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Research Article

JCR. 2020; 7(14): -


Churn Analysis using Fuzzy Hybrid K-Mean Clustering with SVM Classifier Techniques

Sudharsan R, Dr.E.N.Ganesh.

Abstract
These Churn Prediction is the most popular Big Data customer evaluation in business.Data
exploration using the churn predictive model extends to the future potential churn rates.Exploratory analysis
problems in any irregularities, correlations, outliner and their relationship has the domain knowledge need for the
analysis. The limitations overcome using the grouping the customer related information. The grouping concepts
applies the k-means clustering and the grouping data instance uses the Support vector Machine (SVM) classifier
techniques. The proposed frame work applies the churn prediction model with fuzzy logic model able to predict
the churn customer and the non-churn customer. The planned work implemented with the help of Matlab R2014a
software and their simulation results analysis the churn prediction with fuzzy model.

Key words: Telecommunication, churn analysis, support vector machine, fuzzy logic, classifier, k-means clustering, training dataset.



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