Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Classifying voice-based customer query using machine learning technique Subject The topic of the resource Feature extraction; Stemming; Tokenization; Vectorization; Voice recognition Description An account of the resource Timely attention to issues raised by customers is critical. It is imperative that the average handling time is lesser, which in turn contributes to productivity. It was found from the data from the banking industry in the US that, on average, a customer service call last for seven minutes. The first two minutes are for the call to get redirected to the respective team. This study investigates a method using machine learning to classify and redirect the customers into the respective department directly based on their initial voice response or voice message. It will substantially reduce the service time. CRISP-DM methodology is being used to design the process of the study. The most frequently occurring issues and the department to which they are associated are created through machine learning from the dataset that contained product reviews and metadata of different issues. The programming languages that are used in this study are Python, HTML and Java. An interface is created by using HTML, which makes it quite user-friendly. The study tests the effectiveness of converting voice to text and interprets which department the call should be transferred to address the issue. A support vector machine and a logistic regression model were used for the prediction, and it was found that the models provided an accuracy of 83 and 84 percent, respectively. The study proves that using ML and voice recognition reduces the average handling time. 2021 Ecological Society of India. All rights reserved. Creator An entity primarily responsible for making the resource Kapse M.; Elangovan N.; Varghese A.; Joy A.; Tom C. Source A related resource from which the described resource is derived Indian Journal of Ecology, Vol-48, pp. 69-72. Publisher An entity responsible for making the resource available Ecological Society of India Date A point or period of time associated with an event in the lifecycle of the resource 2021-01-01 Identifier An unambiguous reference to the resource within a given context <a href="" target="_blank" rel="noreferrer noopener"></a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113799410&amp;partnerID=40&amp;md5=44e4f957c86c2241c77b893bc18806d9" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113799410&amp;partnerID=40&amp;md5=44e4f957c86c2241c77b893bc18806d9</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 3045250 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Kapse M., School of Business and Management, Christ (Deemed to be University, Bengaluru, 560 029, India; Elangovan N., School of Business and Management, Christ (Deemed to be University, Bengaluru, 560 029, India; Varghese A., School of Business and Management, Christ (Deemed to be University, Bengaluru, 560 029, India; Joy A., St Joseph's College of Arts and Science (Autonomous), Bengaluru, 660 027, India; Tom C., St Joseph's College of Arts and Science (Autonomous), Bengaluru, 660 027, India