ew 18(18): e4

Research Article

Comparative Analysis of Machine Learning Algorithms on IVR data

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  • @ARTICLE{10.4108/eai.12-6-2018.154814,
        author={Aswin. T.S and Himanshu Batra and Mathangi Ramachandran},
        title={Comparative Analysis of Machine Learning Algorithms on IVR data},
        journal={EAI Endorsed Transactions on Energy Web and Information Technologies},
        volume={5},
        number={18},
        publisher={EAI},
        journal_a={EW},
        year={2018},
        month={6},
        keywords={IVR data classification, machine learning on IVR data, text classification, analysis of machine learning methods},
        doi={10.4108/eai.12-6-2018.154814}
    }
    
  • Aswin. T.S
    Himanshu Batra
    Mathangi Ramachandran
    Year: 2018
    Comparative Analysis of Machine Learning Algorithms on IVR data
    EW
    EAI
    DOI: 10.4108/eai.12-6-2018.154814
Aswin. T.S1,*, Himanshu Batra1, Mathangi Ramachandran1
  • 1: Data Science Group, [24]7.ai India
*Contact email: aswin.ts@247.ai

Abstract

We aim to classify IVR data (Interactive Voice Response) and provide a detailed summary of the methods and techniques we employed to create a classifier model of reasonably high accuracy. This model is built to process large datasets of customer grievance lines (in IVR form converted to text), clustering and classifying these lines as accurately as possible. Here machine learning algorithms have been used to build text classifier models, in both supervised and unsupervised approaches on IVR datasets, and their labels have been checked for accuracy. Documentation of the methods applied gives insight into how best we can extract meaningful information and perform classification. By ensuring optimization in the classification of large datasets, resources like time, money and human effort are saved in the long run, and the data is more purposeful.