Telecom Churn Prediction Using Voting Classifier Ensemble Method and Supervised Machine Learning Techniques

Abstract

In the current fast-paced world, there are a lot of changes and developments in the telecom sector, due to which the telecom companies find themselves in difficulties in retaining the customers who have availed of their services. In order to solve this problem, churn prediction system is needed to predict customer churn. So far, there are many supervised machine learning churn prediction models that compare various machine learning and deep learning models, select one model, and create a whole churn prediction model. The solution proposed has various supervised machine learning models like Support Vector Machine (SVM), Random Forest Classifier, Decision Tree Classifier, and Logistic Regression Classifier and combine all the models together using an ensemble method called Voting Classifier to produce a single model that considers all the mentioned algorithms and produces an optimum result. The above-mentioned model will be trained by the telecom dataset containing the records of 7043 customers, and the target field is classified into churned and stayed. The machine learning algorithm is evaluated using various performance metrics such as the F1 score, precision, confusion matrix, classification report, and accuracy.As the result the churn prediction model has shown 84% accuracy.