input: Write a function that implements a basic machine learning model in Python to perform binary classification on the given data. The function should use the Logistic Regression model from the scikit-learn library and calculate the accuracy of the model on the provided data. The function should return the accuracy of the model.
  
  assert binary_classification_accuracy([[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_accuracy([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6], [0.7, 0.8, 0.9]], [0, 1, 1]) == 0.0
  assert binary_classification_accuracy([[0.5, 0.5, 0.5]], [1]) == 1.0
  assert binary_classification_accuracy([], []) == 0.0
output: def binary_classification_accuracy(data, labels):
      if not data or not labels:
          return 0.0
      from sklearn.linear_model import LogisticRegression
      model = LogisticRegression()
      model.fit(data, labels)
      accuracy = model.score(data, labels)
      return accuracy