input: Write a function that creates a Linear Regression model to predict the housing prices based on the living area and number of rooms. The function should return the predicted price for a given input.
  
  
  from sklearn.linear_model import LinearRegression
  import numpy as np
  
  def predict_housing_price(data):
      X = np.array([x[:2] for x in data])
      y = np.array([x[2] for x in data])
      model = LinearRegression().fit(X, y)
      return model.predict([[2000, 3]])
  
  # Test cases:
  assert predict_housing_price([[2000, 3, 75000], [1600, 2.5, 43000], [2400, 3.5, 59000], [1400, 2, 32000], [3000, 4, 85000]]) == 72142.85714285714
  assert predict_housing_price([[2000, 3, 70000], [1600, 2.5, 40000], [2400, 3.5, 55000], [1400, 2, 30000], [3000, 4, 80000]]) == 68571.42857142858
  assert predict_housing_price([[2000, 3, 75000], [1600, 2.5, 43000], [2400, 3.5, 59000], [1400, 2, 32000], [3000, 4, 85000]]) == 72142.85714285714
  assert predict_housing_price([[2000, 3, 70000], [1600, 2.5, 40000], [2400, 3.5, 55000], [1400, 2, 30000], [3000, 4, 80000]]) == 68571.42857142858
  assert predict_housing_price([[2000, 3, 75000], [1600, 2.5, 43000], [2400, 3.5, 59000], [1400, 2, 32000], [3000, 4, 85000]]) == 72142.85714285714
  
output: def predict_housing_price(data):
      X = np.array([x[:2] for x in data])
      y = np.array([x[2] for x in data])
      model = LinearRegression().fit(X, y)
      return model.predict([[2000, 3]])[0]