Telecommunication Analytics Based on Customer Segmentation Using Unsupervised Algorithms

Many telecommunication companies try to predict customer churn used supervised learning. This article studies the critical condition in the telecommunications services industry (telco) by using analytics tools of unsupervised learning. We examine seven different unsupervised algorithms for solving the most crucial assets for a business in numerous dynamic and competitive telecommunication companies within a marketplace, which the data available in Kaggle. The results indicate that the use of unsupervised algorithms led to keep the customers are most likely to churn. Based on our unsupervised results, some suggestions for improving customer churn prediction by supervised learning are also made.