input: def evaluate_model_accuracy(X_train, y_train, X_test, y_test):
      """
      Evaluate the accuracy of a machine-learning model.
      >>> from sklearn.svm import SVC
      >>> clf = SVC(kernel='poly', degree=2, gamma=0.05)
      >>> X_train, y_train, X_test, y_test = ...  # Assumed to be defined
      >>> evaluate_model_accuracy(X_train, y_train, X_test, y_test)
      Accuracy: 0.95
      """
      import numpy as np
      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:     import numpy as np
      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)