input: Write a function to perform linear regression for predicting the numbers in a given dataset. Implement the function using numpy and sklearn's LinearRegression model. The function should accept two lists, X and Y, as input and return the predicted value for a new input. If the input lists are empty, the function should return None.
  
  assert predict_linear_regression([1,2,3,4,5], [3,5,7,9,13]) == 15.0
  assert predict_linear_regression([1], [4]) == 7.0
  assert predict_linear_regression([], []) == None
output: def predict_linear_regression(X, Y):
      if not X or not Y:
          return None
      import numpy as np
      from sklearn.linear_model import LinearRegression
      X = np.array(X).reshape(-1, 1)
      Y = np.array(Y).reshape(-1, 1)
      model = LinearRegression().fit(X, Y)
      return model.predict(np.array([X[-1][0] + 1]).reshape(-1, 1))[0][0]