input: def perform_binary_classification_with_logistic_regression(data, labels):
      """
      Implement a basic machine learning model in Python that performs binary classification on the given data using logistic regression.
      The function takes a list of lists (data) and a list of labels (0 or 1) as input. It trains a logistic regression model on the data and 
      predicts the labels. The function returns the model accuracy.
      
      >>> perform_binary_classification_with_logistic_regression([[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: 0.8'
      """
output:     
      from sklearn.linear_model import LogisticRegression
      from sklearn.model_selection import train_test_split
      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)
      accuracy = accuracy_score(y_test, y_pred)
      return f'Model accuracy: {accuracy:.1f}'
  