input: Write a function to create an AI-based system in Python for predicting stock market movements. The function should include data import, data splitting, model building, model training, and model evaluation. If the input data file is not found, the function should return a relevant error message.
  #import libraries
  import pandas as pd
  import numpy as np
  import tensorflow as tf
  from tensorflow.keras.layers import Dense
  from tensorflow.keras.models import Sequential
  
  def predict_stock_market():
      try:
          #import data
          data = pd.read_csv('data.csv') 
  
          #split data into train and test data
          X_train=data.iloc[:, 0] #Choosing the independent variable 
          y_train=data.iloc[:, 1]  #Choosing the dependent variable
          X_test=data.iloc[:, 0] #Choosing the independent variable 
          y_test=data.iloc[:, 1]  #Choosing the dependent variable
  
          #Build a deep learning model
          #import necessary libraries
          import tensorflow as tf
          from tensorflow.keras.layers import Dense
          from tensorflow.keras.models import Sequential
  
          #create a model and add layers
          model = Sequential()
          model.add(Dense(64, input_shape=(1,), activation='relu'))
          model.add(Dense(128, activation='relu'))
          model.add(Dense(64, activation='relu'))
          model.add(Dense(1))
  
          #compile the model
          model.compile(optimizer='adam', loss='mse', metrics=['mse'])
  
          #train the model
          model.fit(X_train, y_train, batch_size=64, epochs=20, validation_data=(X_test, y_test))
  
          #Evaluate the model
          score = model.evaluate(X_test, y_test, verbose=0)
          return score
      except FileNotFoundError:
          return "Data file 'data.csv' not found."
  
  assert predict_stock_market()==True
  assert predict_stock_market()==False
  assert predict_stock_market().startswith("Data file 'data.csv' not found.")==True
output: def predict_stock_market():
      try:
          data = pd.read_csv('data.csv') 
          X_train = data.iloc[:, 0]
          y_train = data.iloc[:, 1]
          X_test = data.iloc[:, 0]
          y_test = data.iloc[:, 1]
          model = Sequential()
          model.add(Dense(64, input_shape=(1,), activation='relu'))
          model.add(Dense(128, activation='relu'))
          model.add(Dense(64, activation='relu'))
          model.add(Dense(1))
          model.compile(optimizer='adam', loss='mse', metrics=['mse'])
          model.fit(X_train, y_train, batch_size=64, epochs=20, validation_data=(X_test, y_test))
          score = model.evaluate(X_test, y_test, verbose=0)
          return score
      except FileNotFoundError:
          return "Data file 'data.csv' not found."