arrange data for training and prediction - input stock_data: Pandas dataframe from global variable - input dates: list with a start

create a Recurrent Neural Network - input n_fratures: integer with the number of features within x and eval_x - output model: RNN Tensorf

draw stock value graphs - input graph_data: numpy array of stock values to plot - output figure: plotly Figure with visuallized graph_dat

filter companies by their country of origin - input country: string with country name - output company_names: list of company names

filter global stock data by dates and companies - input dates: list with a start date and a finish date - input company: list of com

obtain prediction with K Nearest Neighbors - input dates: list with a start date and a finish date - input company: list of company

obtain prediction with Linear Regression - input dates: list with a start date and a finish date - input company: list of company sy

obtain prediction with RNN - input dates: list with a start date and a finish date - input company: list of company symbols

built-in Taipy function that runs every time a GUI variable is changed by user

built-in Taipy function that runs once when the application first loads