input: Write a function to create a Linear Regression model in Python that can predict housing prices based on the living area and number of rooms. The function should take a list of input data where each element is a list containing the living area and the number of rooms, and a corresponding list of housing prices. It should then fit the model and return the predicted price for a given set of features.
  
  def predict_housing_price(data, prices, new_data):
      import numpy as np
      from sklearn import linear_model
      
      X = np.array(data)
      y = np.array(prices)
      
      model = linear_model.LinearRegression()
      model.fit(X, y)
      
      prediction = model.predict(np.array(new_data).reshape(1, -1))
      return prediction[0]
  
  assert predict_housing_price([[2000, 3], [1600, 2.5], [2400, 3.5], [1400, 2], [3000, 4]], [75000, 43000, 59000, 32000, 85000], [[2200, 3]]) == 75000
  assert predict_housing_price([[2000, 3], [1600, 2.5], [2400, 3.5], [1400, 2], [3000, 4]], [75000, 43000, 59000, 32000, 85000], [[2700, 3.5]]) == 90000
  assert predict_housing_price([[2000, 3], [1600, 2.5], [2400, 3.5], [1400, 2], [3000, 4]], [75000, 43000, 59000, 32000, 85000], [[1500, 2]]) == 40000
  assert predict_housing_price([[2000, 3], [1600, 2.5], [2400, 3.5], [1400, 2], [3000, 4]], [75000, 43000, 59000, 32000, 85000], [[3000, 4]]) == 85000
  assert predict_housing_price([[2000, 3], [1600, 2.5], [2400, 3.5], [1400, 2], [3000, 4]], [75000, 43000, 59000, 32000, 85000], [[1400, 2]]) == 32000
output: def predict_housing_price(data, prices, new_data):
      import numpy as np
      from sklearn import linear_model
      
      X = np.array(data)
      y = np.array(prices)
      
      model = linear_model.LinearRegression()
      model.fit(X, y)
      
      prediction = model.predict(np.array(new_data).reshape(1, -1))
      return prediction[0]