Optimizing Churn Identification in Telecommunications Using Natural Language Processing and XG Boost Machine Learning Paradigm

Abstract

With the increasing competition in the telecom sector, accurate churn prediction has become indispensable for service providers seeking to retain customers. This research paper introduces a novel approach that combines Machine Learning (ML) and Natural Language Processing (NLP) and specifically leveraging the XGBoost algorithm, to enhance the precision and efficiency of churn prediction in the telecom industry. The integration of NLP enables the extraction of meaningful insights from diverse data sources, while XGBoost, a powerful gradient boosting algorithm, is employed to build a robust predictive model for identifying potential churners. A machine learning method called the XGBoost churn prediction model is utilized in the telecom industry to forecast client churn. To construct a predictive model that can precisely identify consumers prone to churn, XGBoost is essentially an ensemble method based on gradient-enhanced trees. Several telecom carriers have used this model to understand their consumers better and identify issues that can contribute to churn. It has been used to predict churn in the telecom sector accurately. The model has been tested for accuracy and effectiveness in identifying factors and forecasting customer attrition. These evaluations' findings indicate that the XGBoost model is a trustworthy and precise method for forecasting customer attrition in the telecom industry.