text: # --------------
  from sklearn.model_selection import train_test_split
  from sklearn.preprocessing import MinMaxScaler
  import pandas as pd
  
  # Code starts here
  
  df = pd.read_csv(path)
  print(df.head())
  X = df.iloc[:,:-1]
  y = df.iloc[:,-1]
  
  X_train, X_test, y_train, y_test =train_test_split(X, y, random_state = 4, test_size = 0.3)
  scaler = MinMaxScaler()
  scaler.fit(X_train)
  X_train = scaler.transform(X_train)
  X_test = scaler.transform(X_test)
  print(X_test)
  print(X_train)
  # Code ends here
  
  
  # --------------
  from sklearn.metrics import classification_report
  from sklearn.linear_model import LogisticRegression
  from sklearn.metrics import roc_auc_score
  
  
  lr = LogisticRegression()
  lr.fit(X_train, y_train)
  y_pred = lr.predict(X_test)
  roc_score = roc_auc_score(y_test,y_pred)
  print(roc_score)
  
  
  # --------------
  from sklearn.tree import DecisionTreeClassifier
  
  
  
  dt = DecisionTreeClassifier(random_state = 4)
  dt.fit(X_train, y_train)
  y_pred = dt.predict(X_test)
  
  roc_score = roc_auc_score(y_test, y_pred)
  print(roc_score)
  
  
  
  # --------------
  from sklearn.ensemble import RandomForestClassifier
  
  
  # Code strats here
  
  
  rfc = RandomForestClassifier( random_state = 4)
  rfc.fit(X_train, y_train)
  y_pred = rfc.predict(X_test)
  roc_score = roc_auc_score(y_test, y_pred)
  print(roc_score)
  # Code ends here
  
  
  # --------------
  # Import Bagging Classifier
  from sklearn.ensemble import BaggingClassifier
  
  
  # Code starts here
  
  bagging_clf = BaggingClassifier(base_estimator = DecisionTreeClassifier(),n_estimators = 100, max_samples = 100, random_state =0)
  bagging_clf.fit(X_train, y_train)
  score_bagging = bagging_clf.score(X_test, y_test)
  
  print(score_bagging)
  
  
  # Code ends here
  
  
  # --------------
  # Import libraries
  from sklearn.ensemble import VotingClassifier
  
  # Various models
  clf_1 = LogisticRegression()
  clf_2 = DecisionTreeClassifier(random_state=4)
  clf_3 = RandomForestClassifier(random_state=4)
  
  model_list = [('lr',clf_1),('DT',clf_2),('RF',clf_3)]
  
  
  # Code starts here
  
  voting_clf_hard = VotingClassifier(estimators= model_list , voting='hard')
  voting_clf_hard.fit(X_train, y_train)
  hard_voting_score = voting_clf_hard.score(X_test, y_test)
  print(hard_voting_score)
  
  # Code ends here
  
  
  