input: Write a function in Python using Linear Regression from the sklearn library to create a model that predicts housing prices based on the given living area and number of rooms. The function should accept a dataset as input and return the predicted value for a given input.
  
  assert predict_housing_price([[2000, 3], [1600, 2.5], [2400, 3.5], [1400, 2], [3000, 4]], [75000, 43000, 59000, 32000, 85000], [2200, 3])==69182.35294117646
  assert predict_housing_price([[2000, 3], [1600, 2.5], [2400, 3.5], [1400, 2], [3000, 4]], [75000, 43000, 59000, 32000, 85000], [1800, 2.8])==58823.52941176471
  assert predict_housing_price([[2000, 3], [1600, 2.5], [2400, 3.5], [1400, 2], [3000, 4]], [75000, 43000, 59000, 32000, 85000], [3200, 4.5])==91823.52941176471
  assert predict_housing_price([[2000, 3], [1600, 2.5], [2400, 3.5], [1400, 2], [3000, 4]], [75000, 43000, 59000, 32000, 85000], [1500, 2])==32352.941176470588
  assert predict_housing_price([[2000, 3], [1600, 2.5], [2400, 3.5], [1400, 2], [3000, 4]], [75000, 43000, 59000, 32000, 85000], [2800, 3.7])==75294.11764705883
output: from sklearn.linear_model import LinearRegression
  
  def predict_housing_price(X, y, new_data):
      model = LinearRegression()
      model.fit(X, y)
      return model.predict([new_data])[0]