input: Write a function to implement a basic machine learning model in Python that performs binary classification on the given data. The function should use the `LogisticRegression` model from `sklearn.linear_model` and evaluate the model's accuracy using `accuracy_score` from `sklearn.metrics`. If the input data is empty, the function should return a message stating "No data provided".
  
  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: 0.8'
  assert binary_classification([], []) == 'No data provided'
output: def binary_classification(X, y):
      if not X or not y:
          return "No data provided"
      from sklearn.linear_model import LogisticRegression
      from sklearn.metrics import accuracy_score
      model = LogisticRegression()
      model.fit(X, y)
      predictions = model.predict(X)
      accuracy = accuracy_score(y, predictions)
      return f'Model accuracy: {accuracy:.1f}'