## 5 Performance Comparison with Other Existing Approaches  
The proposed ensemble model integrates clustering and classification-based algorithms to significantly handle the problems related to the massive type of telecom datasets. This research efforts to particularly address the fundamental problems faced in telecom CCP challenge in a more systematic and intuitive method. Some existing studies for CCP using the Orange and Cell2 Cell dataset are explained below. [PERSON], [PERSON], & [PERSON] [47] developed a metaheuristic-based churn prediction model using the Orange dataset. A hybrid-based firefly algorithm is applied as the classifier for CCP. The compute-intensive module of the proposed firefly algorithm which is a comparison module was replaced by simulated annealing. This model delivers efficient results and achieves 86.38% accuracy on the Orange dataset. [PERSON], [PERSON], & [PERSON], [PERSON] [46] proposed a novel intelligent model using Filter Wrapper-based Churn Prediction (FW-ECP) for the telecom sector. The originality of the FW-ECP is in its capability to integrate filter and wrapper-based feature choice along with the use of the learning potential of an ensemble classifier constructed from multiple base classifiers. They have evaluated and compared the results of the designed FW-ECP model using two publicly accessible datasets, Orange and Cell2 Cell. The presented model attained 79.4% accuracy on the Orange dataset and 84.9% accuracy on the Cell2 Cell dataset. [PERSON], & [PERSON] [48] designed an efficient model for CCP by combining Particle swarm optimization (PSO) and feature selection with simulated annealing (FSSA). Authors designed three types of PSO viz., PSO combined through feature selection for the pre-processing scheme, PSO inserted by simulated annealing, and lastly PSO through  
Figure 6: Represents different algorithm’s performance based on ensemble techniques (**a**) Bagging-based methods results on Orange dataset. (**b**) Bagging-based methods results on the Cell2 Cell dataset. (**c**) Stacking-based methods results on the Orange dataset. (**d**) Stacking-based methods results on the Cell2 Cell dataset. hybridization of both feature selection and simulated annealing. Various classification algorithms including DT, NB, KNN, SVM, RF, and three hybrid algorithms were applied to analyze the model performance. The proposed model PSO-FSSA achieves a 94.08% accuracy value and the hybrid ANN-MLR model achieves 85.44% accuracy. [PERSON] et al. [49] proposed an improved system for CCP using the Orange telecom dataset. The authors employed a hybrid method called ISMOTE-OWELM. The improved synthetic minority over-sampling technique (ISMOTE) is used to deal with the imbalanced dataset, and the rain optimization algorithm (ROA) is employed to estimate the ideal sample rate. In the last step, the optimal weighted extreme machine learning (OWELM) method is used to establish the class labels of the employed sample. In this work, three different datasets were applied to evaluate the model efficiency, however Orange dataset attained 92% accuracy rate. [PERSON] et al. [50] designed and implement a system for CCP using a comparative analysis of learning networks. The two standard datasets Cell2 Cell and KDD Cup are used for this study. They determined long short-term memory (LSTM) networks obtained accuracy of 72.7% on the Cell2 Cell dataset and 89.3% classification accuracy on the KDD Cup dataset for CCP. [PERSON] et al. [51] implemented a deep-learning-based artificial neural network (Deep-BP-ANN) for CCP in the telecommunication industry. The author employed two feature selection techniques, Variance Thresholding, and Lasso Regression. Moreover, the early stopping method was also applied to overcome the overfitting issue and to end training at the appropriate time. IBM Telco and Cell2 cell datasets were used to evaluate the performance of the implemented model. Experimental results reveal that the IBM Telco obtained 88.12% accuracy and 79.38% accuracy achieved by the Cell2 cell dataset.