input: 
  def binary_classification_model(data: List[List[float]], labels: List[int]) -> float:
      """
      Implement a basic machine learning model to perform binary classification on the given data.
      The function returns the accuracy of the model on the provided data.
      >>> 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])
      1.0
      """
  
output:     from sklearn.linear_model import LogisticRegression
      from sklearn.metrics import accuracy_score
  
      model = LogisticRegression()
      model.fit(data, labels)
      predictions = model.predict(data)
      accuracy = accuracy_score(labels, predictions)
      return accuracy