Predictive Model on Churn Customers using SMOTE and XG-Boost Additive Model and Machine Learning Techniques in Telecommunication Industries

Abstract

In this research paper the researcher builds a predictive model on churn customers using SMOTE and XG-Boost additive model and machine learning techniques in Telecommunication Industries. Customer’s churning is one of the global research issues in telecommunication industries. In somehow customers are not satisfying from telecommunication customer services, call rate, international plan, data pack, and others which are having a significant impact on customer’s services. The researcher used the SMOTE and XGboost technique to handle the imbalanced dataset and gives the higher-level accuracy for predictive model to identify the category of customer whether they are in churn or not churn. The researcher used the comparative study between logistics regression and random forest algorithms to classify the category of churn customers and non-churn customers in Telecommunication Industries. The predictive model is verifying at 96% accuracy level and can be capable to handle imbalance dataset. As per the data analysis the score of the confusion matrix is such as accuracy 94%, Precision for “ did not leave “ is 0.97 whereas recall is 0.96, and F1score is 0.97 with the support features of 903. For the churn customers precision is 0.80, recall is 0.81, F1-score is 0.80 and support features is 160, the data analysis report shows that the predictive model is having 94% accuracy whereas at 6% does not predict accurately about the customers status. Finally, the researcher concluded that the predictive model is more accurate and can be capable to handle imbalance dataset. The researchers assure that the predictive model would be benefited for the telecommunication industries to categories the churn/ non-churn customers and accordingly the organization can make changes their business plan and policies which would be benefited for the customers.