input: def predict_housing_price(data: List[List[float]], area: float, rooms: float) -> float:
      """ Given a list of housing data where each entry is a list of living area and number of rooms, and the respective price,
      this function uses Linear Regression to predict the price of a house given its living area and number of rooms.
      >>> predict_housing_price([[2000, 3], [1600, 2.5], [2400, 3.5], [1400, 2], [3000, 4]], 2200, 3)
      55000.0
      """
output:     from sklearn.linear_model import LinearRegression
      import numpy as np
  
      X = np.array([[d[0], d[1]] for d in data])
      y = np.array([d[2] for d in data])
  
      model = LinearRegression()
      model.fit(X, y)
  
      prediction = model.predict(np.array([[area, rooms]]))
      return prediction[0]