input: def binary_classification_model(data: List[List[float]], labels: List[int]) -> float:
      """ Implement a basic machine learning model in Python that performs binary classification on the given 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.model_selection import train_test_split
      from sklearn.linear_model import LogisticRegression
      from sklearn.metrics import accuracy_score
  
      X_train, X_test, y_train, y_test = train_test_split(data, labels, test_size=0.2, random_state=42)
      model = LogisticRegression()
      model.fit(X_train, y_train)
      y_pred = model.predict(X_test)
      return accuracy_score(y_test, y_pred)