input: Write a python function to predict stock price direction using Linear Regression.
  assert predict_stock_direction([41.50, 41.75]) == 'The stock price is expected to go up.'
  assert predict_stock_direction([40.00, 41.00]) == 'The stock price is expected to go down.'
  assert predict_stock_direction([50.25, 50.50]) == 'The stock price is expected to go up.'
output: def predict_stock_direction(prices):
      from sklearn.linear_model import LinearRegression
      import numpy as np
      
      # Reshape the data for sklearn
      X = np.array(range(len(prices))).reshape(-1, 1)
      y = np.array(prices).reshape(-1, 1)
      
      # Create and train the model
      model = LinearRegression().fit(X, y)
      
      # Predict the next value
      next_value = model.predict(np.array([[len(prices)]]))[0][0]
      
      # Determine the direction
      if next_value > prices[-1]:
          return 'The stock price is expected to go up.'
      else:
          return 'The stock price is expected to go down.'