Analysis of Telecom Churn in Algeria: A Case Study of Ooredoo Using R

Abstract

This study aims to analyze and predict customer churn in the telecommunications sector, with a particular focus on Ooredoo Algeria. Customer churn—the loss of subscribers—is a critical challenge that directly affects revenue and growth. Using R, we apply machine-learning techniques, specifically Decision Tree and Support Vector Machine (SVM) classifiers, to identify key factors driving churn and build effective models for predicting at-risk customers. Our findings reveal that the Decision Tree model outperforms the SVM model, delivering more accurate and reliable predictions of customer attrition.