input: 
  def evaluate_model_accuracy(X_train, y_train, X_test, y_test):
      """Evaluate the accuracy of a machine-learning model using a Support Vector Classifier (SVC).
      >>> evaluate_model_accuracy(np.array([[1, 2], [2, 3], [3, 4]]), np.array([0, 1, 1]), np.array([[1, 2], [2, 3]]), np.array([0, 1]))
      Accuracy: 1.0
      """
      from sklearn.svm import SVC
      from sklearn.metrics import accuracy_score
      
      clf = SVC(kernel='poly', degree=2, gamma=0.05)
      clf.fit(X_train, y_train)
      y_pred = clf.predict(X_test)
      
      # Calculating the accuracy
      accuracy = accuracy_score(y_test, y_pred)
      return "Accuracy: " + str(accuracy)
  
output:     from sklearn.svm import SVC
      from sklearn.metrics import accuracy_score
      
      clf = SVC(kernel='poly', degree=2, gamma=0.05)
      clf.fit(X_train, y_train)
      y_pred = clf.predict(X_test)
      
      # Calculating the accuracy
      accuracy = accuracy_score(y_test, y_pred)
      return "Accuracy: " + str(accuracy)