input: Write a function to create and use a neural network model that predicts the next day's stock price based on a given trajectory of stock prices. The function should take a list of stock prices as input and return the predicted price for the next day. If the input list is empty, the function should return None.
  
  assert predict_next_day_stock_price([10, 10.4, 10.5, 10.6, 10.7, 10.9, 11]) != None
  assert predict_next_day_stock_price([]) == None
output: def predict_next_day_stock_price(stock_prices):
      if not stock_prices:
          return None
      import numpy as np
      from sklearn.linear_model import LinearRegression
      X = np.array(stock_prices[:-1]).reshape(-1, 1)
      y = np.array(stock_prices[1:])
      model = LinearRegression().fit(X, y)
      next_day_price = model.predict(np.array([stock_prices[-1]]).reshape(-1, 1))
      return next_day_price[0]