• SABITHA JETTY
• PADALA PAVAN KUMAR

Ticketing systems have gained significance and constitute a critical feature as businesses strive for superior customer experience and satisfaction in terms of business viability. Various software businesses have the development of very effective software solutions for problem tracking, several sub-processes and duties inside ticketing systems are still carried out manually. These were done by hand. Tasks, especially in large companies, are bottlenecks. They lead to a decrease in productivity and an increase in reaction time. Machine learning advances may be linked with existing issue tracking and ticketing systems on the market in a unique way to provide maximum operational efficiency in large-scale enterprises' Client Service and Support (CSS) departments that deal with customer reports. This study provides a holistic approach to customer service by addressing three apparently unrelated bottlenecks in the ticketing system: spam detection, ticket assignment, and sentiment analysis.

Issue Ticketing System Based on Machine Learning

"Issue Ticketing System Based on Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 2, page no.67-72, February-2019, Available :http://www.jetir.org/papers/JETIRFE06013.pdf

"Issue Ticketing System Based on Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 2, page no. pp67-72, February-2019, Available at : http://www.jetir.org/papers/JETIRFE06013.pdf