I do not have ML experience but I want to start getting my hands dirty with it. I have some inputs that I would like MLTK to crunch to advice the best usage of some resources. The use case here is: I have some booths to where people get attended and I have a waiting queue with check in and checkout for those booths. I want to predict is how many booths should I have open to keep the queue waiting times below a certain threshold. The pax volume can vary wildly during the day.
What do I know? I know when someone arrived at the queue (timestamp) and when that person is ready to use the booth as in leaving the waiting queue (timestamp) I know the booth possible capacity (x pax/h) I know my waiting time threshold (x minutes)
What do I want to know? Predict based on historical data how many booths should have open to be able to have the waiting times below the threshold. as a way to do capacity planning for the future (e.g tomorrow at 1pm I should have 5 booths open based on data from same day last week, month or year) Based on near real time data (waiting times, queue size, open booths, etc) advice opening or closing booths to accomplish optimal usage of resources and avoid crossing the waiting time threshold. (e.g there's an different volume of people so in order to keep waiting times low I should open 2 more booths)
------------ Hope I was able to help you. If so, an upvote would be appreciated.