This thesis work, conducted at Telenor Sweden, aims to build a model that would try to accurately predict the resolution time of Priority 4 Trouble Tickets. (Priority 4 trouble tickets are those tickets that get generated more often-e in higher volumes per month). It explores and investigates the possibility of applying Machine Learning and Deep Learning techniques to trouble ticket data to find an optimal solution that performs better than the current method in place (which is explained in Section 3.5). The model would be used by Telenor to inform the end-users of when the networks team expects to resolve the issues that are affecting them.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:liu-176779 |
| Date | January 2021 |
| Creators | Enver, Asad |
| Publisher | Linköpings universitet, Statistik och maskininlärning |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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