Churn Prediction in Telecom Subscriptions Using Boosted Learning Models
Аннотация
Customer turnover significantly threatens telecom service providers' long-term viability and profitability. Especially for business models built on subscriptions, early identification of possible churners is essential for companies. Increased costs related to client acquisition and the loss of income resulting from high churn rates drive home the need for companies to find possible churners. A very effective ensemble learning method, the AdaBoost algorithm, is being investigated in the framework of this Research to improve the precision of customer churn forecasting. The method comprises preprocessing, feature selection, and iterative model training with AdaBoost.Using a real-world telecom dataset, the goal of the approach is to improve the predictive performance of the classifier in comparison to the usual applications of the classifier. The most important results indicate that AdaBoost considerably raises the accuracy of churn prediction, hence attaining an overall precision of 89%. Moreover, regarding recall and F1-score, there are notable gains compared to baseline models such as Decision Trees and Logistic Regression. With this method, one can properly spot high-risk clients, which helps carry out tailored retention plans. Finally, AdaBoost shows it is a resourceful and successful tool for churn prediction in telecommunications services. Its ability to provide actionable insights could help improve client loyalty and operational efficiency.