A Study on Efficiency of Decision Tree and Multi Layer Perceptron to Predict the Customer Churn in Telecommunication using WEKA

S. Babu; N.R. Ananthanarayanan; V. Ramesh

Research Article

A Study on Efficiency of Decision Tree and Multi Layer Perceptron to Predict the Customer Churn in Telecommunication using WEKA

by S. Babu, N.R. Ananthanarayanan, V. Ramesh

International Journal of Computer Applications

Foundation of Computer Science (FCS), NY, USA

Volume 140 - Issue 4

Published: April 2016

Authors: S. Babu, N.R. Ananthanarayanan, V. Ramesh

10.5120/ijca2016909274

PDF

S. Babu, N.R. Ananthanarayanan, V. Ramesh . A Study on Efficiency of Decision Tree and Multi Layer Perceptron to Predict the Customer Churn in Telecommunication using WEKA. International Journal of Computer Applications. 140, 4 (April 2016), 26-30. DOI=10.5120/ijca2016909274

                        @article{ 10.5120/ijca2016909274,
                        author  = { S. Babu,N.R. Ananthanarayanan,V. Ramesh },
                        title   = { A Study on Efficiency of Decision Tree and Multi Layer Perceptron to Predict the Customer Churn in Telecommunication using WEKA },
                        journal = { International Journal of Computer Applications },
                        year    = { 2016 },
                        volume  = { 140 },
                        number  = { 4 },
                        pages   = { 26-30 },
                        doi     = { 10.5120/ijca2016909274 },
                        publisher = { Foundation of Computer Science (FCS), NY, USA }
                        }

                        %0 Journal Article
                        %D 2016
                        %A S. Babu
                        %A N.R. Ananthanarayanan
                        %A V. Ramesh
                        %T A Study on Efficiency of Decision Tree and Multi Layer Perceptron to Predict the Customer Churn in Telecommunication using WEKA%T 
                        %J International Journal of Computer Applications
                        %V 140
                        %N 4
                        %P 26-30
                        %R 10.5120/ijca2016909274
                        %I Foundation of Computer Science (FCS), NY, USA

Abstract

The Data mining is the technique to discover the knowledge which is hidden in the large data sets. It involves with different methods and algorithms to perform efficient analysis over the data sets. The classification is the technique, which is used to mine the data and helps to make the prediction about the future. Different data mining algorithms are available for classification, like C4.5, Simple Cart, NavieBayesen, Logistic Regression and Multi Layer Perceptron based on Artificial Neural Network. The main objective of this paper is to analyse the efficiency of various classification algorithms in terms of performance, accuracy and time complexity. Telecommunication churn dataset is used for the analysis. The obtained results revealed that MLP algorithm outperformed in terms of accuracy and C4.5 algorithm provides better performance in terms of time complexity.

References

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Index Terms

Computer Science

Information Sciences

No index terms available.

Keywords

Data mining Classification algorithm Decision tree Telecommunication Weka.