Building ML Based Intelligent System to Analyze Production LSI (Live Site Incidents)
Himanshu Bajpai
Himanshu Bajpai*, Engineering Services, Infosys Limited, Pune (M.H), India.
Manuscript received on December 28, 2021. | Revised Manuscript received on January 03, 2021. | Manuscript published on February 28, 2021. | PP: 41-46 | Volume-10 Issue-3, February 2021. | Retrieval Number: 100.1/ijeat.C21780210321 | DOI: 10.35940/ijeat.C2178.0210321
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Providing support on the rolled-out application/services is one of the major factors in increasing the customer satisfaction which in turn increases the customer retention. Since we are in the era of automation where most of the day-to-day jobs are taken care of or are facilitated by the technologies around us, hence there is a need to reduce manual effort in triaging the support tickets and hence facilitating the person on call to better close the tickets on time with proper remediation. The machine learning model which will be the product of this complete paper will not only help in classifying the tickets but also, if applicable will give the best possible remediation of the ticket there by reducing the manual effort and the time taken on providing necessary solution on the ticket. The objectives of the work are as follows – a) Understand the data that is present in the ticket and figure out the basic understanding like, categories of issues, trends etc. b) Prepare the data which is ready for applying different classification algorithms. d) Identify the best machine learning model which can classify the new incident with utmost accuracy. e) Prepare a machine learning model which can suggest the best possible remediation of the ticket. f) Integrate the best classification model and solution recommender model and wrap it as an API which can be used by end user.
Keywords: Application Development, Classification, Model Evaluation, Natural Language Processing, Semantic Similarity
Scope of the Article: Natural Language Processing