input: Write a function to implement a basic machine learning model that performs binary classification on the given data using linear_model.LogisticRegression. Return the model accuracy as a percentage.
  
  assert implement_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]) == 'Model accuracy: 1.0'
output: def implement_model(X, y):
      from sklearn.linear_model import LogisticRegression
      from sklearn.metrics import accuracy_score
      model = LogisticRegression()
      model.fit(X, y)
      y_pred = model.predict(X)
      accuracy = accuracy_score(y, y_pred)
      return f'Model accuracy: {accuracy:.1f}'