input: Write a function to create a Linear Regression model using sklearn that can predict housing prices based on the living area and number of rooms. The function should return the predicted price for a given input. If the input area is [2200, 3], it should return the predicted price using the model.
  import numpy as np
  from sklearn import linear_model
  
  def predict_housing_price(area):
      data = [[2000, 3], [1600, 2.5], [2400, 3.5], [1400, 2], [3000, 4]]
      prices = [75000, 43000, 59000, 32000, 85000]
      
      X = np.array(data)
      y = np.array(prices)
      
      model = linear_model.LinearRegression()
      model.fit(X, y)
      
      return round(model.predict([area])[0], 2)
  
  assert predict_housing_price([2200, 3])==54500.0
  assert predict_housing_price([1800, 2])==62750.0
  assert predict_housing_price([2600, 4])==73000.0
output: import numpy as np
  from sklearn import linear_model
  
  def predict_housing_price(area):
      data = [[2000, 3], [1600, 2.5], [2400, 3.5], [1400, 2], [3000, 4]]
      prices = [75000, 43000, 59000, 32000, 85000]
      
      X = np.array(data)
      y = np.array(prices)
      
      model = linear_model.LinearRegression()
      model.fit(X, y)
      
      return round(model.predict([area])[0], 2)