Churn Prediction in Telecommunication Industry: A Comparative Analysis of Boosting Algorithms

Abstract

The issue of customer churn, which is a major problem in the telecommunications industry, poses several challenges, such as financial implications, client attrition, and increased marketing costs. The advancements achieved in the domain of machine learning and artificial intelligence have significantly expanded the possibilities for forecasting customer churn, presenting a promising resolution for effectively handling customer attrition and enhancing customer retention. This study presents a customer churn prediction model that uses machine learning approaches to assist telecom firms in enhancing customer retention and mitigating churn rates. The study employs machine learning techniques, such as Adaboost, Gradient Boosting, and Extreme Gradient Boosting (XGBoost), in order to evaluate extensive datasets and provide predictions on customer churn via a comparative evaluation. The methodology involves extracting data from the Kaggle data pool, doing further data preparation, and identifying relevant features. The Synthetic Minority Oversampling Technique (SMOTE) is used as a strategy to mitigate the challenges posed by imbalanced data. The dataset is partitioned into training and testing sets at a ratio of 75% to 25%. The XGBoost model demonstrated superior accuracy and recall, positioning itself as the top-performing model among the studied models. The attained accuracy rate was 89.51%. The XGBoost method has a recall rate of 92.48%, which is the highest of the three algorithms evaluated. Gradient boosting follows with a recall rate of 87.69%, while Adaboost achieves a recall rate of 85.13%. These findings underscore the potential of machine learning techniques for addressing the challenges posed by customer churn in the telecommunications industry.