input: Write a function to implement a basic machine learning model in Python that performs binary classification on the given data. The function should fit the model to the provided training data and return the model accuracy.
  
  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([[0.3, 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, 0, 0, 1, 1]) == 0.8
  assert binary_classification_model([], []) == None
  assert binary_classification_model([[0.3, 0.2, 0.6], [0.3, 0.1, 0.4], [0.2, 0.3, 0.4]], [1, 0, 0]) == 1.0
output: 
  from sklearn.linear_model import LogisticRegression
  from sklearn.metrics import accuracy_score
  
  def binary_classification_model(X, y):
      if not X or not y:
          return None
      model = LogisticRegression()
      model.fit(X, y)
      y_pred = model.predict(X)
      return accuracy_score(y, y_pred)
  