A Churn Prediction System for Telecommunication Company Using Random Forest and Convolution Neural Network Algorithms

EAI Endorsed Transactions on Mobile Communications and Applications

article

Sulaiman Olaniyi Abdulsalam¹  Jumoke Falilat Ajao¹  Bukola Fatimah Balogun¹  Micheal Olaolu Arowolo² 

[1] Kwara State University;Landmark University

关键词: Telecoms;    Churn;    Relief-F;    CNN;    Random Forest;   

DOI  :  10.4108/eetmca.v6i21.2181

学科分类：社会科学、人文和艺术（综合）

来源: Bern Open Publishing

【 摘 要 】

INTRODUCTION: Customer churn is a severe problem of migrating from one service provider to another. Due to the direct influence on the company's sales, companies are attempting to promote strategies to identify the churn of prospective consumers. Hence it is necessary to examine issues that influence customer churn to yield effective solutions to minimize churn. OBJECTIVES: The major purpose of this work is to create a model of churn prediction that assists telecom operatives to envisage clients that are more probably to be prone to churn. METHODS: The experimental strategy for this study leverages the machine learning techniques on the telecom churn dataset, employing an improved Relief-F feature selection algorithm to extract related features from the enormous dataset. RESULTS: The result demonstrates that CNN has a high prediction capability of 94 percent compared to the 91 percent Random Forest classifier. CONCLUSION: The results are of enormous relevance to the telecommunication business in improving churners and loyal clients.

【 授权许可】

CC BY