input: Write a function to perform linear regression for predicting the numbers in a given dataset. If the input dataset is empty, return an error message. Otherwise, use the sklearn LinearRegression model to fit the data and make predictions.
  
  from sklearn.linear_model import LinearRegression
  import numpy as np
  
  def linear_regression(X, Y):
      if not X or not Y:
          return "Error: Input dataset is empty."
      X = np.array(X).reshape(-1, 1)
      Y = np.array(Y)
      model = LinearRegression()
      model.fit(X, Y)
      return model.predict([[6]]).tolist()[0]
  
  assert linear_regression([1,2,3,4,5], [3,5,7,9,13])==11.0
  assert linear_regression([], []) == "Error: Input dataset is empty."
  assert linear_regression([2,4,6,8], [1,1.5,2,2.2])==3.2
  assert linear_regression([0, 0, 0, 0], [0, 0, 0, 0]) == 0.0
  assert linear_regression([-1, 0, 1], [1, 0, -1]) == -2.0
output: def linear_regression(X, Y):
      if not X or not Y:
          return "Error: Input dataset is empty."
      X = np.array(X).reshape(-1, 1)
      Y = np.array(Y)
      model = LinearRegression()
      model.fit(X, Y)
      return model.predict([[6]]).tolist()[0]