Prediction of Churning Behavior of Customers in Telecom Sector Using Supervised Learning Techniques

• Published in: 2018 IEEE 3rd International Conference on Computing, Communication and Security (ICCCS) pp. 143 - 147
• Main Authors: Ali, Muhammad, Rehman, Aziz Ur, Hafeez, Shamaz
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-10-2018
• Subjects: Bibliographies; Classification algorithms; Companies; Customer churn; Data mining; Decision trees; Supervised learning; Telecommunications; Weka
• DOI: 10.1109/CCCS.2018.8586836

Data mining is vast area that co-relates diverse branches i.e Statistics, Data Base, Machine learning and Artificial intelligence. Various applications are accessible in various areas. Churning of the Customer is the behavior when client never again needs to stay with his association with the company. Customer Churn Management is assuming essential job in client management. Nowadays different telecommunication companies are concentrating on distinguishing high esteemed and potential churning clients to expand benefit and share market. It is comprehended that making new clients are costlier than to holding existing client. There is a current issue that customer leave the organization because of obscure reasons. In our investigation, we predict churn behavior of the client by utilizing diverse data mining methods. It will in the long run help in breaking down client's behavior and characterize whether it is a churning client or not. We utilize online accessible data set available at Kaggle repository and for forecasting of Customer behavior we utilized different algorithms while we achieved 99.8% accuracy level using Bagging Algorithms.