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 Logistic Regression from the sklearn library and print the accuracy of the model.
  
  def binary_classification_model(X, y):
      from sklearn import linear_model
      from sklearn.metrics import accuracy_score
  
      model = linear_model.LogisticRegression()
      model.fit(X, y)
  
      y_pred = model.predict(X)
      accuracy = accuracy_score(y, y_pred)
  
      print('Model accuracy:', 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])
  assert binary_classification_model([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6], [0.7, 0.8, 0.9], [0.1, 0.1, 0.1], [0.9, 0.9, 0.9]], [0, 1, 1, 0, 1])
output: def binary_classification_model(X, y):
      from sklearn import linear_model
      from sklearn.metrics import accuracy_score
  
      model = linear_model.LogisticRegression()
      model.fit(X, y)
  
      y_pred = model.predict(X)
      accuracy = accuracy_score(y, y_pred)
  
      print('Model accuracy:', accuracy)