihXXOFbt/R429Tu2IFG8/OzDDHqjJVGM3lLP6GX1rKsrTB57lZiFtmTtoUjXLsr0KHaWpPCMpGMi4gkASUcDj2a5uJlZTcvSodvOxPHogjcKtpMaQcub/Y1/eZgJGx9jt7zKy+gt9fR+s36rWsbSPcfzKC1jf3bv2Y2xQ9tcxLJVrSYFSc+R9CF0Bz4m6aV0ezhQcvU0M7NdTjsTx+7rXoXdW38GeMOIk7nm5cIawVFvPcw53R8j/6xea5YwFAoWOyrXD9KWclNntyoiqrLSjfsUzGxnMPf+W1jVZa+CfeVGO5V65uHr669hFEto6tfylr2pcQtdiuaiOPBrz722fPX6ovGtW2u1plD8pi9pH2CbxmZJmgRcTzJL6s0RcV2JMh8EriKphTwbER/elnuYmdWiQ/cfulXH9jMLX4W+rdceji8xp97dmydyTncKag/FT2/3WrMEpEyPdGcZkjoZ+A6wH7CMpPmoHji0zHldgZuAU4EGYKak6RExL6/MKOAy4NiIeDNNPGZmO78SzU5d3riT9StfyW1v2fw2vd9xYG77jOFbPwU99bGTmcXJXNfGEwgj6q4B5mQKK0tH8zXAMcBDETFW0okkw1LLmQAsiIhFAJLuAM6msD/i08BNEfEmQEQsyxS1mdlOqHhKi8fvu53u65e3eY6a+rFwbTemPtZSESh+CC5Zl6LjksLmiFghqYukLhHxiKRvZjhvMLA0b7sBOLqozDsBJD1K0sR0VUTcV3whSVOAKQDDhg3LcGszs9q3YeTJ/H395sJ9jU0M3at3bnvChtfh+fkEyQJFi9IWqfyk8NpBHwN+n+meWZLCKkl9SJ5k/qWkZUDbT2okSk65XeL+o4ATgCHA3yQdFhGrCk6KmAa7jhteAAARtElEQVRMg6SjOcO9zcxqXqn1FqbPLlwM4uSD38EHez3F5t0GAi2d0VPLT9NUUpakcDawAfgc8BGgH3B1hvMagKF520OAV0qUeSIiNgMvSppPkiRmZri+mdkup+9u3Vm+pnCWoc2burMvSTPTSQN7oaae0NRyfOHabmwJbcly/bJJISLWSdqXpI9gJXB/RKzIcO2ZwChJI4GXgfOB4pFF95D0T9wqaQBJc9KiLIGbme2KStYeOIHu6VDWY0YmncD5rv7DXF4JZWplKTtESdJFwFPAB4BzgSckXVjuvIhoBC4B7icZrfSbiJgr6ep0RBPpsRWS5gGPAF/OmHDMzCyj4f17ly+UyjIh3nxgYvObtaT+wGMRcdD2BNlefnjNzKzQI/OXsSavQ7q4MxrgzLHDX33zrbX7lbtWlj6FBpJZUputoXBUkZmZVVFxk1JxZzTAW41d12e5VltzH30h/fZl4ElJ95KMHjqbpDnJzMx2Mm3VFPqm/y5Mv5rdW7lwzMxse5UaoURs2b7RRxHxtfxtSX2T3bG2HTGamdkOUmqEUmRMCllGHx0m6RngeWCupFmS2pz3yMzMalOWWfOmAV+IiOERMRz4IvDTyoZlZmYdKTa+vaZ8qWxJoXdEPJK7cMQMIPugVzMzqxlZhqQuknQF8PN0+1+AFysXkpmZVUuWmsKFwEDgt+nXAOCTlQzKzMyqo82aQrpQzuUR8dkdFI+ZmVVRmzWFiGgCjtpBsZiZWZVl6VN4RtJ04E5gXfPOiPhtxaIyM7OqyNKnsDewAjgJeH/6dVaWi0uaJGm+pAWSppY4/glJyyXNTr8u2pbgzcysY2WpKXw5It7Y1gun/RE3AaeSTKo3U9L0iJhXVPTXEXHJtl7fzMw6Xqs1BUnvl7QcmCOpQdLEbbz2BGBBRCyKiE3AHSST6ZmZWSfVVvPRtcB7ImI/4BzgG9t47cEUTrHdkO4rdo6kOZLukjS0xHEkTZFUJ6lu+fLl2xiGmZll1VZSaIyIvwNExJO0zJqalUrsK17R5/fAiIgYAzwE3FbqQhExLSLGRcS4gQMHbmMYZmaWVVt9Cvvkramw1XZEfLfMtRuA/E/+Q4BX8gsULb35U+CbZa5pZmYV1FZN4acktYPmr+LtcmYCoySNlNQDOB+Ynl9A0qC8zckkazmbmVmVZF5PYVtFRKOkS4D7ga7ALRExV9LVQF1ETAc+K2ky0AisBD6xPfc0M7Pto4jiZv7Obdy4cVFXV1ftMMzMaoqkWRExrly5LA+vmZnZLsJJwczMclrtUygaebSVDKOPzMysxrQ1JHVbn0swM7MaV7HRR2ZmVnvKTognqRfwKeBQoFfz/oi4sIJxmZlZFWTpaP45sC9wOvAXkieT11QyKDMzq44sSeHAiLgCWBcRtwHvA0ZXNiwzM6uGLElhc/rvKkmHAf2AERWLyMzMqibLIjvTJO0FXEEyd1Gf9HszM9vJZEkK/xURTST9CftXOB4zM6uiLM1HL0qaJulkSaXWSDAzs51ElqRwEMkCOBcDiyXdKOm4yoZlZmbVUDYpRMT6iPhNRHwAOALYg6QpqSxJkyTNl7RA0tQ2yp0rKSSVncHPzMwqJ9OEeJKOl/RD4GmSB9g+mOGcrsBNwBnAIcAFkg4pUa4v8FngyW2I28zMKqBsUpD0IvA54G/AYRHxwYi4O8O1JwALImJRRGwC7gDOLlHuGuBbwIbsYZuZWSVkGX10eES81Y5rDwaW5m03AEfnF5A0FhgaEX+Q9KXWLiRpCjAFYNiwYe0IxczMsmhr6uyvRMS3gGslbbU8W0R8tsy1S41Uyl1HUhfge2RYgjMipgHTIFl5rVx5MzNrn7ZqCvXpv+1d+7IBGJq3PQR4JW+7L3AYMCMd6bovMF3S5IjweptmZlXQ1tTZv0+/nRMRz7Tj2jOBUZJGAi8D5wMfzrv+amBA87akGcCXnBDMzKony+ij70r6u6RrJB2a9cIR0QhcAtxPUuv4TUTMlXS1pMntjNfMzCpIEeWb6CXtSzIM9UMkzyn8OiL+o8KxlTRu3Lioq3NlwsxsW0iaFRFlnwXL9JxCRLwWET8A/hWYDVy5nfGZmVknlOU5hYMlXSXpeeBG4DGSTmMzM9vJZJolFbgdOC0iXilX2MzMalebSSGdqmJhRFy/g+IxM7MqarP5KF1Hob+kHjsoHjMzq6IszUdLgEclTQfWNe+MiO9WLCozM6uKLEnhlfSrC8lTyGZmtpMqmxQi4ms7IhAzM6u+sklB0iPkTWTXLCJOqkhEZmZWNVmaj/KntO4FnAM0ViYcMzOrpizNR7OKdj0qKdNynGZmVluyNB/tnbfZBTiKZJprMzPbyWRpPppF0qcgkmajF4FPZbm4pEnA9UBX4OaIuK7o+L8CFwNNwFpgSkTMyxy9mZl1qCzNRyPbc+H0aeibgFNJFtyZKWl60Zv+ryLix2n5ycB3gUntuZ+ZmW2/Vp9oljQ+nTK7eftjku6V9IOiJqXWTAAWRMSiiNgE3AGcnV+gaO3n3pQY5WRmZjtOW9Nc/ATYBCDpvcB1wH8Dq0nXSy5jMLA0b7sh3VdA0sWSFgLfAkqu+yxpiqQ6SXXLly/PcGszM2uPtpJC14hYmX7/IWBaRNwdEVcAB2a4tkrsK/W8w00RcQDw78D/K3WhiJgWEeMiYtzAgQMz3NrMzNqjzaQgqbnP4WTgf/KOZemgbgCG5m0PIZkuozV3AP+U4bpmZlYhbSWF24G/SLoXWA/8DUDSgSRNSOXMBEZJGpnOsno+MD2/gKRReZvvA17YhtjNzKyDtfqJPyKulfQwMAh4IFoWc+4CfKbchSOiUdIlwP0kQ1JviYi5kq4G6iJiOnCJpFOAzcCbwMe37+WYmdn2UMt7fW0YN25c1NXVVTsMM7OaImlWRIwrV67sGs1mZrbrcFIwM7McJwUzM8txUjAzsxwnBTMzy3FSMDOzHCcFMzPLcVIwM7McJwUzM8txUjAzsxwnBTMzy3FSMDOznIomBUmTJM2XtEDS1BLHvyBpnqQ5kh6WNLyS8ZiZWdsqlhQkdQVuAs4ADgEukHRIUbFngHERMQa4i2RJTjMzq5JK1hQmAAsiYlFEbCJZWe3s/AIR8UhEvJ1uPkGyOpuZmVVJJZPCYGBp3nZDuq81nwL+XOqApCmS6iTVLV++vANDNDOzfJVMCiqxr+SKPpL+BRgH/Gep4xExLSLGRcS4gQMHdmCIZmaWr9XlODtAAzA0b3sI8EpxoXQ5zv8LHB8RGysYj5mZlVHJmsJMYJSkkZJ6AOcD0/MLSBoL/ASYHBHLKhiLmZllULGkEBGNwCXA/UA98JuImCvpakmT02L/CfQB7pQ0W9L0Vi5nZmY7QCWbj4iIPwF/Ktp3Zd73p1Ty/mZmtm38RLOZmeU4KZiZWY6TgpmZ5TgpmJlZjpOCmZnlOCmYmVmOk4KZmeU4KZiZWY6TgpmZ5TgpmJlZjpOCmZnlOCmYmVlORZOCpEmS5ktaIGlqiePvlfS0pEZJ51YyFjMzK69iSUFSV+Am4AzgEOACSYcUFXsJ+ATwq0rFYWZm2VVy6uwJwIKIWAQg6Q7gbGBec4GIWJwe21LBOMzMLKNKNh8NBpbmbTek+8zMrJOqZFJQiX3RrgtJUyTVSapbvnz5doZlZmatqWRSaACG5m0PAV5pz4UiYlpEjIuIcQMHDuyQ4MzMbGuVTAozgVGSRkrqAZwPeA1mM7NOrGJJISIagUuA+4F64DcRMVfS1ZImA0gaL6kBOA/4iaS5lYrHzMzKq+ToIyLiT8CfivZdmff9TJJmJTMz6wT8RLOZmeU4KZiZWY6TgpmZ5TgpmJlZjpOCmZnlOCmYmVmOk4KZmeU4KZiZWY6TgpmZ5TgpmJlZjpOCmZnlOCmYmVmOk4KZmeVUNClImiRpvqQFkqaWON5T0q/T409KGlHJeMzMrG0VSwqSugI3AWcAhwAXSDqkqNingDcj4kDge8A3KxWPmZmVV8mawgRgQUQsiohNwB3A2UVlzgZuS7+/CzhZUqm1nc3MbAeo5CI7g4GledsNwNGtlYmIRkmrgf7AG/mFJE0BpqSbGyU9X5GIK28ARa+tRtRq3FC7sddq3FC7sddq3JAt9uFZLlTJpFDqE3+0owwRMQ2YBiCpLiLGbX94O16txl6rcUPtxl6rcUPtxl6rcUPHxl7J5qMGYGje9hDgldbKSOoG9ANWVjAmMzNrQyWTwkxglKSRknoA5wPTi8pMBz6efn8u8D8RsVVNwczMdoyKNR+lfQSXAPcDXYFbImKupKuBuoiYDvwM+LmkBSQ1hPMzXHpapWLeAWo19lqNG2o39lqNG2o39lqNGzowdvmDuZmZNfMTzWZmluOkYGZmOTWVFMpNm9FZSLpF0rL85ykk7S3pQUkvpP/uVc0YWyNpqKRHJNVLmivp0nR/p45fUi9JT0l6No37a+n+kekUKi+kU6r0qHaspUjqKukZSX9It2sl7sWSnpM0W1Jduq9T/600k7SnpLsk/T39e393Z49d0kHpz7r56y1Jn+vIuGsmKWScNqOzuBWYVLRvKvBwRIwCHk63O6NG4IsRcTBwDHBx+nPu7PFvBE6KiMOBI4BJko4hmTrle2ncb5JMrdIZXQrU523XStwAJ0bEEXnj5Dv730qz64H7IuJdwOEkP/9OHXtEzE9/1kcARwFvA7+jI+OOiJr4At4N3J+3fRlwWbXjaiPeEcDzedvzgUHp94OA+dWOMePruBc4tZbiB3YHniZ5gv4NoFupv6HO8kXyDM/DwEnAH0ge6uz0caexLQYGFO3r9H8rwB7Ai6SDbWop9rxYTwMe7ei4a6amQOlpMwZXKZb2eEdEvAqQ/rtPleMpK521dizwJDUQf9oEMxtYBjwILARWRURjWqSz/s18H/gKsCXd7k9txA3JDAQPSJqVTkcDNfC3AuwPLAf+K222u1lSb2oj9mbnA7en33dY3LWUFDJNiWEdQ1If4G7gcxHxVrXjySIimiKpVg8hmZDx4FLFdmxUbZN0FrAsImbl7y5RtFPFnefYiDiSpFn3YknvrXZAGXUDjgR+FBFjgXV0sqaitqR9TJOBOzv62rWUFLJMm9GZvS5pEED677Iqx9MqSd1JEsIvI+K36e6aiT8iVgEzSPpE9kynUIHO+TdzLDBZ0mKSmYRPIqk5dPa4AYiIV9J/l5G0bU+gNv5WGoCGiHgy3b6LJEnUQuyQJOGnI+L1dLvD4q6lpJBl2ozOLH9Kj4+TtNV3OunU5T8D6iPiu3mHOnX8kgZK2jP9fjfgFJKOw0dIplCBThh3RFwWEUMiYgTJ3/T/RMRH6ORxA0jqLalv8/ckbdzP08n/VgAi4jVgqaSD0l0nA/OogdhTF9DSdAQdGXe1O0u2sWPlTOAfJG3F/7fa8bQR5+3Aq8Bmkk8knyJpJ34YeCH9d+9qx9lK7MeRNFXMAWanX2d29viBMcAzadzPA1em+/cHngIWkFS1e1Y71jZewwnAH2ol7jTGZ9Ovuc3/Jzv730pe/EcAdenfzD3AXrUQO8lAihVAv7x9HRa3p7kwM7OcWmo+MjOzCnNSMDOzHCcFMzPLcVIwM7McJwUzM8up2MprZrVEUvOQPoB9gSaSaRAAJkTEpqoEZraDeUiqWRFJVwFrI+LbFbxHt2iZ28is03DzkVkZkj6ertUwW9IPJXWR1E3SKknXpWs4PC5pn7T8LyT9U975a9N/T5H0kKQ7SB60K3ntqrxIs5T/AM3aIOkw4J+BiZFMtteNZDoKgH7AXyJZw+Fx4MIMlzwG+EpEjC5zbbOqcJ+CWdtOAcYDdcm0UOxGyxTu6yPiz+n3s4D3ZLje4xHxUoZrm1WFk4JZ2wTcEhFXFOxMZjDN73xuouX/UyNpLTxdMTD//9m6ctc2qyY3H5m17SHgg5IGQDJKSdKwMucsJlkqEZLmoa4deG2zinJSMGtDRDwHfA14SNIc4AHgHWVO+wlwqqSnSGbi3NiB1zarKA9JNTOzHNcUzMwsx0nBzMxynBTMzCzHScHMzHKcFMzMLMdJwczMcpwUzMws5/8Hj6mW2kN9t1IAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"male = (survivaldata['gender_Male'] == 1)\\n\",\n    \"female = (survivaldata['gender_Male'] == 0)\\n\",\n    \"\\n\",\n    \"plt.figure()\\n\",\n    \"ax = plt.subplot(1,1,1)\\n\",\n    \"\\n\",\n    \"kmf.fit(timevar[male],event_observed = eventvar[male],label = \\\"Male\\\")\\n\",\n    \"plot1 = kmf.plot(ax = ax)\\n\",\n    \"\\n\",\n    \"kmf.fit(timevar[female],event_observed = eventvar[female],label = \\\"Female\\\")\\n\",\n    \"plot2 = kmf.plot(ax = plot1)\\n\",\n    \"                 \\n\",\n    \"plt.title('Survival of customers: Gender')\\n\",\n    \"plt.xlabel('Tenure')\\n\",\n    \"plt.ylabel('Survival Probability')\\n\",\n    \"plt.yticks(np.linspace(0,1,11))\\n\",\n    \"groups = logrank_test(timevar[male], timevar[female], event_observed_A=eventvar[male], event_observed_B=eventvar[female])\\n\",\n    \"groups.print_summary()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Senior Citizen\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 91,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"<lifelines.StatisticalResult>\\n\",\n      \"               t_0 = -1\\n\",\n      \" null_distribution = chi squared\\n\",\n      \"degrees_of_freedom = 1\\n\",\n      \"\\n\",\n      \"---\\n\",\n      \" test_statistic      p  -log2(p)\\n\",\n      \"         109.49 <0.005     82.71\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"SeniorCitizen = (survivaldata['SeniorCitizen_1'] == 1)\\n\",\n    \"no_SeniorCitizen = (survivaldata['SeniorCitizen_1'] == 0)\\n\",\n    \"\\n\",\n    \"plt.figure()\\n\",\n    \"ax = plt.subplot(1,1,1)\\n\",\n    \"\\n\",\n    \"kmf.fit(timevar[SeniorCitizen],event_observed = eventvar[SeniorCitizen],label = \\\"Senior Citizen\\\")\\n\",\n    \"plot1 = kmf.plot(ax = ax)\\n\",\n    \"\\n\",\n    \"kmf.fit(timevar[no_SeniorCitizen],event_observed = eventvar[no_SeniorCitizen],label = \\\"Not a Senior Citizen\\\")\\n\",\n    \"plot2 = kmf.plot(ax = plot1)\\n\",\n    \"                 \\n\",\n    \"plt.title('Survival of customers: Senior Citizen')\\n\",\n    \"plt.xlabel('Tenure')\\n\",\n    \"plt.ylabel('Survival Probability')\\n\",\n    \"plt.yticks(np.linspace(0,1,11))\\n\",\n    \"groups = logrank_test(timevar[SeniorCitizen], timevar[no_SeniorCitizen], event_observed_A=eventvar[SeniorCitizen], event_observed_B=eventvar[no_SeniorCitizen])\\n\",\n    \"groups.print_summary()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Partner\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 92,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"<lifelines.StatisticalResult>\\n\",\n      \"               t_0 = -1\\n\",\n      \" null_distribution = chi squared\\n\",\n      \"degrees_of_freedom = 1\\n\",\n      \"\\n\",\n      \"---\\n\",\n      \" test_statistic      p  -log2(p)\\n\",\n      \"         423.54 <0.005    310.21\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"partner = (survivaldata['Partner_Yes'] == 1)\\n\",\n    \"no_partner = (survivaldata['Partner_Yes'] == 0)\\n\",\n    \"\\n\",\n    \"plt.figure()\\n\",\n    \"ax = plt.subplot(1,1,1)\\n\",\n    \"\\n\",\n    \"kmf.fit(timevar[partner],event_observed = eventvar[partner],label = \\\"Has partner\\\")\\n\",\n    \"plot1 = kmf.plot(ax = ax)\\n\",\n    \"\\n\",\n    \"kmf.fit(timevar[no_partner],event_observed = eventvar[no_partner],label = \\\"Does not have a partner\\\")\\n\",\n    \"plot2 = kmf.plot(ax = plot1)\\n\",\n    \"                 \\n\",\n    \"plt.title('Survival of customers: Partner')\\n\",\n    \"plt.xlabel('Tenure')\\n\",\n    \"plt.ylabel('Survival Probability')\\n\",\n    \"plt.yticks(np.linspace(0,1,11))\\n\",\n    \"groups = logrank_test(timevar[partner], timevar[no_partner], event_observed_A=eventvar[partner], event_observed_B=eventvar[no_partner])\\n\",\n    \"groups.print_summary()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Dependents\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 93,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"<lifelines.StatisticalResult>\\n\",\n      \"               t_0 = -1\\n\",\n      \" null_distribution = chi squared\\n\",\n      \"degrees_of_freedom = 1\\n\",\n      \"\\n\",\n      \"---\\n\",\n      \" test_statistic      p  -log2(p)\\n\",\n      \"         232.70 <0.005    172.12\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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4gGImUHcWtYMPKHdvbtsK68rSWx2jIon44JiaPdEZIpTsvI5moySTdvg9PHG5Xkc17NFdIGgU8QzAkdaKZzZE0Figzs6nAYOCPkoyg+Sgj92xOKvHDfst6eP32HdsFNEIpH0RZNyqZVMkjk7WORN5c5VxqWZ2nkA0p5ynUxbtTdjQpVTYnDR5d9ZgG3qS0syWbh5EoE/MykqmpuQqqd5JHSRRReKe5yxdRO5p37aSw4NmqayZ9/iG0LsxJbw3JzkwcdUkUqaSbSKIkDvDk4erHk0Jdlf09aE5KHKHUsr3XHvJQlMQB6SWPdPo1Mj0sN5moySMVTy67Jk8K6YhvToKgSWntEtirW9XjvPZQMBKTx85smoqiLslkZzZpgSePhsaTQjrim5MAFr8AH74GjZvu2Jes9uA1h4KRzRpGOuoyixyqJo9MJYmaeH9Iw+JJIVOi1B685tDg7KwaRlSZnEUeVZSks3r9Zk7o0yFrMbjMyYcZzQ1D4vyG9n2DUUqV1i6pPgmu8jyvPRSsdIfcRpGJOR3pNldFFXV9q80V23yORwPjNYV0JA5lTdbvsHcv6P69qvs8UTjyrxaSTNSaSdQ5HpnoIAdPMPXhNYVsiq89JNYcIEgSAAefUXV/fI3D7bKyNfEvk6LUTKJOBsxkzOlOIozCE07AawqZ4KOW3E6Qy4l/yUSd45HtDvFMaegd697RvDP5qCWXJ6KOrooiE3M8Mt0hnusEk25TWD4kE08KuRal9uD9Di6PZaLvI5Md4tkecZXNhJMPo7S8TyHXUo1aWvle+PNu1fP27lW11lFZlicKt5NF6fvI1gKHyWRzxFV9FkuMItkorXyoPSST1ZqCpCHAOIJVUu82s+sTHu8M3APsER4z2sz+XVuZBVNTSCa+9vDB09Vv8lNZkxhyfdX9G1YGNwpyLs8ka67Kx9FUqdRnDaxMrnmVzUSR8+YjSUXAB8BRBDfcmQGcbmZz446ZAMwys9sl9QT+bWZdayu3oJNCYt/D1s2wZ5cd21GHt3rNweWxQhhyG0XU5dghc4kjMVFkMknkQ/NRf2ChmS0OA5oMDAXmxh1jQMvw91Yk3JmtwUn8IE+c8FYyuPo5yZqZvInJ5bFMTfzLdTKJ0vSV6ft4JD7fTE2YrItsJoUOwPK47XLg0IRjxgDTJF0EfA1I6HUNSBoJjATo3LlzxgPNmWr9DgdD2/2r1h4Sm5l8DoQrMOl+0831LPIo0k0cURNFLvoistl8dApwjJn9NNw+C+hvZhfFHXNpGMOfJX0L+BtwYHh7zqQKuvkoisQmJqjazORNTM7VWbpDdXfmqrrL1nxJl9bN+P3xB9R6brp9EfnQfFQOdIrb7kj15qGfAEMAzOw1ScVAG2BVFuPKb8k+yOObmUoGV3/cm5icq1WmaiuZShLJahhjn5rDsjVfMvapObF9UZqYoPpM7/rUJrKZFGYA3SV1Az4CTgOGJxzzIXAkMEnS/kAxsDqLMRWmKstq1KOJ6bNlvnCfc3WwM/tHBn6jDbCj9rBszZfAp5E6qBPLTrYcSKOmzVsSQbaHpH4fuJlguOlEM7tW0ligzMymhiOO7gKaE3Q6/8bMptVWZoNvPooi3SamksHwzSFV9322DJoU79j2JOFcxk2d/RFtWxSnPjBOZc2hS+tmVfanOwR2UK+Sxdu+XPeNVMdldfJaOOfg3wn7fh/3+1xgYDZjaJDq08SUODeiZHDVROG1CecyrsXuTeo88S+x5gDZn2QHPqO54ahrExMkTxQlg5PXJhITRbLre+JwLqlk7fupZogn63eoqYM6ajNTFL72UUOVqokJqieKle8F/+59YOrySwZXTR4+69q5estUM1OymkNeNB+5HEr2rX3Bs7XPi0hWm0gmWQ1j717R4vIahXMZldjMVFMTU1SeFHYlqRJFsmanZJLVMJIt7lcyuHpTlE+yc65GiX0PUUYtRbkhUtDEFI03H7mqojQ7JaqpvwKqN0X5JDvnIsvUgoNjn5rDtCt+4M1HLg1Rmp2gaqL45pDqNYLaOrZTTbJLxpOH2wWl00GdTJfW0ZOI1xRcelKt+JpM1I7tksE+n8K5OojSQe0dzS67Ej+QU9UmoHqNIuowWYg2nyIZTx7O1UnKpCDpQDN7b2cE4wrYzmh2SjUyqmRw6jkWniRcA5ROB3VNUjYfSXoZ2A2YBDxgZp+ndaUM8eajApeJZqdkojZFJTZDgScK1+Ak66A+8Vv7Ldy2aX33VOdG6lOQ1B04FzgFeBP4u5k9m1649eNJoYFJZ7RTMlFHQJUMTt1fAZ4oXIPTqPhrH2zfvLFHquMidzSHt9f8IXAL8AUg4Ldm9s/6BFpXnhR2AdlKFPXp2E7GE4crIBlLCpJ6Az8GjgOeBf5mZm9Jag+8ZmY1/k+VNAQYR7BK6t1mdn3C438BDg83mwHtzGyP2uLxpLCLSpYoEmVyPkXJ4OqJIpGPiHIFJJNJ4UWC5a2nmNmmhMfOMrN/1HBeEfABcBTBDXdmAKeHK6MmO/4i4GAzO7e2eDwpuBple+JdopLB3l/hCkYm77z2z8QPfkkXm9m4mhJCqD+w0MwWh+dMBoYCSZMCcDpwVYR4nEsuygioVMNkIXrHdrorzHqicHksSk3hLTPrm7BvlpkdnOK8k4EhCfdoPtTMRiU5tgvwOtDRzLYleXwkMBKgc+fO/ZYtW1b7s3KuJvnYX+FJwu0E9a4pSDqd4PaZ3SRNjXuoBbAmSgxJ9tWUgU4jaJ6qlhAAzGwCMAGC5qMI13YuuXTmUyTjE/FcA1Vb89GrwMdAG+DPcfvXA+9EKLsc6BS33RFYUcOxpwEXRijTucyLmigSZWoiXsng1J3a4BPx3E6RtbWPJDUm6Gg+EviIoKN5uJnNSTiuB/AM0M0iBOMdzS5vZHP9p2RKBnvHtktbJpqPXjazQZLWU7XZR4CZWcvaCjazCkmjCD7wi4CJZjZH0ligzMwqm6ROByZHSQjO5ZVsrf+UjHdsu53EV0l1LpvysWM7GU8cDV7UmkKNSUHSXrWdaGZr04ytXjwpuIKXTrNTomxPxANPFA1MJpLCEoJmo6SjiMyspH4hpseTgmtwdvb6T8mUDPahsw1cvZNCvvKk4HYJ2UwUieqzwmwynjzyUiZqCvuZ2XxJfZM9bmZv1TPGtHhScLusTDQ7JVOfFWaT8RpGXspEUphgZiMlPZ/kYTOzI+obZDo8KTgXylRtIpn6dGwn8hpGXvDmI+d2RZlYTTaZTPdXJLNhJfQ6uW5xucgylhQkFQM/BwYRdDy/BNxhZptrPTFLPCk4V0/52F8B3uyUZZlMCg8TLG1xX7jrdGBPMzul3lGmwZOCc1mQraao+gyd9WGyGZXJpPC2mR2Uat/O4knBuZ0k101RJYO9NpFBmbyfwixJA8zs9bDgQ4FX6hugcy7PRfmwzcQKs1A9UaxdsuPYeInl+rIeGVfb6KN3CfoQmgA9gA/D7S7AXDOLsIJX5nlNwbk8l4kaxtOjg8SwV7fayykZHG1ZD08UGakpHJ/BeJxzu4pM1DBKBqcuI2ptAnzZ8TqIPCRVUjsgln7N7MMI5wwBxhGsknq3mV2f5JhTgTEEtZC3zWx4bWV6TcG5BurdKdB87+jHR61NgHdik8E+BUknENxkpz2wiqD5aB5wQIrzioDxwFEEN9yZIWmqmc2NO6Y7cAUw0Mw+CxOPc25XVNyq9vtpJyoZHK3cZDWKKLWJypgacKJIJkpH8x+AAcB/zexgSYcTDEtNpT+w0MwWA0iaDAwF5sYdcx4w3sw+AzCzVXUJ3jnXgNT1/hTJOqyTqaxRPD16x76Swek1OyXTwBJHlKSw1czWSGokqZGZPS/p/yKc1wFYHrddDhyacMw3ASS9QtDENMbMno4SuHOugYtym9QoQ2JLBlfdjnrvbIg23LaBJY4oSeFzSc0JZjLfL2kVUBHhvKRLbie5fndgMME9nF+SdKCZfV6lIGkkMBKgc+fOES7tnGuQsnW3u5oSRaKSwZlLHHmaKKIkhaHAZuAS4AygFTA2wnnlQKe47Y7AiiTHvG5mW4Elkt4nSBIz4g8yswnABAg6miNc2zm3K4hSm4DUzU5Rl+yIkjigevJIljgSY8wTkUYfSdqHoI/AgBlm9kmEcxoDHwBHAh8RfNAPN7M5cccMAU43s3MktQFmAX3MbE1N5froI+dcndV1ZFMydbmfNtR9djZktfaQydFHPwV+D/yPoEnoVkljzWxibeeZWYWkUcAzBP0FE81sjqSxQJmZTQ0fO1rSXGAbcFltCcE559JS15FNyUTt2E627HjUPow8mE8RZe2j94FvV35YS2oNvGpmPXZCfNV4TcE5V29RZl1D9hYFrJxfMaTa1K2qMlibyOTaR+UEq6RWWk/VUUXOOVdYon6opjPaKVGyGkayYbKQF30RNSYFSZeGv34EvCHpCYI+haHAm1mNyjnn8kGU0U7JpDPxLlkzU8ng6gll6+asjmSqrabQIvx3UfhT6YmMXNk55wpNpmoYmVwpFjJae6jL2kctCO7NvCFjV0+D9yk45wpOOjcxqmltp5LBad1nIpOjjw4E/gHsFW5/CpwdP7TUOedcLZLVMFLNgi4ZXH1f1JFM9ag5ROlongBcambPA0gaDNwFfDvtqzrn3K4ucZgspDfJLlkzU5J+hz2LaRklrChJ4WuVCQHAzKZL+lqUwp1zztUgnbWdahrJlChJs1RRIxVFCStKUlgs6UqCJiSAM4ElUQp3zjlXB4mJIlUTU6Uoq8BGFCUpnAtcDfwz3H4R+HFaV3POORddqiYmiL4KbES1JoXwRjm/NbNfpFW6c8659EXpoI6yCuza6I07tSYFM9smqV/k0pxzzmVXOh3UT48GUq5jCkRrPpolaSrwCLCxcqeZ/bPmU5xzzmVFOsNb6yBKUtgLWAMcEbfP2NHH4JxzLp/tVQK8FunQKEnhMjP7NJ04wvsljCNYOvtuM7s+4fERwI0E6ysB3GZmd6dzLeec22WlWhq8/0jg/khF1bYg3g+AicBWSduBU83s1agxhp3U44GjCFZanSFpqpnNTTj0ITMbFbVc55xzCSIs3Ldtu22PUlRtNYVrge+Y2XxJhwI3AN+tQ5j9gYVmthhA0mSCFVYTk4JzzrlMStLvsN2IlBQa1fJYhZnNBzCzN9ixampUHah634XycF+iH0l6R9IUSZ2SPI6kkZLKJJWtXr26jmE455yLqraaQru4eypU2zazm1KUrST7EpdkfRJ40My2SDofuIeqHdqV15pAsAYTpaWl0ZZ1dc45V2e11RTuIqgdVP4kbqdSDsR/8+8IrIg/wMzWmNmWuOv5nAjnnMuCdVuq3EGzRjXWFMzs6nrGMAPoLqkbweii04Dh8QdI+rqZfRxungDMq+c1nXPO1UOUIalpMbMKSaOAZwiGpE40szmSxgJlZjYV+IWkE4AKYC0wIlvxOOecSy3yndfyhd95zTnn6i7qnddq61Nwzjm3i6lt8tqlNT0GkUYfOeecKzC19SnUdV6Cc865ApfN0UfOOecKTMrRR5KKgZ8ABwDFlfvN7NwsxuWccy4HonQ0/wPYBzgGeIFgElqkSRDOOecKS5SksK+ZXQlsNLN7gOOAXtkNyznnXC5ESQpbw38/l3Qg0AromrWInHPO5UyUGc0TJO0JXAlMBZqHvzvnnGtgoiSFv5vZNoL+hJIsx+Occy6HojQfLZE0QdKRkpIth+2cc66BiJIUegD/BS4Elkq6TdKg7IblnHMuF1ImBTPbZGYPm9lJQB+gJUFTUkqShkh6X9JCSaNrOe5kSSYp5WJNzjnnsifSgniSvivpr8BbBBPYTo1wThEwHjgW6AmcLqlnkuNaAL8A3qhD3M4557IgZVKQtAS4BHgJONDMTjWzRyOU3R9YaGaLzewrYDIwNMlxfwBuADZHD9s551w2RBl9dJCZfZFG2R2A5XHb5cCh8QdIOhjoZGZPSfp1TQVJGgmMBOjcuXMaoTjnnIuitqWzf2NmNwDXSqp2Jx4z+0WKspONVIqVI6kR8Bci3G3NzCYAEyC4yU6q451zzqWntppC5f2S073NWTnQKW67I7AibrsFcCAwPRzpug8wVdIJZua3VnPOuRyobensJ8Nf3zGzWWm0CBPRAAAZYElEQVSUPQPoLqkb8BFwGjA8rvx1QJvKbUnTgV97QnDOudyJMvroJknzJf1B0gFRCzazCmAU8AxBreNhM5sjaaykE9KM1znnXBbJLHUTvaR9CIahDiOYp/CQmV2T5diSKi0ttbIyr0w451xdSJppZinngkWap2Bmn5jZLcD5wGzg9/WMzznnXB6KMk9hf0ljJL0H3Aa8StBp7JxzroGJtEoq8CBwtJmtSHWwc865wlVrUgiXqlhkZuN2UjzOOedyqNbmo/A+Cq0l7baT4nHOOZdDUZqPlgGvSJoKbKzcaWY3ZS0q55xzORElKawIfxoRzEJ2zjnXQKVMCmZ29c4IxDnnXO6lTAqSniduIbtKZnZEViJyzjmXM1Gaj+KXtC4GfgRUZCcc55xzuRSl+Whmwq5XJEW6HadzzrnCEqX5aK+4zUZAP4Jlrp1zzjUwUZqPZhL0KYig2WgJ8JMohUsaAowDioC7zez6hMfPBy4EtgEbgJFmNjdy9M455zIqSvNRt3QKDmdDjweOIrjhzgxJUxM+9B8wszvC408AbgKGpHM955xz9VfjjGZJh4RLZlduny3pCUm3JDQp1aQ/sNDMFpvZV8BkYGj8AQn3fv4aSUY5Oeec23lqW+biTuArAEmHAdcD9wLrCO+XnEIHYHncdnm4rwpJF0paBNwAJL3vs6SRksokla1evTrCpZ1zzqWjtqRQZGZrw9+HARPM7FEzuxLYN0LZSrIv2XyH8Wb2DeBy4HfJCjKzCWZWamalbdu2jXBp55xz6ag1KUiq7HM4Evhf3GNROqjLgU5x2x0JlsuoyWTghxHKdc45lyW1JYUHgRckPQFsAl4CkLQvQRNSKjOA7pK6hausngZMjT9AUve4zeOABXWI3TnnXIbV+I3fzK6V9BzwdWCa7biZcyPgolQFm1mFpFHAMwRDUiea2RxJY4EyM5sKjJL0PWAr8BlwTv2ejnPOufrQjs/6wlBaWmplZWW5DsM55wqKpJlmVprquJT3aHbOObfr8KTgnHMuxpOCc865GE8KzjnnYjwpOOeci/Gk4JxzLsaTgnPOuRhPCs4552I8KTjnnIvxpOCccy7Gk4JzzrkYTwrOOedispoUJA2R9L6khZJGJ3n8UklzJb0j6TlJXbIZj3POudplLSlIKgLGA8cCPYHTJfVMOGwWUGpmvYEpBLfkdM45lyPZrCn0Bxaa2WIz+4rgzmpD4w8ws+fN7Mtw83WCu7M555zLkWwmhQ7A8rjt8nBfTX4C/CfZA5JGSiqTVLZ69eoMhuiccy5eNpOCkuxLekcfSWcCpcCNyR43swlmVmpmpW3bts1giM455+LVeDvODCgHOsVtdwRWJB4U3o7z/wHfNbMtWYzHOedcCtmsKcwAukvqJmk34DRgavwBkg4G7gROMLNVWYzFOedcBFlLCmZWAYwCngHmAQ+b2RxJYyWdEB52I9AceETSbElTayjOOefcTpDN5iPM7N/AvxP2/T7u9+9l4jpbt26lvLyczZs3Z6I457KiuLiYjh070qRJk1yH4lyNspoUdpby8nJatGhB165dkZL1bzuXW2bGmjVrKC8vp1u3brkOx7kaNYhlLjZv3kzr1q09Ibi8JYnWrVt7bdblvQaRFABPCC7v+XvUFYIGkxScc87VnyeFDGnevHmV7UmTJjFq1KiMXmPMmDH86U9/ymiZUU2fPp3jjz8+7fOvu+66DEbjnMsWTwpup/Ck4FxhaBCjj+Jd/eQc5q74IqNl9mzfkqt+cEDa5z/55JNcc801fPXVV7Ru3Zr777+fvffemxdeeIGLL74YCNqbX3zxRVq0aFHl3GuvvZZ7772XTp060bZtW/r16wfAokWLuPDCC1m9ejXNmjXjrrvuYr/99mPEiBEUFxczZ84cVq5cyU033cTxxx/Ptm3bGD16NNOnT2fLli1ceOGF/OxnP2P69OmMGTOGNm3a8N5779GvXz/uu+8+JPH0009zySWX0KZNG/r27RuLaePGjVx00UW8++67VFRUMGbMGIYOHcqkSZOYOnUqX375JYsWLeLEE0/khhtuYPTo0WzatIk+ffpwwAEHMGHCBE499VTKy8vZtm0bV155JcOGDUv79XXOZU6DSwq5UvmhV2nt2rWccEIwR2/QoEG8/vrrSOLuu+/mhhtu4M9//jN/+tOfGD9+PAMHDmTDhg0UFxdXKXPmzJlMnjyZWbNmUVFRQd++fWNJYeTIkdxxxx10796dN954g5///Of873//A2Dp0qW88MILLFq0iMMPP5yFCxdy77330qpVK2bMmMGWLVsYOHAgRx99NACzZs1izpw5tG/fnoEDB/LKK69QWlrKeeedx//+9z/23XffKh/a1157LUcccQQTJ07k888/p3///nzve8GUk9mzZzNr1iyaNm1Kjx49uOiii7j++uu57bbbmD17NgCPPvoo7du351//+hcA69aty8afxDmXhgaXFOrzjb4+dt9999iHHgR9CmVlZUAwj2LYsGF8/PHHfPXVV7Fx6gMHDuTSSy/ljDPO4KSTTqJjx6orh7/00kuceOKJNGvWDCCWZDZs2MCrr77KKaecEjt2y5Ydy0adeuqpNGrUiO7du1NSUsL8+fOZNm0a77zzDlOmTAGCD+IFCxaw22670b9//9i1+/Tpw9KlS2nevDndunWje/fuAJx55plMmDABgGnTpjF16tRY/8bmzZv58MMPATjyyCNp1aoVAD179mTZsmV06hS/BBb06tWLX//611x++eUcf/zxfOc730nvRXfOZZz3KewEF110EaNGjeLdd9/lzjvvjI1VHz16NHfffTebNm1iwIABzJ8/v9q5yYYxbt++nT322IPZs2fHfubNm1fjOZIwM2699dbY8UuWLInVFJo2bRo7tqioiIqKihqvDcFErEcffTRW1ocffsj+++9fa1nxvvnNbzJz5kx69erFFVdcwdixY5O/cM65nc6Twk6wbt06OnQIbiVxzz33xPYvWrSIXr16cfnll1NaWlotKRx22GE89thjbNq0ifXr1/Pkk08C0LJlS7p168YjjzwCBB/Sb7/9duy8Rx55hO3bt7No0SIWL15Mjx49OOaYY7j99tvZunUrAB988AEbN26sMeb99tuPJUuWsGjRIgAefPDB2GPHHHMMt956K2bBSuizZs1K+Ro0adIkdu0VK1bQrFkzzjzzTH7961/z1ltvpTzfObdzNLjmo3w0ZswYTjnlFDp06MCAAQNYsmQJADfffDPPP/88RUVF9OzZk2OPPbbKeX379mXYsGH06dOHLl26VGlmuf/++7ngggu45ppr2Lp1K6eddhoHHXQQAD169OC73/0uK1eu5I477qC4uJif/vSnLF26lL59+2JmtG3blscff7zGmIuLi5kwYQLHHXccbdq0YdCgQbz33nsAXHnllVxyySX07t0bM6Nr16489dRTtb4GI0eOpHfv3vTt25ezzz6byy67jEaNGtGkSRNuv/32tF5X51zmqfLbXlYKl4YA44Ai4G4zuz7h8cOAm4HewGlmNiVVmaWlpVbZVl9p3rx5seaLXd2IESM4/vjjOfnkk3MdikvC36suVyTNNLPSVMdlrflIUhEwHjgW6AmcLqlnwmEfAiOAB7IVh3POueiy2XzUH1hoZosBJE0GhgJzKw8ws6XhY9uzGMcuZdKkSbkOwTlXwLLZ0dwBWB63XR7uc845l6eymRSSjWdMqwND0khJZZLKVq9eXc+wnHPO1SSbSaEciJ+11BFYkU5BZjbBzErNrLRt27YZCc4551x12UwKM4DukrpJ2g04DfB7MDvnXB7LWlIwswpgFPAMMA942MzmSBor6QQASYdIKgdOAe6UNCdb8WRbUVFRbMG3gw46iJtuuont2/On/3z69Om8+uqrSR8r5CW56ytxyfO6mDRpEitWpFX5dS5vZXXympn9G/h3wr7fx/0+g6BZqeDFr320atUqhg8fzrp167j66qtzHFlg+vTpNG/enG9/+9u5DqXBmDRpEgceeCDt27fPdSjOZUzDm9H8n9HwybuZLXOfXnDs9amPC7Vr144JEyZwyCGHMGbMGLZs2cIFF1xAWVkZjRs35qabbuLwww+vcTnrjz/+mGHDhvHFF19QUVHB7bffXm3RuK5du3LOOefw5JNPsnXrVh555BH2228/1q5dy7nnnsvixYtp1qwZEyZMoGXLltxxxx0UFRVx3333ceutt1Yrb+7cuQwePJgPP/yQSy65hF/84hcA/PCHP2T58uVs3ryZiy++mJEjR3L77bezZMkSbrjhBiD4cJw5cya33nor9913H7fccgtfffUVhx56KH/9618pKiqqcq10luR+7LHH2LJlC0uWLGH48OFcddVVADVer3nz5lx88cU89dRT7L777jzxxBPsvffesfMrKioYMmRIlbhuvPFGHn74YbZs2cKJJ57I1VdfzdKlSzn22GMZNGgQr776Kh06dOCJJ57gX//6F2VlZZxxxhnsvvvuvPbaa1x99dVMnTqVxo0bc/TRR+es9uVcffjaR1lSUlLC9u3bWbVqFePHjwfg3Xff5cEHH+Scc85h8+bN/O1vf4stZz1jxgzuuusulixZwgMPPMAxxxzD7Nmzefvtt6ssyR2vTZs2vPXWW1xwwQWxD6CrrrqKgw8+mHfeeYfrrruOs88+m65du3L++efzy1/+ktmzZyddlXT+/Pk888wzvPnmm1x99dWxdYomTpzIzJkzKSsr45ZbbmHNmjWcfPLJ/POf/4yd+9BDDzFs2DDmzZvHQw89xCuvvMLs2bMpKiri/vvvr3KdzZs3c9555/Hkk0/y0ksv8cknn8Qeq1ySe8aMGTz//PNcdtllsfWZ3nzzTe6//35mz57NI488QllZWa3X27hxIwMGDODtt9/msMMO46677gLg4osv5oILLmDGjBnss88+sWtPmzaNBQsW8OabbzJ79mxmzpzJiy++CMCCBQu48MILmTNnDnvssQePPvooJ598MqWlpbGYNm3axGOPPcacOXN45513+N3vfleHd4tz+aPh1RTq8I0+2yqXEHn55Ze56KKLgGChuS5duvDBBx/UuJz1IYccwrnnnsvWrVv54Q9/WGNSOOmkkwDo169f7EP65Zdf5tFHHwXgiCOOYM2aNZHuV3DcccfRtGlTmjZtSrt27Vi5ciUdO3bklltu4bHHHgNg+fLlLFiwgAEDBlBSUsLrr79O9+7def/99xk4cCDjx49n5syZHHLIIUBwj4l27dpVuc78+fPTWpL7qKOOonXr1rHn/fLLL9O4ceMar7fbbrvF+ir69evHs88+C8Arr7wSe33OOussLr/88ti1p02bxsEHHwwEy5MvWLCAzp07061bt9jfoF+/fixdurTa69eyZcvYGlPHHXdcTvtJnKuPhpcU8sTixYspKiqiXbt21LS+VOVy1sccc0y1x1588UX+9a9/cdZZZ3HZZZdx9tlnVzumcpnq+CWqk12rpiWwk5UVX9706dP573//y2uvvUazZs0YPHhwbNnvYcOG8fDDD7Pffvtx4oknxpbnPuecc/jjH/9Y67VSLcndo0ePKvvfeOONGpcDr+l6TZo0iZ2TuIR3suubGVdccQU/+9nPquxfunRptddm06ZN1c5v3Lgxb775Js899xyTJ0/mtttui930yLlC4s1HWbB69WrOP/98Ro0ahSQOO+ywWLPGBx98wIcffljrctbLli2jXbt2nHfeefzkJz+p09LS8deaPn06bdq0oWXLlrRo0YL169fX6XmsW7eOPffck2bNmjF//nxef/312GMnnXQSjz/+OA8++GDsrmxHHnkkU6ZMYdWqVUBw97lly5ZVKTPdJbmfffZZ1q5dy6ZNm3j88ccZOHBgpOslGjhwIJMnTwao0rR1zDHHMHHiRDZs2ADARx99FCu3JvGv6YYNG1i3bh3f//73ufnmm6vccMm5QuI1hQypvB3n1q1bady4MWeddRaXXnopAD//+c85//zz6dWrF40bN2bSpEk0bdq0xuWsp0+fzo033kiTJk1o3rw59957b+Q4xowZw49//GN69+5Ns2bNYvdv+MEPfsDJJ5/ME088kbSjOZkhQ4Zwxx130Lt3b3r06MGAAQNij+2555707NmTuXPn0r9/fyC409o111zD0Ucfzfbt22nSpAnjx4+nS5cusfPSXZJ70KBBnHXWWSxcuJDhw4dTWhos9pjqeonGjRvH8OHDGTduHD/60Y9i+48++mjmzZvHt771LSAYqnrfffdV6ySPN2LECM4//3x23313/vOf/zB06FA2b96MmfGXv/wl5evrXD7K6tLZ2eBLZ+96Km9tetttt+U6lHrz96rLlZwvne2cc67wePORy3sjRoxgxIgRuQ7DuV1Cg6kpFFozmNv1+HvUFYIGkRSKi4tZs2aN/6dzecvMWLNmDcXFxbkOxblaNYjmo44dO1JeXo7fa8Hls+LiYjp2bBBLfbkGrEEkhSZNmtCtW7dch+GccwUvq81HkoZIel/SQkmjkzzeVNJD4eNvSOqazXicc87VLmtJQVIRMB44FugJnC6pZ8JhPwE+M7N9gb8A/5eteJxzzqWWzZpCf2ChmS02s6+AycDQhGOGAveEv08BjlSUhXqcc85lRTb7FDoAy+O2y4FDazrGzCokrQNaA5/GHyRpJDAy3Nwi6b2sRJx9bUh4bgWiUOOGwo29UOOGwo29UOOGaLHXvP5LnGwmhWTf+BPHjEY5BjObAEwAkFQWZap2PirU2As1bijc2As1bijc2As1bshs7NlsPioHOsVtdwQSb2gbO0ZSY6AVsDaLMTnnnKtFNpPCDKC7pG6SdgNOA6YmHDMVOCf8/WTgf+Yz0JxzLmey1nwU9hGMAp4BioCJZjZH0ligzMymAn8D/iFpIUEN4bQIRU/IVsw7QaHGXqhxQ+HGXqhxQ+HGXqhxQwZjL7ils51zzmVPg1j7yDnnXGZ4UnDOORdTUEkh1bIZ+ULSREmr4udTSNpL0rOSFoT/7pnLGGsiqZOk5yXNkzRH0sXh/ryOX1KxpDclvR3GfXW4v1u4hMqCcEmV3XIdazKSiiTNkvRUuF0ocS+V9K6k2ZLKwn15/V6pJGkPSVMkzQ/f79/K99gl9Qhf68qfLyRdksm4CyYpRFw2I19MAoYk7BsNPGdm3YHnwu18VAH8ysz2BwYAF4avc77HvwU4wswOAvoAQyQNIFg65S9h3J8RLK2Sjy4G5sVtF0rcAIebWZ+4cfL5/l6pNA542sz2Aw4ieP3zOnYzez98rfsA/YAvgcfIZNxmVhA/wLeAZ+K2rwCuyHVctcTbFXgvbvt94Ovh718H3s91jBGfxxPAUYUUP9AMeItgBv2nQONk76F8+SGYw/MccATwFMGkzryPO4xtKdAmYV/ev1eAlsASwsE2hRR7XKxHA69kOu6CqSmQfNmMDjmKJR17m9nHAOG/7XIcT0rhqrUHA29QAPGHTTCzgVXAs8Ai4HMzqwgPydf3zM3Ab4Dt4XZrCiNuCFYgmCZpZrgcDRTAewUoAVYDfw+b7e6W9DUKI/ZKpwEPhr9nLO5CSgqRlsRwmSGpOfAocImZfZHreKIws20WVKs7EizIuH+yw3ZuVLWTdDywysxmxu9OcmhexR1noJn1JWjWvVDSYbkOKKLGQF/gdjM7GNhInjUV1SbsYzoBeCTTZRdSUoiybEY+Wynp6wDhv6tyHE+NJDUhSAj3m9k/w90FE7+ZfQ5MJ+gT2SNcQgXy8z0zEDhB0lKClYSPIKg55HvcAJjZivDfVQRt2/0pjPdKOVBuZm+E21MIkkQhxA5BEn7LzFaG2xmLu5CSQpRlM/JZ/JIe5xC01eedcOnyvwHzzOymuIfyOn5JbSXtEf6+O/A9go7D5wmWUIE8jNvMrjCzjmbWleA9/T8zO4M8jxtA0tcktaj8naCN+z3y/L0CYGafAMsl9Qh3HQnMpQBiD53OjqYjyGTcue4sqWPHyveBDwjaiv9fruOpJc4HgY+BrQTfSH5C0E78HLAg/HevXMdZQ+yDCJoq3gFmhz/fz/f4gd7ArDDu94Dfh/tLgDeBhQRV7aa5jrWW5zAYeKpQ4g5jfDv8mVP5fzLf3ytx8fcBysL3zOPAnoUQO8FAijVAq7h9GYvbl7lwzjkXU0jNR84557LMk4JzzrkYTwrOOediPCk455yL8aTgnHMuJmt3XnOukEiqHNIHsA+wjWAZBID+ZvZVTgJzbifzIanOJZA0BthgZn/K4jUa2461jZzLG9585FwKks4J79UwW9JfJTWS1FjS55KuD+/h8JqkduHx90n6Ydz5G8J/vyfpv5ImE0y0S1p2Tp6kcyF/AzpXC0kHAicC37Zgsb3GBMtRALQCXrDgHg6vAedGKHIA8Bsz65WibOdywvsUnKvd94BDgLJgWSh2Z8cS7pvM7D/h7zOB70Qo7zUz+zBC2c7lhCcF52onYKKZXVllZ7CCaXzn8zZ2/H+qIKyFh3cMjP9/tjFV2c7lkjcfOVe7/wKnSmoDwSglSZ1TnLOU4FaJEDQPFWWwbOeyypOCc7Uws3eBq4H/SnoHmAbsneK0O4GjJL1JsBLnlgyW7VxW+ZBU55xzMV5TcM45F+NJwTnnXIwnBeecczGeFJxzzsV4UnDOORfjScE551yMJwXnnHMx/x/qZGrltM9ukwAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"Dependents = (survivaldata['Dependents_Yes'] == 1)\\n\",\n    \"no_Dependents = (survivaldata['Dependents_Yes'] == 0)\\n\",\n    \"\\n\",\n    \"plt.figure()\\n\",\n    \"ax = plt.subplot(1,1,1)\\n\",\n    \"\\n\",\n    \"kmf.fit(timevar[Dependents],event_observed = eventvar[Dependents],label = \\\"Has dependents\\\")\\n\",\n    \"plot1 = kmf.plot(ax = ax)\\n\",\n    \"\\n\",\n    \"kmf.fit(timevar[no_Dependents],event_observed = eventvar[no_Dependents],label = \\\"Does not have dependents\\\")\\n\",\n    \"plot2 = kmf.plot(ax = plot1)\\n\",\n    \"                 \\n\",\n    \"plt.title('Survival of customers: Dependents')\\n\",\n    \"plt.xlabel('Tenure')\\n\",\n    \"plt.ylabel('Survival Probability')\\n\",\n    \"plt.yticks(np.linspace(0,1,11))\\n\",\n    \"groups = logrank_test(timevar[Dependents], timevar[no_Dependents], event_observed_A=eventvar[Dependents], event_observed_B=eventvar[no_Dependents])\\n\",\n    \"groups.print_summary()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### PhoneService\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 94,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"<lifelines.StatisticalResult>\\n\",\n      \"               t_0 = -1\\n\",\n      \" null_distribution = chi squared\\n\",\n      \"degrees_of_freedom = 1\\n\",\n      \"\\n\",\n      \"---\\n\",\n      \" test_statistic    p  -log2(p)\\n\",\n      \"           0.43 0.51      0.97\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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bbufvuuxk+fDhz5sxh3LhxAIwdO5bzzjuPd999l/79+zNz5kxOOOGE4H5z5sxh5syZzJ8/n7p165KTk1Nmv/TQ7omh92vVqhW8H6gKijZfe+j2aWlpFBYWoqp06dKF+fPnR32eEydO5PPPP+edd96hZ8+eLFmyBFXlmWeeYejQoWHbzpkzh3r16oWtGzlyJNOnT+f777/niiuuKHV8VS3VDdNvbKa0atGm0K54Mw3m/z54+3blgmSHZExMu3btonXr1gC88MILwfXr1q0L1qf37t2bVatWldqvcePG1K1bl1WrVvHZZ5+VeY5Au8C0adM45ZRTosYzcOBApk2bRlFREXl5eXzyySf07du3zO07depEXl5e8Iu3oKCA5cuXl9pu3bp19OvXj/Hjx9OsWTM2b97M0KFD+dvf/kZBQQEA33zzDfv27fM8zxVXXMErr7zC9OnTPXscDRkyhIkTJ1JYWAg4VXR+YzOlpXxJYW/Hi9i4YlpwzIx2RZvQXcVR9zGmKhg3bhyXXnoprVu3pn///mzYsAGAP//5z8yePZu0tDQ6d+7MOeecE7bfsGHDmDhxIt27d6dTp07079+/zHMcOnSIfv36UVxczNSpU6PGc9FFFzF//nx69OiBiPDEE09wzDHHlEpKATVr1mT69Onccccd7Nq1i8LCQu666y66dAkvld17772sWbMGVWXw4MH06NGD7t27s3HjRk466SRUlebNm/PGG294nqdLly7s2bOH1q1be1YB3XTTTXzzzTd0796djIwMbr75ZsaMGeMrNlOaRCsyVkW9e/fWyEl2lr8/mZ01GgNQf97jCEq3i+8L39HaGY46K1eu5MQTT0x2GEkTmJCqWbNmyQ7FVDKvz76ILFLV3rH2TfmSAkCXVg0g0/ngL/+8BvsKlHPeqRW2zeCW27nH2hmMMSaqatGmEKpZHaF2DSU9rUbw9u3eGny0tVbsnY2pRjZu3GilBHPEqkVJIVTLukLT/Zt4peYjwXXra8Lsor7AxckLzBhjUkD1SAq1G8Jep681rU7i0P59FBaVNDZ3KN7MgWLl3N+9HrZbThbcd40lCmOMCUhoUhCRYcBfgDTgeVV9POLxdsBkoDmwA/h/qpp7xCcKbUDuNpJ6X0+HzJbBVT+8fj+19xaRllFShbR+F8zJPUREc7QxxhzVEjl0dhowATgbZ0KdBSIyQ1VXhGz2JM48zS+IyJnAY8DV8Y6lZV2htiiP9ytZN3YeFB0uZuFbzwXXZWQ2oscZl8b79MYYkzIS2dDcF1irqutV9TDwCnBhxDadgVnu/dkej8dNjRrCzv2Hg7fComI27K/Nw6taBW/vrj2cqNObo1RaWho9e/akS5cu9OjRgz/+8Y9h4/Yk25w5c5g3b57nY+PGjePJJ5+s5IjKpyoMSR4P5557bnB8qGRJZPVRa2BzyHIu0C9im6+AS3CqmC4C6otIU1XND91IREYDowHatm1brmDq79vMgLUlH/ApGcq0Ov34kLMApzqpsKA2XZZ8F75fnQzO6NSiXOc0JnTso23btnHllVeya9cuHn744SRH5pgzZw6ZmZmceuqpyQ7lqFFUVERaWprnY++++24lR1NaIksKXnPCRV4pdw8wSEQWA4OA74DCUjupTlLV3qrau3nz5rHPHGh4DtxanQQNWoVt0vLgRq6tO5fHT4XHT4XshvDt/po8/94XYbfPPvnQGWwv9LbmQ58vgTElWrRowaRJk3j22WdRVQ4ePMj1119Pt27d6NWrF7NnzwbKHlZ669atDBw4kJ49e9K1a1f+97//lTpH+/bteeihhzjppJPo1q1b8GrkHTt2MGLECLp3707//v1ZunQpGzduZOLEifzpT3+iZ8+ensdbsWIFOTk5ZGdn8/TTTwfXew3d/be//Y377itppZsyZQq33347AC+99BJ9+/alZ8+e/PSnP/WcK2H8+PH06dOHrl27Mnr0aM+xmK677jpuueUWTj/9dI4//vjgqK/gTLwzbNgwOnbsGBbH1KlT6datG127duX+++8Prs/MzOSBBx6gR48e9O/fPzgwYF5eHpdccgl9+vShT58+zJ07t1Qcy5cvDz6f7t27s2bNmqjPMzMzkwcffJB+/frxu9/9jssuuyx4rDlz5nDBBRcE37/t27cD8OKLL9K9e3d69OjB1Vdf7Tu2ikpkSSEXaBOynAVsCd1AVbfg9hMVkUzgElXdVeEzR1653G2k82Ue0vjMe2Opl7+erM+dX2zPFApv1D6VWWlnBTf5Oh+W7qzJJ/nhxf2zmm/lFwdtVNaU8t+x8P3X8T3mMd3gnMdjbxciOzub4uJitm3bxksvvQTA119/zapVqxgyZAjffPMNL774ouew0q+//jpDhw7lgQceoKioiP3793ueo1mzZnz55Zf89a9/5cknn+T555/noYceolevXrzxxht89NFHXHPNNSxZsoRbbrmFzMxM7rnnHs9jrVq1itmzZ7Nnzx46derEz372MzIyMjyH7h45ciSnnHIKTzzxBEBwuOqVK1cybdo05s6dS0ZGBrfeeisvv/wy11xzTdi5xowZw4MPPgjA1Vdfzdtvvx38sgy1ceNGPv74Y9atW8cZZ5wRnCdiyZIlLF68mFq1atGpUyduv/120tLSuP/++1m0aBGNGzdmyJAhvPHGG4wYMYJ9+/bRv39/Hn30Ue677z6ee+45fv3rX3PnnXfy85//nNNOO41vv/2WoUOHsnLlyrAYJk6cyJ133slVV13F4cOHKSoqivo89+3bR9euXRk/fjyFhYVkZ2ezb98+6tWrx7Rp07j88svDjr98+XIeffRR5s6dS7NmzdixYweAr9gqKpFJYQHQUUQ64JQArgCuDN1ARJoBO1S1GPglTk+kypGdQzrQ0F1seGAD19edy9n9SpLCfzfBx99BaIFq/S6YSWN+kdmo5Fg/bgofuhssUZgyBX4Bf/rpp8Ff0ieccALt2rXjm2++KXNY6T59+nDDDTdQUFDAiBEjwuZpCBUYnvrkk0/m9ddfD57r3//+NwBnnnkm+fn57NoV+/fXeeedR61atahVqxYtWrTghx9+ICsry3Po7v79+5Odnc1nn31Gx44dWb16NQMGDGDChAksWrSIPn36AM4cEy1alK6SnT17Nk888QT79+9nx44ddOnSxTMpXHbZZdSoUYOOHTuSnZ0dLA0NHjyYhg2d/+jOnTuzadMm8vPzycnJIVDDcNVVV/HJJ58wYsQIatasyfnnnx98rT780KkBmDlzJitWlPSH2b17N3v27KF+/frBdaeccgqPPvooubm5XHzxxXTs2JFZs2aV+TzT0tK45JJLAEhPT2fYsGG89dZbjBw5knfeeSeYSAM++ugjRo4cGbz4sEmTJr5jq6iEJQVVLRSRMcD7OF1SJ6vqchEZDyxU1RlADvCYiCjwCXBbouIp5fhhzi0gouQAcDNwfot+FB1Xst3YebB2p3DOtJLGoJyshtw/ICRJQMl1E6ZqOMJf9Imyfv160tLSaNGiRZlDVZc1rDQ4M6a98847XH311dx7772lfm1DyTDXgSGuA8eMFDnctBevIbOjDd19+eWX8+qrr3LCCSdw0UUXISKoKtdeey2PPfZYmec5ePAgt956KwsXLqRNmzaMGzfO13DgoctlDe9dloyMjOC+oa9VcXEx8+fPp06dOmXue+WVV9KvXz/eeecdhg4dyvPPPx/1edauXTusHeHyyy9nwoQJNGnShD59+pT6UvcaDtxvbBWV0GEuVPVdVT1eVX+iqo+66x50EwKqOl1VO7rb3KSqhxIZT1TZOaQ3zaZhnYyS2+7VnLDhRbI+fzh4e6ZwPD+tMyt8CI3NMHft9rDbytxt1hZhwuTl5XHLLbcwZswYRISBAwfy8ssvA87Q0d9++y2dOnUqc1jpTZs20aJFC26++WZuvPFGvvzyS9/nDj3XnDlzaNasGQ0aNKB+/fqe031GE23o7osvvpg33niDqVOnBqtEBg8ezPTp09m2bRvgtG9s2rQp7JiBBNCsWTP27t0bdf7l1157jeLiYtatW8f69evp1KlTmdv269ePjz/+mO3bt1NUVMTUqVMZNGhQ1Oc3ZMgQnn322eBy6CRJAevXryc7O5s77riD4cOHs3TpUl/PMyAnJ4cvv/yS5557rlTVETiv2auvvkp+fn7wWH5jq6jqcUWzH6FXPQcUHITG7Zz7kSUHgG/eg/VzglVMAA13L+MuVnFzfWec9vU14d/7T+XxJYPDdj2paSuGpIf/SmlSuIYTD4YU2a2KqdoLTMdZUFBAeno6V199NXfffTcAt956K7fccgvdunUjPT2dKVOmUKtWLW666SbPYaXnzJnDH/7wBzIyMsjMzOTFF1/0Hce4ceO4/vrr6d69O3Xr1g3O33DBBRcwcuRI3nzzTZ555hlOP/30mMeKNnR348aN6dy5MytWrAjOxdC5c2d++9vfMmTIEIqLi8nIyGDChAm0a9cuuF+jRo24+eab6datG+3btw9WwXjp1KkTgwYN4ocffmDixIme05UGHHvssTz22GOcccYZqCrnnnsuF14Yvef7008/zW233Ub37t0pLCxk4MCBTJw4MWybadOm8dJLL5GRkcExxxzDgw8+SJMmTWI+z4C0tDTOP/98pkyZEjaXRkCXLl144IEHGDRoEGlpafTq1YspU6b4iq2iqsXQ2eUW2fjsxzfvsWflTIqLndet4e7VzqFqlAxTu68Q3iw6lWWNwhNF3+aF/GZQk5IVP26CjIgPtCWKuDnah86ujq677jrOP/98z8l2TImjfujsSnX8MOqHlii+eY9dyz8kO6TbcdquTdRLh19RkhTW74LDhTWYu3Z7cF3dWg3p1SaiLcIarY0xSXR0J4XIKqXQ6iS/jh/G+jr92X+o5PKKrl8/xgn7NpYaqTWymumkpgXsP7Q97HCeicIarY0BnGsfTGId3Ukh8td35C90n0p9iRef7VQxhYzU2lE3c1G6ssgtPXydD1/nZ/BlfviufZsX0qtN+DoKDsaOzUoTnsrqxWFMdVXRJoGjOylEitUY7VdkFRPAe2Ppsn1dsPSwozHsPAQcKNlkXyG8ufNUzvn+jLBdPbu8RvKqdvKjGieT2rVrk5+fT9OmTS0xmKOCqpKfnx+18T0WSwqhvL4c13wYn0SRncOBQ4XB0kODDMhMh5rpJb2C6/24kv41VvL14ZIByg4UwbyNpzC35cDgurq10kuXTo40noBqXDWVlZVFbm4ueXl5yQ7FmEpTu3ZtsrKyyr2/JYVY/CQKP0nCo/SwePPOsLaIlt/Poem2+WSHvCv64yYogEeWlFxp7d0W4ZEo/IismqpGJYeMjAw6dOiQ7DCMSSlHd5fUeFnzIRyMGDKgPKUJnIvgGtWtGVxu8OkjND6wiQ01nGOV1d31pKYFDGlT8l6WO0l4dZP1Uo2ShzFHA+uSWpm8vhzL2Whdt1Y6O/eXzOtQq1U/CrYVB0sPwSqmAyVVTIG2iMfzY/Vs8pEo/CayyDYMSxLGVAtWUkiU8lwYV4bQaqZAFVNkWwSUXEC3z62RqheR8jMzlCa1wt/vXccOoG3/OMxTbSUMY6o0KykkW7x6MhHR5fW4kSzefFbUtogdB92eTSH2FcK+QmFvQUkvnA7Fmyg6NI+5zWI0YvthJQxjqgUrKVSmyLaHciYJL5GN1oeLimlRv+SXe8kw4CUe2PMInWUTm9JKYtjQqB9NT8wJ267cicIPK2EYUyn8lhQSmhREZBjOVJtpwPOq+njE422BF4BG7jZjVTXqfHQpnRQixbGBOlJkkoDSieK7r2Zx7PaStoluxeHVUAFe1U55zU/hh2NygssJTRzgP3lEsmRiDFAFkoKIpAHfAGfjzMK2ABilqitCtpkELFbVv4lIZ+BdVW0f7bjVKil4SWCiiOzZFCkySYB3+4RX8qj0EoZfkcnEkoQ5SlWFNoW+wFpVXe8G9ApwIbAiZBsFGrj3GxIxXedRKY49mSJF9myC8NJD6x6DgfCurp96VDsNLZjFoMLwEka3HSvZt+Lz8GMXFrMrIn/HrWHbr8hkWt4rv8vLkpBJMYksKYwEhqnqTe7y1UA/VR0Tss2xwAdAY6AecJaqLvI41mhgNEDbtm27sdWnAAAdD0lEQVRPLmviimqrEtsivERWO0Wa/fEs+h6aR520MjcBSkoYuxqUTIpS6Umisvmp9rLEYSpBVSgpeA02E5mBRgFTVPUpETkF+KeIdHXnbC7ZSXUSMAmc6qOERFuVRX5heA29Ecln4vBTvbN4886oJYyD7QfzyHeDvXYN03XnLC5Mm0e9vc5yt+KVNNy9ml1b54ZtV6OGUL9WxEczO6f0JEipwE/yrsZDjZjUk8ikkAuEjveZRenqoRuBYQCqOl9EagPNgG0JjCv1+flV6SdxgK/k4ZU4QhPFKc3h5CbRSxMA/900mEdDkkdkkgjIzFCKiwuCyw13r4YflrFrecl0ptUqcXiNgmulB5Mkiaw+SsdpaB4MfIfT0Hylqi4P2ea/wDRVnSIiJwKzgNYaJahq39Bc2eJ0kZ2f3k6RvLrJfu0OJd6tacm6oQVO8mgScqjDhcXUCCmLBmbAC62aghSuntr7A3Sz2cVM/CS995EbxLnAn3G6m05W1UdFZDywUFVnuD2OngMycaqW7lPVD6Id05JCnFVye0W8EkUkr8QRuNI7JdswrAuuibMqkRQSwZJCgnl1ifVSzuQR6yI7L16JIpKfEoZXkoAyqqLiJTuncqu0bN5vUwZLCiax4lTC8NP7CeJTwojsShvgdXFeXBLFD8ucvy27Rt8uOyexicN6QBksKZjKVo1KGFB6KHIvMS/O++Y9WD8negA7NkCTDjDs8ejbJZq1YVR7lhRM1ZTAhm0v8WrDGNQazonIZdv2HKRmWsloteW6gvu9sSWJIVR2TvKrnSJZaSKlVYXrFIwpLXL02HiMHBtF5DUWkUninHalv+wjE8XX+c4tMnkMal07bN9tew4yd234HBZewpJHdk7pDXZscP5WZlLw8x54XQ1uiaLasZKCSa4EVztFqsweUVDOEkZZpQc/snOqXgnDiyWTSmfVR6Z68ZM84tjYXZ5EEamsxBGZKCKTRMvv59A8b36p48Vs/K4q7RN+WC+pSmdJwRx94jjCbDzGhKoyJYzsnNS40tsauxPKkoIx4L96qhyWb9nNgcNFweWC4iKa1gtPEjWKDnIos2S0l0SXMFrmfxZemqgq3WL9KG9VVCQrcXiKW1JwB6hbFrfIKsiSgqmqZq/exp4DBWHrmm96iwbN2pSxh7d49ojyqooqVQ2VSonDDytxeIpnUvgUqAlMAf6lqjvjEmE5WVIwqeSr2a9RsDf6v0xxwX7qtTwu6jZePaIgdjUUxKkNw9orUl5cq49EpCNwA3Ap8AXwf6r6YfS9EsOSgqlulr8/mZ01Goeti0dDN8SvDaPr149Rb/9m0ptmh2+UnZMapQebgS/+bQru9JojgKeB3TjzJfxKVV+vSKBHypKCqXY82j1W5m5jR/qxYev8XMUdKV5tGI1zZ1En91PPkWljVjt5yc5JbjI5CksT8aw+6g5cD5wHfAj8Q1W/FJFWwHxVLbNrh4gMA/6CM0rq86r6eMTjfwLOcBfrAi1UNepVSZYUzFHB48rvePSI8lKRkWnPk7mlxo2K5LsNIzun6iWK8qqCCSaeSeETnOGtp6vqgYjHrlbVf5axXxrOfApn40y4swAYpaorytj+dqCXqt4QLR5LCuaoUM7rMuKVOCr7Oox6+77lQP121L/wyegnTRXxTDB++EhC8UwKd6nqnyPW3amqf4mx3ynAOFUd6i7/EkBVHytj+3nAQ7HaKiwpGOMq53UZ5blYz0tFeklFar/wEWru3sj+em2D66rV7HqJ5qPHVTyTwpeqelLEusWq2ivGfiOBYap6k7t8NdBPVcd4bNsO+AzIUtUij8dHA6MB2rZte/KmTZuiPytjjlZVLFH4beg+ktn1qk3X2Xjy0ZBe4QHxRGQUcCXQQURmhDxUH8j3EaZ4rCsrA12BUz1VKiEAqOokYBI4JQUf5zbm6ORVhRA5iJ2HWPNwlyXWAIN+e0k9uXswTzI4agljX6CxO+RakFKliWQMJlgVRCZ9j8ELG9emgZ9DRRsldR6wFWgGPBWyfg+w1Mexc4HQq3aygC1lbHsFcJuPYxpjjlTkyLRePEoTfkai9UocoYnCaxRaL/5Gph3MoNaDo7ZXdP36Merlryf9vbHhJ8jOOboShUfJMK2GpPnZNWHDXIhIOk5D82DgO5yG5itVdXnEdp2A94EO6iMYa1MwJgHiOOBgeSZEilTe9opoXWdTcq7uOGreddD6vH3FP4m1XbTqo09V9TQR2UN4tY8AqqpRiyKqWigiY3C+8NOAyaq6XETGAwtVNVAlNQp4xU9CMMYkiJ/ukz6qoaB0CcNPNRTELmGUtzSxL3cWDb+fF/yyq/fjShruXs2urXPDjn80JgovNiCeMcafOI5C6+VISxgVub4islG73o8rgfDShBffc3dn51S56iq/JYUyk4KINIm2o6ruKGdsFWJJwZgqpJrMc+GVKLxE9ojyUqErvcsrOydmEopHUtiAU23k2YtIVbM91iecJQVjUkwcZ9erzPYK8HeNRSSvdg3fJYzy8DlYYYXbFFS1HHMBGmNMBL/DPZSj62ys3k9e/LRXQNlzc0eKTBw/Zg3mx6zBYdtE9pICj0mSyiuyp1UFRWtoPkFVV4nISV6Pq+qXcY3EGHN0K0fXWb/XV8QrUUTynzhqlzq+n8Z333ZsCE8O2TnlbtOIVp65G+cq4qc8HlPgzHKd0RhjvPgpUaz5MGbi6JV5ENqUHhOqohfiealI4jipqQDbw9aVq/SQnRO+XMEL+Kz3kTGmevEYYdaPeA31EckrcazfBdkN4fFTw9fHnIfbj0CJIaKNocJtCgEiUhu4FTgNp4TwP2Ciqh48skiNMabqitdQH5G8Shxj5zmJYey88PWR1UzlrmKKrE46An6aw1/EGdriGXd5FPBPnFnYjDGmavFqmyhnt9jyDvURKTJxDGpdepv1bget0KRwuKiYuWuPsIopO6f0ukCVkg9+Rkn9SlV7xFpXWaz6yBhzxCK7xcbxojs/5q7dTqO6NaNuEyg9ZDcsWRdrmtSAmInivbE0v2d+fKqPgMUi0l9VPwMQkX7A3Bj7GGNM1RHZiO3VYJ3ARFG3VnrMHlGRpYeyGqgT3ZMpWpfUr3HaEDKAa0TkW3e5HeA5e5oxxqQEr55OPno2+eaj62xktZCfYce9qpigdDVTRa6BiFZSOL9cRzTGmFQUzzmVfVyIF6v0UJEG6m17DoYlia4HCiC9Vl0/oUe7ojlsejMRaQEcUd8sERkG/AVnlNTnVbXUddgichkwDqcU8pWqXnkk5zDGmCrHx4V4vRqXXrd8y24O7CgZVq6guIim9Uq+ds9sXhspqgUh05Gt25uOFB3mnHYl20X2hips1AFkWXhDRBn8NDQPx7mArRWwDaf6aKWqdomxXxrOfApn40y4swAYpaorQrbpCLwKnKmqP4pIC1XdFu241tBsjDlazFjyHc1jXCcx/u3lfJu3i+yGJd/5Xg3UOeddui1v5/6YF3D4aWh+BOgPzFTVXiJyBk631Fj6AmtVdT2AiLwCXEh4e8TNwARV/REgVkIwxpijSf06GeTtCb8k7GBhEW0a1wsuD/hJMzIO5KM4vZv8Dr1RFj9JoUBV80WkhojUUNXZIvJ7H/u1BjaHLOcC/SK2OR5ARObiVDGNU9X3Ig8kIqNxhtygbdu2Pk5tjDGp74xOLUqtm7Ek/Nt+8Iktuaz2FxTUaQ5Eb6D2w09S2CkimThXMr8sItuAwhj7QBlDbnucvyOQgzOH8/9EpKuq7gzbSXUSMAmc6iMf5zbGmGrJq/RQcDiDY8gDYHgLGNH0IIcy2wQfHzvPqfv3w09SuBA4CNwFXAU0BMb72C8XaBOynAVs8djmM1UtADaIyGqcJLHAx/GNMeao41l6IIeMkLaHphveCns8uwF85vP4MVujVXUf0Bw4F9gBvKqq+T6OvQDoKCIdRKQmcAUwI2KbN4AzAESkGU510nqfsRtjjKGk9BC45e8LL0mMPoJJ4PwMiHcT8CDwEU6V0DMiMl5VJ0fbT1ULRWQM8D5Oe8FkVV0uIuOBhao6w31siIiswOlkda/PhGOMMcYVWXpYuDmt3MfyU310L9Ar8GUtIk2BeUDUpACgqu8C70asezDkvuLM23D3EcRsjDEmiozMRhzYEV5bX6yhVzeUzU9SyMUZJTVgD+G9iowxxlQhPc4oPYh1UfEdfjoIRR37KPDr/TvgcxF5E6f30IXAF0cepjHGmKouWkmhvvt3nXsLeDNx4RhjjEmIgoMH/GwWbeyjh0OXRaS+s1r3VjA0Y4wxVVTMLqki0lVEFgPLgOUiskhEoo57ZIwxJjX5GTVvEnC3qrZT1XbAL4DnEhuWMcaYZPCTFOqp6uzAgqrOAeqVvbkxxphU5adL6noR+Q3wT3f5/wH+Z4E2xhiTdLsOhV1aUCY/JYUbcIa5eN29NQOuL39oxhhjqqqoJQV3opxfqeodlRSPMcaYJIpaUlDVIuDkSorFGGNMkvlpU1gsIjOA14B9gZWq+nrCojLGGJMUftoUmgD5wJnABe7tfD8HF5FhIrJaRNaKyFiPx68TkTwRWeLebjqS4I0xxsSXr1FSVXX7kR7YbY+YAJyNM6jeAhGZoaorIjadpqpjjvT4xhhj4q/MkoKIXCAiecBSEckVkVOP8Nh9gbWqul5VDwOv4AymZ4wxpoqKVn30KHC6qrYCLgEeO8JjtyZ8iO1cd12kS0RkqYhMF5E2Ho8bY4ypJNGSQqGqrgJQ1c8pGTXVL/FYpxHLbwHtVbU7MBN4wfNAIqNFZKGILMzLyzvCMIwxxvgVrU2hRcicCqWWVfWPMY6dC4T+8s8CwqYCiph68zng914HUtVJOGMw0bt378jEYowxJk6ilRSewykdBG6Ry7EsADqKSAcRqQlcAcwI3UBEjg1ZHA6s9B+6McaYePM9n8KRUtVCERkDvA+kAZNVdbmIjAcWquoM4A4RGQ4UAjuA6ypyTmOMMRUjqqlVG9O7d29duHBhssMwxpiUIiKLVLV3rO38XLxmjDHmKGFJwRhjTFCZbQoRPY9K8dH7yBhjTIqJ1iX1SK9LMMYYk+IS1vvIGGNM6ok5IJ6I1AZuBLoAtQPrVfWGBMZljDEmCfw0NP8TOAYYCnyMc2Wyr7k+jTHGpBY/SeE4Vf0NsE9VXwDOA7olNixjjDHJ4CcpFLh/d4pIV6Ah0D5hERljjEkaP5PsTBKRxsBvcMYuynTvG2OMqWb8JIX/U9UinPaE7ATHY4wxJon8VB9tEJFJIjJYRLzmSDDGGFNN+EkKnXAmwLkN2Cgiz4rIaYkNyxhjTDLETAqqekBVX1XVi4GeQAOcqqSYRGSYiKwWkbUiMjbKdiNFREUk5gh+xhhjEsfXgHgiMkhE/gp8iXMB22U+9kkDJgDnAJ2BUSLS2WO7+sAdwOdHELcxxpgEiJkURGQDcBfwP6Crql6mqv/2cey+wFpVXa+qh4FXgAs9tnsEeAI46D9sY4wxieCn91EPVd1djmO3BjaHLOcC/UI3EJFeQBtVfVtE7inrQCIyGhgN0LZt23KEYowxxo9oQ2ffp6pPAI+KSKnp2VT1jhjH9uqpFDyOiNQA/oSPKThVdRIwCZyZ12Jtb4wxpnyilRRWun/LO/dlLtAmZDkL2BKyXB/oCsxxe7oeA8wQkeGqavNtGmNMEkQbOvst9+5SVV1cjmMvADqKSAfgO+AK4MqQ4+8CmgWWRWQOcI8lBGOMSR4/vY/+KCKrROQREeni98CqWgiMAd7HKXW8qqrLRWS8iAwvZ7zGGGMSSFRjV9GLyDE43VAvx7lOYZqq/jbBsXnq3bu3LlxohQljjDkSIrJIVWNeC+brOgVV/V5VnwZuAZYAD1YwPmOMMVWQn+sUThSRcSKyDHgWmIfTaGyMMaaa8TVKKjAVGKKqW2JtbIwxJnVFTQruUBXrVPUvlRSPMcaYJIpafeTOo9BURGpWUjzGGGOSyE/10SZgrojMAPYFVqrqHxMWlTHGmKTwkxS2uLcaOFchG2OMqaZiJgVVfbgyAjHGGJN8MZOCiMwmZCC7AFU9MyERGWOMSRo/1UehQ1rXBi4BChMTjjHGmGTyU320KGLVXBHxNR2nMcaY1OKn+qhJyGIN4GScYa6NMcZUM36qjxbhtCkITrXRBuBGPwcXkWHAX4A04HlVfTzi8VuA24AiYC8wWlVX+I7eGGNMXPmpPupQngO7V0NPAM7GmXBngYjMiPjS/5eqTnS3Hw78ERhWnvMZY4ypuDKvaBaRPu6Q2YHla0TkTRF5OqJKqSx9gbWqul5VDwOvABeGbhAx93M9PHo5GWOMqTzRhrn4O3AYQEQGAo8DLwK7cOdLjqE1sDlkOdddF0ZEbhORdcATgOe8zyIyWkQWisjCvLw8H6c2xhhTHtGSQpqq7nDvXw5MUtV/q+pvgON8HFs81nld7zBBVX8C3A/82utAqjpJVXurau/mzZv7OLUxxpjyiJoURCTQ5jAY+CjkMT8N1LlAm5DlLJzhMsryCjDCx3GNMcYkSLSkMBX4WETeBA4A/wMQkeNwqpBiWQB0FJEO7iirVwAzQjcQkY4hi+cBa44gdmOMMXFW5i9+VX1URGYBxwIfaMlkzjWA22MdWFULRWQM8D5Ol9TJqrpcRMYDC1V1BjBGRM4CCoAfgWsr9nSMMcZUhJR816eG3r1768KFC5MdhjHGpBQRWaSqvWNtF3OOZmOMMUcPSwrGGGOCLCkYY4wJsqRgjDEmyJKCMcaYIEsKxhhjgiwpGGOMCbKkYIwxJsiSgjHGmCBLCsYYY4IsKRhjjAmypGCMMSYooUlBRIaJyGoRWSsiYz0ev1tEVojIUhGZJSLtEhmPMcaY6BKWFEQkDZgAnAN0BkaJSOeIzRYDvVW1OzAdZ0pOY4wxSZLIkkJfYK2qrlfVwzgzq10YuoGqzlbV/e7iZzizsxljjEmSRCaF1sDmkOVcd11ZbgT+6/WAiIwWkYUisjAvLy+OIRpjjAmVyKQgHus8Z/QRkf8H9Ab+4PW4qk5S1d6q2rt58+ZxDNEYY0yoMqfjjINcoE3IchawJXIjdzrOB4BBqnoogfEYY4yJIZElhQVARxHpICI1gSuAGaEbiEgv4O/AcFXdlsBYjDHG+JCwpKCqhcAY4H1gJfCqqi4XkfEiMtzd7A9AJvCaiCwRkRllHM4YY0wlSGT1Ear6LvBuxLoHQ+6flcjzG2OMOTJ2RbMxxpggSwrGGGOCLCkYY4wJsqRgjDEmyJKCMcaYIEsKxhhjgiwpGGOMCbKkYIwxJsiSgjHGmCBLCsYYY4IsKRhjjAmypGCMMSYooUlBRIaJyGoRWSsiYz0eHygiX4pIoYiMTGQsxhhjYktYUhCRNGACcA7QGRglIp0jNvsWuA74V6LiMMYY418ih87uC6xV1fUAIvIKcCGwIrCBqm50HytOYBzGGGN8SmT1UWtgc8hyrrvOGGNMFZXIpCAe67RcBxIZLSILRWRhXl5eBcMyxhhTlkQmhVygTchyFrClPAdS1Umq2ltVezdv3jwuwRljjCktkUlhAdBRRDqISE3gCsDmYDbGmCosYUlBVQuBMcD7wErgVVVdLiLjRWQ4gIj0EZFc4FLg7yKyPFHxGGOMiS2RvY9Q1XeBdyPWPRhyfwFOtZIxxpgqwK5oNsYYE2RJwRhjTJAlBWOMMUGWFIwxxgRZUjDGGBNkScEYY0yQJQVjjDFBlhSMMcYEWVIwxhgTZEnBGGNMkCUFY4wxQZYUjDHGBFlSMMYYE5TQpCAiw0RktYisFZGxHo/XEpFp7uOfi0j7RMZjjDEmuoQlBRFJAyYA5wCdgVEi0jlisxuBH1X1OOBPwO8TFY8xxpjYEllS6AusVdX1qnoYeAW4MGKbC4EX3PvTgcEi4jW3szHGmEqQyEl2WgObQ5ZzgX5lbaOqhSKyC2gKbA/dSERGA6PdxUMisiwhESdeMyKeW4pI1bghdWNP1bghdWNP1bjBX+zt/BwokUnB6xe/lmMbVHUSMAlARBaqau+Kh1f5UjX2VI0bUjf2VI0bUjf2VI0b4ht7IquPcoE2IctZwJaythGRdKAhsCOBMRljjIkikUlhAdBRRDqISE3gCmBGxDYzgGvd+yOBj1S1VEnBGGNM5UhY9ZHbRjAGeB9IAyar6nIRGQ8sVNUZwD+Af4rIWpwSwhU+Dj0pUTFXglSNPVXjhtSNPVXjhtSNPVXjhjjGLvbD3BhjTIBd0WyMMSbIkoIxxpiglEoKsYbNqCpEZLKIbAu9nkJEmojIhyKyxv3bOJkxlkVE2ojIbBFZKSLLReROd32Vjl9EaovIFyLylRv3w+76Du4QKmvcIVVqJjtWLyKSJiKLReRtdzlV4t4oIl+LyBIRWeiuq9KflQARaSQi00Vklft5P6Wqxy4indzXOnDbLSJ3xTPulEkKPofNqCqmAMMi1o0FZqlqR2CWu1wVFQK/UNUTgf7Abe7rXNXjPwScqao9gJ7AMBHpjzN0yp/cuH/EGVqlKroTWBmynCpxA5yhqj1D+slX9c9KwF+A91T1BKAHzutfpWNX1dXua90TOBnYD/yHeMatqilxA04B3g9Z/iXwy2THFSXe9sCykOXVwLHu/WOB1cmO0efzeBM4O5XiB+oCX+JcQb8dSPf6DFWVG841PLOAM4G3cS7qrPJxu7FtBJpFrKvynxWgAbABt7NNKsUeEusQYG68406ZkgLew2a0TlIs5dFSVbcCuH9bJDmemNxRa3sBn5MC8btVMEuAbcCHwDpgp6oWuptU1c/Mn4H7gGJ3uSmpETc4IxB8ICKL3OFoIAU+K0A2kAf8n1tt97yI1CM1Yg+4Apjq3o9b3KmUFHwNiWHiQ0QygX8Dd6nq7mTH44eqFqlTrM7CGZDxRK/NKjeq6ETkfGCbqi4KXe2xaZWKO8QAVT0Jp1r3NhEZmOyAfEoHTgL+pqq9gH1UsaqiaNw2puHAa/E+diolBT/DZlRlP4jIsQDu321JjqdMIpKBkxBeVtXX3dUpE7+q7gTm4LSJNHKHUIGq+ZkZAAwXkY04IwmfiVNyqOpxA6CqW9y/23DqtvuSGp+VXCBXVT93l6fjJIlUiB2cJPylqv7gLsct7lRKCn6GzajKQof0uBanrr7KcYcu/wewUlX/GPJQlY5fRJqLSCP3fh3gLJyGw9k4Q6hAFYxbVX+pqlmq2h7nM/2Rql5FFY8bQETqiUj9wH2cOu5lVPHPCoCqfg9sFpFO7qrBwApSIHbXKEqqjiCecSe7seQIG1bOBb7BqSt+INnxRIlzKrAVKMD5RXIjTj3xLGCN+7dJsuMsI/bTcKoqlgJL3Nu5VT1+oDuw2I17GfCguz4b+AJYi1PUrpXsWKM8hxzg7VSJ243xK/e2PPA/WdU/KyHx9wQWup+ZN4DGqRA7TkeKfKBhyLq4xW3DXBhjjAlKpeojY4wxCWZJwRhjTJAlBWOMMUGWFIwxxgRZUjDGGBOUsJnXjEklIhLo0gdwDFCEMwwCQF9VPZyUwIypZNYl1ZgIIjIO2KuqTybwHOlaMraRMVWGVR8ZE4OIXOvO1bBERP4qIjVEJF1EdorI4+4cDvNFpIW7/UsiMiJk/73u37NEZKaIvIJzoZ3nsZPyJI1x2QfQmChEpCtwEXCqOoPtpeMMRwHQEPhYnTkc5gM3+Dhkf+A+Ve0W49jGJIW1KRgT3VlAH2ChMywUdSgZwv2Aqv7Xvb8ION3H8ear6rc+jm1MUlhSMCY6ASar6m/CVjojmIY2PhdR8v9UiFsKd2cMDP0/2xfr2MYkk1UfGRPdTOAyEWkGTi8lEWkbY5+NOFMlglM9lBbHYxuTUJYUjIlCVb8GHgZmishS4AOgZYzd/g6cLSJf4IzEeSiOxzYmoaxLqjHGmCArKRhjjAmypGCMMSbIkoIxxpggSwrGGGOCLCkYY4wJsqRgjDEmyJKCMcaYoP8PoKZBtGC68YEAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"PhoneService = (survivaldata['PhoneService_Yes'] == 1)\\n\",\n    \"no_PhoneService = (survivaldata['PhoneService_Yes'] == 0)\\n\",\n    \"\\n\",\n    \"plt.figure()\\n\",\n    \"ax = plt.subplot(1,1,1)\\n\",\n    \"\\n\",\n    \"kmf.fit(timevar[PhoneService],event_observed = eventvar[PhoneService],label = \\\"Has a phone service\\\")\\n\",\n    \"plot1 = kmf.plot(ax = ax)\\n\",\n    \"\\n\",\n    \"kmf.fit(timevar[no_PhoneService],event_observed = eventvar[no_PhoneService],label = \\\"Does not have a phone service\\\")\\n\",\n    \"plot2 = kmf.plot(ax = plot1)\\n\",\n    \"                 \\n\",\n    \"plt.title('Survival of customers: Phone Service')\\n\",\n    \"plt.xlabel('Tenure')\\n\",\n    \"plt.ylabel('Survival Probability')\\n\",\n    \"plt.yticks(np.linspace(0,1,11))\\n\",\n    \"groups = logrank_test(timevar[PhoneService], timevar[no_PhoneService], event_observed_A=eventvar[PhoneService], event_observed_B=eventvar[no_PhoneService])\\n\",\n    \"groups.print_summary()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### MultipleLines\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 95,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"<lifelines.StatisticalResult>\\n\",\n      \"               t_0 = -1\\n\",\n      \" null_distribution = chi squared\\n\",\n      \"degrees_of_freedom = 2\\n\",\n      \"             alpha = 0.95\\n\",\n      \"\\n\",\n      \"---\\n\",\n      \" test_statistic      p  -log2(p)\\n\",\n      \"          30.97 <0.005     22.34\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"no_phone = (survivaldata['MultipleLines_No phone service'] == 1)\\n\",\n    \"multiLines = (survivaldata['MultipleLines_Yes'] == 1)\\n\",\n    \"no_multiLines = ((survivaldata['MultipleLines_Yes'] == 0) & (survivaldata['MultipleLines_No phone service'] == 0))\\n\",\n    \"\\n\",\n    \"plt.figure()\\n\",\n    \"ax = plt.subplot(1,1,1)\\n\",\n    \"\\n\",\n    \"kmf.fit(timevar[no_phone],event_observed = eventvar[no_phone],label = \\\"No Phone Service\\\")\\n\",\n    \"plot1 = kmf.plot(ax = ax)\\n\",\n    \"\\n\",\n    \"kmf.fit(timevar[multiLines],event_observed = eventvar[multiLines],label = \\\"Multiple Lines\\\")\\n\",\n    \"plot2 = kmf.plot(ax = plot1)\\n\",\n    \"\\n\",\n    \"kmf.fit(timevar[no_multiLines],event_observed = eventvar[no_multiLines],label = \\\"Single Line\\\")\\n\",\n    \"plot3 = kmf.plot(ax = plot2)\\n\",\n    \"                 \\n\",\n    \"plt.title('Survival of customers: Mutliple Lines')\\n\",\n    \"plt.xlabel('Tenure')\\n\",\n    \"plt.ylabel('Survival Probability')\\n\",\n    \"plt.yticks(np.linspace(0,1,11))\\n\",\n    \"twoplusgroups_logrank = multivariate_logrank_test(df['tenure'], df['MultipleLines'], df['Churn'], alpha = 0.95)\\n\",\n    \"twoplusgroups_logrank.print_summary()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Internet Service\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 96,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"<lifelines.StatisticalResult>\\n\",\n      \"               t_0 = -1\\n\",\n      \" null_distribution = chi squared\\n\",\n      \"degrees_of_freedom = 2\\n\",\n      \"             alpha = 0.95\\n\",\n      \"\\n\",\n      \"---\\n\",\n      \" test_statistic      p  -log2(p)\\n\",\n      \"         520.12 <0.005    375.19\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"Fiber_optic = (survivaldata['InternetService_Fiber optic'] == 1)\\n\",\n    \"No_Service = (survivaldata['InternetService_No'] == 1)\\n\",\n    \"DSL = ((survivaldata['InternetService_Fiber optic'] == 0) & (survivaldata['InternetService_No'] == 0))\\n\",\n    \"\\n\",\n    \"plt.figure()\\n\",\n    \"ax = plt.subplot(1,1,1)\\n\",\n    \"\\n\",\n    \"kmf.fit(timevar[Fiber_optic],event_observed = eventvar[Fiber_optic],label = \\\"Fiber optic\\\")\\n\",\n    \"plot1 = kmf.plot(ax = ax)\\n\",\n    \"\\n\",\n    \"kmf.fit(timevar[No_Service],event_observed = eventvar[No_Service],label = \\\"No Service\\\")\\n\",\n    \"plot2 = kmf.plot(ax = plot1)\\n\",\n    \"\\n\",\n    \"kmf.fit(timevar[DSL],event_observed = eventvar[DSL],label = \\\"DSL\\\")\\n\",\n    \"plot3 = kmf.plot(ax = plot2)\\n\",\n    \"                 \\n\",\n    \"plt.title('Survival of customers: Internet Service')\\n\",\n    \"plt.xlabel('Tenure')\\n\",\n    \"plt.ylabel('Survival Probability')\\n\",\n    \"plt.yticks(np.linspace(0,1,11))\\n\",\n    \"twoplusgroups_logrank = multivariate_logrank_test(df['tenure'], df['InternetService'], df['Churn'], alpha = 0.95)\\n\",\n    \"twoplusgroups_logrank.print_summary()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Online Security\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 97,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"<lifelines.StatisticalResult>\\n\",\n      \"               t_0 = -1\\n\",\n      \" null_distribution = chi squared\\n\",\n      \"degrees_of_freedom = 2\\n\",\n      \"             alpha = 0.95\\n\",\n      \"\\n\",\n      \"---\\n\",\n      \" test_statistic      p  -log2(p)\\n\",\n      \"        1013.86 <0.005    731.35\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"no_internetService = (survivaldata['OnlineSecurity_No internet service'] == 1)\\n\",\n    \"onlineSecurity = (survivaldata['OnlineSecurity_Yes'] == 1)\\n\",\n    \"no_onlineSecurity = ((survivaldata['OnlineSecurity_No internet service'] == 0) & (survivaldata['OnlineSecurity_Yes'] == 0))\\n\",\n    \"\\n\",\n    \"plt.figure()\\n\",\n    \"ax = plt.subplot(1,1,1)\\n\",\n    \"\\n\",\n    \"kmf.fit(timevar[no_internetService],event_observed = eventvar[no_internetService],label = \\\"No Internet Service\\\")\\n\",\n    \"plot1 = kmf.plot(ax = ax)\\n\",\n    \"\\n\",\n    \"kmf.fit(timevar[onlineSecurity],event_observed = eventvar[onlineSecurity],label = \\\"Online Security\\\")\\n\",\n    \"plot2 = kmf.plot(ax = plot1)\\n\",\n    \"\\n\",\n    \"kmf.fit(timevar[no_onlineSecurity],event_observed = eventvar[no_onlineSecurity],label = \\\"No online Security\\\")\\n\",\n    \"plot3 = kmf.plot(ax = plot2)\\n\",\n    \"                 \\n\",\n    \"plt.title('Survival of customers: Online Security')\\n\",\n    \"plt.xlabel('Tenure')\\n\",\n    \"plt.ylabel('Survival Probability')\\n\",\n    \"plt.yticks(np.linspace(0,1,11))\\n\",\n    \"twoplusgroups_logrank = multivariate_logrank_test(df['tenure'], df['OnlineSecurity'], df['Churn'], alpha = 0.95)\\n\",\n    \"twoplusgroups_logrank.print_summary()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Online Backup\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 98,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"<lifelines.StatisticalResult>\\n\",\n      \"               t_0 = -1\\n\",\n      \" null_distribution = chi squared\\n\",\n      \"degrees_of_freedom = 2\\n\",\n      \"             alpha = 0.95\\n\",\n      \"\\n\",\n      \"---\\n\",\n      \" test_statistic      p  -log2(p)\\n\",\n      \"         821.34 <0.005    592.47\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"no_internetService = (survivaldata['OnlineBackup_No internet service'] == 1)\\n\",\n    \"onlineBackup = (survivaldata['OnlineBackup_Yes'] == 1)\\n\",\n    \"no_onlineBackup = ((survivaldata['OnlineBackup_No internet service'] == 0) & (survivaldata['OnlineBackup_Yes'] == 0))\\n\",\n    \"\\n\",\n    \"plt.figure()\\n\",\n    \"ax = plt.subplot(1,1,1)\\n\",\n    \"\\n\",\n    \"kmf.fit(timevar[no_internetService],event_observed = eventvar[no_internetService],label = \\\"No Internet Service\\\")\\n\",\n    \"plot1 = kmf.plot(ax = ax)\\n\",\n    \"\\n\",\n    \"kmf.fit(timevar[onlineBackup],event_observed = eventvar[onlineBackup],label = \\\"Online Backup\\\")\\n\",\n    \"plot2 = kmf.plot(ax = plot1)\\n\",\n    \"\\n\",\n    \"kmf.fit(timevar[no_onlineBackup],event_observed = eventvar[no_onlineBackup],label = \\\"No online Backup\\\")\\n\",\n    \"plot3 = kmf.plot(ax = plot2)\\n\",\n    \"                 \\n\",\n    \"plt.title('Survival of customers: Online Backup')\\n\",\n    \"plt.xlabel('Tenure')\\n\",\n    \"plt.ylabel('Survival Probability')\\n\",\n    \"plt.yticks(np.linspace(0,1,11))\\n\",\n    \"twoplusgroups_logrank = multivariate_logrank_test(df['tenure'], df['OnlineBackup'], df['Churn'], alpha = 0.95)\\n\",\n    \"twoplusgroups_logrank.print_summary()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Device Protection\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 99,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"<lifelines.StatisticalResult>\\n\",\n      \"               t_0 = -1\\n\",\n      \" null_distribution = chi squared\\n\",\n      \"degrees_of_freedom = 2\\n\",\n      \"             alpha = 0.95\\n\",\n      \"\\n\",\n      \"---\\n\",\n      \" test_statistic      p  -log2(p)\\n\",\n      \"         763.51 <0.005    550.75\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"no_internetService = (survivaldata['DeviceProtection_No internet service'] == 1)\\n\",\n    \"DeviceProtection = (survivaldata['DeviceProtection_Yes'] == 1)\\n\",\n    \"no_DeviceProtection = ((survivaldata['DeviceProtection_No internet service'] == 0) & (survivaldata['DeviceProtection_Yes'] == 0))\\n\",\n    \"\\n\",\n    \"plt.figure()\\n\",\n    \"ax = plt.subplot(1,1,1)\\n\",\n    \"\\n\",\n    \"kmf.fit(timevar[no_internetService],event_observed = eventvar[no_internetService],label = \\\"No Internet Service\\\")\\n\",\n    \"plot1 = kmf.plot(ax = ax)\\n\",\n    \"\\n\",\n    \"kmf.fit(timevar[DeviceProtection],event_observed = eventvar[DeviceProtection],label = \\\"Device Protection\\\")\\n\",\n    \"plot2 = kmf.plot(ax = plot1)\\n\",\n    \"\\n\",\n    \"kmf.fit(timevar[no_DeviceProtection],event_observed = eventvar[no_DeviceProtection],label = \\\"No Device Protection\\\")\\n\",\n    \"plot3 = kmf.plot(ax = plot2)\\n\",\n    \"                 \\n\",\n    \"plt.title('Survival of customers: Device Protection')\\n\",\n    \"plt.xlabel('Tenure')\\n\",\n    \"plt.ylabel('Survival Probability')\\n\",\n    \"plt.yticks(np.linspace(0,1,11))\\n\",\n    \"twoplusgroups_logrank = multivariate_logrank_test(df['tenure'], df['DeviceProtection'], df['Churn'], alpha = 0.95)\\n\",\n    \"twoplusgroups_logrank.print_summary()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Tech Support\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 100,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"<lifelines.StatisticalResult>\\n\",\n      \"               t_0 = -1\\n\",\n      \" null_distribution = chi squared\\n\",\n      \"degrees_of_freedom = 2\\n\",\n      \"             alpha = 0.95\\n\",\n      \"\\n\",\n      \"---\\n\",\n      \" test_statistic      p  -log2(p)\\n\",\n      \"         989.56 <0.005    713.82\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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2qCxYcXiV10HRnyaQJfSZUVJH4aMVWdv/k/c0fWzzBJBoncBweSVWtcD8HjjzcSf7eOvg4xndc74NuU1Up5Rxygsjiam0jzn+c2YP3q58fcU4K91NUi/l6Zw0AOtT/mdLyBhcTXCoH4lqZGeVyr6oYgERkoap2jZmuMgSFXQd2cUOHG0LnsmpkcVbzsyqieKUSbzDx4w0w3iARjTd4xKNB3gfU2xwZTOrscALF3gZt/S4BYPt+2BmwpcobYMKDS2lU9WCyfNNuANoek1XBJSmuPINVeQagVPGrM05eWbh/94mx0iU1KIhIH+BJIAN4QVVHep4/DhgPNMbZuvM3qppXUp5+QWHJD0s4ru7hmcjtGrXjgR4PJOx1pANvgAlaC4lVw4iXX6CIV5AAE82upj3Y0eJwMKm5ZwOHPM1s8faHpKN4R9klW7zBKlVrPamoZ07r1YX7dh0fK13SgoKIZADfAhfibKgzHxigqsvC0rwBvKuqL4rIecAgVb22pHy9QeGNb9/g5WUvU72as7Dd+t3rOabOMbzzy3cS/prSSby1kMab/0v1gyUMdYoio3B/sQl8QfxceIijs6L3hUD8AcYvmHiDBPgHiiCqUjBJtniCVVlqPVUxmKRCUOgOjFDV3u7xPQCq+vewNEuB3qqaJyKCs9FOXd8MXd6gADBtzTQa13aWq3hk3iMcPHSwygeFcufdwwLg4P7ItaR8fLVhJ/sOFJSYJl5NNrsTBF31flwBBKtx+AUPLwsmFassc4sgNZvQkmnGPZcGCgrJHH3UHNgQdpyHs8NauEXA5ThNTL8CskTkKFXdVpobZdXIIv8nZ3TOwUMHqSalb2s2ZeQ3KW7lTNizpcTLOjcImH+AAFPMCf2AfoePv53O7uWzOFR4qMTL6v24gjo7llPn+09Ldbv8xt3Z0rRXzHRHbM+j+q63QsflPTckVZV24EVZh4Mbf8kMCn7jE73Vkj8DT4vI9cB/ge+BYj8bRWQIMAQgOzu7WKbhncpvrHiD3T/vjrfMJpESOXs6QIDxFR5MTuxD1ol9Yl/jrlNVqu7oLUuo9+MKTtizIHba1r0gvBw71kHG3GipD6vkM9LLawhwvMEk3c24J1i6ZAaFPCIXDmoBbAxPoKobgV8DiMiRwOWqWmwkvaqOAcaA03yUrAKbFBbvl6E3mASpcZzYJ/JLO4hoq+V6FS2E6N3xL8j9dqxz9gApUsmCRFbt6oEmj4ZL5LBu40hmUJgPtBGRVjg1gP7A1eEJRKQRsF1VDwH34IxEKrNCLWTammkR59JlmKpJMO+XZtAaR2mbq4IGkmir5QYJKK17Fa9hhAcJSOtAEc+8gaCrEHhZMIkumXs0F4jIMOB9nCGp41V1qYg8CCxQ1alAL+DvIqI4zUcJ2bM5b3ce4xdHxpd2jdpZUDDBvzCDBI94+jm8wSPRNQy/QBFEmgaTeCegxbOkTVWR9pPXvLxDVMGGqZok8RtxFUQ8wcSvhgHQpH1kuta9St/0BU4wqR5jJFWaBo6ySOQE04qWEpPXkiFWUIDIIapgw1RNivEGk0QECYgeKIJo3St2MAkSOKBKBo90EHSZi0q5IF74EFVwhqkC1s9gUkOQfo5YgcKvDyNoU5RXtH6N1r0i7xE0cFWxJqzKplIGBe8X/Rsr3mD7/u0RtQcgInAYU2GCzvGIJ1AEEa3WEaQDvHWv4vcsba2nSCUfXZUuKmVQ8LNxz0YemfdIxLl2jdpxSatLKqhExpQgSKCIp9nJT7y1jtKMnPJq3St2MAlS47DAkXBVIihc0voStu/fHnFu/e71oWYlY9JCeQ2vhWC1jkQ3V3m17hWsnyOepio/FmCAStrR7Mev8/lA4QH+0OkPoXPWx2AqpfIcJRVE0FoIJG50VRBBO9KDSMEAU6U7mv1E63wODxTWx2AqpfKcDR5EvLWQRDdXeSUyAKZx/0iVqSl42T4MxpRS0BpHRdYw/CR7qG4QfrWQcg4UVlOIwdvPYH0MxsSQyNngfspzdFUQiayZ+L2uFF2mpMoGhStOvII61etE7MNwoPCAzWUwpqzSsbnKTyKDSetewYbuxhNME6zKBgU/W/ZtsTWTjKkoiZjUl0iJCiZBJweC8/oquC+iSgeF8M7ndo3acaAwchf59bvXW+3BmIoS58ZNvioymPjVOLavOZw2XJC5GkkOFEntaBaRPji7qmUAL6jqSM/z2cCLQH03zd2qOq1YRmES1dHsx2/Y6vrd68nOitzYxzqkjUkzqTYsd/rdTmBo2CryfOtesWsne7ZATr+S0/io8I5mEckAngEuxNlwZ76ITFXVZWHJhgOvq+qzInIKMA1omawyldbpx3h3D7UOaWPSUqL6OYKKFUxa9yp+LmhfhLeJCRJae0hm81E3YJWqrgYQkclAXyA8KChQ131cD8/ObOXNO5ehXaN2nFD/BFpktQid8+uQtuYkYyqpZHWaB1lapDR9EQnsoE5mUGgObAg7zgO8P71HADNE5GagDnBBEssTk98Xu7c/AYp3SFtntDEmQjyd5mXpi0igZAYF8Tnn7cAYAExU1X+KSHfgJRFp727PeTgjkSHAEIDs7GzKk1/tIbxD2jqjjTEx+dU4Yq3Z5FebKOqLmH535PkmOTGL0KBWqFWmRMkMCnnAsWHHLSjePPRboA+Aqs4VkVpAI2BreCJVHQOMAaejOVkF9uP9Yr+k1SURHdJFK6/astzGmFKpVa/0Q25b9yp+rqj20PmaEm+XUU0yghQrmUFhPtBGRFoB3wP9gas9adYD5wMTRaQtUAtIu2/T9bvX27LcxpjSSWTtIYGSFhRUtUBEhgHv4ww3Ha+qS0XkQWCBqk4FbgfGishtOE1L12saLMZk8xuMMUnhrT2U5/wKV5VdEC+RbH6DMSYp/OZXeAOF35yH1r2K1Sgatz9ndf7eQ8fHumWVntGcLNHmN9hQVmNMqQRpYmrdK/K4LAv5YUEhIYLObwDbv8EYU0beJqZmnaFx28O1h5KGsgZgQSEBgs5v8HZIn9TgpGJprPZgjClRrNpD1M7ozYGyt6BQTrxNSut3rwfgl21+GXHeag/GmIRr2BqYGyipBYUkidWkVNQZ7R3KarUHY0zCdRsCTAqU1IJCksRqUorWGQ1WezDGlFICh7JaUChH3vkNfp3RQWoPVnMwxkQIsNZS4aHI5YOiiRkU3LWIlpSieCYK7xe5tzM6aO0hb3eeTYwzxkTn0xl9SElMUACeE5EawETgFVXdWarCmaiCDmX11h5OP+Z0eh3bKyIva2IyxiRCzKCgqj1FpA0wGFggIvOACao6M+mlq+SCDGWNNmrJGxQOFNjEOGNM2QXqU1DVlSIyHFgAjAY6i4gA96rqW8ksYFUT76glb+3Bag7GmHC7DrA7SLogfQodgEHA/wAzgUtV9UsRaYYz8NWCQgKVZdRSeFDw1hzAag/GmNiC1BSeBsbi1Ap+Kjqpqhvd2kNUItIHeBJnldQXVHWk5/lRwLnu4RHA0apavxTlrxLiGbVk/Q7GmHgECQpvqepL4SdE5I+q+qT3vCdNBvAMcCHOhjvzRWSqqob2aFbV28LS3wx0Lu0LqApKO2ppxY4VrNixgi82fRFx3ibGGWNiCRIUBgJPeM5dj1MDKEk3YJWqrgYQkclAX2BZlPQDgPsDlKfKi9XvMHvD7GIBIdrEOO/wVgsSxlRtUYOCiAzA2SmtlYhMDXsqC9gWIO/mwIaw4zygeIO4c6/jgFbAhwHyrfJi9Tv0OrZXsaajoB3UNgfCmKqtpJrCHGATzp7J/ww7vxv4OkDe4nMu2o4+/YEpqlrom5HIEGAIQHZ2tl+SKs9bezhQcCCi38Gvg9qvmcn6Ioyp2qIGBVVdB6wDuseZdx5wbNhxC2BjlLT9gaEllGUMMAacndfiLE+lFqvfwa/24G1mCjoHAqz2YExlFXU7ThH51J24tpvIX/gCqKrWLTFjkUzgW+B84HtgPnC1qi71pDsJZx/nVkH2Z07F7ThT0Sfff8LunyOHJXtrD17RthH1qz3k7c6jZmbN0LEFCWNSm4gsVNWusdKVVFPo6f6dFU8BVLVARIbhfOFnAONVdamIPAgsUNWifooBwOQgAcEEF3Tjn3BBm5iK0lpfhDGVT0k1hYYlXaiq25NSohisphC/eGoPfiOZVuxYAUQOcQ1SmwALFMZUlDLXFICFOM1G0TqMW8dZNlNB/L6MP/n+kxI7qMvSF+EXbKzT2pjUFrWmkKqsppBc1hdhTOVU5pqCiJysqt+ISBe/51X1y7IU0KSm8u6LsJqDMamlpOajP+HMDfinz3MKnJeUEpmU450DAZG1hyBNTGAL9xmTDqz5yMTF28wUq4kJ/JuZrIPamPKRiI7mooxqATcBPXFqCJ8Az6nq/jKX0qQt7xe0t8MaYs+qDtrEBLZGkzHlJWZNQURex1na4mX31ACggapekeSy+bKaQvqI1WkddLgr+M+LsNqEMcEFrSkECQqLVLVjrHPlxYJCepu2ZhqNazeO+rzNizAmORLWfAR8JSJnqOrnbsanA5+VtYCmaoq1cF+QTmtrdjImeUqa0bwYpw+hOnASsN49Pg5Ypqrty6uQ4aymULnEqjn4iTayKTsrm7u63VXitVabMFVVmZuP3D0OonJXUS13FhQql3gmy/kpzQQ6LwsUpipIxIJ4EV/6InI0UCsBZTMmJMjSGxA7UJRmAp3ftdbsZIwjSEfzZTgT2JoBW3Gaj5araruYmYv0wdm2MwN4QVVH+qS5EhiB0zS1SFWvLilPqylUTfHMi/BrZvKyZidTVSR09BHO7OVZqtpZRM4FBqjqkBjXZeDsp3AhzoY7893rloWlaQO8DpynqjtE5GhV3VpSvhYUDKRGs1P+T/lc0uqSUt3PmIqSyNFHB1V1m4hUE5FqqvqRiDwS+zK6AatUdbVboMlAX2BZWJrfAc+o6g6AWAHBmCLxrNHkpyzblPot0eFltQmTboIEhZ0iciTOTOZJIrIVKAhwXXNgQ9hxHuD9H3gigIh8htPENEJVpwfI25hiYq3R5CfRQ2C9bME/k26CNB/VAfbj7KtwDVAPmKSq22JcdwXQW1VvcI+vBbqp6s1had4FDgJX4uzh/AnQXlV3evIagrM4H9nZ2aeuW1chA59MGvJrZvKyjYZMVZCwPgU3s6Y4zUEKzFfVzQGu6Y7zy7+3e3wPgKr+PSzNc8DnqjrRPf4AuFtV50fL1/oUTKIlYq5E0OU5wD9QeFngMImWyAXxbgD+CnyIU1t4SkQeVNXxMS6dD7QRkVbA90B/wDuy6G2ctZQmikgjnOak1bHKZEwiJaLZKehS4eC/I52XNTuZihKk+WgF0KOouUhEjgLmqOpJJV7opL0EeAKnv2C8qj4sIg8CC1R1qogIznDXPkAh8LCqTi4pT6spmPIQzxBYr2gjm7ys2cmUh0QOSf0AuFhVf3aPawDTVPWChJS0lCwomIoQzxDYIPMkytLsZEHClEYituP8k/vwe+ALEXkHp0+hLzAvIaU0Jk0EmXkdZHE/r7I0O3lnXYMFClN2Ja19dH9JF6rqA0kpUQxWUzCpKhUm1FkntokmoaOP3AyzAFXVPWUtXFlYUDDpJFGrwAYdAhuE9VdUTYnsU2gPvAQ0dE/9AAxU1aVlLmUcLCiYdJKIeRJQtiGwQVigqPwSGRTmAH9R1Y/c417A31S1RyIKWloWFExlk6hO7GQHCgsS6c224zQmjSWz2cmP7TtR+SVyQbzVInIfThMSwG+ANWUpnDGmZMlax8lP0LWd/O5tk+wqnyA1hQbAA0BP99R/gQeKVjYtb1ZTMFVVIibU+bHtTauGhDQfuXsijFTVOxJZuLKwoGCMI1FDYP0kcja2BYnUkJDmI1UtFJFTE1csY0yiJGorUz9++0x42SS7yilI89E/gTbAG8DeovOq+lZyi+bPagrGlE6ihsV6JXqSnQWK5EpkR3NDYBvOlpxFFKiQoGCMKZ0gX7Tx1DDKsmuddVqnriBB4Q5V/SHpJTHGVJh4tjcNMtopWhOTH+/2plZzqBglrX10KTAeZ2e0Q8CVqjqnVJmL9AGexFk6+wVVHel5/nrgUZxF9wCeVtUXSsrTmo+MKR/luXw4FK9RWBNTYiWi+ehh4CxV/Ua7HNIWAAAgAElEQVRETgf+AZxTigJkAM8AF+LszzxfRKaq6jJP0tdUdVjQfI0x5cP75ZuoJiY/fjUKv3y9ndYWJBKvpKBQoKrfAKjqF+6CeKXRDVilqqsBRGQyzrLb3qBgjEkDiWpi8lNUo3hk3iOhc0H6ImxkU+KVFBSODttTodixqj4eI+/mwIaw4zzA72fD5SJyNvAtcJuqbvAmEJEhwBCA7OzY1VBjTPmIZ+a1H2+NwmZZV5yk7acgIlcAvVX1Bvf4WqCbqt4cluYoYI+qHhCR3+P0W5znn6PD+hSMSW2J6Isoy/Lh1hfhL+H7KcRRgO7ACFXt7R7fA6Cqf4+SPgPYrqr1SsrXgoIx6SVRM6/9RjYFWYoDbJY1pEZQyMRpEjofZ3TRfODq8H0YROQYVd3kPv4VcJeqnlFSvhYUjEl/iQgUZZk8l/9TPpe0uiTwvSqDRE5ei4uqFojIMOB9nCGp41V1qYg8CCxQ1anALSJyGVAAbAeuT1Z5jDGpI55Oa6+gk+eK0oYHCu+cCKiatQc/SaspJIvVFIypnJLVF2ErvjrK3HzkGXlUTIDRR0lhQcGYqiFRfRF+zUxBd6OrTH0RiWg+Ku28BGOMSZggq8AGCRLxDneFqjkvwpqPjDFpKxX2t/ZrdvJKhcCRyD2aawG/BdoBtYrOq+rgshYyHhYUjDElSeb+1kGbnbxSYbRTIkcfvQR8A/QGHgSuAZaXrXjGGJMcydrfujTNTl7pNNopSFA4QVWvEJG+qvqiiLyCM8zUGGNSTiKGu0LxQBFtZFNR2pKk03IcQYLCQffvnSLSHtgMtExaiYwxJsG8tYd4RjH51Sb8FvLz41ebSNXaQ5CgMEZEGgD3AVOBI93HxhiTFhKxDLifRO5lDalRewjS0ZyhqoXlVJ6YrKPZGJMMiZoX4VWa5TiSOS8ikR3Na0RkOvAa8KGm2xhWY4wJIFHzIrz8ahNBaw8VUXMIUlOoDVwK9AdOBf4PmKyqnya/eMVZTcEYU1GSOcsakrslacJqCqr6E/A68Lrbt/Ak8DHOInfGGFNlJGpkU9DaQ0X0OwRaJVVEzgGuAi7GWQL7yoDX9cEJIhnAC6o6Mkq6fsAbwGmqatUAY0za8JsX4eWtTUQbyRREskctxQwKIrIGyMWpLdyhqnuDZOxumvMMcCHOVpzzRWSqqi7zpMsCbgG+KJ6LMcaktiBfxkFrE/HsUw3F12QqS5AIUlPoqKo/xpF3N2CVqq4GEJHJQF9gmSfd/wL/AP4cxz2MMSblBZll7W1SCjoxDhLbQR01KIjInar6D+BhESnWG62qt8TIuzmwIew4D4h41SLSGThWVd8VkahBQUSGAEMAsrOzoyUzxpiUFKQvwtukFG1iXLzrLwVVUk2haH2jeNv4xedcKLiISDVgFAF2W1PVMcAYcEYfxVkeY4xJG4neWa7aEdXqBrlv1KCgqv/nPvxaVb8KkplHHnBs2HELYGPYcRbQHpgtIgBNgakicpl1NhtjKrtYTUpBFumD4KOWpJoEGjEaZJ7CR8AxOKODJqvq0kAZi2QC3wLnA9/jjFq6Otr1IjIb+HOsgGDzFIwxlVU8y34H3Vmue5vuqwt2FxwfK78g8xTOFZGmOMNQx4hIXeA1VX0oxnUFIjIMZ0XVDGC8qi4VkQeBBao6Nda9jTGmKoln4b6ydFD7KdXOayKSA9wJXKWqNeK6YxlZTcEYU1XEW3MAuKvbXRHnE1ZTEJG2OBPX+gHbgMnA7aUqpTHGmHITZDnvaILMU5gAvApcpKobYyU2xhiTGPHsIlfSEhpBlBgU3FnJ36nqk4FzNMYYkxBBVm6F2KOWHpn3CF/yZaB7lhgUVLVQRI4SkRqq+nOgHI0xxiRNohbliyZI89E64DMRmQqE1j1S1ccTVgpjjDFxizVq6disY/0u8xUkKGx0/1TDmXBmjDEmhXhrD96aw9Vtr+YpngqUV5B5Cg+UomzGGGPSWJAhqR8RtmZREVU9LyklMsYYU2GCNB+Fr15aC7gcKEhOcYwxxpSV78Y/yqEg1wZpPlroOfWZiHwcuHTGGGPKld8IJT2kiQkKItIw7LAacCrOiqbGGGMqmSDNRwtx+hQEp9loDfDbZBbKGGNMxQjSfNQq3sxFpA/wJM4qqS+o6kjP878HhgKFwB5giHcPZ2OMMWV36KdDu4OkqxbtCRE5zV0yu+h4oIi8IyKjPU1K0a7PAJ4BLgZOAQaIyCmeZK+oao6qdsLZp9kmxBljTAWKGhSA54GfAUTkbGAk8C9gF+7WmDF0A1ap6mp3iYzJQN/wBKr6Y9hhHXyGvhpjjCk/JTUfZajqdvfxVcAYVX0TeFNEcgPk3RzYEHacBxRbvk9EhgJ/AmoAvnMfRGQIMAQgOzvbL4kxxpgEKKmmkOFuqQnOlpofhj0XpINafM75TYJ7RlWPB+4ChvtlpKpjVLWrqnZt3Lh0G04YY4wJrqQv91eBj0XkB+An4BMAETkBpwkpljwgfBWmFjhrKEUzGXg2QL7GGGOSJGpQUNWHReQD4Bhghh7et7MacHOAvOcDbUSkFfA90B+4OjyBiLRR1ZXu4f8AKzHGGFNhYu2n8LnPuW+DZKyqBSIyDHgfZ0jqeFVdKiIPAgtUdSowTEQuAA4CO4DrSvsCjDHGJE6QvoG4qeo0YJrn3F/DHv8xmfc3xhhTOiV1NBtjjKliLCgYY4wJsaBgjDEmxIKCMcaYkKR2NJeXgwcPkpeXx/79+yu6KKaC1apVixYtWlC9evWKLooxaalSBIW8vDyysrJo2bIlIn4TqU1VoKps27aNvLw8WrWKe3FfY6q0StF8tH//fo466igLCFWciHDUUUdZjdGYMqgUQQGwgGAA+xwYU1aVJigYY4wpOwsKCSIi3H777aHjxx57jBEjRgS+fuLEiQwbNqzENGvXruWVV16Jt4il8vbbb7Nsmf8meCtWrKBXr1506tSJtm3bMmTIkITcc+PGjfTr1y8heRlj4mNBIUFq1qzJW2+9xQ8//JC0e8QTFAoLC+O6V0lB4ZZbbuG2224jNzeX5cuXc/PNQdZHjF2eZs2aMWXKlFKX1RiTOJVi9FG4B/5vKcs2/hg7YSmc0qwu91/arsQ0mZmZDBkyhFGjRvHwww9HPLdu3ToGDx5Mfn4+jRs3ZsKECSVuFnT99ddTt25dFixYwObNm/nHP/5Bv379uPvuu1m+fDmdOnXiuuuu45ZbbuHuu+9m9uzZHDhwgKFDh3LjjTcye/ZsHnjgAY455hhyc3OZNm0aF198MT179mTOnDk0b96cd955h9q1a/Pdd98xdOhQ8vPzOeKIIxg7dizbt29n6tSpfPzxxzz00EO8+eabHH/88aHybdq0iRYtWoSOc3JyAOcLP0h5Lr30Uo477jhuuukmAEaMGEFWVhaXX345v/jFL1iyZAmFhYXcddddvP/++4gIv/vd77j55ptZuHAhf/rTn9izZw+NGjVi4sSJHHPMMaX+NzXG+EtqTUFE+ojIChFZJSJ3+zz/JxFZJiJfi8gHInJcMsuTbEOHDmXSpEns2hW53cSwYcMYOHAgX3/9Nddccw233HJLzLw2bdrEp59+yrvvvsvddztv3ciRIznrrLPIzc3ltttuY9y4cdSrV4/58+czf/58xo4dy5o1awCYN28eDz/8cOjX/sqVKxk6dChLly6lfv36vPnmmwAMGTKEp556ioULF/LYY49x00030aNHDy677DIeffRRcnNzIwICwG233cZ5553HxRdfzKhRo9i5cydA4PL079+f1157LZTf66+/zhVXXBFxjzFjxrBmzRq++uqr0Pt28OBBbr75ZqZMmcLChQsZPHgwf/nLXwL/+xhjYktaTUFEMoBngAtxNtyZLyJTVTW8TeIroKuq7hORPwD/wNn6M26xftEnU926dRk4cCCjR4+mdu3aofNz587lrbfeAuDaa6/lzjvvjJnXL3/5S6pVq8Ypp5zCli1bfNPMmDGDr7/+OtTksmvXLlauXEmNGjXo1q1bxFj9Vq1a0alTJwBOPfVU1q5dy549e5gzZ07EF/KBAwdilm3QoEH07t2b6dOn88477/D888+zaNGiwOXp3LkzW7duZePGjeTn59OgQQOys7NZu3Zt6B6zZs3i97//PZmZzke0YcOGLFmyhCVLlnDhhRcCTs3EagnGJFYym4+6AatUdTWAiEwG+gKhoKCqH4Wl/xz4TRLLUy5uvfVWunTpwqBBg6KmCTJssmbNmqHHh/c3iqSqPPXUU/Tu3Tvi/OzZs6lTp07U/DIyMvjpp584dOgQ9evXJzc3yJbbkZo1a8bgwYMZPHgw7du3Z8mSJaUqT79+/ZgyZQqbN2+mf//+vq/N+z6pKu3atWPu3LmlLq8xJphkNh81BzaEHee556L5LfCe3xMiMkREFojIgvz8/AQWMfEaNmzIlVdeybhx40LnevToweTJkwGYNGkSPXv2jCvvrKwsdu/eHTru3bs3zz77LAcPHgTg22+/Ze/evYHzq1u3Lq1ateKNN94AnC/dRYsW+d4r3PTp00P33Lx5M9u2baN58+alKk///v2ZPHkyU6ZM8R1xdNFFF/Hcc89RUFAAwPbt2znppJPIz88PBYWDBw+ydOnSwK/XGBNbMoOC389h35+8IvIboCvwqN/zqjpGVbuqatfGjRsnsIjJcfvtt0eMQho9ejQTJkygQ4cOvPTSSzz55JNx5duhQwcyMzPp2LEjo0aN4oYbbuCUU06hS5cutG/fnhtvvDH0JRrUpEmTGDduHB07dqRdu3a88847gPOl/eijj9K5c2e+++67iGtmzJhB+/bt6dixI7179+bRRx+ladOmpSpPu3bt2L17N82bN/dtArrhhhvIzs6mQ4cOdOzYkVdeeYUaNWowZcoU7rrrLjp27EinTp2YM2dOqV6vMaZkEq1poswZi3QHRqhqb/f4HgBV/bsn3QXAU8A5qro1Vr5du3bVBQsWRJxbvnw5bdu2TVTRTZqzz4MxxYnIQlXtGitdMmsK84E2ItJKRGoA/YGp4QlEpDPwPHBZkIBgjDEmuZIWFFS1ABgGvA8sB15X1aUi8qCIXOYmexQ4EnhDRHJFZGqU7IwxxpSDpE5eU9VpwDTPub+GPb4gmfc3xhhTOrbMhTHGmBALCsYYY0IsKBhjjAmxoJAA27Zto1OnTnTq1ImmTZvSvHnz0PHPP/8cOJ/hw4fzxBNPxEw3duxYcnJy6NixIzk5Obz77rtlKX6ZjB8/ns2bN1fY/Y0xiVXpVkmtCEcddVRoqYgRI0Zw5JFH8uc//zkp91q3bh2PPvooCxcuDM063rZtW1LuFUtBQQHjx4+nS5cuNG3atELKYIxJrMoXFN67GzYvTmyeTXPg4pFxXfriiy/yzDPP8PPPP9OjRw+efvppqlWrxn/+8x/uu+8+CgsLadKkCTNmzABg8eLFnHPOOWzYsIHbb7+doUOHRuS3ZcsW6tatG1pLKCsri6ysLAB69uzJ008/TadOndi8eTM9e/Zk1apVvPDCC/znP/9h3759rF27lmuvvZbhw4ezatUq+vbtS5cuXcjNzaVt27a8+OKL1K5dm5kzZ3LHHXdQWFjIGWecwTPPPEONGjVo0aIFN954I9OnT+eWW24hNzeXq666itq1azNv3jxq1KhRhjfaGFPRrPkoiZYsWcK///1v5syZQ25uLgUFBUyePJnNmzfzhz/8gX//+98sWrQotC4SOOsFzZw5k88//5y//vWvxTal6dKlC/Xr16dVq1YMHjw4cNPRvHnzmDx5Ml9++SWvvPJKqGazbNkyhg4dyuLFi6lVqxbPP/88+/btY/Dgwbz55pssXryYffv2MWbMmFBederU4bPPPuOqq66iU6dOvPbaa+Tm5lpAMKYSqHw1hTh/0SfDrFmzmD9/Pl27OjPLf/rpJ4499lhq167Nueeey3HHOdtHNGzYMHTNL37xC2rUqMHRRx9Nw4YNyc/Pj2iayczMZObMmXzxxRd8+OGHoV/rw4cPL7EsvXv3pkGDBoCzLPenn35Knz59aNWqFWeccQYAv/nNbxgzZgxnnXUWbdq0Ce2jMHDgQMaNGxfaLvSqq8q0urkxJoVZTSGJVJXBgweTm5tLbm4uK1as4L777vNdFrqId4lrvwXlRIQzzjiDe++9l1deeSW0YU5mZiaHDh0CYP/+/cWu8Tv2Ox9rPSzvMtjGmMrDgkISXXDBBbz++uuhFVO3bdvG+vXrOfPMM/nwww9Zt24d4CwLHVReXl7E/ge5ubmhGkfLli1ZuHAhQLG9jmfMmMHOnTvZt28f77zzDmeeeSYAa9asYf78+QC8+uqr9OzZk1NOOYWVK1eyevVqAF5++WXOOecc3/KUtMS2MSb9WFBIopycHO6//34uuOACOnTowEUXXcSWLVto0qQJzz77LH379qVjx45cc801gfM8ePAgt912GyeffDIdO3bkrbfeYtSoUQDccccdPPnkk/To0YMdO3ZEXNezZ0+uvvpqOnfuzIABA0K7sLVr146xY8fSoUMH9u7dy5AhQzjiiCMYN24cv/71r8nJyaFmzZr87ne/8y3PoEGDuOGGG0o9/NYYk5qStnR2stjS2aX3wgsvsGTJkmJzIFatWkW/fv3i2nktldnnwZjiUmHpbESkj4isEJFVInK3z/Nni8iXIlIgIsW33zLGGFOukjb6SEQygGeAC3G24pwvIlNVdVlYsvXA9UByZnoZwNnFzM8JJ5xQ6WoJxpiySeaQ1G7AKlVdDSAik4G+QCgoqOpa97lDSSyHMcaYgJLZfNQc2BB2nOeeKzURGSIiC0RkQX5+fkIKZ4wxprhkBgW/gfhx9Wqr6hhV7aqqXRs3blzGYhljjIkmmUEhDzg27LgFsDGJ9zPGGFNGyQwK84E2ItJKRGoA/YFKuweziHD77beHjh977DFGjBgR6NoJEyaEltquUaMGOTk5dOrUibvvLjZgK6YWLVqwc+fOEtPs2bOH/v37k5OTQ/v27TnrrLPYt29fqe+VCNu3b+e5556rkHsbY4pLWlBQ1QJgGPA+sBx4XVWXisiDInIZgIicJiJ5wBXA8yKyNFnlSbaaNWvy1ltvhWYvl8agQYNCS2E0a9aMjz76iNzcXEaOTM46TqNGjSI7O5vFixezZMkSxo4dS/Xq1ZNyr5IUFBRYUDAmxSR1QTxVnQZM85z7a9jj+TjNSgnzyLxH+Gb7N4nMkpMbnsxd3e4qMU1mZiZDhgxh1KhRPPzwwxHPrVu3jsGDB5Ofn0/jxo2ZMGEC2dnZge69Z88ehg0bxrJlyzh48CAPPvggl156KQUFBdxxxx3MnDmTatWq8fvf/56bbroJgCeeeIJ33nmHwsJCpkyZwoknnhiR56ZNmzjppJMOv76TTwaKT2YbOXIkBQUFDB8+nJ49e9K1a1e++OIL9uzZw4QJE+jatSvDhw9nw4YNrF+/nry8PO655x4GDx7MoUOH+POf/8yMGTMQEe6//3769evHrFmzGDlyJI0aNWLp0qWcdNJJrFixgk6dOtGnT5+kBUJjTDCVb5XUCjR06FA6dOjAnXfeGXF+2LBhDBw4kOuuu47x48dzyy238PbbbwfK88EHH6RPnz5MnDiRHTt2cPrpp3PhhRcyduxYNm7cyKJFi8jIyIhYP6lJkyZ89dVXjB49mscff7zYL/Hf/va39OnTh9dee43zzz+f6667jhNOOCFmWQ4cOMDcuXP58MMPueGGG0LBY/HixcyZM4cff/yRLl268D//8z/Mnj2bZcuWsWjRIvLz8znttNM4++yzAfj8889ZtmwZ2dnZrFq1ilWrVtl8CWNSRKULCrF+0SdT3bp1GThwIKNHj6Z27dqh83PnzuWtt94C4Nprry0WNEoyY8YM3nvvvdAv6P3797N+/XpmzZrFrbfeSkZGBhC5/Pavf/1rAE499VSmTZtWLM9TTz2V1atXM2PGDGbNmkXXrl2ZN28e1aqV3Jo4YMAAAM477zy2bt3Knj17AGcp7lq1alGrVi3OPvts5s+fz6effsrVV19NRkYGTZs2pWfPnixYsIAaNWrQvXv3wDUlY0z5qnRBoaLdeuutdOnShUGDBkVNE23ZbD+qyttvvx3a2yD8fKzlt6MtvQ3O6qaXX345l19+OarKe++9R9++fUNLb4MTgDIzD39EErX8ti29bUzqslVSE6xhw4ZceeWVjBs3LnSuR48eod3VJk2aRM+ePQPn17t3b0aPHh06/uqrrwC46KKLePbZZ0M7s5Vm+e1PP/00NELpwIEDLF++nOOOO46mTZuyceNGduzYwf79+/nPf/4Tcd1rr70GwOzZs2nSpEnoy/3tt9/mwIED/PDDD3zyySd07dqVs88+m8mTJ1NYWMiWLVv47LPPQpsNhbOlt41JLRYUkuD222+PGIU0evRoJkyYQIcOHXjppZd48sknA+d1//33s2/fPnJycmjXrl1omOuNN95I06ZN6dChAx07duT1118PnOfKlSs566yzyMnJoUuXLnTv3p2+fftSq1Yt7r33Xk477TQuu+wyTjnllIjr6tatS48ePbj55psZO3Zs6Pxpp53GxRdfTPfu3XnggQdo0qQJ/fr1Cy3vfcEFF/D4449z9NFHFytLkyZN6Nq1Kzk5OXENwTXGJJYtnW0C6dmzJ08//XRoH4Yiw4cPp1GjRtx6660VVLLi7PNgTHEpsXS2McaY9GIdzSaQTz/91Pf8Qw89VM4lMcYkU6WpKaRbM5hJDvscGFM2lSIo1KpVi23bttkXQhWnqmzbto1atWpVdFGMSVuVovmoRYsW5OXlYXstmFq1atGiRUJXTjGmSqkUQaF69eq0atWqoothjDFpL6nNRyLSR0RWiMgqESk2CF1EaorIa+7zX4hIy2SWxxhjTMmSFhREJAN4BrgYOAUYICKneJL9FtihqicAo4BHklUeY4wxsSWzptANWKWqq1X1Z2Ay0NeTpi/wovt4CnC+lGZhIGOMMQmVzD6F5sCGsOM84PRoaVS1QER2AUcBETvViMgQYIh7eEBEliSlxMnXCM9rSxPpWm5I37Kna7khfcueruWGYGU/LkhGyQwKfr/4vWNGg6RBVccAYwBEZEGQqdqpKF3Lnq7lhvQte7qWG9K37Olabkhs2ZPZfJQHHBt23ALYGC2NiGQC9YDgy30aY4xJqGQGhflAGxFpJSI1gP7AVE+aqcB17uN+wIdqM9CMMabCJK35yO0jGAa8D2QA41V1qYg8CCxQ1anAOOAlEVmFU0PoHyDrMckqczlI17Kna7khfcueruWG9C17upYbElj2tFs62xhjTPJUirWPjDHGJIYFBWOMMSFpFRRiLZuRKkRkvIhsDZ9PISINRWSmiKx0/25QkWWMRkSOFZGPRGS5iCwVkT+651O6/CJSS0Tmicgit9wPuOdbuUuorHSXVKlR0WX1IyIZIvKViLzrHqdLudeKyGIRyRWRBe65lP6sFBGR+iIyRUS+cT/v3VO97CJykvteF/35UURuTWS50yYoBFw2I1VMBPp4zt0NfKCqbYAP3ONUVADcrqptgTOAoe77nOrlPwCcp6odgU5AHxE5A2fplFFuuXfgLK2Siv4ILA87TpdyA5yrqp3Cxsmn+melyJPAdFU9GeiI8/6ndNlVdYX7XncCTgX2Af8mkeVW1bT4A3QH3g87vge4p6LLVUJ5WwJLwo5XAMe4j48BVlR0GQO+jneAC9Op/MARwJc4M+h/ADL9PkOp8gdnDs8HwHnAuziTOlO+3G7Z1gKNPOdS/rMC1AXW4A62Saeyh5X1IuCzRJc7bWoK+C+b0byCyhKPJqq6CcD9++gKLk9M7qq1nYEvSIPyu00wucBWYCbwHbBTVQvcJKn6mXkCuBM45B4fRXqUG5wVCGaIyEJ3ORpIg88K0BrIBya4zXYviEgd0qPsRfoDr7qPE1budAoKgZbEMIkhIkcCbwK3quqPFV2eIFS1UJ1qdQucBRnb+iUr31KVTER+AWxV1YXhp32SplS5w5ypql1wmnWHisjZFV2ggDKBLsCzqtoZ2EuKNRWVxO1jugx4I9F5p1NQCLJsRirbIiLHALh/b63g8kQlItVxAsIkVX3LPZ025VfVncBsnD6R+u4SKpCan5kzgctEZC3OSsLn4dQcUr3cAKjqRvfvrTht291Ij89KHpCnql+4x1NwgkQ6lB2cIPylqm5xjxNW7nQKCkGWzUhl4Ut6XIfTVp9y3KXLxwHLVfXxsKdSuvwi0lhE6ruPawMX4HQcfoSzhAqkYLlV9R5VbaGqLXE+0x+q6jWkeLkBRKSOiGQVPcZp415Cin9WAFR1M7BBRE5yT50PLCMNyu4awOGmI0hkuSu6s6SUHSuXAN/itBX/paLLU0I5XwU2AQdxfpH8Fqed+ANgpft3w4ouZ5Sy98RpqvgayHX/XJLq5Qc6AF+55V4C/NU93xqYB6zCqWrXrOiylvAaegHvpku53TIucv8sLfo/meqflbDydwIWuJ+Zt4EG6VB2nIEU24B6YecSVm5b5sIYY0xIOjUfGWOMSTILCsYYY0IsKBhjjAmxoGCMMSbEgoIxxpiQpO28Zkw6EZGiIX0ATYFCnGUQALqp6s8VUjBjypkNSTXGQ0RGAHtU9bEk3iNTD69tZEzKsOYjY2IQkevcvRpyReT/iUg1EckUkZ0iMtLdw2GuiBztpn9ZRH4Zdv0e9+8LRGSWiEzGmWjnm3eFvEhjXPYBNKYEItIe+BXQQ53F9jJxlqMAqAd8rM4eDnOBwQGyPAO4U1VzYuRtTIWwPgVjSnYBcBqwwFkWitocXsL9J1V9z328EDgrQH5zVXV9gLyNqRAWFIwpmQDjVfW+iJPOCqbhnc+FHP7/VIBbC3d3DAz/f7Y3Vt7GVCRrPjKmZLOAK0WkETijlEQkO8Y1a3G2SgSneSgjgXkbk1QWFIwpgaouBh4AZonI18AMoDqRkh0AAABMSURBVEmMy54HLhSReTgrcR5IYN7GJJUNSTXGGBNiNQVjjDEhFhSMMcaEWFAwxhgTYkHBGGNMiAUFY4wxIRYUjDHGhFhQMMYYE/L/AfEkoNGg46CZAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"no_internetService = (survivaldata['TechSupport_No internet service'] == 1)\\n\",\n    \"TechSupport = (survivaldata['TechSupport_Yes'] == 1)\\n\",\n    \"no_TechSupport = ((survivaldata['TechSupport_No internet service'] == 0) & (survivaldata['TechSupport_Yes'] == 0))\\n\",\n    \"\\n\",\n    \"plt.figure()\\n\",\n    \"ax = plt.subplot(1,1,1)\\n\",\n    \"\\n\",\n    \"kmf.fit(timevar[no_internetService],event_observed = eventvar[no_internetService],label = \\\"No Internet Service\\\")\\n\",\n    \"plot1 = kmf.plot(ax = ax)\\n\",\n    \"\\n\",\n    \"kmf.fit(timevar[TechSupport],event_observed = eventvar[TechSupport],label = \\\"Tech Support\\\")\\n\",\n    \"plot2 = kmf.plot(ax = plot1)\\n\",\n    \"\\n\",\n    \"kmf.fit(timevar[no_TechSupport],event_observed = eventvar[no_TechSupport],label = \\\"No Tech Support\\\")\\n\",\n    \"plot3 = kmf.plot(ax = plot2)\\n\",\n    \"                 \\n\",\n    \"plt.title('Survival of customers: Tech Support')\\n\",\n    \"plt.xlabel('Tenure')\\n\",\n    \"plt.ylabel('Survival Probability')\\n\",\n    \"plt.yticks(np.linspace(0,1,11))\\n\",\n    \"twoplusgroups_logrank = multivariate_logrank_test(df['tenure'], df['TechSupport'], df['Churn'], alpha = 0.95)\\n\",\n    \"twoplusgroups_logrank.print_summary()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Streaming TV\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 101,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"<lifelines.StatisticalResult>\\n\",\n      \"               t_0 = -1\\n\",\n      \" null_distribution = chi squared\\n\",\n      \"degrees_of_freedom = 2\\n\",\n      \"             alpha = 0.95\\n\",\n      \"\\n\",\n      \"---\\n\",\n      \" test_statistic      p  -log2(p)\\n\",\n      \"         368.31 <0.005    265.68\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"no_internetService = (survivaldata['StreamingTV_No internet service'] == 1)\\n\",\n    \"StreamingTV = (survivaldata['StreamingTV_Yes'] == 1)\\n\",\n    \"no_StreamingTV = ((survivaldata['StreamingTV_No internet service'] == 0) & (survivaldata['StreamingTV_Yes'] == 0))\\n\",\n    \"\\n\",\n    \"plt.figure()\\n\",\n    \"ax = plt.subplot(1,1,1)\\n\",\n    \"\\n\",\n    \"kmf.fit(timevar[no_internetService],event_observed = eventvar[no_internetService],label = \\\"No Internet Service\\\")\\n\",\n    \"plot1 = kmf.plot(ax = ax)\\n\",\n    \"\\n\",\n    \"kmf.fit(timevar[StreamingTV],event_observed = eventvar[StreamingTV],label = \\\"Streaming TV\\\")\\n\",\n    \"plot2 = kmf.plot(ax = plot1)\\n\",\n    \"\\n\",\n    \"kmf.fit(timevar[no_StreamingTV],event_observed = eventvar[no_StreamingTV],label = \\\"No Streaming TV\\\")\\n\",\n    \"plot3 = kmf.plot(ax = plot2)\\n\",\n    \"                 \\n\",\n    \"plt.title('Survival of customers: Streaming TV')\\n\",\n    \"plt.xlabel('Tenure')\\n\",\n    \"plt.ylabel('Survival Probability')\\n\",\n    \"plt.yticks(np.linspace(0,1,11))\\n\",\n    \"twoplusgroups_logrank = multivariate_logrank_test(df['tenure'], df['StreamingTV'], df['Churn'], alpha = 0.95)\\n\",\n    \"twoplusgroups_logrank.print_summary()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Streaming Movies\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 102,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"<lifelines.StatisticalResult>\\n\",\n      \"               t_0 = -1\\n\",\n      \" null_distribution = chi squared\\n\",\n      \"degrees_of_freedom = 2\\n\",\n      \"             alpha = 0.95\\n\",\n      \"\\n\",\n      \"---\\n\",\n      \" test_statistic      p  -log2(p)\\n\",\n      \"         378.43 <0.005    272.98\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"no_internetService = (survivaldata['StreamingMovies_No internet service'] == 1)\\n\",\n    \"StreamingMovies = (survivaldata['StreamingMovies_Yes'] == 1)\\n\",\n    \"no_StreamingMovies = ((survivaldata['StreamingMovies_No internet service'] == 0) & (survivaldata['StreamingMovies_Yes'] == 0))\\n\",\n    \"\\n\",\n    \"plt.figure()\\n\",\n    \"ax = plt.subplot(1,1,1)\\n\",\n    \"\\n\",\n    \"kmf.fit(timevar[no_internetService],event_observed = eventvar[no_internetService],label = \\\"No Internet Service\\\")\\n\",\n    \"plot1 = kmf.plot(ax = ax)\\n\",\n    \"\\n\",\n    \"kmf.fit(timevar[StreamingMovies],event_observed = eventvar[StreamingMovies],label = \\\"Streaming Movies\\\")\\n\",\n    \"plot2 = kmf.plot(ax = plot1)\\n\",\n    \"\\n\",\n    \"kmf.fit(timevar[no_StreamingMovies],event_observed = eventvar[no_StreamingMovies],label = \\\"No Streaming Movies\\\")\\n\",\n    \"plot3 = kmf.plot(ax = plot2)\\n\",\n    \"                 \\n\",\n    \"plt.title('Survival of customers: Streaming Movies')\\n\",\n    \"plt.xlabel('Tenure')\\n\",\n    \"plt.ylabel('Survival Probability')\\n\",\n    \"plt.yticks(np.linspace(0,1,11))\\n\",\n    \"twoplusgroups_logrank = multivariate_logrank_test(df['tenure'], df['StreamingMovies'], df['Churn'], alpha = 0.95)\\n\",\n    \"twoplusgroups_logrank.print_summary()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Contract\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 103,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"<lifelines.StatisticalResult>\\n\",\n      \"               t_0 = -1\\n\",\n      \" null_distribution = chi squared\\n\",\n      \"degrees_of_freedom = 2\\n\",\n      \"             alpha = 0.95\\n\",\n      \"\\n\",\n      \"---\\n\",\n      \" test_statistic      p  -log2(p)\\n\",\n      \"        2352.87 <0.005       inf\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"Contract_One_year = (survivaldata['Contract_One year'] == 1)\\n\",\n    \"Contract_Two_year = (survivaldata['Contract_Two year'] == 1)\\n\",\n    \"Contract_month_to_month = ((survivaldata['Contract_One year'] == 0) & (survivaldata['Contract_Two year'] == 0))\\n\",\n    \"\\n\",\n    \"plt.figure()\\n\",\n    \"ax = plt.subplot(1,1,1)\\n\",\n    \"\\n\",\n    \"kmf.fit(timevar[Contract_One_year],event_observed = eventvar[Contract_One_year],label = \\\"One year Contract\\\")\\n\",\n    \"plot1 = kmf.plot(ax = ax)\\n\",\n    \"\\n\",\n    \"kmf.fit(timevar[Contract_Two_year],event_observed = eventvar[Contract_Two_year],label = \\\"Two year Contract\\\")\\n\",\n    \"plot2 = kmf.plot(ax = plot1)\\n\",\n    \"\\n\",\n    \"kmf.fit(timevar[Contract_month_to_month],event_observed = eventvar[Contract_month_to_month],label = \\\"Month to month Contract\\\")\\n\",\n    \"plot3 = kmf.plot(ax = plot2)\\n\",\n    \"                 \\n\",\n    \"plt.title('Survival of customers: Contract')\\n\",\n    \"plt.xlabel('Tenure')\\n\",\n    \"plt.ylabel('Survival Probability')\\n\",\n    \"plt.yticks(np.linspace(0,1,11))\\n\",\n    \"twoplusgroups_logrank = multivariate_logrank_test(df['tenure'], df['Contract'], df['Churn'], alpha = 0.95)\\n\",\n    \"twoplusgroups_logrank.print_summary()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Payment Method\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 104,\n   \"metadata\": {\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"<lifelines.StatisticalResult>\\n\",\n      \"               t_0 = -1\\n\",\n      \" null_distribution = chi squared\\n\",\n      \"degrees_of_freedom = 3\\n\",\n      \"             alpha = 0.95\\n\",\n      \"\\n\",\n      \"---\\n\",\n      \" test_statistic      p  -log2(p)\\n\",\n      \"         865.24 <0.005    619.58\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"automatic_Credit_Card = (survivaldata['PaymentMethod_Credit card (automatic)'] == 1)\\n\",\n    \"electronic_check = (survivaldata['PaymentMethod_Electronic check'] == 1)\\n\",\n    \"mailed_check = (survivaldata['PaymentMethod_Mailed check'] == 1)\\n\",\n    \"automatic_Bank_Transfer = ((survivaldata['PaymentMethod_Credit card (automatic)'] == 0) & (survivaldata['PaymentMethod_Electronic check'] == 0) & (survivaldata['PaymentMethod_Mailed check'] == 0))\\n\",\n    \"\\n\",\n    \"plt.figure()\\n\",\n    \"ax = plt.subplot(1,1,1)\\n\",\n    \"\\n\",\n    \"kmf.fit(timevar[automatic_Credit_Card],event_observed = eventvar[automatic_Credit_Card],label = \\\"Automatic Credit card Payment\\\")\\n\",\n    \"plot1 = kmf.plot(ax = ax)\\n\",\n    \"\\n\",\n    \"kmf.fit(timevar[electronic_check],event_observed = eventvar[electronic_check],label = \\\"Electronic Check\\\")\\n\",\n    \"plot2 = kmf.plot(ax = plot1)\\n\",\n    \"\\n\",\n    \"kmf.fit(timevar[mailed_check],event_observed = eventvar[mailed_check],label = \\\"Mailed_check\\\")\\n\",\n    \"plot3 = kmf.plot(ax = plot2)\\n\",\n    \"\\n\",\n    \"kmf.fit(timevar[automatic_Bank_Transfer],event_observed = eventvar[automatic_Bank_Transfer],label = \\\"Automatic Bank Transfer\\\")\\n\",\n    \"plot4 = kmf.plot(ax = plot3)\\n\",\n    \"                 \\n\",\n    \"plt.title('Survival of customers: PaymentMethod')\\n\",\n    \"plt.xlabel('Tenure')\\n\",\n    \"plt.ylabel('Survival Probability')\\n\",\n    \"plt.yticks(np.linspace(0,1,11))\\n\",\n    \"twoplusgroups_logrank = multivariate_logrank_test(df['tenure'], df['PaymentMethod'], df['Churn'], alpha = 0.95)\\n\",\n    \"twoplusgroups_logrank.print_summary()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Paperless Billing\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 105,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"<lifelines.StatisticalResult>\\n\",\n      \"               t_0 = -1\\n\",\n      \" null_distribution = chi squared\\n\",\n      \"degrees_of_freedom = 1\\n\",\n      \"\\n\",\n      \"---\\n\",\n      \" test_statistic      p  -log2(p)\\n\",\n      \"         189.51 <0.005    140.82\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"PaperlessBilling = (survivaldata['PaperlessBilling_Yes'] == 1)\\n\",\n    \"no_PaperlessBilling = (survivaldata['PaperlessBilling_Yes'] == 0)\\n\",\n    \"\\n\",\n    \"plt.figure()\\n\",\n    \"ax = plt.subplot(1,1,1)\\n\",\n    \"\\n\",\n    \"kmf.fit(timevar[PaperlessBilling],event_observed = eventvar[PaperlessBilling],label = \\\"Paperless Billing\\\")\\n\",\n    \"plot1 = kmf.plot(ax = ax)\\n\",\n    \"\\n\",\n    \"kmf.fit(timevar[no_PhoneService],event_observed = eventvar[no_PhoneService],label = \\\"No Paperless Billing\\\")\\n\",\n    \"plot2 = kmf.plot(ax = plot1)\\n\",\n    \"                 \\n\",\n    \"plt.title('Survival of customers: Paperless Billing')\\n\",\n    \"plt.xlabel('Tenure')\\n\",\n    \"plt.ylabel('Survival Probability')\\n\",\n    \"plt.yticks(np.linspace(0,1,11))\\n\",\n    \"groups = logrank_test(timevar[PaperlessBilling], timevar[no_PaperlessBilling], event_observed_A=eventvar[PaperlessBilling], event_observed_B=eventvar[no_PaperlessBilling])\\n\",\n    \"groups.print_summary()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Survival Regression\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def datapreparation(filepath):\\n\",\n    \"    \\n\",\n    \"    df = pd.read_csv(filepath)\\n\",\n    \"    df.drop([\\\"customerID\\\"], inplace = True, axis = 1)\\n\",\n    \"    \\n\",\n    \"    df.TotalCharges = df.TotalCharges.replace(\\\" \\\",np.nan)\\n\",\n    \"    df.TotalCharges.fillna(0, inplace = True)\\n\",\n    \"    df.TotalCharges = df.TotalCharges.astype(float)\\n\",\n    \"    \\n\",\n    \"    cols1 = ['Partner', 'Dependents', 'PaperlessBilling', 'Churn', 'PhoneService']\\n\",\n    \"    for col in cols1:\\n\",\n    \"        df[col] = df[col].apply(lambda x: 0 if x == \\\"No\\\" else 1)\\n\",\n    \"   \\n\",\n    \"    df.gender = df.gender.apply(lambda x: 0 if x == \\\"Male\\\" else 1)\\n\",\n    \"    df.MultipleLines = df.MultipleLines.map({'No phone service': 0, 'No': 0, 'Yes': 1})\\n\",\n    \"    \\n\",\n    \"    cols2 = ['OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport', 'StreamingTV', 'StreamingMovies']\\n\",\n    \"    for col in cols2:\\n\",\n    \"        df[col] = df[col].map({'No internet service': 0, 'No': 0, 'Yes': 1})\\n\",\n    \"    \\n\",\n    \"    df = pd.get_dummies(df, columns=['InternetService', 'Contract', 'PaymentMethod'], drop_first=True)\\n\",\n    \"    \\n\",\n    \"    return df\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>gender</th>\\n\",\n       \"      <th>SeniorCitizen</th>\\n\",\n       \"      <th>Partner</th>\\n\",\n       \"      <th>Dependents</th>\\n\",\n       \"      <th>tenure</th>\\n\",\n       \"      <th>PhoneService</th>\\n\",\n       \"      <th>MultipleLines</th>\\n\",\n       \"      <th>OnlineSecurity</th>\\n\",\n       \"      <th>OnlineBackup</th>\\n\",\n       \"      <th>DeviceProtection</th>\\n\",\n       \"      <th>...</th>\\n\",\n       \"      <th>MonthlyCharges</th>\\n\",\n       \"      <th>TotalCharges</th>\\n\",\n       \"      <th>Churn</th>\\n\",\n       \"      <th>InternetService_Fiber optic</th>\\n\",\n       \"      <th>InternetService_No</th>\\n\",\n       \"      <th>Contract_One year</th>\\n\",\n       \"      <th>Contract_Two year</th>\\n\",\n       \"      <th>PaymentMethod_Credit card (automatic)</th>\\n\",\n       \"      <th>PaymentMethod_Electronic check</th>\\n\",\n       \"      <th>PaymentMethod_Mailed check</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>29.85</td>\\n\",\n       \"      <td>29.85</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>34</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>56.95</td>\\n\",\n       \"      <td>1889.50</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>53.85</td>\\n\",\n       \"      <td>108.15</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>45</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>42.30</td>\\n\",\n       \"      <td>1840.75</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>70.70</td>\\n\",\n       \"      <td>151.65</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"<p>5 rows × 24 columns</p>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   gender  SeniorCitizen  Partner  Dependents  tenure  PhoneService  \\\\\\n\",\n       \"0       1              0        1           0       1             0   \\n\",\n       \"1       0              0        0           0      34             1   \\n\",\n       \"2       0              0        0           0       2             1   \\n\",\n       \"3       0              0        0           0      45             0   \\n\",\n       \"4       1              0        0           0       2             1   \\n\",\n       \"\\n\",\n       \"   MultipleLines  OnlineSecurity  OnlineBackup  DeviceProtection  ...  \\\\\\n\",\n       \"0              0               0             1                 0  ...   \\n\",\n       \"1              0               1             0                 1  ...   \\n\",\n       \"2              0               1             1                 0  ...   \\n\",\n       \"3              0               1             0                 1  ...   \\n\",\n       \"4              0               0             0                 0  ...   \\n\",\n       \"\\n\",\n       \"   MonthlyCharges  TotalCharges  Churn  InternetService_Fiber optic  \\\\\\n\",\n       \"0           29.85         29.85      0                            0   \\n\",\n       \"1           56.95       1889.50      0                            0   \\n\",\n       \"2           53.85        108.15      1                            0   \\n\",\n       \"3           42.30       1840.75      0                            0   \\n\",\n       \"4           70.70        151.65      1                            1   \\n\",\n       \"\\n\",\n       \"   InternetService_No  Contract_One year  Contract_Two year  \\\\\\n\",\n       \"0                   0                  0                  0   \\n\",\n       \"1                   0                  1                  0   \\n\",\n       \"2                   0                  0                  0   \\n\",\n       \"3                   0                  1                  0   \\n\",\n       \"4                   0                  0                  0   \\n\",\n       \"\\n\",\n       \"   PaymentMethod_Credit card (automatic)  PaymentMethod_Electronic check  \\\\\\n\",\n       \"0                                      0                               1   \\n\",\n       \"1                                      0                               0   \\n\",\n       \"2                                      0                               0   \\n\",\n       \"3                                      0                               0   \\n\",\n       \"4                                      0                               1   \\n\",\n       \"\\n\",\n       \"   PaymentMethod_Mailed check  \\n\",\n       \"0                           0  \\n\",\n       \"1                           1  \\n\",\n       \"2                           1  \\n\",\n       \"3                           0  \\n\",\n       \"4                           0  \\n\",\n       \"\\n\",\n       \"[5 rows x 24 columns]\"\n      ]\n     },\n     \"execution_count\": 4,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"regression_df = datapreparation(\\\"C:/Data/Telco-Customer-Churn.csv\\\")\\n\",\n    \"regression_df.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Survival Regression Ananlysis using Cox Proportional Hazard model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"<lifelines.CoxPHFitter: fitted with 7043 observations, 5174 censored>\\n\",\n      \"      duration col = 'tenure'\\n\",\n      \"         event col = 'Churn'\\n\",\n      \"number of subjects = 7043\\n\",\n      \"  number of events = 1869\\n\",\n      \"partial log-likelihood = -12659.69\\n\",\n      \"  time fit was run = 2020-09-22 14:53:48 UTC\\n\",\n      \"\\n\",\n      \"---\\n\",\n      \"                                       coef exp(coef)  se(coef)      z      p  -log2(p)  lower 0.95  upper 0.95\\n\",\n      \"gender                                 0.04      1.04      0.05   0.85   0.40      1.33       -0.05        0.13\\n\",\n      \"SeniorCitizen                          0.03      1.04      0.06   0.61   0.54      0.88       -0.08        0.15\\n\",\n      \"Partner                               -0.18      0.84      0.06  -3.23 <0.005      9.67       -0.29       -0.07\\n\",\n      \"Dependents                            -0.09      0.91      0.07  -1.31   0.19      2.40       -0.23        0.05\\n\",\n      \"PhoneService                           0.83      2.29      0.47   1.75   0.08      3.63       -0.10        1.76\\n\",\n      \"MultipleLines                          0.09      1.09      0.13   0.69   0.49      1.03       -0.16        0.33\\n\",\n      \"OnlineSecurity                        -0.21      0.81      0.13  -1.60   0.11      3.20       -0.47        0.05\\n\",\n      \"OnlineBackup                          -0.06      0.95      0.13  -0.44   0.66      0.60       -0.31        0.19\\n\",\n      \"DeviceProtection                       0.09      1.09      0.13   0.69   0.49      1.03       -0.16        0.34\\n\",\n      \"TechSupport                           -0.08      0.92      0.13  -0.64   0.52      0.93       -0.34        0.17\\n\",\n      \"StreamingTV                            0.28      1.32      0.24   1.19   0.23      2.10       -0.18        0.74\\n\",\n      \"StreamingMovies                        0.29      1.33      0.24   1.22   0.22      2.16       -0.18        0.75\\n\",\n      \"PaperlessBilling                       0.15      1.16      0.06   2.65   0.01      6.95        0.04        0.26\\n\",\n      \"MonthlyCharges                         0.01      1.01      0.02   0.57   0.57      0.82       -0.03        0.06\\n\",\n      \"TotalCharges                          -0.00      1.00      0.00 -39.16 <0.005       inf       -0.00       -0.00\\n\",\n      \"InternetService_Fiber optic            1.02      2.77      0.58   1.76   0.08      3.67       -0.12        2.15\\n\",\n      \"InternetService_No                    -2.34      0.10      0.60  -3.93 <0.005     13.51       -3.51       -1.17\\n\",\n      \"Contract_One year                     -1.27      0.28      0.10 -12.55 <0.005    117.58       -1.46       -1.07\\n\",\n      \"Contract_Two year                     -3.70      0.02      0.20 -18.32 <0.005    246.60       -4.10       -3.31\\n\",\n      \"PaymentMethod_Credit card (automatic) -0.01      0.99      0.09  -0.13   0.90      0.16       -0.19        0.17\\n\",\n      \"PaymentMethod_Electronic check         0.39      1.47      0.07   5.31 <0.005     23.13        0.24        0.53\\n\",\n      \"PaymentMethod_Mailed check             0.51      1.67      0.09   5.87 <0.005     27.74        0.34        0.68\\n\",\n      \"---\\n\",\n      \"Concordance = 0.93\\n\",\n      \"Log-likelihood ratio test = 5986.69 on 22 df, -log2(p)=inf\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"cph = CoxPHFitter()\\n\",\n    \"cph.fit(regression_df, duration_col='tenure', event_col='Churn')\\n\",\n    \"\\n\",\n    \"cph.print_summary()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.9285636735265471\"\n      ]\n     },\n     \"execution_count\": 6,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"cph.score_\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x504 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig, ax = plt.subplots(figsize = (10,7))\\n\",\n    \"cph.plot(ax = ax);\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"test_id = regression_df.sample(1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig, ax = plt.subplots()\\n\",\n    \"cph.predict_cumulative_hazard(test_id).plot(ax = ax, color = 'red')\\n\",\n    \"plt.axvline(x=test_id.tenure.values[0], color = 'blue', linestyle='--')\\n\",\n    \"plt.legend(labels=['Hazard','Current Position'])\\n\",\n    \"ax.set_xlabel('Tenure', size = 10)\\n\",\n    \"ax.set_ylabel('Cumulative Hazard', size = 10)\\n\",\n    \"ax.set_title('Cumulative Hazard Over Time');\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig, ax = plt.subplots()\\n\",\n    \"cph.predict_survival_function(test_id).plot(ax = ax, color = 'red')\\n\",\n    \"plt.axvline(x=test_id.tenure.values[0], color = 'blue', linestyle='--')\\n\",\n    \"plt.legend(labels=['Survival Function','Current Position'])\\n\",\n    \"ax.set_xlabel('Tenure', size = 10)\\n\",\n    \"ax.set_ylabel('Survival Probability', size = 10)\\n\",\n    \"ax.set_title('Survival Probability Over Time');\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Saving the model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import pickle\\n\",\n    \"pickle.dump(cph, open('survivemodel.pkl','wb'))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Customer Lifetime Value\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"To calculate customer lifetime value, I would multiply the Monthly charges the customer is paying to Telcom and the expected life time of the customer. \\n\",\n    \"\\n\",\n    \"I utilize the survival function of a customer to calculate its expected life time. I would like to be little bit conservative and consider the customer is churned when the survival probability of him is 10%. \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 87,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def LTV(info):\\n\",\n    \"    life = cph.predict_survival_function(info).reset_index()\\n\",\n    \"    life.columns = ['Tenure', 'Probability']\\n\",\n    \"    max_life = life.Tenure[life.Probability > 0.1].max()\\n\",\n    \"    \\n\",\n    \"    LTV = max_life * info['MonthlyCharges'].values[0]\\n\",\n    \"    return LTV\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 89,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"LTV of a testid is: 922.25 dollars.