input: def evaluate_model_accuracy():
      """Evaluate the accuracy of a machine-learning model using the sklearn library.
      Given an instance of an SVC model with specific parameters, train the model on
      training data (X_train, y_train) and then predict on test data (X_test). Finally,
      calculate and print the accuracy of the model predictions.
      
      >>> from sklearn.svm import SVC
      >>> clf = SVC(kernel='poly', degree=2, gamma=0.05)
      >>> import numpy as np
      >>> from sklearn.datasets import make_classification
      >>> from sklearn.model_selection import train_test_split
      >>> from sklearn.metrics import accuracy_score
      >>> X, y = make_classification(n_samples=1000, n_features=10, n_classes=2, random_state=42)
      >>> X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)
      >>> clf.fit(X_train, y_train)
      >>> y_pred = clf.predict(X_test)
      >>> accuracy = accuracy_score(y_test, y_pred)
      >>> print("Accuracy: " + str(accuracy))
      Accuracy: 0.8866666666666667
      """
output:     clf.fit(X_train, y_train)
      y_pred = clf.predict(X_test)
      accuracy = accuracy_score(y_test, y_pred)
      print("Accuracy: " + str(accuracy))