This case study shows how to create a model for text analysis and classification and deploy it as a web service in Azure cloud in order to automatically classify support tickets. This project is a proof of concept made by Microsoft (Commercial Software Engineering team) in collaboration with Endava http://endava.com/en
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Tickets attributes category, impact, ticketype are not shown, instead numbers are shown #6
Tickets attributes category, impact, ticketype are not shown, instead numbers are shown.
In the process of preparation of the machine learning model, the categorical features are replaced with numbers and then, upon classification, numbers are shown instead of these text (human understandable) categories. We would like to have these text categories, not numbers, as results (restore the labels).
In the link is shown how it is expected to look like.
https://github.com/gabrielpreda/Support-Tickets-Classification/blob/master/docs/postman_1.jpg
Tickets attributes category, impact, ticketype are not shown, instead numbers are shown. In the process of preparation of the machine learning model, the categorical features are replaced with numbers and then, upon classification, numbers are shown instead of these text (human understandable) categories. We would like to have these text categories, not numbers, as results (restore the labels). In the link is shown how it is expected to look like. https://github.com/gabrielpreda/Support-Tickets-Classification/blob/master/docs/postman_1.jpg