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 the `sklearn` library to fit the data and predict the accuracy using provided labels. The function should return the accuracy as a float.
  
  assert basic_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]) == 1.0
output: 
  def basic_classification(data, labels):
      from sklearn.linear_model import LogisticRegression
      model = LogisticRegression()
      model.fit(data, labels)
      accuracy = model.score(data, labels)
      return accuracy
  