input: def predict_housing_price(data: List[List[float]], new_data: List[List[float]]) -> List[float]:
      """ This function uses Linear Regression to predict housing prices based on living area and number of rooms.
      It takes as input a list of training data where each element is a list containing living area and number of rooms.
      The corresponding prices are provided in a separate list.
      The function returns predictions for new data points.
      >>> data = [[2000, 3], [1600, 2.5], [2400, 3.5], [1400, 2], [3000, 4]]
      >>> corresponding_prices = [75000, 43000, 59000, 32000, 85000]
      >>> new_data = [[2200, 3]]
      >>> predict_housing_price(data, corresponding_prices, new_data)
      [62000.0]
      """
output:     from sklearn.linear_model import LinearRegression
      import numpy as np
  
      X = np.array([d[:2] for d in data])
      y = np.array([d[2] for d in data])
  
      model = LinearRegression()
      model.fit(X, y)
  
      new_X = np.array([d[:2] for d in new_data])
      predictions = model.predict(new_X)
  
      return predictions.tolist()