Application of machine learning techniques for churn prediction in the telecom business

The telecom business generates a significant amount of data on a daily basis due to its massive client base. Acquiring a fresh client base is more expensive than retaining existing customers, whereas churn refers to customers transitioning from one company to another within a specified timeframe. Telecom managers and analysts investigate why customers cancel their subscriptions and analyze the behavior patterns of customers who have stopped using the services. In this work employs categorization methodologies to determine the instances of leave subscriptions and gathers the rationales behind client leave subscriptions in the telecommunications sector. The primary objective of this work is to examine various machine learning algorithms necessary for creating customer churn prediction (CP) models and identifying the reasons for churn. This work aims to provide retention strategies and plans to address churn. This work utilizes machine learning (ML) technique such as random forests (RF) to collect and classify client data for leave subscriptions. These results compare with other ML algorithm such as support vector machines (SVM), gradient boosting (GB), Extreme Gradient Boosting (XGBoost), and light gradient boosting machines (LGBM), The business model provides a practical analysis of customer churn data, enabling accurate forecasts of customers likely to churn. This allows business management to take timely action to prevent churn and minimize profit loss. In this work obtains an accuracy of 98.1 % by utilizing the random forest classifier for churn prediction. The classifier matrix has obtained a precision of 92.8 % and a recall factor of 92.7 %, resulting in an overall accuracy of 95.6 %. Similarly, our research endeavors enhance churn prediction, encompass additional business domains, and furnish prediction models to retain their current consumers, improve customer service, and efficiently prevent churn.