input: Write a function to create a Deep Learning model in Python using Keras to predict stock prices. The function should include defining the model architecture with three LSTM layers, dropout for regularization, and a final Dense layer for output. The function should also handle data preparation, including loading data from a CSV file, splitting data into training and testing sets, and reshaping the data to fit the model's input format. Finally, the function should train the model and return the trained model.
  
  def create_stock_price_prediction_model():
      from keras.models import Sequential
      from keras.layers import Dense
      from keras.layers import LSTM
      from keras.layers import Dropout
      import numpy as np
  
      # define constants
      time_steps = 60
      batch_size = 32
  
      # build the model
      model = Sequential()
      model.add(LSTM(units=50, return_sequences=True, input_shape=(time_steps, 1)))
      model.add(Dropout(0.2))
  
      model.add(LSTM(units=50, return_sequences=True))
      model.add(Dropout(0.2))
  
      model.add(LSTM(units=50))
      model.add(Dropout(0.2))
  
      model.add(Dense(units=1))
  
      model.compile(optimizer='adam', loss='mean_squared_error')
  
      # read the data 
      dataset = np.loadtxt('data.csv', delimiter=',')
  
      data = dataset[:, 0:1]
  
      # create train and test data
      x_train = data[:int(len(data)*0.8)]
      x_test = data[int(len(data)*0.8):]
  
      # reshape the data for input format
      x_train = np.reshape(x_train, (x_train.shape[0], time_steps, 1))
      x_test = np.reshape(x_test, (x_test.shape[0], time_steps, 1))
  
      # train the model
      model.fit(x_train, x_train, epochs=50, batch_size=batch_size, validation_data=(x_test, x_test))
  
      return model
  
  # assert test case
  assert type(create_stock_price_prediction_model()) == Sequential
  assert create_stock_price_prediction_model().layers[0].units == 50
  assert create_stock_price_prediction_model().layers[0].return_sequences == True
  assert create_stock_price_prediction_model().layers[-1].units == 1
  assert create_stock_price_prediction_model().optimizer == 'adam'
  assert create_stock_price_prediction_model().loss == 'mean_squared_error'
output: def create_stock_price_prediction_model():
      from keras.models import Sequential
      from keras.layers import Dense, LSTM, Dropout
      import numpy as np
  
      time_steps = 60
      batch_size = 32
  
      model = Sequential()
      model.add(LSTM(units=50, return_sequences=True, input_shape=(time_steps, 1)))
      model.add(Dropout(0.2))
  
      model.add(LSTM(units=50, return_sequences=True))
      model.add(Dropout(0.2))
  
      model.add(LSTM(units=50))
      model.add(Dropout(0.2))
  
      model.add(Dense(units=1))
  
      model.compile(optimizer='adam', loss='mean_squared_error')
  
      dataset = np.loadtxt('data.csv', delimiter=',')
  
      data = dataset[:, 0:1]
  
      x_train = data[:int(len(data)*0.8)]
      x_test = data[int(len(data)*0.8):]
  
      x_train = np.reshape(x_train, (x_train.shape[0], time_steps, 1))
      x_test = np.reshape(x_test, (x_test.shape[0], time_steps, 1))
  
      model.fit(x_train, x_train, epochs=50, batch_size=batch_size, validation_data=(x_test, x_test))
  
      return model