## 1 Introduction  
The size of the information and communication technology (ICT) market is growing as more communication companies approach the market as a result of globalization and liberalization. Customers are encouraged to switch from one service to another as a result of the rising in efficient and modern service [1]. Customer happiness and improvement are essential for customer retention. Customer relationship management (CRM) relies on this [2, 3]. User retention can only be achieved by calculating the numerous characteristics of a customer who is about to churn, but this computation is challenging due to manual data collecting which is an ineffective and laborious task [4]. Continuous customer turnover is extremely damaging for a company's business. If the mobile telecommunications business could predict customer defection, it would do well and make some measures to retain those customers who are going to switch in many sectors for example e-commerce [5; 6], finance [7], banks [8] and telecommunications industries [9]. The industry strives to make minor adjustments in order to keep clients and revenue [10]. It is tough to lose a present customer in any business situation. Customer churn occurs in three modes. Some individuals may leave due to service provider issues such as network outages, costly rates, billing hassles, and so on. Second, certain clients have a habit to jump from one company to the other. Finally, some clients tend to switch service providers for reasons that the communication sector does not understand. This type of user, as well as the factors for their shifting, should be determined in advance [11]. Registering a fresh client is a long and costly process. Therefore, churn prediction and anticipation are the only ways for a telecommunications firm to survive [12]. Once a potential churn customer has been detected, the employer might give incentives and presents to sustain those clients. Leading telecommunications firms use the strategy of attracting clients using promotions and e-mail marketing. The churn client patterns are examined, and appropriate deals are offered for various groups based on their categorization. Some may receive call discounts, while others may receive data discounts. Customers who are important to the firm are recognized during this classification, and appropriate high-value incentives are offered to them [11]. Many machine learning-based classification algorithms including decision trees (DT), k-nearest neighbor (KNN), naive bayes (NB), neural network (NN), and support vector machine (SVM) are used to identify customer churn [13; 14; 15; 16]. Previously, researchers employed single classification methods for CCP. Ensemble-based classification algorithms have been the subject of recent development. With the use of a fusion method, these new strategies employ hybrid techniques that combine numerous single classifiers, and their predictions are integrated into a single aggregated result [17]. Therefore, this study proposed an ensemble method using the advantages of clustering and classification learning algorithms for telecom churn prediction. We proposed the integration of a supervised and unsupervised learning approach for CCP. In this research, a hybrid-based CCP approach entirely incorporating (k-means, k-medoids, and Random) clustering and classification (GBT, DT, RF, DL, and NB) models are suggested. We compare different ensemble models to recognize the top-performing model. * Clustering: the proposed model uses k-means, k-medoids, and random in the clustering stage. This stage gives us the top clustering technique. The k-medoids technique performs better and overcomes several hitches of the k-mean and random algorithm. * Classification: the proposed system evaluates different classifiers as single and hybrid models using two datasets. Through the classification stage, we select the most appropriate individual and hybrid classification model.