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print('LTV of a testid is:', LTV(test_id), 'dollars.')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.3\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "Exploratory Data Analysis.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# EDA\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 35,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import pandas as pd\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import seaborn as sns\\n\",\n    \"sns.set_style('darkgrid')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 36,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>customerID</th>\\n\",\n       \"      <th>gender</th>\\n\",\n       \"      <th>SeniorCitizen</th>\\n\",\n       \"      <th>Partner</th>\\n\",\n       \"      <th>Dependents</th>\\n\",\n       \"      <th>tenure</th>\\n\",\n       \"      <th>PhoneService</th>\\n\",\n       \"      <th>MultipleLines</th>\\n\",\n       \"      <th>InternetService</th>\\n\",\n       \"      <th>OnlineSecurity</th>\\n\",\n       \"      <th>...</th>\\n\",\n       \"      <th>DeviceProtection</th>\\n\",\n       \"      <th>TechSupport</th>\\n\",\n       \"      <th>StreamingTV</th>\\n\",\n       \"      <th>StreamingMovies</th>\\n\",\n       \"      <th>Contract</th>\\n\",\n       \"      <th>PaperlessBilling</th>\\n\",\n       \"      <th>PaymentMethod</th>\\n\",\n       \"      <th>MonthlyCharges</th>\\n\",\n       \"      <th>TotalCharges</th>\\n\",\n       \"      <th>Churn</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>7590-VHVEG</td>\\n\",\n       \"      <td>Female</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>No phone service</td>\\n\",\n       \"      <td>DSL</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>Month-to-month</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"      <td>Electronic check</td>\\n\",\n       \"      <td>29.85</td>\\n\",\n       \"      <td>29.85</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>5575-GNVDE</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>34</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>DSL</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>One year</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>Mailed check</td>\\n\",\n       \"      <td>56.95</td>\\n\",\n       \"      <td>1889.5</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>3668-QPYBK</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>DSL</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>Month-to-month</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"      <td>Mailed check</td>\\n\",\n       \"      <td>53.85</td>\\n\",\n       \"      <td>108.15</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>7795-CFOCW</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>45</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>No phone service</td>\\n\",\n       \"      <td>DSL</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>One year</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>Bank transfer (automatic)</td>\\n\",\n       \"      <td>42.30</td>\\n\",\n       \"      <td>1840.75</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>9237-HQITU</td>\\n\",\n       \"      <td>Female</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>Fiber optic</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>Month-to-month</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"      <td>Electronic check</td>\\n\",\n       \"      <td>70.70</td>\\n\",\n       \"      <td>151.65</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"<p>5 rows × 21 columns</p>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   customerID  gender  SeniorCitizen Partner Dependents  tenure PhoneService  \\\\\\n\",\n       \"0  7590-VHVEG  Female              0     Yes         No       1           No   \\n\",\n       \"1  5575-GNVDE    Male              0      No         No      34          Yes   \\n\",\n       \"2  3668-QPYBK    Male              0      No         No       2          Yes   \\n\",\n       \"3  7795-CFOCW    Male              0      No         No      45           No   \\n\",\n       \"4  9237-HQITU  Female              0      No         No       2          Yes   \\n\",\n       \"\\n\",\n       \"      MultipleLines InternetService OnlineSecurity  ... DeviceProtection  \\\\\\n\",\n       \"0  No phone service             DSL             No  ...               No   \\n\",\n       \"1                No             DSL            Yes  ...              Yes   \\n\",\n       \"2                No             DSL            Yes  ...               No   \\n\",\n       \"3  No phone service             DSL            Yes  ...              Yes   \\n\",\n       \"4                No     Fiber optic             No  ...               No   \\n\",\n       \"\\n\",\n       \"  TechSupport StreamingTV StreamingMovies        Contract PaperlessBilling  \\\\\\n\",\n       \"0          No          No              No  Month-to-month              Yes   \\n\",\n       \"1          No          No              No        One year               No   \\n\",\n       \"2          No          No              No  Month-to-month              Yes   \\n\",\n       \"3         Yes          No              No        One year               No   \\n\",\n       \"4          No          No              No  Month-to-month              Yes   \\n\",\n       \"\\n\",\n       \"               PaymentMethod MonthlyCharges  TotalCharges Churn  \\n\",\n       \"0           Electronic check          29.85         29.85    No  \\n\",\n       \"1               Mailed check          56.95        1889.5    No  \\n\",\n       \"2               Mailed check          53.85        108.15   Yes  \\n\",\n       \"3  Bank transfer (automatic)          42.30       1840.75    No  \\n\",\n       \"4           Electronic check          70.70        151.65   Yes  \\n\",\n       \"\\n\",\n       \"[5 rows x 21 columns]\"\n      ]\n     },\n     \"execution_count\": 36,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df = pd.read_csv(\\\"C:/Data/Telco-Customer-Churn.csv\\\")\\n\",\n    \"df.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 37,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(7043, 21)\"\n      ]\n     },\n     \"execution_count\": 37,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 38,\n   \"metadata\": {\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"customerID          0\\n\",\n       \"gender              0\\n\",\n       \"SeniorCitizen       0\\n\",\n       \"Partner             0\\n\",\n       \"Dependents          0\\n\",\n       \"tenure              0\\n\",\n       \"PhoneService        0\\n\",\n       \"MultipleLines       0\\n\",\n       \"InternetService     0\\n\",\n       \"OnlineSecurity      0\\n\",\n       \"OnlineBackup        0\\n\",\n       \"DeviceProtection    0\\n\",\n       \"TechSupport         0\\n\",\n       \"StreamingTV         0\\n\",\n       \"StreamingMovies     0\\n\",\n       \"Contract            0\\n\",\n       \"PaperlessBilling    0\\n\",\n       \"PaymentMethod       0\\n\",\n       \"MonthlyCharges      0\\n\",\n       \"TotalCharges        0\\n\",\n       \"Churn               0\\n\",\n       \"dtype: int64\"\n      ]\n     },\n     \"execution_count\": 38,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df.isna().sum()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Analysis\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We do not need customer ID in our analysis as it does not help us predict whether the cutomer will churn or not also, it increases the dimensionality.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 39,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"df.drop([\\\"customerID\\\"], inplace = True, axis = 1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 40,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def stacked_plot(df, group, target):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Function to generate a stacked plots between two variables\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    fig, ax = plt.subplots(figsize = (6,4))\\n\",\n    \"    temp_df = (df.groupby([group, target]).size()/df.groupby(group)[target].count()).reset_index().pivot(columns=target, index=group, values=0)\\n\",\n    \"    temp_df.plot(kind='bar', stacked=True, ax = ax, color = [\\\"green\\\", \\\"darkred\\\"])\\n\",\n    \"    ax.xaxis.set_tick_params(rotation=0)\\n\",\n    \"    ax.set_xlabel(group)\\n\",\n    \"    ax.set_ylabel('Churn Percentage')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Gender, SeniorCitizen, Partner, Dependents\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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FHI6ICfBq4IzN/O9LOensr9PdPLKhUbcu29nP/wx8q9PZu2d85M2bsROY9T9vu4Ycf4q677mSPPfZgu+0mANDb20dvbw+VSoWensqzXs+YsTMTJjwPqHXyzp79Enp7e5g+fQaPP/7oFte1UaXiz8G2pJs+i6KC4AlgcsN0T2Zu2DgREROAy4BVwIdH29nQUJUVKwZbXmSrDQxMHn0lbZGt/dyr1SpDQ8NbtM3cufvz1a/+I4cf/g523vlFbNiwgQsvPJ9Xv/o1AE/b39DQMOPGjWfZsmXMnDmLe+75T6ZNG2BoaJhKpfKndavVKsPDtfWr1WGGh7e8rsbvaWv/Pzw2W6+bficVFQSLgcOAK+t9BHdvXFBvCXwXuCkzzyvo/aUtNmnS9vzd353JeeedzfDwMIODg+y//4Hsuutu/PrXtz9r/Xe+81189rPn8YIXvJBp0wY6ULHUGpVqtdrynTZcNbQ3UAGOBg4FlgK9wBXArQ2bLMjMWza3v/Xrh6pjJX0rZ3q9d6tUF1ZZtmzVVm37yCMPMH36rBZX1FnP5Xvy2Gyt53JsttPAwOTbeHof7SYV0iKodwIf94zZ9zS8nlDE+0qStpx3FktSyRkEklRyBoEklZxBIEklZxBIUsk5+qhKYdLUXiaOb92doIPrBlm9cmjEdW6//T849dR5fPWr3+CFL5wOwBe/+HlmzdqVQw89rGW1SM+VQaBSmDh+Ykuvo68urLKa0a8j7+sbxznnnMWFF17sMwW0zTIIpAK96lX7Mjxc5TvfuZJ3vONdf5p/xRX/yo9+9G/09vYyZ84r+fCHT+hglSo7+wikgs2bN59vfvPrPPjgfwEwOLiam266kUsuuYxLLrmM3//+QRYv/mmHq1SZGQRSwaZO7eeEE07inHPOoFodZt26dbzsZXvR19dHpVJhzpxXcP/993a6TJWYQSC1wQEHHMTMmbO4/vrrGD9+PL/5zRI2bNhAtVrlzjvvYObM7hoXSWOLfQRSm3zsYydx222/YuLESbz+9W/gQx86hmq1yt57z+Gggw7udHkqMYNApTC4bpDqwtaNtDu4bvTRcPfZZ1/22ed/Bn6cNGl7rrrquj9NH3HEkS2rR3ouDAKVwuqVQ01d7imVkX0EklRyBoEklZxBIEklZxBIUskZBJJUcl41pFKYOqmX8RNbN/rousFBVq4eefTR0047mYiX8jd/cxQAg4ODHHPMkZx11qfYc88Xt6wW6bkyCFQK4ydO5DMtHP1zXrUKq0e+HHXevFM55pgjOeCAg9htt925+OILedvb/sIQ0DbHIJAK0t/fz4knnsx5553NBz/4ER566PfMm7eAe+9dyoUXfppqtcrUqVNZsGAh69evZ+HCBQwPDzM0tIF5805ljz3+rNPfgkrCIJAKdMABB3HzzT/mnHPOZNGiS6lUKpx33tksWHA6u+22O9dddw2XX/5V9tprDpMmbc8ZZ5zN/fffz+rVT3a6dJWIQSAV7M1v/j889dRaBgZeAMADD9zP+ed/CoChoQ3MnDmL/fZ7Lb///X8xf/5J9PX18b73HdPJklUyBoHUZrvsMovTTjuL6dOnc9ddd/LYY49yxx23seOO07jggotZsuQuvvSli/n857/U6VJVEgaB1GYnnbSAs88+neHhYQDmz/8kU6dO5fTTT+XKK6+gp6eHo49+f4erVJkYBCqFdYODtSt9Wri/Zj1zFNKXvGQ2X/jCl5+13kUXLWpJbdKWMghUCitXD416uadUVt5ZLEklZxBIUskZBOpa1Rb2CXRaN30v2vYYBOpKfX3jWb36ia74BVqtVlm9+gn6+sZ3uhR1KTuL1ZV22GGA5cuX8eSTKzpdSkv09Y1nhx0GOl2GupRBoK7U29vHtGkzOl2GNCYUEgQR0QMsAuYATwHHZubShuXvBz4IbADOzszriqhDkjS6ovoIDgcmZOZcYD5w/sYFETEdOAHYH3gTcG5EPK+gOiRJoygqCA4AbgDIzFuBfRuW/S9gcWY+lZkrgaXA3gXVIUkaRVF9BFOAlQ3TQxHRl5kbNrFsFTB1pJ2NG9f76MDA5AdaX2brVReO/atUtiUDA5M7XULX8NhsrTFybM5qZqWiguAJoPF/qaceAptaNhkY7dIOL5eQpIIUdWpoMXAoQETsB9zdsOyXwIERMSEipgKzgSUF1SFJGkWliBtuGq4a2huoAEdTC4almfm9+lVDH6AWROdk5lUtL0KS1JRCgkCSNHY4xIQklZxBIEklZxB0sYg4OCJWRMTMhnmfioijOliWSi4ivh0R8xumt4+IjIg5nayrzAyC7rcO+KeIqHS6EKnuOOBDEfHS+vRngC9n5q87WFOpOehc97uJWuB/BPjCxpkRcRJwBLXxnm7OzFM6U57KJjMfjYjjgUsjYgGwB7Vg2Av4HLUrDR8D/i8wHvgmtWN4HHBcZt696T1ra9kiKIcPASdGxJ716cnAXwGvrX/tGRFv7VRxKp/MvBa4B/hn4KjMrAJfAT6SmQcD1wMnUxuSZiXwFmpjlE3pRL3dziAogcx8DPg4tR+6HmACcGtmrq//AP4UeFnnKlRJfQ34RWY+VJ+eDSyKiH+n1hrYCfgB8BPgu8BZwHAH6ux6BkFJ1P8CS+AoYC3wmojoq/cdHAT8toPlSVA7Pt9bbxGcDHwfOBj478w8BDgbOKdj1XUx+wjK5ePA/6Y20N+V1IYC6QF+BlzTwbokqJ3C/FpE9Nanj6HWV/DNiPg4MEStVaAW885iSSo5Tw1JUskZBJJUcgaBJJWcQSBJJWcQSFLJefmoSi8iDqZ2Oe1vgCqwHXB5Zn6+iW33AnbIzJsLLVIqkC0CqeamzDw4M18H/DlwUkT0N7HdO4CXjrqWtA2zRSA922RqNy/NiYiF9XkTgfdSG831Wmo3Ov2Y2p3a6yLiduBSasMh7E2tZfH2zFwZEedSu3u7B/hsZn6rPozCMmAH4E2ZOdSm7016FoNAqnl9/ZfzMLAe+Ci18ZeOzMyHI+JU4J3A5cB04FWZua4+RMcjmfnLiJgCXJGZH42Iy4G3RMRKYLfM3D8iJgC3RsSN9ff8emZe3d5vU3o2g0CquSkzj2icERFvBz4XEU8CO1MbkgPg/sxct5n93FH/90Fqg/vtAryqHjJQG0p5Vv11tqh26Tmxj0DavEuBozPzKOBhauPkw9NHwBzm6T9Hzxyz5R7gx/WB1F5PrVP6vk3sR+oYg0DavH8BfhERi6n1G+y0iXVuA46PiNdtZh/XAk9GxE/r61Yzc1Uh1UpbyUHnJKnkbBFIUskZBJJUcgaBJJWcQSBJJWcQSFLJGQSSVHIGgSSVnEEgSSX3/wFlrXzLVFB5vgAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"stacked_plot(df, \\\"gender\\\", \\\"Churn\\\")\\n\",\n    \"stacked_plot(df, \\\"SeniorCitizen\\\", \\\"Churn\\\")\\n\",\n    \"stacked_plot(df, \\\"Partner\\\", \\\"Churn\\\")\\n\",\n    \"stacked_plot(df, \\\"Dependents\\\", \\\"Churn\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"From above plots, we can say following:\\n\",\n    \"- Gender alone does not help us predict the customer churn.\\n\",\n    \"- If a person is young and has a family, he or she is less likely to stop the service as we can see below. The reason might be the busy life, more money or another factors.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"No     1437\\n\",\n       \"Yes     229\\n\",\n       \"Name: Churn, dtype: int64\"\n      ]\n     },\n     \"execution_count\": 9,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df[(df.SeniorCitizen == 0) & (df.Partner == 'Yes') & (df.Dependents == 'Yes')].Churn.value_counts()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"No     921\\n\",\n       \"Yes    242\\n\",\n       \"Name: Churn, dtype: int64\"\n      ]\n     },\n     \"execution_count\": 10,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df[(df.SeniorCitizen == 0) & (df.Partner == 'Yes') & (df.Dependents == 'No')].Churn.value_counts()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"No     278\\n\",\n       \"Yes     75\\n\",\n       \"Name: Churn, dtype: int64\"\n      ]\n     },\n     \"execution_count\": 11,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df[(df.SeniorCitizen == 0) & (df.Partner == 'No') & (df.Dependents == 'Yes')].Churn.value_counts()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"No     1872\\n\",\n       \"Yes     847\\n\",\n       \"Name: Churn, dtype: int64\"\n      ]\n     },\n     \"execution_count\": 12,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df[(df.SeniorCitizen == 0) & (df.Partner == 'No') & (df.Dependents == 'No')].Churn.value_counts()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Tenure\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"count    7043.000000\\n\",\n       \"mean       32.371149\\n\",\n       \"std        24.559481\\n\",\n       \"min         0.000000\\n\",\n       \"25%         9.000000\\n\",\n       \"50%        29.000000\\n\",\n       \"75%        55.000000\\n\",\n       \"max        72.000000\\n\",\n       \"Name: tenure, dtype: float64\"\n      ]\n     },\n     \"execution_count\": 10,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df['tenure'].describe()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"1     613\\n\",\n       \"72    362\\n\",\n       \"2     238\\n\",\n       \"3     200\\n\",\n       \"4     176\\n\",\n       \"71    170\\n\",\n       \"5     133\\n\",\n       \"7     131\\n\",\n       \"8     123\\n\",\n       \"70    119\\n\",\n       \"Name: tenure, dtype: int64\"\n      ]\n     },\n     \"execution_count\": 11,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df['tenure'].value_counts().head(10)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x576 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.figure(figsize=(16,8))\\n\",\n    \"sns.countplot(x=\\\"tenure\\\", hue=\\\"Churn\\\", data=df)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"As we can see the higher the tenure, the lesser the churn rate. This tells us that the customer becomes loyal with the tenure.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Converting into 5 groups to reduce model complexity.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 41,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def tenure(t):\\n\",\n    \"    if t<=12:\\n\",\n    \"        return 1\\n\",\n    \"    elif t>12 and t<=24:\\n\",\n    \"        return 2\\n\",\n    \"    elif t>24 and t<=36:\\n\",\n    \"        return 3\\n\",\n    \"    elif t>36 and t<=48:\\n\",\n    \"        return 4\\n\",\n    \"    elif t>48 and t<=60:\\n\",\n    \"        return 5\\n\",\n    \"    else:\\n\",\n    \"        return 6\\n\",\n    \"\\n\",\n    \"df[\\\"tenure_group\\\"]=df[\\\"tenure\\\"].apply(lambda x: tenure(x))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 42,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"1    2186\\n\",\n       \"6    1407\\n\",\n       \"2    1024\\n\",\n       \"3     832\\n\",\n       \"5     832\\n\",\n       \"4     762\\n\",\n       \"Name: tenure_group, dtype: int64\"\n      ]\n     },\n     \"execution_count\": 42,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df[\\\"tenure_group\\\"].value_counts()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 43,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.axes._subplots.AxesSubplot at 0x2128cfcf080>\"\n      ]\n     },\n     \"execution_count\": 43,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"sns.countplot(x=\\\"tenure_group\\\", hue=\\\"Churn\\\", data=df)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Phone Service and MultipleLines \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"stacked_plot(df, \\\"PhoneService\\\", \\\"Churn\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"stacked_plot(df, \\\"MultipleLines\\\", \\\"Churn\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"As we can see multiplelines and phoneservice do not add value in the model having similar churn rate.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### OnlineSecurity, OnlineBackup, DeviceProtection, TechSupport, StreamingTV, StreamingMovies\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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8BOwKLgZ/HWpGIiBRUlCDo6+53A43u/hLBC+xFRKRMRAkCzGyn8N8tgUysFYmISEFFuVh8LnAPMBR4DD1QJiJSVtoNAjPrBbzr7qMKVI+IiBRYm6eGzGwiMBeYa2aHFq4kEREppPauEZwEGDAKvYxGRKRstRcEa919vbsvBboXqiARESmsSHcNoVtGRUTKVnsXi3cxswcJQqDlZ0B9DYmIlJP2guC4nJ9vi7sQEREpjjaDIHxpvYiIlLmo1whERKRMKQhERBKuwy4mzGwr4ESgqmWcu18ZZ1EiIlI4UVoEjwK9CF5V2fKfiIiUiSidzq1098tjr0RERIoiShDMM7MTgDlAFsDdF8RalYiIFEyUINgj/K9FFjgonnJERKTQogTBU+7+o9grERGRoohysXicmaVjr0RERIoiSougDvjYzN4nOC2UdffR8ZYlIiKFEiUIvt7ZlZpZBXALsDuwDhjv7gtbmee3wBPurr6MRESKJEoQnNrKuI4eKDsGqHL3UWY2ErgeOHqDea4C+kTYvoiIxCjKNYKWh8g+A7YEto6wzL7AdAB3nwUMz51oZt8CmoHfdaZYERHJvw5bBO5+e+6wmUX58O4FLM8ZzphZpbs3mdmuBK/B/BbwX1GKTKdT1Nb2jDKrRKT9Wbp07EpbVzx+Ufoa2jFncCDRWgQrgJqc4Qp3bwp/PgUYBPwB2BZYb2YfuPv0tlaWyWSpr18TYbP5UVdX0/FMJa6Q+7PQyv346diVtq74WRblGkFui2AtcEGEZWYCRwKPhNcI3m6Z4O4XtfxsZlcAi9sLARERiVeUU0MHfon1Pg6MNbOXCF51ebqZnQ8sdPfffIn1iYhITKKcGvovYCLQcmoHd9+ivWXcvRmYsMHo+a3Md0WkKkVEJDZRTg0dCWzj7g1xFyMiIoUX5fbRz4DGuAsREZHiaLNFYGYPEXQp0R+YY2bzwklZdz+pEMWJiEj82js1pG4fREQSoM1TQ+4+AzBgZvhzMzA0/FlERMpEm0FgZpOBQ4Du4ahFwCFm9p+FKExERAqjvYvFhwP/5u5rANz9A+B44KgC1CUiIgXSXhCscvds7gh3bwRWxluSiIgUUntB0GBmg3NHhMPZNuYXEZES1N5dQxcDvzazZ4H3CDqbO5TW308gIiIlqr27hv4E7AfMAaqBN4Ax7j6nQLWJiEgBtNvFhLsvB+4rUC0iIlIEUbqYEBGRMqYgEBFJOAWBiEjCKQhERBJOQSAiknAKAhGRhFMQiIgknIJARCThFAQiIgmnIBARSTgFgYhIwikIREQSTkEgIpJwCgIRkYRTEIiIJJyCQEQk4RQEIiIJpyAQEUk4BYGISMIpCEREEk5BICKScJVxrNTMKoBbgN2BdcB4d1+YM/084IRw8Cl3nxJHHSIi0rG4WgTHAFXuPgq4BLi+ZYKZDQZOBkYDo4BDzGy3mOoQEZEOxBUE+wLTAdx9FjA8Z9oi4DB3z7h7M9ANWBtTHSIi0oFYTg0BvYDlOcMZM6t09yZ3bwSWmlkK+BEwx90XtLeydDpFbW3PmEpNJu3P0qVjV9q64vGLKwhWADU5wxXu3tQyYGZVwN3ASuA/OlpZJpOlvn5N3otsS11dTcczlbhC7s9CK/fjp2NX2rriZ1lcp4ZmAocDmNlI4O2WCWFL4Algrruf5e6ZmGoQEZEI4moRPA6MNbOXgBRwupmdDywE0sD+wCZmNi6c/1J3fzmmWkREpB2xBEF4EXjCBqPn5/xcFcd2RUSk8/RAmYhIwikIREQSTkEgIpJwCgIRkYRTEIiIJJyCQEQk4RQEIiIJpyAQEUk4BYGISMIpCEREEk5BICKScAoCEZGEUxCIiCScgkBEJOEUBCIiCacgEBFJOAWBiEjCKQhERBJOQSAiknAKAhGRhFMQiIgknIJARCThFAQiIgmnIBARSTgFgYhIwikIREQSTkEgIpJwCgIRkYRTEIiIJJyCQEQk4RQEIiIJpyAQEUk4BYGISMIpCEREEq4yjpWaWQVwC7A7sA4Y7+4Lc6Z/FzgLaAKucvf/i6MOERHpWFwtgmOAKncfBVwCXN8ywcwGAN8HxgCHAlPNbJOY6hARkQ7EFQT7AtMB3H0WMDxn2ghgpruvc/flwEJgt5jqEBGRDsRyagjoBSzPGc6YWaW7N7UybSWwWXsr69YtvbSurubD/JfZtuzkbCE3V3B1dTXFLiFW5Xz8dOxKW4GP3zZRZoorCFYAub9tRRgCrU2rAeo7WF9dHmsTEZEccZ0amgkcDmBmI4G3c6a9AuxnZlVmthkwFJgXUx0iItKBVDab/2ZYzl1DuwEp4HSCYFjo7r8J7xo6kyCIrnH3X+a9CBERiSSWIBARkdKhB8pERBJOQSAiknAKggIyswPMrN7MtsoZd62ZnVbEsrqsjd1fZnajmW3dzvSvmlksz7CY2bFmtkUc6w7Xf5iZnRnX+suFmT1mZpfkDG9qZm5muxezrq5GQVB464F7zCxV7EJKxJfeX+4+yd3/2s4s3wHi+rA+l+CZmVi4+3R3vyOu9ZeRCcDZZrZzODwNuMPd5xaxpi4nrucIpG1/IAjg7wE/bRlpZj8ATiDof+l5d7+4OOV1Oa3uL+h4n5nZHwk+CE4AtgM2J3jA5jxgKXAYsJeZ/RnYBzgfyAAvuvslZnYFMBrYFDgDuBtYBGwPvOLuZ4e3QP8c6Btu9vvA1sAewH1mtq+7rw/r2RG4F2gMaz7F3T8ys6nAV8Pf88fu/mhY+xKgN8FDlze6+wwz2xu4HHgc2Cms83KCbl0qgVvd/XYzOwc4CcgCD7v7TZ3d8eXA3Zea2UTgLjO7lODYnW1mw4CbCO5q/DvBl4LuwP8SHIduwAR3f7v1NZcXtQiK42zgPDPbIRyuAY4j+NAZDexgZl8vVnFd0Ib7i/B/5M7ss3XuPo7gm/p57v46QTcoFwGrgCnA19x9X2CQmY0Nl3vH3UcDDcCOBIEwAjg87Dfrh8Cz7n4gwS3Rt7r7b4E3CT7o1+fUMBZ4HTgYuBrobWbjgO3cfQxwIHCZmdWG8z/o7gcDdwCnhuNOA+7M2Q97AuMIgmw0sLOZ7QIcT9DVy77AMWZm7eybsubuTwLzCUL4NHfPEuzD77n7AcBTBH8HIwh6PRhHEOixtei6GgVBEbj734FJBH+YFUAVMMvdG8M/0heAXYpXYdfSyv4C2InO7bM54b+LCPZ3riEET68/FX4T3xkY3LL5nPkWuvtKd88An4TrGQZ8J1zuToJv8G35OUFLZDowkaBVMAz4Srj8dIJvoi3dArRs+2lghJn1AfYDfpezTiNonWTcfY27n0uwH7YBniVoUfUNf8ckuw+Y7e4fhcNDgVvC/d5yivB3wAzgCeBKoLkIdRaFgqBIwm8pTvANby2wj5lVhufCvwosKGJ5Xc4G+wuCb3id2WetPTDTTPD/wPsEATE2/IZ4MzA7Z5721jEfuCFc7jjgFxusO9fRwAvu/jXgUeDicPnnwuUPAh4B3svdtrs3h/PfCvw6DKLc7e9lZhVm1s3MniHYT38CDgzXey9ffLpfgn10Srh/LgJ+CxwAfOLuhwBXAdcUrboCUxAU1ySCUw4rCT4AZhJ0wfEB8OvildVltewvwnO3G7vPZgPXAv2AHwMzzGw2wamBqEF8NXBczjf6lu5SXiK4RtAnZ97XgKvN7AWCaxc3A08Cq8JxrwNZd1/ZynbuBr4R/vs5d38z3O5M4EXgF+GF0GeBF83sNWAH4CMk19kEx+cFgr+Bt4C5wHfN7GXgR8DUItZXUHqyWEQk4dQiEBFJOAWBiEjCKQhERBJOQSAiknAKAhGRhFMXE1LyzGw7gj5k+hI8kDUXuLiN2zAxs8XuPsDMbiTo0qG9/ohaW/4SgqeDmwmeLfhh+KRy3pjZHsBR7n6lmR1L8DDUx/nchkgLBYGUNDPrAfwGGO/us8NxpwIPAe120+Huk77E9nYGjgLGuHs2/MD+HyCvvVmGzwe8GQ6eS/DcgYJAYqHnCKSkmdm3gAPcfeIG42cRPBTWAGwLDCToZ+aNnBbBH2mjUzp3f9rM9id4YCwDvAucBWxG8AE9GZgedhq3ibuva6Mjs5XhuBEEnZpNJujPZoK7nxDW2lLPvQStmr4EDzQdD9xP8LTyAuAuYAd3v9DM0mEdw919Xd52qCSSrhFIqRtM8CG9ofcJup340N0PJXiKt73++7/QKV3YbcWdwDfcfX+CJ3NPc/elhC0C4GUzm88/Wx6tdWR2NNDP3UcQ9Ha6dwe/zx/CTu6WAeR2YEfQyjkmDIHDCLqmUAjIRtOpISl1HxF8297QDsDzfLGzuTHtrGfDTunqCFoRj4Qdd/YAfm9mQ4AV7v4dADMbTtBZ3XP8syMzCK5VLCDoFO5lAHdfDFxuZgdssO3cdy04bXD3lWY2AzgUOJ2gYzSRjaYWgZS6J4CxZvZ5GJjZeIK+/Fsu5kax4XxLgb8BR4ff8K8GngN2A241s5YeTBcQnOrJ0HpHZu8QtgLMbDMze5qgk8GB4bhtgNz+iFrr8TK3A7s7gfHA5u7+VsTfTaRdahFISXP3VWZ2JHCDmfUl+Jt+CzgRuHEj1ttsZucCvzWzCmAFwYf8Z2Y2FJhtZqsIPqAvdPflZtbSkVk6XM0ZwF+Ag83sxbC2KQSdz9WHHdy9Q3Aaqz0tHdgd4u6zw1bJz77s7yayIV0sFikhYSjNBA519xXFrkfKg04NiZSI8HmJN4D7FAKST2oRiIgknFoEIiIJpyAQEUk4BYGISMIpCEREEk5BICKScAoCEZGE+386k140djz/+AAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"stacked_plot(df, \\\"OnlineSecurity\\\", \\\"Churn\\\")\\n\",\n    \"stacked_plot(df, \\\"OnlineBackup\\\", \\\"Churn\\\")\\n\",\n    \"stacked_plot(df, \\\"DeviceProtection\\\", \\\"Churn\\\")\\n\",\n    \"stacked_plot(df, \\\"TechSupport\\\", \\\"Churn\\\")\\n\",\n    \"stacked_plot(df, \\\"StreamingTV\\\", \\\"Churn\\\")\\n\",\n    \"stacked_plot(df, \\\"StreamingMovies\\\", \\\"Churn\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"In all above categories we see consistent results. If a person does not opt for internet service, the customer churning is less. The reason might be the less cost of the service. Also, if they have internet service and does not opt for specific service their probability of churning is high.