with ML/DL model Is possible predict numbers of items required? I have a dataset is regarding ambulance call data.
Data sample:
     v_type      district   gender  complaint      age  Month
0   Advanced    District 1  Male    Chest Pain      28  jan
1   Advanced    District 2  Male    Heart Problem   50  dec
2   General     District 3  Male    Cardiac Arrest  76  jun
3   Advanced    District 4  Male    Heart Problem   45  oct
4   General     District 5  Female  Cardiac Arrest  52  nov
5   Advanced    District 1  Male    Chest Pain      34  feb
6   Advanced    District 2  Male    Cardiac Arrest  44  jun
7   General     District 3  Female  Heart Problem   55  july
8   Advanced    District 4  Female  Heart Problem   86  may
9   General     District 5  Male    Heart Problem   65  aug
10  General     District 1  Male    Heart Problem   60  nov
11  Advanced    District 2  Male    Chest Pain      36  mar

In the data v_type (Vehicle type) we have Advanced (Advanced featured emergency vehicle) and General (Basic featured emergency vehicle).
How to predict how many advanced or general ambulances should arrange in a district for a particular month?
Example: If in a month (jan) and in district3 has huge complaint then predict and need to show 5 or 6.. Advanced vehicle type is required
The first thing to do is to reorganize the data in a way which suits the problem that you're trying to solve: since the goal is to predict the number of each type of vehicle by month, your data should contain a column for this number.
Here you need to count the number of rows for each month and each type of vehicle so that you obtain something like this:
v_type   district    month number
Advanced District 1  jan   1
General  District 1  jan   2
Advanced District 2  jan   0
General  District 2  jan   3
Advanced District 1  feb   2
General  District 1  feb   3
...

After that you will probably need to represent the month (and year) in a way that a regression algorithm can use, typically an integer starting at month 1.