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 sklearn library and its components to fit a model, make predictions, and output the model's accuracy score. If the data is empty, the function should return a message indicating that no data is available.
  assert binary_classifier([
   [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_classifier([
   [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]
  ], [0, 1, 1, 0, 1])=='Model accuracy: 0.6'
  
  assert binary_classifier([], [])=='No data available'
output: def binary_classifier(data, labels):
      if not data:
          return 'No data available'
      
      from sklearn.model_selection import train_test_split
      from sklearn.linear_model import LogisticRegression
      from sklearn.metrics import accuracy_score
      
      X_train, X_test, y_train, y_test = train_test_split(data, labels, test_size=0.2, random_state=42)
      model = LogisticRegression()
      model.fit(X_train, y_train)
      y_pred = model.predict(X_test)
      accuracy = accuracy_score(y_test, y_pred)
      
      return f'Model accuracy: {accuracy:.1f}'