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 33,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"sns.distplot(df.tenure[df.OnlineSecurity == \\\"No\\\"], hist_kws=dict(alpha=0.3), label=\\\"No\\\")\\n\",\n    \"sns.distplot(df.tenure[df.OnlineSecurity == \\\"Yes\\\"], hist_kws=dict(alpha=0.3), label=\\\"Yes\\\")\\n\",\n    \"sns.distplot(df.tenure[df.OnlineSecurity == \\\"No internet service\\\"], hist_kws=dict(alpha=0.3), label=\\\"No Internet Service\\\")\\n\",\n    \"plt.title(\\\"Tenure Distribution by Online Security Service Subscription\\\")\\n\",\n    \"plt.legend()\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 32,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"sns.distplot(df.tenure[df.StreamingTV == \\\"No\\\"], hist_kws=dict(alpha=0.3), label=\\\"No\\\")\\n\",\n    \"sns.distplot(df.tenure[df.StreamingTV == \\\"Yes\\\"], hist_kws=dict(alpha=0.3), label=\\\"Yes\\\")\\n\",\n    \"sns.distplot(df.tenure[df.StreamingTV == \\\"No internet service\\\"], hist_kws=dict(alpha=0.3), label=\\\"No Internet Service\\\")\\n\",\n    \"plt.title(\\\"Tenure Distribution by Streaming TV Service Subscription\\\")\\n\",\n    \"plt.legend()\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"sns.distplot(df.tenure[df.StreamingMovies == \\\"No\\\"], hist_kws=dict(alpha=0.3), label=\\\"No\\\")\\n\",\n    \"sns.distplot(df.tenure[df.StreamingMovies == \\\"Yes\\\"], hist_kws=dict(alpha=0.3), label=\\\"Yes\\\")\\n\",\n    \"sns.distplot(df.tenure[df.StreamingMovies == \\\"No internet service\\\"], hist_kws=dict(alpha=0.3), label=\\\"No Internet Service\\\")\\n\",\n    \"plt.title(\\\"Tenure Distribution by Streaming Movies Service Subscription\\\")\\n\",\n    \"plt.legend()\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"As we can see, when the customers are new they do not opt for various services and their churning rate is very high.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### InternetService\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 34,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"stacked_plot(df, \\\"InternetService\\\", \\\"Churn\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"When the internet service is Fiber Optic, the churn rate is very high. Fiber Optics provides highr speed compared to DSL. The reason might be the higher cost of fiber optics.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 37,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.axes._subplots.AxesSubplot at 0x1ef073862e8>\"\n      ]\n     },\n     \"execution_count\": 37,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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xY49okSQ1gTYfH/hx4GtgEmF5s66C6lpMkqR8oDYrM/AdwJ3BnRLwOGNyT/SRJ648eveFHxMVUPzfir0AT1c+KeMUJdJKk9U9PewY7AVv7AUKS1P/09PDYR3hp2EmS1I/0tEcxGpgbEY8U1yuZ6dCTJPUDPQ2KT9a0CklSw+ppUHy6m21n9mYhkqTG1NOg+FvxvQnYAZf+kKR+o0dBkZnTO1+PiJtrU44kqdH09DyKN3W6+nqqk9uSpH6gp0NPnXsUy4Bja1CLJKkB9XToafeI2ATYBpiTmfNrW5YkqVH0dJnxfwd+A5wE/DYiDqhpVZKkhtHTo5f+ExifmR8B3gUcVbuSJEmNpKdB0ZGZSwAyczHVeQpJUj/Q08nsRyPia8CvgPfi51FIUr/R0x7F5cBzwJ7AwcC0mlUkSWooPQ2K84HrMnMy8O7iuiSpH+hpULRl5oMAmTmH6sehSpL6gZ7OUcyNiLOAe4D3AE/VriRJUiPpaY/iYODvwD7As8AhNatIktRQenpm9jLgghrXIklqQC4XLkkqZVBIkkoZFJKkUgaFJKmUQSFJKmVQSJJK9fSEu7UWETsB52TmhIjYFrgKqAAPAEdmZkdEnAbsC7QBR2fmvatrW6s6JUnlatKjiIjjgSuAwcWm84EpmfleoAnYLyJ2AHYDdgL2By5eXdta1ChJ6plaDT09Cny00/XxwF3F5ZuBPYBdgFszs5KZ84CWiGhdTVtJUp3UZOgpM6+NiLGdNjVlZqW4vBgYAQwHFnRqs2p7d21LDRjQxMiRQ15z3Y1sfX9+6ztfP63LajZH0UXnOYZhwEJgUXG56/bu2pZqb6+wcOHSHhXS2jpszY0aUE+f3/rO10/qPT39e+qro55mR8SE4vLewEzgbuD9EdEcEaOB5sycv5q2kqQ66asexTHANyNiA+Ah4JrMbI+ImVSXLm8Gjlxd2z6qUZLUjZoFRWY+DuxcXP4z1SOcurY5HTi9y7Zu20qS6sMT7iRJpQwKSVIpg0KSVMqgkCSVMigkSaX66vBYSWKj4YPZcNDAepfRYy8uX8mSRcvqXUbdGRSS+syGgwYy/rgZ9S6jx2adexBLMCgcepIklTIoJEmlDApJUimDQpJUysnsdUClbfk6t7x2+4plPPf8ynqXIakXGBTrgKaWQcw7c1y9y1gro0/9E2BQSOsDh54kSaUMCklSKYNCklTKoJAklTIoJEmlDApJUimDQpJUyqCQJJUyKCRJpQwKSVIpl/CQamxdW6vLdbrUlUEh1di6tlaX63SpK4NCklZjXesNQm16hAaFJK3GutYbhNr0CJ3MliSVMigkSaUMCklSKYNCklTKoJAklTIoJEmlDApJUqk+PY8iImYDzxdXHwOmA98A2oBbM/OMiGgGLgG2B5YDh2XmI31ZpyTpJX0WFBExGCAzJ3Tadj/wMWAO8LOI2AEYCwzOzH+JiJ2BrwH79VWdkqSX68sexfbAkIi4tXjc04FBmfkoQETcArwPeD3wc4DM/G1E7NiHNUqSuujLoFgKnAdcAWwH3Aws7HT7YmBrYDgvDU8BtEdES2a2re6OBwxoYuTIIb1fsV4TX5N1l6/duq23X7++DIo/A49kZgX4c0Q8D2zc6fZhVINjSHF5leaykABob6+wcOHSHhWxri3wtS7r6WuyNnz9+kYtXjvw9esrvf1+2JdHPR1Cdb6BiHgD1UB4ISK2iYgm4P3ATOBuYJ+i3c7An/qwRklSF33Zo/gWcFVE/BqoUA2ODuB7wACqRz39LiL+B9gzIn4DNAEH92GNkqQu+iwoMnMF8Klubtq5S7sO4PN9UpQkaY084U6SVMqgkCSVMigkSaUMCklSKYNCklTKoJAklTIoJEmlDApJUimDQpJUyqCQJJUyKCRJpQwKSVIpg0KSVMqgkCSVMigkSaUMCklSKYNCklTKoJAklTIoJEmlDApJUimDQpJUyqCQJJUyKCRJpQwKSVIpg0KSVMqgkCSVMigkSaUMCklSKYNCklTKoJAklTIoJEmlDApJUimDQpJUyqCQJJUyKCRJpVrqXUB3IqIZuATYHlgOHJaZj9S3Kknqnxq1R/ERYHBm/gtwAvC1OtcjSf1WowbFLsDPATLzt8CO9S1HkvqvpkqlUu8aXiEirgCuzcybi+vzgK0zs201uzwLzO2r+iRpPTEGaF1To4acowAWAcM6XW8uCQnowROVJL06jTr0dDewD0BE7Az8qb7lSFL/1ag9iuuAPSPiN0ATcHCd65Gkfqsh5ygkSY2jUYeeJEkNwqCQJJVq1DmKfiMiJgA/Ah6kOh8zELggM38UEZ8GPg20F7d9NTNvjYjTgWcy87L6VL1+iYixwB+B+zpt/iXwU+DDmXlmRDyTmZv3YU3jgFGZ+auI+CFwUGau6KvH74+Kv8XrgXGZ+USx7Wzg4cy8qo6l1Z1B0Rh+mZn7A0TERsBdEfE34BTgrZm5IiLeANwbEaPrWeh67MHMnNDN9vv7upDCx4BngF+t+t1Qn1gBfDsi9sxMJ3ALBkWDycwlETEd+ADV1+fwiLgxMx+NiG0ysyMi6lxl/1D8h/n54o16UPGf/ZZUex9HAMOBbwGbFLt8ITP/FBFzgYeBhzLz6E73tycwFVgGLAAOAd4JnAx0AJsDl1P9r/YzwIqIuI9qj/PNxWNfAWwALAX2z8xna/X8+6lfUh2SPxKYtmpjRBwD7A+0UQ3vL9WnvPpwjqIx/Q0YBewBbAf8vHjzOaSuVa3f3hoRd3b62qLL7RsCX8rMf6MaDB8CTgJuz8zdgUnApUXbLYFPdQmJJqoh8NHM3A24C5hS3LwF8GFgZ+CLwErgKuD8zLy3Uw3nAV8p1kCbDryrd566ujgc+GJEbFdcHwZ8HPjX4mu7iPhgvYqrB3sUjWkM8Fdgw8ycDBARb6IaGL+ua2Xrr1cMPXV6owCYl5mrlon5DRDAOGBiRHyi2D6q+D4/Mxd0uf9NgUWZ+VRx/VfAWcCNwG8yc3nxmA8A26ymxgDuAcjMH63Fc9NayMwFEXE01bC+GxgM/DYzVwJExEzgbVRfu37BHkWDiYhhwGeBW4DvRcSqN5+5wHyqY6jqe2+MiNcXl3cBHqA6vPT1ImA+DnyvuL2jm/3nA8M73cduwJ+Ly++MiAERMYTqG9Bfivvo+vf5EPBugIj4j4j4v6/5WalbmXkDkFSHAJcBO0VES9Ez3JWXXrt+wR5FY5gYEXdSPbqpBTgtM38XERcCv4yIpcX2KzIzizmKEyPisGL/xcXwh2pnAXBhRLyRag/g5oi4F/hWREyiOl9x+up2zsxKRHwW+ElEdAD/oPom9HaqR7rdTHVIa2pmzo+IWcC5EfFQp7s5DpgeEVOozlEc0NtPUi9zNPA+YDHVeaK7qYb3r6nOI/Ubnpkt1VGXCXOpITn0JEkqZY9CklTKHoUkqZRBIUkqZVBIkkoZFFqvRcTYiPhtye2TImJgjR57cqfLJ0TELyLi1oi4JSLGv8b7/mFEbPDaq5TWzKBQf3cSMKBG9z0FICLeSnWJjj0zcy/gS8CVr+WOM3N/V5NVX/GEO/ULxQmN91M9wW048O9U19LaHPgh8JGI+ArVs26bqa6z9ONiv2epLs/xA+D9wBCqy2yck5lXFUuCX0h1KfhVi/1NBjaOiEuAU4HRwCER8fPMvD8i3lPU1d2+7wLOoXoW/i+A3TJzYtH+RqqrCl/HahYKpLrkxOXF92XApFXLZkuvhj0K9Sf3ZuYewG3AJzPzW1SX8t4/IvYGtioW/dsdODkiRhb7fb/Yrx0YkZkfpNpDOKG4/ZvAkcVSHjcBx2fm/wOey8wjMnN+0f7fgHsi4mHgg6vbt9g+ODPfm5lnABtGxJhi+Y9NM3N2p+fU3UKB5wEXFmfrnwec3Rs/PPVf9ijUn6x6g32Cak+is3HA+KIHAdVlNcYUl7NTu1WfT/EE1f/YAd4CXFIsrTKQLusARcS2VBcEPKS4viNwU0TcUbJv58f8FnAQsBz4dpe6X7FQYERcAJwUEV+i2lNxiEqviT0K9SfdnV26avG9h4E7iv/sJ1Jd22dOpzZl95FUP4FuAtUewc+K7U3F93cAl0bEqmD5M/A81R7K6vbt/Jg/pNoD+SjV4a/Oulso8GGqS6JPAD4HXNNNzVKP2aNQfzeT6pDP7sCEYgnpjYDrMnNxDz8k6nBgRkSsmhQ/tPj+YET8V2YeEBFvAX4XEUuoBtNxmfl8RHS37xs633nxYVZ/AFoyc1GXx+5uocCf8VIwbQgc1cOfhdQtl/CQJJVy6EmSVMqgkCSVMigkSaUMCklSKYNCklTKoJAklTIoJEmlDApJUqn/BbvdH5SxlJ9YAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"sns.countplot(df.InternetService, hue = df.Dependents)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 43,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"stacked_plot(df[df.InternetService == \\\"Fiber optic\\\"], \\\"Dependents\\\", \\\"Churn\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Mostly people without dependents go for fiber optic option as Internnet Service and their churning percentage is high.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 38,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.axes._subplots.AxesSubplot at 0x1ef0755d390>\"\n      ]\n     },\n     \"execution_count\": 38,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"sns.countplot(df.InternetService, hue = df.Partner)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 39,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.axes._subplots.AxesSubplot at 0x1ef081c9e80>\"\n      ]\n     },\n     \"execution_count\": 39,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"sns.countplot(df.InternetService, hue = df.SeniorCitizen)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 42,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"stacked_plot(df[df.InternetService == \\\"Fiber optic\\\"], \\\"SeniorCitizen\\\", \\\"Churn\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"As we can see, Partner and Senior Citizen do not tell us anything about why fiber optics have higher churning rate.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 40,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"sns.distplot(df.tenure[df.InternetService == \\\"No\\\"], hist_kws=dict(alpha=0.3), label=\\\"No\\\")\\n\",\n    \"sns.distplot(df.tenure[df.InternetService == \\\"DSL\\\"], hist_kws=dict(alpha=0.3), label=\\\"DSL\\\")\\n\",\n    \"sns.distplot(df.tenure[df.InternetService == \\\"Fiber optic\\\"], hist_kws=dict(alpha=0.3), label=\\\"Fiber optic\\\")\\n\",\n    \"plt.title(\\\"Tenure Distribution by Internet Service type\\\")\\n\",\n    \"plt.legend()\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Also, the tenure distribution of customers with different internet service is similar.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 44,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>gender</th>\\n\",\n       \"      <th>SeniorCitizen</th>\\n\",\n       \"      <th>Partner</th>\\n\",\n       \"      <th>Dependents</th>\\n\",\n       \"      <th>tenure</th>\\n\",\n       \"      <th>PhoneService</th>\\n\",\n       \"      <th>MultipleLines</th>\\n\",\n       \"      <th>InternetService</th>\\n\",\n       \"      <th>OnlineSecurity</th>\\n\",\n       \"      <th>OnlineBackup</th>\\n\",\n       \"      <th>...</th>\\n\",\n       \"      <th>TechSupport</th>\\n\",\n       \"      <th>StreamingTV</th>\\n\",\n       \"      <th>StreamingMovies</th>\\n\",\n       \"      <th>Contract</th>\\n\",\n       \"      <th>PaperlessBilling</th>\\n\",\n       \"      <th>PaymentMethod</th>\\n\",\n       \"      <th>MonthlyCharges</th>\\n\",\n       \"      <th>TotalCharges</th>\\n\",\n       \"      <th>Churn</th>\\n\",\n       \"      <th>tenure_group</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>11</th>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>16</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>No internet service</td>\\n\",\n       \"      <td>No internet service</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>No internet service</td>\\n\",\n       \"      <td>No internet service</td>\\n\",\n       \"      <td>No internet service</td>\\n\",\n       \"      <td>Two year</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>Credit card (automatic)</td>\\n\",\n       \"      <td>18.95</td>\\n\",\n       \"      <td>326.8</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>16</th>\\n\",\n       \"      <td>Female</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>52</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>No internet service</td>\\n\",\n       \"      <td>No internet service</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>No internet service</td>\\n\",\n       \"      <td>No internet service</td>\\n\",\n       \"      <td>No internet service</td>\\n\",\n       \"      <td>One year</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>Mailed check</td>\\n\",\n       \"      <td>20.65</td>\\n\",\n       \"      <td>1022.95</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>5</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>21</th>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>12</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>No internet service</td>\\n\",\n       \"      <td>No internet service</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>No internet service</td>\\n\",\n       \"      <td>No internet service</td>\\n\",\n       \"      <td>No internet service</td>\\n\",\n       \"      <td>One year</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>Bank transfer (automatic)</td>\\n\",\n       \"      <td>19.80</td>\\n\",\n       \"      <td>202.25</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>22</th>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>No internet service</td>\\n\",\n       \"      <td>No internet service</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>No internet service</td>\\n\",\n       \"      <td>No internet service</td>\\n\",\n       \"      <td>No internet service</td>\\n\",\n       \"      <td>Month-to-month</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>Mailed check</td>\\n\",\n       \"      <td>20.15</td>\\n\",\n       \"      <td>20.15</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>33</th>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>No internet service</td>\\n\",\n       \"      <td>No internet service</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>No internet service</td>\\n\",\n       \"      <td>No internet service</td>\\n\",\n       \"      <td>No internet service</td>\\n\",\n       \"      <td>Month-to-month</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>Bank transfer (automatic)</td>\\n\",\n       \"      <td>20.20</td>\\n\",\n       \"      <td>20.2</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"<p>5 rows × 21 columns</p>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"    gender  SeniorCitizen Partner Dependents  tenure PhoneService  \\\\\\n\",\n       \"11    Male              0      No         No      16          Yes   \\n\",\n       \"16  Female              0      No         No      52          Yes   \\n\",\n       \"21    Male              0     Yes         No      12          Yes   \\n\",\n       \"22    Male              0      No         No       1          Yes   \\n\",\n       \"33    Male              0      No         No       1          Yes   \\n\",\n       \"\\n\",\n       \"   MultipleLines InternetService       OnlineSecurity         OnlineBackup  \\\\\\n\",\n       \"11            No              No  No internet service  No internet service   \\n\",\n       \"16            No              No  No internet service  No internet service   \\n\",\n       \"21            No              No  No internet service  No internet service   \\n\",\n       \"22            No              No  No internet service  No internet service   \\n\",\n       \"33            No              No  No internet service  No internet service   \\n\",\n       \"\\n\",\n       \"    ...          TechSupport          StreamingTV      StreamingMovies  \\\\\\n\",\n       \"11  ...  No internet service  No internet service  No internet service   \\n\",\n       \"16  ...  No internet service  No internet service  No internet service   \\n\",\n       \"21  ...  No internet service  No internet service  No internet service   \\n\",\n       \"22  ...  No internet service  No internet service  No internet service   \\n\",\n       \"33  ...  No internet service  No internet service  No internet service   \\n\",\n       \"\\n\",\n       \"          Contract PaperlessBilling              PaymentMethod MonthlyCharges  \\\\\\n\",\n       \"11        Two year               No    Credit card (automatic)          18.95   \\n\",\n       \"16        One year               No               Mailed check          20.65   \\n\",\n       \"21        One year               No  Bank transfer (automatic)          19.80   \\n\",\n       \"22  Month-to-month               No               Mailed check          20.15   \\n\",\n       \"33  Month-to-month               No  Bank transfer (automatic)          20.20   \\n\",\n       \"\\n\",\n       \"    TotalCharges Churn tenure_group  \\n\",\n       \"11         326.8    No            2  \\n\",\n       \"16       1022.95    No            5  \\n\",\n       \"21        202.25    No            1  \\n\",\n       \"22         20.15   Yes            1  \\n\",\n       \"33          20.2    No            1  \\n\",\n       \"\\n\",\n       \"[5 rows x 21 columns]\"\n      ]\n     },\n     \"execution_count\": 44,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df[df.InternetService == 'No'].head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 45,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"No internet service    1526\\n\",\n       \"Name: OnlineSecurity, dtype: int64\"\n      ]\n     },\n     \"execution_count\": 45,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df[df.InternetService == 'No'].OnlineSecurity.value_counts()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 46,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"No internet service    1526\\n\",\n       \"Name: OnlineBackup, dtype: int64\"\n      ]\n     },\n     \"execution_count\": 46,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df[df.InternetService == 'No'].OnlineBackup.value_counts()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 48,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"No internet service    1526\\n\",\n       \"Name: DeviceProtection, dtype: int64\"\n      ]\n     },\n     \"execution_count\": 48,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df[df.InternetService == 'No'].DeviceProtection.value_counts()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 49,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"No internet service    1526\\n\",\n       \"Name: TechSupport, dtype: int64\"\n      ]\n     },\n     \"execution_count\": 49,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df[df.InternetService == 'No'].TechSupport.value_counts()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 50,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"No internet service    1526\\n\",\n       \"Name: StreamingMovies, dtype: int64\"\n      ]\n     },\n     \"execution_count\": 50,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df[df.InternetService == 'No'].StreamingMovies.value_counts()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 52,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"No internet service    1526\\n\",\n       \"Name: StreamingTV, dtype: int64\"\n      ]\n     },\n     \"execution_count\": 52,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df[df.InternetService == 'No'].StreamingTV.value_counts()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We need to encode these variables to remove dependancy in the model.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Contract\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"stacked_plot(df, \\\"Contract\\\", \\\"Churn\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"In the case of Month-to-month contract Churn rate is very high. There is also a posibility of having customers in the dataframe who are still in their two-year or one-year contract plan.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 41,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.axes._subplots.AxesSubplot at 0x1ef08c2cfd0>\"\n      ]\n     },\n     \"execution_count\": 41,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"sns.countplot(df.InternetService, hue = df.Contract)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Many of the people of who opt for month-to-month Contract choose Fiber optic as Internet service and this is the reason for higher churn rate for fiber optic Internet service type.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### PaymentMethod\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 44,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 864x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"group = \\\"PaymentMethod\\\"\\n\",\n    \"target = \\\"Churn\\\"\\n\",\n    \"fig, ax = plt.subplots(figsize = (12,5))\\n\",\n    \"temp_df = (df.groupby([group, target]).size()/df.groupby(group)[target].count()).reset_index().pivot(columns=target, index=group, values=0)\\n\",\n    \"temp_df.plot(kind='bar', stacked=True, ax = ax, color = [\\\"green\\\", \\\"darkred\\\"])\\n\",\n    \"ax.xaxis.set_tick_params(rotation=0)\\n\",\n    \"ax.set_xlabel(group)\\n\",\n    \"ax.set_ylabel('Churn Percentage');\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"In the case of Electronic check, churn is very high. \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 47,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.axes._subplots.AxesSubplot at 0x1ef081edf60>\"\n      ]\n     },\n     \"execution_count\": 47,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 864x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig, ax = plt.subplots(figsize = (12,5))\\n\",\n    \"sns.countplot(df.PaymentMethod, hue = df.Contract, ax = ax)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"People having month-to-month contract prefer paying by Electronic Check mostly or mailed check. The reason might be short subscription cancellation process compared to automatic payment. \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### PaperlessBilling\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 48,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"stacked_plot(df, \\\"PaperlessBilling\\\", \\\"Churn\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### TotalCharges\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"count     7043\\n\",\n       \"unique    6531\\n\",\n       \"top           \\n\",\n       \"freq        11\\n\",\n       \"Name: TotalCharges, dtype: object\"\n      ]\n     },\n     \"execution_count\": 22,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df.TotalCharges.describe()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 53,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"11\"\n      ]\n     },\n     \"execution_count\": 53,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df['TotalCharges'] = df[\\\"TotalCharges\\\"].replace(\\\" \\\",np.nan)\\n\",\n    \"df['TotalCharges'].isna().sum() \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 54,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>gender</th>\\n\",\n       \"      <th>SeniorCitizen</th>\\n\",\n       \"      <th>Partner</th>\\n\",\n       \"      <th>Dependents</th>\\n\",\n       \"      <th>tenure</th>\\n\",\n       \"      <th>PhoneService</th>\\n\",\n       \"      <th>MultipleLines</th>\\n\",\n       \"      <th>InternetService</th>\\n\",\n       \"      <th>OnlineSecurity</th>\\n\",\n       \"      <th>OnlineBackup</th>\\n\",\n       \"      <th>...