## 1 Introduction  
Electronic commerce dramatically boosted the quantity of information accessible to customers when it first began. In the digital age, consumers have become more informed about the products they buy. Armed with access to information about a wide range of related products or services, the behavior of consumers has shifted towards being less impulsive. Instead, getting a range of information on a wide range of products to make a more calculated purchasing decision has become the norm. This change in attitude makes attracting and retaining new customers one of the most critical difficulties confronting businesses in marketing today. While new businesses focus on obtaining new consumers, established businesses are more focused on retaining current customers to increase cross-selling opportunities. Customer satisfaction is one of the keystone concepts in strategic marketing [1]. Efforts have been made to enhance customer value, such as the 7 Cs framework proposed by [2]. In several businesses, customer churn rates are a significant problem. Many studies have demonstrated that even a minor shift in churn rates may have a big effect on profits. Understanding client behavior in advance can provide businesses with a competitive edge. Even though mobile phones account for over 75% of all potential phone calls worldwide, the mobile telephone market is one of the most rapidly growing segments of the telecom sector. For the same reasons that every other competitive market has witnessed a movement in the competitive landscape from customer acquisition to customer retention, retail has also seen a shift in the means of competition from customer acquisition to customer retention. In the telecom industry, churn refers to a company losing customers to other service providers [3]. Like many businesses that work with long-term clientele, telecom firms utilize customer churn research as one of their primary business indicators [4]. According to a survey presented by [5], 30-35% of clients leave their telecom company annually post-COVID. This churn rate may continue to rise with market growth or the emergence of new, big telecom players in the future. In addition, for cellular corporations, client acquisition costs can be comparable to 5-10 times the amount spent on customer retention or satisfaction costs [6]. Therefore, it is essential to learn the reasons why customers leave to reduce the harm churn has on a business's bottom line. Predictive marketing analytics and churn analysis can help identify factors influencing consumers' voluntary churn by using advanced machine learning algorithms [7]. By investigating the existing studies, it was discovered that ensemble learning, including classical machine learning algorithms like Random Forest, Decision Trees, and Naive Bayes [8; 9; 10], and deep learning methods, such as particle-classification-optimization-based BP networks [11] and Deep-BP-ANN [12], have been employed to improve the accuracy of churn prediction. However, the models should not only focus on accuracy in predicting churning [9] but also be comprehensible, which means it should provide reasons for churning so that experts can validate its results and check that it predicts intuitively and correctly. If the company had understandable and transparent models to work with, it would increase the understanding of what was causing the churn and how to enhance customer happiness to boost retention. As a result, this research aims to construct an accurate, efficient, responsible, and explainable prediction model for customer attrition in the telecom industry using ensemble learning methods. Various data mining methods like Decision trees, Random Forests, and Logistic Regression were utilized by experts to build the predictive model. The performance of the models was evaluated using the accuracy measures, area under the curve, and sensitivity and specificity measures.