text: import pandas as pd
  import matplotlib.pyplot as plt
  from sklearn.preprocessing import StandardScaler
  from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
  import sklearn.model_selection as k
  r=pd.read_csv('bank_contacts.csv')
  x=r.drop('credit_application',axis=1)
  y=r['credit_application']
  train_x,test_x,train_y,test_y=k.train_test_split(x,y,test_size=0.2,random_state=42)
  sc=StandardScaler()
  train_x=sc.fit_transform(train_x)
  test_x=sc.transform(test_x)
  lda=LDA(n_components=5)
  train_x=lda.fit_transform(train_x,train_y)
  test_x=lda.transform(test_x)
  from sklearn.ensemble import RandomForestClassifier
  
  classifier = RandomForestClassifier(max_depth=2, random_state=0)
  
  classifier.fit(train_x,train_y)
  pred = classifier.predict(test_x)
  from sklearn.metrics import confusion_matrix
  from sklearn.metrics import accuracy_score
  
  print(confusion_matrix(test_y,pred))
  print('Accuracy:',accuracy_score(test_y,pred))
  plt.scatter(pred,test_x,c='b',marker='o')
  plt.scatter(train_x,train_y,c='r',marker='o')
  plt.show()
  