input: Write a function to create a Linear Regression model in Python that predicts housing prices based on the living area and number of rooms. Use the given input data to train the model and make a prediction for a new data point. If the given data is empty return None.
  data = [[2000, 3], [1600, 2.5], [2400, 3.5], [1400, 2], [3000, 4]]
  assert predict_housing_price(data)==67000
  assert predict_housing_price([])==None
  assert predict_housing_price([[1500, 2]])==37500
output: if not data:
      return None
  
  import numpy as np
  from sklearn.linear_model import LinearRegression
  
  X = np.array([d[:-1] for d in data])
  y = np.array([d[-1] for d in data])
  
  model = LinearRegression()
  model.fit(X, y)
  
  new_data = np.array([[2000, 3]])
  prediction = model.predict(new_data)
  
  return int(prediction[0] * 10000)