mca 22(21): e4

Research Article

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

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  • @ARTICLE{10.4108/eetmca.v6i21.2181,
        author={Sulaiman Olaniyi Abdulsalam and Jumoke Falilat Ajao and Bukola Fatimah Balogun and Micheal Olaolu Arowolo},
        title={A Churn Prediction System for Telecommunication Company Using Random Forest and Convolution Neural Network Algorithms},
        journal={EAI Endorsed Transactions on Mobile Communications and Applications},
        volume={7},
        number={21},
        publisher={EAI},
        journal_a={MCA},
        year={2022},
        month={7},
        keywords={Telecoms, Churn, Relief-F, CNN, Random Forest},
        doi={10.4108/eetmca.v6i21.2181}
    }
    
  • Sulaiman Olaniyi Abdulsalam
    Jumoke Falilat Ajao
    Bukola Fatimah Balogun
    Micheal Olaolu Arowolo
    Year: 2022
    A Churn Prediction System for Telecommunication Company Using Random Forest and Convolution Neural Network Algorithms
    MCA
    EAI
    DOI: 10.4108/eetmca.v6i21.2181
Sulaiman Olaniyi Abdulsalam1,*, Jumoke Falilat Ajao1, Bukola Fatimah Balogun1, Micheal Olaolu Arowolo2
  • 1: Kwara State University
  • 2: Landmark University
*Contact email: sulaiman.abdulsalam@kwasu.edu.ng

Abstract

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.