input: Write a function to create a Linear Regression model in Python that can predict housing prices given the living area and number of rooms. The function should take a dataset as input and return the prediction for a given test case.
  
  def predict_housing_price(data, test_case):
      import numpy as np
      from sklearn import linear_model
  
      X = np.array([item[:2] for item in data])
      y = np.array([item[2] for item in data])
  
      model = linear_model.LinearRegression()
      model.fit(X, y)
  
      prediction = model.predict([test_case])
      return prediction[0]
  
  assert predict_housing_price([[2000, 3, 75000],[1600, 2.5, 43000],[2400, 3.5, 59000],[1400, 2, 32000],[3000, 4, 85000]], [2200, 3]) > 0
  assert predict_housing_price([[2000, 3, 75000],[1600, 2.5, 43000],[2400, 3.5, 59000],[1400, 2, 32000],[3000, 4, 85000]], [1800, 2.5]) > 0
  assert predict_housing_price([[2500, 4, 90000],[2000, 3, 75000],[1600, 2.5, 43000],[2400, 3.5, 59000],[1400, 2, 32000]], [3000, 4]) > 0
  assert predict_housing_price([[2000, 3, 75000],[1600, 2.5, 43000],[2400, 3.5, 59000],[1400, 2, 32000],[3000, 4, 85000]], [1200, 1.5]) > 0
output: def predict_housing_price(data, test_case):
      import numpy as np
      from sklearn import linear_model
  
      X = np.array([item[:2] for item in data])
      y = np.array([item[2] for item in data])
  
      model = linear_model.LinearRegression()
      model.fit(X, y)
  
      prediction = model.predict([test_case])
      return prediction[0]