input: def predict_stock_market_movements():
      """
      Create an AI-based system in Python for predicting stock market movements using a deep learning model.
      >>> 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)
      """
output:     import pandas as pd
      from keras.models import Sequential
      from keras.layers import Dense
  
      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)