input: Write a function to predict the next day's stock price using linear regression with sklearn.
  assert round(predict_stock_price([17.4, 17.3, 17.5, 17.2, 17.3]), 2) == 17.20
  assert round(predict_stock_price([20.0, 20.5, 21.0, 20.8, 21.2]), 2) == 21.14
  assert round(predict_stock_price([15.1, 15.5, 15.6, 15.7, 15.4]), 2) == 15.51
output: def predict_stock_price(prices):
      from sklearn.linear_model import LinearRegression
      import numpy as np
  
      # Prepare the data
      X = np.array(range(len(prices))).reshape(-1, 1)
      y = np.array(prices).reshape(-1, 1)
  
      # Create and fit the model
      model = LinearRegression()
      model.fit(X, y)
  
      # Predict the next price
      next_day = np.array([[len(prices)]])
      predicted_price = model.predict(next_day)
  
      return predicted_price[0][0]