input: Write a python function to predict stock prices using linear regression for the next 30 days.
  assert len(predict_stock_prices(data)) == 30
  assert isinstance(predict_stock_prices(data), np.ndarray)
  assert predict_stock_prices(data).shape == (30, 1)
output: def predict_stock_prices(data):
      import numpy as np
      from sklearn.linear_model import LinearRegression
      
      # Assuming 'data' is a 2D array with shape (n_samples, 1)
      n_samples = len(data)
      X = np.arange(n_samples).reshape(-1, 1)
      y = data
      
      # Fit the model
      model = LinearRegression()
      model.fit(X, y)
      
      # Predict the next 30 days
      X_future = np.arange(n_samples, n_samples + 30).reshape(-1, 1)
      future_predictions = model.predict(X_future)
      
      return future_predictions.reshape(30, 1)