input: Write a function to implement a basic machine learning model in Python that performs binary classification on the given data. The function should return the accuracy score of the model on the provided data. If the input data is empty then return 0.
  assert binary_classification_model([
   [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]) == 1.0
  assert binary_classification_model([], []) == 0.0
output: def binary_classification_model(data, labels):
      if not data:
          return 0.0
      from sklearn.linear_model import LogisticRegression
      from sklearn.metrics import accuracy_score
      model = LogisticRegression()
      model.fit(data, labels)
      predictions = model.predict(data)
      return accuracy_score(labels, predictions)