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(5))
  X = df.iloc[:,:-1]
  y = df.iloc[:,-1]
  X_train,X_test,y_train,y_test = train_test_split(X,y,test_size = 0.3,random_state =4)
  scaler = MinMaxScaler()
  scaler.fit(X_train)
  X_train = scaler.transform(X_train)
  X_test = scaler.transform(X_test)
  # 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_auc_score',roc_auc_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('Decision_roc_auc_score',roc_auc_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('Random Forrest model score:',roc_auc_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('Bagging score:',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)
  # Code ends here
  
  
  