input: def predict_stock_market(data: pd.DataFrame, model: tf.keras.Model) -> float:
      """ Predict stock market movements using an AI-based deep learning model.
      The function takes a DataFrame containing stock market data and a compiled Keras model.
      It splits the data into training and testing sets, trains the model, and evaluates it.
      The function returns the evaluation score of the model.
      >>> data = pd.read_csv('data.csv')
      >>> model = build_stock_model()  # Assume build_stock_model() is defined to build the model
      >>> score = predict_stock_market(data, model)
      >>> print(score)
      0.023
      """
      # 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
      
      # 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
output:     # split data into train and test data
      X_train = data.iloc[:, :-1]  # Choosing all columns except the last as independent variables
      y_train = data.iloc[:, -1]  # Choosing the last column as the dependent variable
      X_test = data.iloc[:, :-1]  # Choosing all columns except the last as independent variables
      y_test = data.iloc[:, -1]  # Choosing the last column as the dependent variable
      
      # 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