input: Write a function to implement a basic machine learning model in Python that performs binary classification on the given data. The function should take the input data and corresponding labels as parameters, train a Logistic Regression model, and then evaluate its accuracy. If the function is called with an empty dataset, it should return a message indicating that the dataset is empty.
  
  assert binary_classification([[0.3, 0.2, 0.6], [0.3, 0.1, 0.4], [0.2, 0.3, 0.4], [0.7, 0.4, 0.2], [0.5, 0.2, 0.1]], [1, 0, 0, 1, 0]) == 'Model accuracy: 1.0'
  assert binary_classification([], []) == 'Dataset is empty'
output: def binary_classification(data, labels):
      if not data:
          return 'Dataset is empty'
      
      model = LogisticRegression()
      model.fit(data, labels)
      accuracy = model.score(data, labels)
      
      return f'Model accuracy: {accuracy}'