input: 
  def evaluate_model_accuracy(model, X_train, y_train, X_test, y_test):
      """ Evaluates the accuracy of a given machine-learning model using the provided training and testing data.
      >>> from sklearn.svm import SVC
      >>> clf = SVC(kernel='poly', degree=2, gamma=0.05)
      >>> X_train, y_train, X_test, y_test = generate_data()  # Hypothetical function to generate data
      >>> evaluate_model_accuracy(clf, X_train, y_train, X_test, y_test)
      Accuracy: 0.9
      """
      import numpy as np
      from sklearn.metrics import accuracy_score
  
      model.fit(X_train, y_train)
      y_pred = model.predict(X_test)
  
      # Calculating the accuracy
      accuracy = accuracy_score(y_test, y_pred)
  
      return f"Accuracy: {accuracy}"
  
output:     import numpy as np
      from sklearn.metrics import accuracy_score
  
      model.fit(X_train, y_train)
      y_pred = model.predict(X_test)
  
      # Calculating the accuracy
      accuracy = accuracy_score(y_test, y_pred)
  
      return f"Accuracy: {accuracy}"