input: Write a function to implement a basic logistic regression model for binary classification using the given data. The function should train the model, predict outcomes on the same data, and print the accuracy.
  
  Input:
  [
   [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]
  ]
  
  Output:
  from sklearn import linear_model
  from sklearn.metrics import accuracy_score
  
  def binary_classification(X, y):
      model = linear_model.LogisticRegression()
      model.fit(X, y)
      y_pred = model.predict(X)
      accuracy = accuracy_score(y, y_pred)
      print('Model accuracy:', accuracy)
  
  X = [[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]]
  y = [1, 0, 0, 1, 0]
  
  binary_classification(X, y)
  
  assert binary_classification([[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])('Model accuracy:', 1.0)
output: def binary_classification(X, y):
      model = linear_model.LogisticRegression()
      model.fit(X, y)
      y_pred = model.predict(X)
      accuracy = accuracy_score(y, y_pred)
      print('Model accuracy:', accuracy)