input: Write a function to predict stock market movements using a deep learning model. The function should load data, split it into train and test sets, build a neural network model, train the model, and evaluate its performance. The function should return the evaluation score of the model. If the dataset is empty, the function should return 'No Data for Training'.
  def predict_stock_market_movements(data):
      if data.empty:
          return 'No Data for Training'
  
      # split data into train and test data
      X_train = data.iloc[:, 0]
      y_train = data.iloc[:, 1]
      X_test = data.iloc[:, 0]
      y_test = data.iloc[:, 1]
  
      # 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
  
  assert predict_stock_market_movements(pd.read_csv('data.csv'))==some_score, "Test Case 1 Failed"
  assert predict_stock_market_movements(pd.DataFrame())=='No Data for Training', "Test Case 2 Failed"
output: def predict_stock_market_movements(data):
      if data.empty:
          return 'No Data for Training'
  
      # split data into train and test data
      X_train = data.iloc[:, 0]
      y_train = data.iloc[:, 1]
      X_test = data.iloc[:, 0]
      y_test = data.iloc[:, 1]
  
      # 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