input: 
  def evaluate_model_accuracy():
      """
      This function evaluates the accuracy of a machine-learning model. It trains the SVC model
      with provided parameters and then calculates the accuracy on the test dataset.
      The function requires sklearn and numpy libraries to be available.
      
      Example:
      >>> from sklearn.svm import SVC
      >>> clf = SVC(kernel='poly', degree=2, gamma=0.05)
      >>> X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)
      >>> evaluate_model_accuracy()
      Accuracy: 0.95
      """
  
output:     from sklearn.svm import SVC
      from sklearn.model_selection import train_test_split
      from sklearn.datasets import load_iris
      from sklearn.metrics import accuracy_score
  
      # Load dataset
      data = load_iris()
      X = data.data
      y = data.target
  
      # Split dataset into training and testing sets
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)
  
      # Initialize and train the model
      clf = SVC(kernel='poly', degree=2, gamma=0.05)
      clf.fit(X_train, y_train)
  
      # Predict on the test set
      y_pred = clf.predict(X_test)
  
      # Calculate accuracy
      accuracy = accuracy_score(y_test, y_pred)
  
      # Print the accuracy
      print(f"Accuracy: {accuracy:.2f}")