input: from sklearn.svm import SVC
  clf = SVC(kernel='poly', degree=2, gamma=0.05)
  
  import numpy as np
  from sklearn.metrics import accuracy_score
  
  clf.fit(X_train, y_train)
  y_pred = clf.predict(X_test)
  
  # Calculating the accuracy
  accuracy = accuracy_score(y_test, y_pred)
  
  print("Accuracy: " + str(accuracy))
output:     
  # Calculating the confusion matrix
  from sklearn.metrics import confusion_matrix
  conf_matrix = confusion_matrix(y_test, y_pred)
  
  print("Confusion Matrix:")
  print(conf_matrix)
  
  # Calculating precision, recall, and F1-score
  from sklearn.metrics import classification_report
  report = classification_report(y_test, y_pred)
  
  print("Classification Report:")
  print(report)
  