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