input: Write a function to implement a basic machine learning model in Python that performs binary classification on the given data. The function should fit the model on the provided dataset and calculate the accuracy of the model's predictions.
  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([[0.1, 0.2, 0.3], [0.2, 0.3, 0.4], [0.3, 0.4, 0.5], [0.4, 0.5, 0.6]], [1, 0, 1, 0])=='Model accuracy: 0.5'
  assert binary_classification([[0.5, 0.5], [0.5, 0.5], [0.5, 0.5]], [1, 0, 1])=='Model accuracy: 0.0'
  assert binary_classification([], [])=='Model accuracy: None'
output: def binary_classification(data, labels):
      if not data or not labels:
          return 'Model accuracy: None'
      
      from sklearn.linear_model import LogisticRegression
      from sklearn.metrics import accuracy_score
      
      model = LogisticRegression()
      model.fit(data, labels)
      predictions = model.predict(data)
      accuracy = accuracy_score(labels, predictions)
      
      return f'Model accuracy: {accuracy:.1f}'