</th>\\n\",\n       \"      <th>TechSupport</th>\\n\",\n       \"      <th>StreamingTV</th>\\n\",\n       \"      <th>StreamingMovies</th>\\n\",\n       \"      <th>Contract</th>\\n\",\n       \"      <th>PaperlessBilling</th>\\n\",\n       \"      <th>PaymentMethod</th>\\n\",\n       \"      <th>MonthlyCharges</th>\\n\",\n       \"      <th>TotalCharges</th>\\n\",\n       \"      <th>Churn</th>\\n\",\n       \"      <th>tenure_group</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>488</th>\\n\",\n       \"      <td>Female</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>No phone service</td>\\n\",\n       \"      <td>DSL</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>Two year</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"      <td>Bank transfer (automatic)</td>\\n\",\n       \"      <td>52.55</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>753</th>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>No internet service</td>\\n\",\n       \"      <td>No internet service</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>No internet service</td>\\n\",\n       \"      <td>No internet service</td>\\n\",\n       \"      <td>No internet service</td>\\n\",\n       \"      <td>Two year</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>Mailed check</td>\\n\",\n       \"      <td>20.25</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>936</th>\\n\",\n       \"      <td>Female</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>DSL</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"      <td>Two year</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>Mailed check</td>\\n\",\n       \"      <td>80.85</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1082</th>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>No internet service</td>\\n\",\n       \"      <td>No internet service</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>No internet service</td>\\n\",\n       \"      <td>No internet service</td>\\n\",\n       \"      <td>No internet service</td>\\n\",\n       \"      <td>Two year</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>Mailed check</td>\\n\",\n       \"      <td>25.75</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1340</th>\\n\",\n       \"      <td>Female</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>No phone service</td>\\n\",\n       \"      <td>DSL</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>Two year</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>Credit card (automatic)</td>\\n\",\n       \"      <td>56.05</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3331</th>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>No internet service</td>\\n\",\n       \"      <td>No internet service</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>No internet service</td>\\n\",\n       \"      <td>No internet service</td>\\n\",\n       \"      <td>No internet service</td>\\n\",\n       \"      <td>Two year</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>Mailed check</td>\\n\",\n       \"      <td>19.85</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3826</th>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>No internet service</td>\\n\",\n       \"      <td>No internet service</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>No internet service</td>\\n\",\n       \"      <td>No internet service</td>\\n\",\n       \"      <td>No internet service</td>\\n\",\n       \"      <td>Two year</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>Mailed check</td>\\n\",\n       \"      <td>25.35</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4380</th>\\n\",\n       \"      <td>Female</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>No internet service</td>\\n\",\n       \"      <td>No internet service</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>No internet service</td>\\n\",\n       \"      <td>No internet service</td>\\n\",\n       \"      <td>No internet service</td>\\n\",\n       \"      <td>Two year</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>Mailed check</td>\\n\",\n       \"      <td>20.00</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>5218</th>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>No internet service</td>\\n\",\n       \"      <td>No internet service</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>No internet service</td>\\n\",\n       \"      <td>No internet service</td>\\n\",\n       \"      <td>No internet service</td>\\n\",\n       \"      <td>One year</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"      <td>Mailed check</td>\\n\",\n       \"      <td>19.70</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>6670</th>\\n\",\n       \"      <td>Female</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"      <td>DSL</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>Two year</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>Mailed check</td>\\n\",\n       \"      <td>73.35</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>6754</th>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"      <td>DSL</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>Two year</td>\\n\",\n       \"      <td>Yes</td>\\n\",\n       \"      <td>Bank transfer (automatic)</td>\\n\",\n       \"      <td>61.90</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>No</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"<p>11 rows × 21 columns</p>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"      gender  SeniorCitizen Partner Dependents  tenure PhoneService  \\\\\\n\",\n       \"488   Female              0     Yes        Yes       0           No   \\n\",\n       \"753     Male              0      No        Yes       0          Yes   \\n\",\n       \"936   Female              0     Yes        Yes       0          Yes   \\n\",\n       \"1082    Male              0     Yes        Yes       0          Yes   \\n\",\n       \"1340  Female              0     Yes        Yes       0           No   \\n\",\n       \"3331    Male              0     Yes        Yes       0          Yes   \\n\",\n       \"3826    Male              0     Yes        Yes       0          Yes   \\n\",\n       \"4380  Female              0     Yes        Yes       0          Yes   \\n\",\n       \"5218    Male              0     Yes        Yes       0          Yes   \\n\",\n       \"6670  Female              0     Yes        Yes       0          Yes   \\n\",\n       \"6754    Male              0      No        Yes       0          Yes   \\n\",\n       \"\\n\",\n       \"         MultipleLines InternetService       OnlineSecurity  \\\\\\n\",\n       \"488   No phone service             DSL                  Yes   \\n\",\n       \"753                 No              No  No internet service   \\n\",\n       \"936                 No             DSL                  Yes   \\n\",\n       \"1082               Yes              No  No internet service   \\n\",\n       \"1340  No phone service             DSL                  Yes   \\n\",\n       \"3331                No              No  No internet service   \\n\",\n       \"3826               Yes              No  No internet service   \\n\",\n       \"4380                No              No  No internet service   \\n\",\n       \"5218                No              No  No internet service   \\n\",\n       \"6670               Yes             DSL                   No   \\n\",\n       \"6754               Yes             DSL                  Yes   \\n\",\n       \"\\n\",\n       \"             OnlineBackup  ...          TechSupport          StreamingTV  \\\\\\n\",\n       \"488                    No  ...                  Yes                  Yes   \\n\",\n       \"753   No internet service  ...  No internet service  No internet service   \\n\",\n       \"936                   Yes  ...                   No                  Yes   \\n\",\n       \"1082  No internet service  ...  No internet service  No internet service   \\n\",\n       \"1340                  Yes  ...                  Yes                  Yes   \\n\",\n       \"3331  No internet service  ...  No internet service  No internet service   \\n\",\n       \"3826  No internet service  ...  No internet service  No internet service   \\n\",\n       \"4380  No internet service  ...  No internet service  No internet service   \\n\",\n       \"5218  No internet service  ...  No internet service  No internet service   \\n\",\n       \"6670                  Yes  ...                  Yes                  Yes   \\n\",\n       \"6754                  Yes  ...                  Yes                   No   \\n\",\n       \"\\n\",\n       \"          StreamingMovies  Contract PaperlessBilling  \\\\\\n\",\n       \"488                    No  Two year              Yes   \\n\",\n       \"753   No internet service  Two year               No   \\n\",\n       \"936                   Yes  Two year               No   \\n\",\n       \"1082  No internet service  Two year               No   \\n\",\n       \"1340                   No  Two year               No   \\n\",\n       \"3331  No internet service  Two year               No   \\n\",\n       \"3826  No internet service  Two year               No   \\n\",\n       \"4380  No internet service  Two year               No   \\n\",\n       \"5218  No internet service  One year              Yes   \\n\",\n       \"6670                   No  Two year               No   \\n\",\n       \"6754                   No  Two year              Yes   \\n\",\n       \"\\n\",\n       \"                  PaymentMethod MonthlyCharges  TotalCharges Churn  \\\\\\n\",\n       \"488   Bank transfer (automatic)          52.55           NaN    No   \\n\",\n       \"753                Mailed check          20.25           NaN    No   \\n\",\n       \"936                Mailed check          80.85           NaN    No   \\n\",\n       \"1082               Mailed check          25.75           NaN    No   \\n\",\n       \"1340    Credit card (automatic)          56.05           NaN    No   \\n\",\n       \"3331               Mailed check          19.85           NaN    No   \\n\",\n       \"3826               Mailed check          25.35           NaN    No   \\n\",\n       \"4380               Mailed check          20.00           NaN    No   \\n\",\n       \"5218               Mailed check          19.70           NaN    No   \\n\",\n       \"6670               Mailed check          73.35           NaN    No   \\n\",\n       \"6754  Bank transfer (automatic)          61.90           NaN    No   \\n\",\n       \"\\n\",\n       \"     tenure_group  \\n\",\n       \"488             1  \\n\",\n       \"753             1  \\n\",\n       \"936             1  \\n\",\n       \"1082            1  \\n\",\n       \"1340            1  \\n\",\n       \"3331            1  \\n\",\n       \"3826            1  \\n\",\n       \"4380            1  \\n\",\n       \"5218            1  \\n\",\n       \"6670            1  \\n\",\n       \"6754            1  \\n\",\n       \"\\n\",\n       \"[11 rows x 21 columns]\"\n      ]\n     },\n     \"execution_count\": 54,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df[df[\\\"TotalCharges\\\"].isnull()]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"All the customers having tenure = 0 have null total charges which means that these customers recently joined and we can fill those missing values as 0.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 55,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"False\"\n      ]\n     },\n     \"execution_count\": 55,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df.loc[df[\\\"TotalCharges\\\"].isnull(), 'TotalCharges'] = 0\\n\",\n    \"df.isnull().any().any()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 56,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"df['TotalCharges'] = df[\\\"TotalCharges\\\"].astype(float)\\n\",\n    \"\\n\",\n    \"Churn = df[df.Churn==\\\"Yes\\\"]\\n\",\n    \"Not_Churn = df[df.Churn==\\\"No\\\"]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 57,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig, ax = plt.subplots()\\n\",\n    \"sns.kdeplot(Churn[\\\"TotalCharges\\\"],label = \\\"Churn\\\", ax= ax)\\n\",\n    \"sns.kdeplot(Not_Churn[\\\"TotalCharges\\\"], label = \\\"Not Churn\\\", ax=ax)\\n\",\n    \"ax.set_xlabel(\\\"Total Charges\\\");\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The density of total charges for churning customers are high around 0. As many customers cancel the subsription in 1-2 months.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Monthly Charges\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"count    7043.000000\\n\",\n       \"mean       64.761692\\n\",\n       \"std        30.090047\\n\",\n       \"min        18.250000\\n\",\n       \"25%        35.500000\\n\",\n       \"50%        70.350000\\n\",\n       \"75%        89.850000\\n\",\n       \"max       118.750000\\n\",\n       \"Name: MonthlyCharges, dtype: float64\"\n      ]\n     },\n     \"execution_count\": 18,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df.MonthlyCharges.describe()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0\"\n      ]\n     },\n     \"execution_count\": 19,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df.MonthlyCharges.isna().sum()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.axes._subplots.AxesSubplot at 0x16f326cfcc0>\"\n      ]\n     },\n     \"execution_count\": 20,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"sns.kdeplot(Churn[\\\"MonthlyCharges\\\"], label = \\\"Churn\\\")\\n\",\n    \"sns.kdeplot(Not_Churn[\\\"MonthlyCharges\\\"], label = \\\"Not Churn\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The customers paying high monthly fees churn more.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's see the correlation of total charges and (monthly charges x tenure) to check if we have redundant information.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[1.        , 0.99956055],\\n\",\n       \"       [0.99956055, 1.        ]])\"\n      ]\n     },\n     \"execution_count\": 28,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.corrcoef(df.TotalCharges, df.MonthlyCharges*df.tenure)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's keep total charges as it shows the interaction between tenure and monthly charges\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Fucntion to prepare data for model building based on EDA\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 62,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def datapreparation(filepath):\\n\",\n    \"    \\n\",\n    \"    df = pd.read_csv(filepath)\\n\",\n    \"    df.drop([\\\"customerID\\\"], inplace = True, axis = 1)\\n\",\n    \"    \\n\",\n    \"    df.TotalCharges = df.TotalCharges.replace(\\\" \\\",np.nan)\\n\",\n    \"    df.TotalCharges.fillna(0, inplace = True)\\n\",\n    \"    df.TotalCharges = df.TotalCharges.astype(float)\\n\",\n    \"    \\n\",\n    \"    cols1 = ['Partner', 'Dependents', 'PaperlessBilling', 'Churn', 'PhoneService']\\n\",\n    \"    for col in cols1:\\n\",\n    \"        df[col] = df[col].apply(lambda x: 0 if x == \\\"No\\\" else 1)\\n\",\n    \"   \\n\",\n    \"    df.gender = df.gender.apply(lambda x: 0 if x == \\\"Male\\\" else 1)\\n\",\n    \"    df.MultipleLines = df.MultipleLines.map({'No phone service': 0, 'No': 0, 'Yes': 1})\\n\",\n    \"    \\n\",\n    \"    cols2 = ['OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport', 'StreamingTV', 'StreamingMovies']\\n\",\n    \"    for col in cols2:\\n\",\n    \"        df[col] = df[col].map({'No internet service': 0, 'No': 0, 'Yes': 1})\\n\",\n    \"    \\n\",\n    \"    df = pd.get_dummies(df, columns=['InternetService', 'Contract', 'PaymentMethod'], drop_first=True)\\n\",\n    \"    \\n\",\n    \"    return df\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.3\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "Images/README.md",
    "content": "Contains all images\n"
  },
  {
    "path": "LICENSE.md",
    "content": "MIT License\n\nCopyright (c) 2020 Arch Desai\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n"
  },
  {
    "path": "Procfile",
    "content": "web: gunicorn app:app\r\n"
  },
  {
    "path": "README.md",
    "content": "# Customer Survival Analysis and Churn Prediction\n\nApp: https://churn-prediction-app.herokuapp.com/\n\nCustomer attrition, also known as customer churn, customer turnover, or customer defection, is the loss of clients or customers.\n\nTelephone service companies, Internet service providers, pay TV companies, insurance firms, and alarm monitoring services, often use customer attrition analysis and customer attrition rates as one of their key business metrics because the cost of retaining an existing customer is far less than acquiring a new one. Companies from these sectors often have customer service branches which attempt to win back defecting clients, because recovered long-term customers can be worth much more to a company than newly recruited clients.\n\nPredictive analytics use churn prediction models that predict customer churn by assessing their propensity of risk to churn. Since these models generate a small prioritized list of potential defectors, they are effective at focusing customer retention marketing programs on the subset of the customer base who are most vulnerable to churn.\n\nIn this project I aim to perform customer survival analysis and build a model which can predict customer churn. I also aim to build an app which can be used to understand why a specific customer would stop the service and to know his/her expected lifetime value.  \n\n## Final Customer Churn Prediction App\n<img src=https://github.com/archd3sai/Customer-Survival-Analysis-and-Churn-Prediction/blob/master/app-pic.png>\n\n## Project Organization\n```\n.\n├── Images/                             : contains images\n├── static/                             : plots to show gauge chart, hazard and survival curve, shap values in Flask App \n│   └── images/\n│       ├── hazard.png\n│       ├── surv.png\n│       ├── shap.png\n│       └── new_plot.png\n├── templates/                          : contains html template for flask app\n│   └── index.html\n├── Customer Survival Analysis.ipynb    : Survival Analysis kaplan-Meier curve, log-rank test and Cox-proportional Hazard model\n├── Exploratory Data Analysis.ipynb     : Data Analysis to understand customer data\n├── Churn Prediction Model.ipynb        : Random Forest model to predict customer churn\n├── app.py                              : Flask App\n├── app-pic.png                         : Final App image  \n├── explainer.bz2                       : Shap Explainer\n├── model.pkl                           : Random Forest model\n├── survivemodel.pkl                    : Cox-proportional Hazard model\n├── requirements.txt                    : requirements to run this model\n├── Procfile                            : procfile for app deployment\n├── LICENSE.md                          : MIT License\n└── README.md                           : Report\n```\n\n## Customer Survival Analysis\n\n**Survival Analysis:** \nSurvival analysis is generally defined as a set of methods for analyzing data where the outcome variable is the time until the occurrence of an event of interest. The event can be death, occurrence of a disease, marriage, divorce, etc. The time to event or survival time can be measured in days, weeks, years, etc.\n\nFor example, if the event of interest is heart attack, then the survival time can be the time in years until a person develops a heart attack.\n\n**Objective:**\nThe objective of this analysis is to utilize non-parametric and semi-parametric methods of survival analysis to answer the following questions.\n- How the likelihood of the customer churn changes over time?\n- How we can model the relationship between customer churn, time, and other customer characteristics?\n- What are the significant factors that drive customer churn?\n- What is the survival and Hazard curve of a specific customer?\n- What is the expected lifetime value of a customer?\n\n**Kaplan-Meier Survival Curve:**\n\n<p align=\"center\">\n<img src=\"https://github.com/archd3sai/Customer-Churn-Analysis-and-Prediction/blob/master/Images/SurvivalCurve.png\" width=\"400\" height=\"300\">\n</p>\n\nFrom above graph, we can say that\n- AS expected, for telcom, churn is relatively low. The company was able to retain more than 60% of its customers even after 72 months.\n- There is a constant decrease in survival probability probability between 3-60 months.\n- After 60 months or 5 years, survival probability decreases with a higher rate. \n\n**Log-Rank Test:** \n\nLog-rank test is carried out to analyze churning probabilities group wise and to find if there is statistical significance between groups. The plots show survival curve group wise.\n\n<p align=\"center\">\n<img src=\"https://github.com/archd3sai/Customer-Churn-Analysis-and-Prediction/blob/master/Images/gender.png\" width=\"250\" height=\"200\"/> \n<img src=\"https://github.com/archd3sai/Customer-Churn-Analysis-and-Prediction/blob/master/Images/Senior%20Citizen.png\" width=\"250\" height=\"200\"/>\n<img src=\"https://github.com/archd3sai/Customer-Churn-Analysis-and-Prediction/blob/master/Images/partner_1.png\" width=\"250\" height=\"200\"/> \n</p>\n\n<p align=\"center\">\n<img src=\"https://github.com/archd3sai/Customer-Churn-Analysis-and-Prediction/blob/master/Images/dependents.png\" width=\"250\" height=\"200\"/> \n<img src=\"https://github.com/archd3sai/Customer-Churn-Analysis-and-Prediction/blob/master/Images/phoneservice.png\" width=\"250\" height=\"200\"/>\n<img src=\"https://github.com/archd3sai/Customer-Churn-Analysis-and-Prediction/blob/master/Images/MultipleLines.png\" width=\"250\" height=\"200\"/> \n</p>\n\n<p align=\"center\">\n<img src=\"https://github.com/archd3sai/Customer-Churn-Analysis-and-Prediction/blob/master/Images/InternetService.png\" width=\"250\" height=\"200\"/> \n<img src=\"https://github.com/archd3sai/Customer-Churn-Analysis-and-Prediction/blob/master/Images/OnlineSecurity.png\" width=\"250\" height=\"200\"/> \n<img src=\"https://github.com/archd3sai/Customer-Churn-Analysis-and-Prediction/blob/master/Images/OnlineBackup.png\" width=\"250\" height=\"200\"/> \n</p>\n\n<p align=\"center\">\n<img src=\"https://github.com/archd3sai/Customer-Churn-Analysis-and-Prediction/blob/master/Images/DeviceProtection.png\" width=\"250\" height=\"200\"/> \n<img src=\"https://github.com/archd3sai/Customer-Churn-Analysis-and-Prediction/blob/master/Images/TechSupport.png\" width=\"250\" height=\"200\"/>\n<img src=\"https://github.com/archd3sai/Customer-Churn-Analysis-and-Prediction/blob/master/Images/Contract.png\" width=\"250\" height=\"200\"/> \n</p>\n\n<p align=\"center\">\n<img src=\"https://github.com/archd3sai/Customer-Churn-Analysis-and-Prediction/blob/master/Images/StreamingMovies.png\" width=\"250\" height=\"200\"/>\n<img src=\"https://github.com/archd3sai/Customer-Churn-Analysis-and-Prediction/blob/master/Images/paymentmethod.png\" width=\"250\" height=\"200\"/> \n<img src=\"https://github.com/archd3sai/Customer-Churn-Analysis-and-Prediction/blob/master/Images/PaperlessBilling.png\" width=\"250\" height=\"200\"/>\n</p>\n\nFrom above graphs we can conclude following:\n- Customer's Gender and the phone service type are not indictive features and their p value of log rank test is above threshold value 0.05.\n- If customer is young and has a family, he or she is less likely to churn. The reason might be the busy life, more money or another factors.