mSVM: An Approach for Customer Churn Prediction using modified Support Vector Machine and various Machine Learning Techniques

Abstract

Consumer attrition is a major issue across the world companies and that is one of their primary worries. Organizations, specifically in the telecommunication sector, are working to build technologies to forecast future customer churn due to the obvious direct impact on profits. It's essential to define the variables that lead to customer churn before making the required efforts to minimize churn. An important impact was the creation of an attrition estimation method that aids telecommunication corporations in predicting which consumers are willing to churn. The methodology proposed in this study employs mathematical methodologies on a big data framework to provide a unique strategy to feature design and evaluation. This research work has proposed a customer churn prediction using a modified Support Vector Machine Learning (mSVM) classifier. The significant contribution of this research is that we have changed the distance function of SVM in both training and testing. Similar machine learning algorithms are also validated on similar datasets such as Naïve Bayes (NB), K-nearest Neighbor (KNN) and Decision Tree (DT) with J48 classifiers. The BigML dataset has been used for detecting churn in the telecom industry in a real-time scenario. In an extensive experimental analysis, we demonstrate mSVM which produces higher accuracy over the traditional machine learning algorithm for different cross-validation methods.