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 sklearn and output the accuracy of the model on the training data.
  
  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)
      
      return 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]
  
  assert binary_classification_model(X, y) == 1.0
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)
      
      return accuracy