text: import pandas as pd
  
  from sklearn.neighbors import  KNeighborsClassifier
  from sklearn.ensemble import BaggingClassifier
  
  from sklearn.model_selection import train_test_split
  from sklearn.metrics import  accuracy_score
  
  from sklearn.svm import LinearSVC
  from sklearn.svm import SVC
  from sklearn.linear_model import SGDClassifier
  from sklearn.tree import DecisionTreeClassifier
  
  if __name__ == "__main__":
      
      path = './data/heart.csv'
      dataset = pd.read_csv(path)
  
      print(dataset.head(5))
      print('')
      print(dataset['target'].describe())
  
      x = dataset.drop(['target'], axis=1, inplace=False)
      y = dataset['target']
  
      x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.35, random_state=42)
  
      knn_class = KNeighborsClassifier().fit(x_train, y_train)
      knn_pred = knn_class.predict(x_test)
  
      print('')
      print('Accuracy KNeighbors:', accuracy_score(knn_pred, y_test))
      print('')
  
      #bag_class = BaggingClassifier(base_estimator=KNeighborsClassifier(), n_estimators=50).fit(x_train, y_train)
      #bag_pred = bag_class.predict(x_test)
  
      #print('')
      #print('Accuracy Bagging with KNeighbors:', accuracy_score(bag_pred, y_test))
      #print('')
  
      classifier = {
          'KNeighbors': KNeighborsClassifier(),
          'LinearSCV': LinearSVC(),
          'SVC': SVC(),
          'SGDC': SGDClassifier(),
          'DecisionTree': DecisionTreeClassifier()
      }
  
      for name, estimator in classifier.items():
          bag_class = BaggingClassifier(base_estimator=estimator, n_estimators=5).fit(x_train, y_train)
          bag_pred = bag_class.predict(x_test)
  
          print('Accuracy Bagging with {}:'.format(name), accuracy_score(bag_pred, y_test))
          print('')