ML - Service Desk classification I'm trying to explore an use-case in ML but stuck at a point. May i please request your advise please.

Have a service desk web application for logging tickets, which is essentially a form having various fields like - 

subject,
content, 
username, 
emailid,
category - {hardware, application, datafix, mobileapp, etc.}, 
service group - {AAA, BBB, CCC, DDD, etc.}, 
domain - {email, walkin, phonecall, etc.},
priority - {high, medium, low}
#(Apologies for the poor quality of sample data provided above.)


Based on this info, the ticket is then manually assigned to respective team owners for resolution.

My intent is to use ML - based on the above fields, predict the Team who will work on this ticket. (Team ex. HR or IT or Desktop support or Pantry or Facilities, etc. )


Can this use-case be categorized as Multi-class classification
problem?
The field values are stored as words in database. How can it be fed
to my ML as numbers?


Please advise.
Welcome to the DataScience Stack exchange.

Since your target variable has many categories your problem is Multi class classification. On how to implement and find a solution for this problem you can find on google. The following 2 links are for reference:


Link-1  
Link-2


Here I'm not giving much information on which algorithm to use. If you need any additional help you can raise one more question so that we can help you better as we don't now much about your data and business problem.

So, to answer your question 2, you can convert them into number it is one line code,I've added the link for your reference. 

Please let us know if you need any additional information.