# Predicting popular times

I have a MySQL database that contains datetime for every check-in with an RFID card. I have millions of records in it. It shouts machine learning prediction for me.

So I'd like to predict popular times to see when most people use the terminal. I'd like to represent it the same way as Google does at places:

This is the rare case when I do not ask for code, but keywords.

I assume I have to predict number of check-ins between time ranges. Also I am sure I have to make a dataset like this:

                        (These are the number of check-ins in hour ranges)
month | day | day-name | 0-1 | 1-2 | ... | 6-7 etc.
jan   | 1   | Monday   |  0  |  1  |     |  12
jan   | 2   | Tuesday  |  1  |  3  |     |  15


So it can predict today's popular times based on what day it is, also by day of given month (as Saturdays and Sundays will be dead, also 25th of December. A good prediction should know these from the dataset).

As I wrote this question I solved most of it it seems. The only thing I need is a keyword as I have little experience in this. What model fits this best?

• An average hourly check-in on the day of the week would not be enough for a forecast? Dec 1 '17 at 18:45