input: Write a function to implement a basic machine learning model in Python that performs binary classification on the given data. The function should use the Logistic Regression model from scikit-learn and return the accuracy of the model on the given data.
  
  from sklearn import linear_model
  from sklearn.metrics import accuracy_score
  
  def binary_classification_model(X, y):
      model = linear_model.LogisticRegression()
      model.fit(X, y)
      y_pred = model.predict(X)
      accuracy = accuracy_score(y, y_pred)
      return 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]) > 0.7
  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, 1, 0, 1, 1]) > 0.4
  assert binary_classification_model([[0.5, 0.4, 0.3], [0.5, 0.6, 0.2], [0.3, 0.5, 0.2]], [1, 0, 0]) > 0.6
  assert binary_classification_model([[0.1, 0.2, 0.7], [0.2, 0.3, 0.5], [0.4, 0.3, 0.3]], [0, 0, 1]) > 0.3
  assert binary_classification_model([], []) == 1.0
output: def binary_classification_model(X, y):
      if not X or not y:
          return 1.0
      model = linear_model.LogisticRegression()
      model.fit(X, y)
      y_pred = model.predict(X)
      accuracy = accuracy_score(y, y_pred)
      return accuracy