input: Write a function to create and train a basic machine learning model in Python that performs binary classification on the given data. The function should return the accuracy of the model on the training data. If the input or the target labels are empty, the function should 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
  assert binary_classification_model([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]], [])==0
  assert binary_classification_model([], [1, 0])==0
output: def binary_classification_model(data, labels):
      if not data or not labels:
          return 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)