TL;DR
XGBoost, LightGBM and CatBoost algorithms perform better than other algorithms in predicting customer churn, and can strengthen long-term customer relationships by increasing customer satisfaction.
XGBoost was the most effective algorithm for customer churn prediction under the experimental conditions of the study, demonstrating that customer churn remains a major challenge for e-commerce organizations.
This study compared explainable machine learning models for predicting customer churn using the IBM Telco Customer Churn dataset in R and found Logistic Regression achieved the best performance, with an accuracy of 82.30% on this dataset.
. Customer churn prediction has a great impact on the long-term profitability and competitiveness of telecommunications companies. This study uses telecommunications customer data, employs machine learning methods to build a customer churn prediction model, and identifies key influencing factors. By using majority clas...
With the rapid growth of digital and technology, a big amount of data is generated on a daily basis from customers, and customer churn is causing a great concern in this highly competitive service sector, especially the telecom service providers. Investment in acquiring new customers is more expensive than retaining ex...
Customer churn prediction is an important task in the telecommunications sector, particularly when class imbalance affects the ability of machine learning models to identify churned customers. This study evaluates the performance of several machine learning algorithms using the Telco Customer Churn dataset, which conta...
The Extreme Gradient Boosting algorithm is applied to telecom customer churn prediction, comparing its performance with Logistic Regression and Random Forest using the public Telco Customer Churn dataset and showing XGBoost outperformed benchmark models.
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