input: Write a function to implement a basic machine learning model that performs binary classification on the given data. If the data is successfully classified by the model, return the accuracy score. If the data is empty or not suitable for classification, return None.
  
  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]) == 0.8
  assert binary_classification_model([[0.7, 0.4, 0.2], [0.5, 0.2, 0.1]], [1, 0]) == 1.0
  assert binary_classification_model([], []) == None
  assert binary_classification_model([[0.3, 0.1], [0.7, 0.4]], []) == None
output: def binary_classification_model(data, labels):
      if not data or not labels or len(data) != len(labels):
          return None
      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)