input: from sklearn.svm import SVC
  
  def evaluate_model_accuracy(clf: SVC, X_train: np.ndarray, y_train: np.ndarray, X_test: np.ndarray, y_test: np.ndarray) -> float:
      """ Evaluate the accuracy of a trained machine-learning model using unseen test data.
      >>> clf = SVC(kernel='poly', degree=2, gamma=0.05)
      >>> X_train, y_train, X_test, y_test = // assume some pre-processed data
      >>> accuracy = evaluate_model_accuracy(clf, X_train, y_train, X_test, y_test)
      >>> print("Accuracy: " + str(accuracy))
      Accuracy: 0.85
      """
      clf.fit(X_train, y_train)
      y_pred = clf.predict(X_test)
      
      # Calculating the accuracy
      accuracy = accuracy_score(y_test, y_pred)
      return accuracy
output:     from sklearn.metrics import accuracy_score