Churn Predictions using NLP and ML

Abstract

The churn prediction system identifies customers at risk of attrition and analyzes the factors contributing to telecom customer turnover by leveraging classification and clustering algorithms. The telecom industry typically gathers vast amounts of data, which can make the application of certain data mining techniques cumbersome and interpreting predictions with standard methods challenging. Numerous studies have focused on minimizing churn in large datasets. However, these systems continue to face significant challenges in effectively detecting churn. In some cases, telecommunications data might already contain churn signals, underscoring the need for precise search and detection methods. Efficient customer relationship management is critical for accurately identifying turnover within extensive datasets. In this study, we demonstrated churn detection and prediction by utilizing comprehensive telecom datasets with natural language processing (NLP) using machine learning methods. The main system emphasizes a planned NLP approach, incorporating feature extraction, feature selection, data normalization, and data preparation. We implemented feature extraction approaches using NLP methodologies. We trained and assessed the system as a whole using hybrid machine learning methods for classification. The experimental analysis highlights the methodology for evaluating the performance of the proposed system, comparing it with existing approaches.