print("citi bike data:\ {}format(citibike head())out[ ]citi bike datastarttime : : : : : : : : : : freq hnameonedtypefloat the following example shows visualization of the rental frequencies for the whole month (figure - )in[ ]plt figure(figsize=( )xticks pd date_range(start=citibike index min()end=citibike index max()freq=' 'plt xticks(xticksxticks strftime("% % -% ")rotation= ha="left"plt plot(citibikelinewidth= plt xlabel("date"plt ylabel("rentals"figure - number of bike rentals over time for selected citi bike station looking at the datawe can clearly distinguish day and night for each -hour interval the patterns for weekdays and weekends also seem to be quite different when evaluating prediction task on time series like thiswe usually want to learn from the past and predict for the future this means when doing split into training and test setwe want to use all the data up to certain date as the training set and all the data past that date as the test set this is how we would usually use time series predictiongiven everything that we know about rentals in the pastwhat do we think will utilizing expert knowledge