input: Write a function to evaluate the accuracy of a machine-learning model using scikit-learn. The function should take as input the training and test data, fit the model, make predictions, and return the accuracy.
  
  
  def evaluate_model_accuracy(X_train, y_train, X_test, y_test):
      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)
      
      accuracy = accuracy_score(y_test, y_pred)
      return accuracy
  
  # Example usage:
  X_train, y_train, X_test, y_test = ...  # Initialize your data here
  assert evaluate_model_accuracy(X_train, y_train, X_test, y_test) == 0.95  # Adjust the expected accuracy as needed
  
output: def evaluate_model_accuracy(X_train, y_train, X_test, y_test):
      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)
      
      accuracy = accuracy_score(y_test, y_pred)
      return accuracy