\n- If customer is not enrolled in services like online backup, online security, device protection, tech support, streaming Tv and streaming movies even though having active internet service, the survival probability is less.\n- The company should traget customers who opt for internet service as their survival probability constantly descreases. Also, Fiber Optilc type of Internet Service is costly and fast compared to DSL and this might be the reason of higher customer churning. \n- More offers should be given to customers who opt for month-to-month contract and company should target customers to subscribe for long-term service. \n- If customer's paying method is automatic, he or she is less likely to churn. The reason is in the case of electronic check and mailed check, a customer has to make an effort to pay and it takes time.\n\n**Survival Regression:**\nI use cox-proportional hazard model to perform survival regression analysis on customer data. This model is used to relate several risk factors or exposures simultaneously to survival time. In a Cox proportional hazards regression model, the measure of effect is the hazard rate, which is the risk or probability of suffering the event of interest given that the participant has survived up to a specific time. The model fits the data well and the coefficients are shown below.\n\n<p align=\"center\">\n<img src=\"https://github.com/archd3sai/Customer-Survival-Analysis-and-Churn-Prediction/blob/master/Images/Survival-analysis.png\" width=\"750\" height=\"500\"/>\n</p>\n\nUsing this model we can calculate the survival curve and hazard curve of any customer as shown below. These plots are useful to know the remaining life of a customer. \n\n<p align=\"center\">\n<img src=\"https://github.com/archd3sai/Customer-Survival-Analysis-and-Churn-Prediction/blob/master/Images/survival.png\" width=\"400\" height=\"300\"/>\n<img src=\"https://github.com/archd3sai/Customer-Survival-Analysis-and-Churn-Prediction/blob/master/Images/hazard.png\" width=\"400\" height=\"300\"/>\n</p>\n\n**Customer Lifetime Value:**\n\nTo calculate customer lifetime value, I would multiply the Monthly charges the customer is paying to Telcom and the expected life time of the customer.\n\nI utilize the survival function of a customer to calculate its expected life time. I would like to be little bit conservative and consider the customer is churned when the survival probability of him is 10%.\n\n## Customer Churn Prediction\nI aim to implement a machine learning model to accurately predict if the customer will churn or not.\n\n### Analysis\n\n**Churn and Tenure Relationship:**\n\n<p align=\"center\">\n<img src=\"https://github.com/archd3sai/Customer-Churn-Analysis-and-Prediction/blob/master/Images/tenure-churn.png\" width=\"600\" height=\"300\"/>\n</p>\n\n- As we can see the higher the tenure, the lesser the churn rate. This tells us that the customer becomes loyal with the tenure.\n\n<br />\n\n**Tenure Distrbution by Various Services:**\n\n<p align=\"center\">\n<img src=\"https://github.com/archd3sai/Customer-Churn-Analysis-and-Prediction/blob/master/Images/tenure-dist.png\" width=\"340\" height=\"250\"/>\n</p>\n\n- When the customers are new they do not opt for various services and their churning rate is very high. This can be seen in above plot for Streaming Movies and this holds true for all various services.\n\n<br />\n\n**Internet Service By Contract Type:**\n\n<p align=\"center\">\n<img src=\"https://github.com/archd3sai/Customer-Churn-Analysis-and-Prediction/blob/master/Images/internetservice-contract.png\" width=\"360\" height=\"250\"/>\n</p>\n\n- Many of the people of who opt for month-to-month Contract choose Fiber optic as Internet service and this is the reason for higher churn rate for fiber optic Internet service type.\n\n<br />\n\n**Payment method By Contract Type:**\n\n<p align=\"center\">\n<img src=\"https://github.com/archd3sai/Customer-Churn-Analysis-and-Prediction/blob/master/Images/payment-contract.png\" width=\"500\" height=\"250\"/>\n</p>\n\n- People having month-to-month contract prefer paying by Electronic Check mostly or mailed check. The reason might be short subscription cancellation process compared to automatic payment.\n\n<br />\n\n**Monthly Charges:**\n\n<p align=\"center\">\n<img src=\"https://github.com/archd3sai/Customer-Churn-Analysis-and-Prediction/blob/master/Images/monthlycharges.png\" width=\"300\" height=\"220\"/>\n</p>\n\n- As we can see the customers paying high monthly fees churn more.\n\n<br />\n\n### Modelling\n\nFor the modelling, I will use tress based Ensemble method as we do not have linearity in this classification problem. Also, we have a class imbalance of 1:3 and to combat it I will assign class weightage of 1:3 which means false negatives are 3 times costlier than false positives. I built a model on 80% of data and validated model on remaining 20% of data keeping in mind that I do not have data leakage. The random forest model has many hyperparameters and I tuned them using Grid Search Cross Validation while making sure that I do not overfit.\n\nThe final model resulted in 0.62 F1 score and 0.85 ROC-AUC. The resulting plots can be seen below.\n\n<p align=\"center\">\n<img src=\"https://github.com/archd3sai/Customer-Survival-Analysis-and-Churn-Prediction/blob/master/Images/model_1.png\" width=\"600\" height=\"300\"/>\n<img src=\"https://github.com/archd3sai/Customer-Survival-Analysis-and-Churn-Prediction/blob/master/Images/model_feat_imp.png\" width=\"600\" height=\"400\"/>\n\n</p>\n\nFrom the feature importance plot, we can see which features govern the customer churn.\n\n### Explainability\n\nWe can explain and understand the Random forest model using explainable AI modules such as Permutation Importance, Partial Dependence plots and Shap values.\n\n1. Permutation Importance shows feature importance by randomly shuffling feature values and measuring how much it degrades our performance.\n\n<p align=\"center\">\n<img src=https://github.com/archd3sai/Customer-Churn-Analysis-and-Prediction/blob/master/Images/eli51.png height=250 width=200>\n<img src=https://github.com/archd3sai/Customer-Churn-Analysis-and-Prediction/blob/master/Images/eli52.png height=130 width=200> \n</p>\n\n2. Partial dependence plot is used to see how churning probability changes across the range of particular feature. For example, in below graph of tenure group, the churn probability decreases at a higher rate if a person is in tenure group 2 compared to 1.\n\n<p align=\"center\">\n<img src=https://github.com/archd3sai/Customer-Churn-Analysis-and-Prediction/blob/master/Images/pdp_tenure.png height=250 width=400>\n<img src=https://github.com/archd3sai/Customer-Churn-Analysis-and-Prediction/blob/master/Images/pdp_contract.png height=250 width=400> \n</p>\n\n<p align=\"center\">\n<img src=https://github.com/archd3sai/Customer-Churn-Analysis-and-Prediction/blob/master/Images/pdp_monthly_charges.png height=250 width=400>\n<img src=https://github.com/archd3sai/Customer-Churn-Analysis-and-Prediction/blob/master/Images/pdp_total_charges.png height=250 width=400> \n</p>\n\n3. Shap values (SHapley Additive exPlanations) is a game theoretic approach to explain the output of any machine learning model. In below plot we can see that why a particual customer's churning probability is less than baseline value and which features are causing them.\n\n![](https://github.com/archd3sai/Customer-Churn-Analysis-and-Prediction/blob/master/Images/shap.png)\n\n## Flask App\n\nI saved the final tuned Random Forest model and deployed it using Flask web app. Flask is a micro web framework written in Python.  It is designed to make getting started quick and easy, with the ability to scale up to complex applications. I saved the shap value explainer tuned using random forest model to show shap plots in app. I have also utilized the cox-proportional hazard model to show survival curve and hazard curve, and to calculate expected customer lifetime value. \n\nThe final app shows churning probability, gauge chart of how severe a customer is and shap values based on customer's data. The final app layout can be seen above.  \n\n\n\n\n"
  },
  {
    "path": "app.py",
    "content": "import numpy as np\r\nimport pickle\r\nimport joblib\r\nimport matplotlib\r\nimport matplotlib.pyplot as plt\r\nfrom matplotlib.patches import Circle, Wedge, Rectangle\r\nimport shap\r\nshap.initjs()\r\nimport time\r\nfrom flask import Flask, request, jsonify, render_template\r\nimport base64\r\nimport io\r\n\r\napp = Flask(__name__)\r\nmodel = pickle.load(open('model.pkl', 'rb'))\r\nsurvmodel = pickle.load(open('survivemodel.pkl', 'rb'))\r\n\r\n@app.route('/')\r\ndef home():\r\n    return render_template('index.html')\r\n\r\n@app.route('/predict',methods=['POST'])\r\ndef predict():\r\n\r\n    gender = 0\r\n    if request.form[\"gender\"] == 1:\r\n        gender = 1\r\n    SeniorCitizen = 0\r\n    if 'SeniorCitizen' in request.form:\r\n        SeniorCitizen = 1\r\n    Partner = 0\r\n    if 'Partner' in request.form:\r\n        Partner = 1\r\n    Dependents = 0\r\n    if 'Dependents' in request.form:\r\n        Dependents = 1\r\n    PaperlessBilling = 0\r\n    if 'PaperlessBilling' in request.form:\r\n        PaperlessBilling = 1\r\n\r\n    MonthlyCharges = int(request.form[\"MonthlyCharges\"])\r\n    Tenure = int(request.form[\"Tenure\"])\r\n    TotalCharges = MonthlyCharges*Tenure\r\n\r\n    PhoneService = 0\r\n    if 'PhoneService' in request.form:\r\n        PhoneService = 1\r\n\r\n    MultipleLines = 0\r\n    if 'MultipleLines' in request.form and PhoneService == 1:\r\n        MultipleLines = 1\r\n\r\n    InternetService_Fiberoptic = 0\r\n    InternetService_No = 0\r\n    if request.form[\"InternetService\"] == 0:\r\n        InternetService_No = 1\r\n    elif request.form[\"InternetService\"] == 2:\r\n        InternetService_Fiberoptic = 1\r\n\r\n    OnlineSecurity = 0\r\n    if 'OnlineSecurity' in request.form and InternetService_No == 0:\r\n        OnlineSecurity = 1\r\n\r\n    OnlineBackup = 0\r\n    if 'OnlineBackup' in request.form and InternetService_No == 0:\r\n        OnlineBackup = 1\r\n\r\n    DeviceProtection = 0\r\n    if 'DeviceProtection' in request.form and InternetService_No == 0:\r\n        DeviceProtection = 1\r\n\r\n    TechSupport = 0\r\n    if 'TechSupport' in request.form and InternetService_No == 0:\r\n        TechSupport = 1\r\n\r\n    StreamingTV = 0\r\n    if 'StreamingTV' in request.form and InternetService_No == 0:\r\n        StreamingTV = 1\r\n\r\n    StreamingMovies = 0\r\n    if 'StreamingMovies' in request.form and InternetService_No == 0:\r\n        StreamingMovies = 1\r\n\r\n    Contract_Oneyear = 0\r\n    Contract_Twoyear = 0\r\n    if request.form[\"Contract\"] == 1:\r\n        Contract_Oneyear = 1\r\n    elif request.form[\"Contract\"] == 2:\r\n        Contract_Twoyear = 1\r\n\r\n    PaymentMethod_CreditCard = 0\r\n    PaymentMethod_ElectronicCheck = 0\r\n    PaymentMethod_MailedCheck = 0\r\n    if request.form[\"PaymentMethod\"] == 1:\r\n        PaymentMethod_CreditCard = 1\r\n    elif request.form[\"PaymentMethod\"] == 2:\r\n        PaymentMethod_ElectronicCheck = 1\r\n    elif request.form[\"PaymentMethod\"] == 3:\r\n        PaymentMethod_MailedCheck = 1\r\n\r\n    features = [gender, SeniorCitizen, Partner, Dependents, Tenure, PhoneService, MultipleLines, OnlineSecurity, OnlineBackup,\r\n       DeviceProtection, TechSupport, StreamingTV, StreamingMovies, PaperlessBilling, MonthlyCharges, TotalCharges,\r\n       InternetService_Fiberoptic, InternetService_No, Contract_Oneyear,Contract_Twoyear,\r\n       PaymentMethod_CreditCard, PaymentMethod_ElectronicCheck, PaymentMethod_MailedCheck]\r\n\r\n    columns = ['gender', 'SeniorCitizen', 'Partner', 'Dependents', 'tenure', 'PhoneService', 'MultipleLines', 'OnlineSecurity', 'OnlineBackup',\r\n       'DeviceProtection', 'TechSupport', 'StreamingTV', 'StreamingMovies','PaperlessBilling', 'MonthlyCharges', 'TotalCharges',\r\n       'InternetService_Fiber optic', 'InternetService_No', 'Contract_One year', 'Contract_Two year',\r\n       'PaymentMethod_Credit card (automatic)', 'PaymentMethod_Electronic check', 'PaymentMethod_Mailed check']\r\n\r\n    final_features = [np.array(features)]\r\n    prediction = model.predict_proba(final_features)\r\n\r\n    output = prediction[0,1]\r\n\r\n    # Shap Values\r\n    explainer = joblib.load(filename=\"explainer.bz2\")\r\n    shap_values = explainer.shap_values(np.array(final_features))\r\n    shap_img = io.BytesIO()\r\n    shap.force_plot(explainer.expected_value[1], shap_values[1], columns, matplotlib = True, show = False).savefig(shap_img, bbox_inches=\"tight\", format = 'png')\r\n    shap_img.seek(0)\r\n    shap_url = base64.b64encode(shap_img.getvalue()).decode()\r\n\r\n    # Hazard and Survival Analysis\r\n    surv_feats = np.array([gender, SeniorCitizen, Partner, Dependents, PhoneService, MultipleLines, OnlineSecurity, OnlineBackup,\r\n       DeviceProtection, TechSupport, StreamingTV, StreamingMovies, PaperlessBilling, MonthlyCharges, TotalCharges,\r\n       InternetService_Fiberoptic, InternetService_No, Contract_Oneyear,Contract_Twoyear,\r\n       PaymentMethod_CreditCard, PaymentMethod_ElectronicCheck, PaymentMethod_MailedCheck])\r\n\r\n    surv_feats = surv_feats.reshape(1, len(surv_feats))\r\n\r\n    hazard_img = io.BytesIO()\r\n    fig, ax = plt.subplots()\r\n    survmodel.predict_cumulative_hazard(surv_feats).plot(ax = ax, color = 'red')\r\n    plt.axvline(x=Tenure, color = 'blue', linestyle='--')\r\n    plt.legend(labels=['Hazard','Current Position'])\r\n    ax.set_xlabel('Tenure', size = 10)\r\n    ax.set_ylabel('Cumulative Hazard', size = 10)\r\n    ax.set_title('Cumulative Hazard Over Time')\r\n    plt.savefig(hazard_img, format = 'png')\r\n    hazard_img.seek(0)\r\n    hazard_url = base64.b64encode(hazard_img.getvalue()).decode()\r\n\r\n    surv_img = io.BytesIO()\r\n    fig, ax = plt.subplots()\r\n    survmodel.predict_survival_function(surv_feats).plot(ax = ax, color = 'red')\r\n    plt.axvline(x=Tenure, color = 'blue', linestyle='--')\r\n    plt.legend(labels=['Survival Function','Current Position'])\r\n    ax.set_xlabel('Tenure', size = 10)\r\n    ax.set_ylabel('Survival Probability', size = 10)\r\n    ax.set_title('Survival Probability Over Time')\r\n    plt.savefig(surv_img, format = 'png')\r\n    surv_img.seek(0)\r\n    surv_url = base64.b64encode(surv_img.getvalue()).decode()\r\n\r\n    life = survmodel.predict_survival_function(surv_feats).reset_index()\r\n    life.columns = ['Tenure', 'Probability']\r\n    max_life = life.Tenure[life.Probability > 0.1].max()\r\n\r\n    CLTV = max_life * MonthlyCharges\r\n\r\n    # Gauge plot\r\n    def degree_range(n):\r\n        start = np.linspace(0,180,n+1, endpoint=True)[0:-1]\r\n        end = np.linspace(0,180,n+1, endpoint=True)[1::]\r\n        mid_points = start + ((end-start)/2.)\r\n        return np.c_[start, end], mid_points\r\n\r\n    def rot_text(ang):\r\n        rotation = np.degrees(np.radians(ang) * np.pi / np.pi - np.radians(90))\r\n        return rotation\r\n\r\n    def gauge(labels=['LOW','MEDIUM','HIGH','EXTREME'], \\\r\n              colors=['#007A00','#0063BF','#FFCC00','#ED1C24'], Probability=1, fname=False):\r\n\r\n        N = len(labels)\r\n        colors = colors[::-1]\r\n\r\n\r\n        \"\"\"\r\n        begins the plotting\r\n        \"\"\"\r\n\r\n        gauge_img = io.BytesIO()\r\n        fig, ax = plt.subplots()\r\n\r\n        ang_range, mid_points = degree_range(4)\r\n\r\n        labels = labels[::-1]\r\n\r\n        \"\"\"\r\n        plots the sectors and the arcs\r\n        \"\"\"\r\n        patches = []\r\n        for ang, c in zip(ang_range, colors):\r\n            # sectors\r\n            patches.append(Wedge((0.,0.), .4, *ang, facecolor='w', lw=2))\r\n            # arcs\r\n            patches.append(Wedge((0.,0.), .4, *ang, width=0.10, facecolor=c, lw=2, alpha=0.5))\r\n\r\n        [ax.add_patch(p) for p in patches]\r\n\r\n\r\n        \"\"\"\r\n        set the labels (e.g. 'LOW','MEDIUM',...)\r\n        \"\"\"\r\n\r\n        for mid, lab in zip(mid_points, labels):\r\n\r\n            ax.text(0.35 * np.cos(np.radians(mid)), 0.35 * np.sin(np.radians(mid)), lab, \\\r\n                horizontalalignment='center', verticalalignment='center', fontsize=14, \\\r\n                fontweight='bold', rotation = rot_text(mid))\r\n\r\n        \"\"\"\r\n        set the bottom banner and the title\r\n        \"\"\"\r\n        r = Rectangle((-0.4,-0.1),0.8,0.1, facecolor='w', lw=2)\r\n        ax.add_patch(r)\r\n\r\n        ax.text(0, -0.05, 'Churn Probability ' + np.round(Probability,2).astype(str), horizontalalignment='center', \\\r\n             verticalalignment='center', fontsize=22, fontweight='bold')\r\n\r\n        \"\"\"\r\n        plots the arrow now\r\n        \"\"\"\r\n\r\n        pos = (1-Probability)*180\r\n        ax.arrow(0, 0, 0.225 * np.cos(np.radians(pos)), 0.225 * np.sin(np.radians(pos)), \\\r\n                     width=0.04, head_width=0.09, head_length=0.1, fc='k', ec='k')\r\n\r\n        ax.add_patch(Circle((0, 0), radius=0.02, facecolor='k'))\r\n        ax.add_patch(Circle((0, 0), radius=0.01, facecolor='w', zorder=11))\r\n\r\n        \"\"\"\r\n        removes frame and ticks, and makes axis equal and tight\r\n        \"\"\"\r\n\r\n        ax.set_frame_on(False)\r\n        ax.axes.set_xticks([])\r\n        ax.axes.set_yticks([])\r\n        ax.axis('equal')\r\n        plt.tight_layout()\r\n\r\n        plt.savefig(gauge_img, format = 'png')\r\n        gauge_img.seek(0)\r\n        url = base64.b64encode(gauge_img.getvalue()).decode()\r\n        return url\r\n\r\n    gauge_url = gauge(Probability = output)\r\n\r\n    t = time.time()\r\n    return render_template('index.html', prediction_text='Churn probability is {} and Expected Life Time Value is ${}'.format(round(output, 2), CLTV), url_1 = gauge_url, url_2 = shap_url, url_3 = hazard_url, url_4 = surv_url)\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    app.run(debug=True)\r\n"
  },
  {
    "path": "requirements.txt",
    "content": "Click==7.0\nFlask==1.1.1\ngunicorn==19.9.0\nitsdangerous==1.1.0\nJinja2==2.10.1\nMarkupSafe==1.1.1\nWerkzeug==0.15.5\npandas==0.24.2\nnumpy==1.16.2\nscikit-learn==0.22.1\npickleshare==0.7.5\nmatplotlib==3.0.3\njoblib==0.13.2\neli5==0.10.1\npdpbox==0.2.0\nshap==0.34.0\nlifelines==0.21.2\nscipy==1.2.1\nstatsmodels==0.9.0\nIPython==7.4.0\n"
  },
  {
    "path": "templates/index.html",
    "content": "<!DOCTYPE html>\r\n<html >\r\n<head>\r\n  <meta charset=\"UTF-8\">\r\n  <title>Customer Churn Prediction</title>\r\n</head>\r\n\r\n<body style=\"background: black\" text=\"white\">\r\n\r\n <div class=\"login\">\r\n\t<center><h1>Customer Churn Prediction</h1></center>\r\n    <center><h4>Author: Arch Desai</h4></center>\r\n\r\n     <!-- Main Input For Receiving Query to our ML -->\r\n    <form action=\"{{ url_for('predict')}}\"method=\"post\">\r\n\r\n    <center>\r\n      <input type=\"checkbox\" id=\"SeniorCitizen\" name=\"SeniorCitizen\" value=1>\r\n      <label for=\"SeniorCitizen\"> Senior Citizen</label> &nbsp;&nbsp;&nbsp;\r\n      <input type=\"checkbox\" id=\"Partner\" name=\"Partner\" value=1>\r\n      <label for=\"Partner\">Has a partner</label> &nbsp;&nbsp;&nbsp;\r\n      <input type=\"checkbox\" id=\"Dependents\" name=\"Dependents\" value=1>\r\n      <label for=\"Dependents\">Has dependents</label>&nbsp;&nbsp;&nbsp;\r\n      <input type=\"checkbox\" id=\"PaperlessBilling\" name=\"PaperlessBilling\" value=1>\r\n      <label for=\"PaperlessBilling\">Paperless Billing</label>&nbsp;&nbsp;&nbsp;\r\n      <input type=\"checkbox\" id=\"PhoneService\" name=\"PhoneService\" value=1>\r\n      <label for=\"PhoneService\">Phone Service</label>&nbsp;&nbsp;&nbsp;\r\n      <input type=\"checkbox\" id=\"MultipleLines\" name=\"MultipleLines\" value=1>\r\n      <label for=\"MultipleLines\">Multiple Lines</label>\r\n    </center>\r\n    <br>\r\n\r\n    <center>\r\n      <input type=\"checkbox\" id=\"OnlineSecurity\" name=\"OnlineSecurity\" value=1>\r\n      <label for=\"OnlineSecurity\">Online Security</label>&nbsp;&nbsp;&nbsp;\r\n      <input type=\"checkbox\" id=\"OnlineBackup\" name=\"OnlineBackup\" value=1>\r\n      <label for=\"OnlineBackup\">Online Backup</label>&nbsp;&nbsp;&nbsp;\r\n      <input type=\"checkbox\" id=\"DeviceProtection\" name=\"DeviceProtection\" value=1>\r\n      <label for=\"DeviceProtection\">Device Protection</label>&nbsp;&nbsp;&nbsp;\r\n      <input type=\"checkbox\" id=\"TechSupport\" name=\"TechSupport\" value=1>\r\n      <label for=\"TechSupport\">Tech Support</label>&nbsp;&nbsp;&nbsp;\r\n      <input type=\"checkbox\" id=\"StreamingTV\" name=\"StreamingTV\" value=1>\r\n      <label for=\"StreamingTV\">Streaming TV</label>&nbsp;&nbsp;&nbsp;\r\n      <input type=\"checkbox\" id=\"StreamingMovies\" name=\"StreamingMovies\" value=1>\r\n      <label for=\"StreamingMovies\">Streaming Movies</label>\r\n     </center>\r\n     <br>\r\n\r\n    <center>\r\n      <label for=\"gender\">Gender:</label>\r\n        <select id=\"gender\" name=\"gender\">\r\n            <option value=0>Male</option>\r\n            <option value=1>Female</option>\r\n        </select> &nbsp;&nbsp;\r\n\r\n      <label for=\"InternetService\">Internet :</label>\r\n      \t<select id=\"InternetService\" name=\"InternetService\">\r\n        \t\t<option value=0>No</option>\r\n        \t\t<option value=1>DSL</option>\r\n              <option value=2>Fiber optic</option>\r\n        </select> &nbsp;&nbsp;\r\n\r\n      <label for=\"Contract\">Contract:</label>\r\n      \t<select id=\"Contract\" name=\"Contract\">\r\n        \t\t<option value=0>Month-to-Month</option>\r\n        \t\t<option value=1>One-Year</option>\r\n              <option value=2>Two-Year</option>\r\n\t      </select>&nbsp;&nbsp;\r\n\r\n        <label for=\"PaymentMethod\">Payment:</label>\r\n        \t<select id=\"PaymentMethod\" name=\"PaymentMethod\">\r\n          \t\t<option value=0>Automatic: Bank Transfer</option>\r\n          \t\t<option value=1>Automatic: Credit Card</option>\r\n                <option value=2>Electronic Check</option>\r\n                <option value=3>Mailed Check</option>\r\n        \t</select>\r\n      </center>\r\n<br>\r\n\r\n<center>\r\n\t\t<input type=\"number\"  min=\"0\" name=\"MonthlyCharges\" placeholder=\"Monthly Charges\" required=\"required\" />\r\n<input type=\"number\"  min=\"0\" name=\"Tenure\" placeholder=\"Tenure in Months\" required=\"required\" />\r\n\r\n</center>\r\n<br><br>\r\n\r\n<center>\r\n<button type=\"submit\" class=\"btn btn-primary btn-block btn-large\" style=\"height:30px;width:200px\">Predict</button>\r\n</center>\r\n    </form>\r\n<br>\r\n   <center>\r\n\r\n   {{ prediction_text }}\r\n\r\n\r\n  <br>\r\n  <br>\r\n  <img src=\"data:image/png;base64,{{url_3}}\" alt=\"Submit Form\" height=\"180\" width=\"233\" onerror=\"this.style.display='none'\"/>\r\n  <img src=\"data:image/png;base64,{{url_1}}\" alt=\"Submit Form\" height=\"180\" width=\"233\" onerror=\"this.style.display='none'\"/>\r\n  <img src=\"data:image/png;base64,{{url_4}}\" alt=\"Submit Form\" height=\"180\" width=\"233\" onerror=\"this.style.display='none'\"/>\r\n  <br>\r\n  <br>\r\n  <img src=\"data:image/png;base64,{{url_2}}\" alt=\"Submit Form\" height=\"150\" width=\"711\" onerror=\"this.style.display='none'\"/>\r\n  </center>\r\n </div>\r\n\r\n\r\n</body>\r\n</html>\r\n"
  }
]