input: 
  def evaluate_model_accuracy(clf, X_train, y_train, X_test, y_test):
      """ Evaluate the accuracy of a given machine-learning model using sklearn's SVC classifier.
      The function trains the model on X_train and y_train data, then predicts on X_test and
      calculates the accuracy score against y_test.
      >>> from sklearn.svm import SVC
      >>> clf = SVC(kernel='poly', degree=2, gamma=0.05)
      >>> X_train = [[0, 0], [1, 1]]
      >>> y_train = [0, 1]
      >>> X_test = [[2., 2.], [3., 3.]]
      >>> y_test = [1, 0]
      >>> evaluate_model_accuracy(clf, X_train, y_train, X_test, y_test)
      Accuracy: 0.5
      """
      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)
      
      return "Accuracy: " + str(accuracy)
  
output:     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)
      
      return "Accuracy: " + str(accuracy)