Churn Prediction in Telecommunications Sector using Machine Learning

Andreea-Maria Copaceanu ()
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Andreea-Maria Copaceanu: The Bucharest University of Economic Studies, Romania

Database Systems Journal, 2021, vol. 12, issue 1, 12-20

Abstract: Churn prediction is becoming increasingly important in business, especially in the telecom industry. The primary objective in telecom churn analysis is to accurately estimate the churn behavior by identifying the customers who are at risk of churning. This paper focuses on various machine learning algorithms for predicting customer churn, including Logistic Regression, Random Forest, Decision Tree, and Support Vector Machine. Prediction performance of the classifiers is evaluated and compared through measures such as Area Under the Curve (AUC), accuracy, and recall rate. Such predictive models have the potential to be used in the telecom industry for making better decisions in customer management.

Keywords: Churn Prediction; Machine Learning; Retention; Telecommunication; Decision Tree (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:aes:dbjour:v:12:y:2021:i:1:p:12-20

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