input: Write a function to create a Linear Regression model in Python that predicts housing prices based on living area and number of rooms. The function should take a dataset as input, train the model, and return the model. Use the model to predict the price for a house with a living area of 2200 square feet and 3 rooms. If the dataset is empty, return None.
  assert linear_regression_model([[2000, 3], [1600, 2.5], [2400, 3.5], [1400, 2], [3000, 4]])==model.predict([[2200, 3]])
  assert linear_regression_model([[2000, 3], [2400, 3.5]], 75000, 59000)==model.predict([[2200, 3]])
  assert linear_regression_model([], None, None)==None
  assert linear_regression_model([[1400, 2], [3000, 4]], 32000, 85000)==model.predict([[2200, 3]])
  assert linear_regression_model([[1600, 2.5]], 43000, None)==model.predict([[2200, 3]])
output: def linear_regression_model(dataset):
      if not dataset:
          return None
      from sklearn.linear_model import LinearRegression
      X = [d[:-1] for d in dataset]
      y = [d[-1] for d in dataset]
      model = LinearRegression()
      model.fit(X, y)
      return model.predict([[2200, 3]])[0]