input: def predict_housing_price(data: list, new_data: list) -> float:
      """Given a list of housing data where each element is a tuple of living area and number of rooms, 
      and a target housing tuple to predict the price for, train a Linear Regression model and predict the price.
      >>> predict_housing_price([[2000, 3], [1600, 2.5], [2400, 3.5], [1400, 2], [3000, 4]], [[2200, 3]])
      72000.0
      """
output:     from sklearn.linear_model import LinearRegression
      import numpy as np
  
      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_X = np.array(new_data)
      predicted_price = model.predict(new_X)
  
      return predicted_price[0]