input: Write a function to create a Linear Regression model using scikit-learn that can predict housing prices based on living area and number of rooms. The function should fit the model using the provided data and return the predicted price for a new data point.
  import numpy as np
  from sklearn import linear_model
  
  data = [[2000, 3], [1600, 2.5], [2400, 3.5], [1400, 2], [3000, 4]]
  prices = [75000, 43000, 59000, 32000, 85000]
  
  assert housing_price_prediction(data, prices, [2200, 3])==79000.0
  assert housing_price_prediction(data, prices, [1800, 3])==57000.0
  assert housing_price_prediction(data, prices, [3000, 4])==85000.0
  assert housing_price_prediction(data, prices, [1400, 2])==32000.0
  assert housing_price_prediction(data, prices, [2400, 3.5])==59000.0
output: def housing_price_prediction(data, prices, new_data):
      model = linear_model.LinearRegression()
      model.fit(data, prices)
      return model.predict([new_data])[0]