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Churn Prediction using Various Machine Learning Algorithms in Telecom Sector

by Vikrant Verma, Padmavati Shrivastava, Dhananjay Sahu
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 184 - Number 36
Year of Publication: 2022
Authors: Vikrant Verma, Padmavati Shrivastava, Dhananjay Sahu
10.5120/ijca2022922462

Vikrant Verma, Padmavati Shrivastava, Dhananjay Sahu . Churn Prediction using Various Machine Learning Algorithms in Telecom Sector. International Journal of Computer Applications. 184, 36 ( Nov 2022), 39-45. DOI=10.5120/ijca2022922462

@article{ 10.5120/ijca2022922462,
author = { Vikrant Verma, Padmavati Shrivastava, Dhananjay Sahu },
title = { Churn Prediction using Various Machine Learning Algorithms in Telecom Sector },
journal = { International Journal of Computer Applications },
issue_date = { Nov 2022 },
volume = { 184 },
number = { 36 },
month = { Nov },
year = { 2022 },
issn = { 0975-8887 },
pages = { 39-45 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume184/number36/32552-2022922462/ },
doi = { 10.5120/ijca2022922462 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:23:20.471493+05:30
%A Vikrant Verma
%A Padmavati Shrivastava
%A Dhananjay Sahu
%T Churn Prediction using Various Machine Learning Algorithms in Telecom Sector
%J International Journal of Computer Applications
%@ 0975-8887
%V 184
%N 36
%P 39-45
%D 2022
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The world around us is growing at a very fast rate as a result of this rapid expansion of digital systems and related information technologies, there is a growing trend in the global economy to develop digital CRM systems. One of the pro competitors in this field is the telecommunications sector, where companies are increasingly digitalized. Telecom companies emphasize better and improved CRM’s and one of main problem Telecom companies face is Customer churning. Customer churning is the discontinuation of services by customer side. This happens very commonly in telecom sectors like in India people switch from Idea to Jio then from Jio to Airtel, Customer gets service from whoever gives better offer. Due to this, service providers have recognized the significance of retaining current customers. Therefore, providers are compelled to exert additional effort in predicting and preventing churn. Hence From existing churn customers data, business analysts and Customer Relationship Management (CRM) analysts must determine the reasons for churning customers and their behavioural patterns. This study conducts a real-world investigation of customer churn prediction and proposes the use of different classification algorithms and feature selection to create a customer churn prediction model. In this study the researchers have used a fictional dataset which contains real life features on the basis of which customer might churn. They performed an analysis on the WA_Fn-UseC_-Telco-Customer-Churn dataset, which is a telecom customer churn dataset that is available on Kaggle. The purpose of this research was to identify patterns concerning the factors that lead to customer churn. This dataset contains features like Customer ID, gender, tenure, PhoneService, InternetService, PaymentMethod, MonthlyCharges, TotalChargesetc and Churn (Whether the customer churned or not (Yes or No)).

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Index Terms

Computer Science
Information Sciences

Keywords

CRM churning

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