## 2 Literature Review
### Customer Churn Prediction Model for Telecom Industry  
Ensemble learning contains the use of multiple ML models to achieve improved performance and accuracy [28; 29] and also provides more independent views, so that decision-making can be better and more accurate [30]. Ensemble-based ML classifiers have recently emerged as a new way of building ML models. Since models have different strengths and weaknesses, researchers have started building hybrid models consisting of two or more models. [PERSON] and [PERSON] [31] took this approach and applied ensemble models, such as Random Forest, Decision Trees, bagging algorithms, and boosting algorithms, to predict customer churn in the telecom industry. They compared the results of these ensemble models with more classical models such as Naive Bayes. From their results, they found ensemble classifiers performed favorably, with Random Forest returning the highest accuracy of 91.66% and all other ensemble classifiers returning results above 90%. Researchers [19] used a particle classification optimization-based BP network for telecom customer churn prediction. The PBCCP algorithm is based on particle classification optimization and particle fitness calculation. In this case, particles refer to vectors containing the thresholds and weights within a BP neural network. The particles are classified into categories using their fitness values and are updated using distinct equations. They found that increasing the number of layers can improve the performance of the algorithm at the cost of training time. As a result of this, they opted to use one hidden layer in the neural network. They used a balanced dataset made up of 50% churn customers and 50% non-churm customers, resulting in the PBCCP network returning an overall accuracy of 73.3%. By comparison, the PSO-BP network returned an overall accuracy of 69.6% and the BP model had 63.6%. Similarly, researchers [9] devised the Logit Leaf model, an ensemble model using aspects of Logistic Regression and Decision Trees, and compared their results with standard Decision Tree, Logistic Model Tree, Logistic Regression, and Random Forest models. The AUC performance criteria were one of the metrics used to evaluate performance--a metric commonly used for evaluating the performance of binary classification systems, such as customer churn prediction. In this regard, the Logit Leaf model performed the best, slightly better than Random Forest. The same was true when the models were evaluated using the TDL (10%) performance criteria. The Logit Leaf model was also more efficient at making predictions, taking less time than the other models used. When real-time predictions are required, this can be a crucial factor when deciding which model to use for a given problem. Finally, scientists [10] applied gravitational search algorithms to perform effective feature selection, resulting in a dimensionality reduction in the dataset used for customer churn prediction. After this, they applied a selection of ML models for comparison: Logistic Regression, Naive Bayes, Support Vector Machine, Decision Trees, Random Forest, and Extra Tree Classifiers and boosting algorithms such as Adaboost, XGBoost, and CatBoost. They found that, when comparing the models used, the boosting algorithms performed the best, with CatBoost achieving the highest accuracy, recall, and precision with scores of 81.8%, 82.2%, and 81.2%, respectively. When comparing their AUC scores, boosting algorithms performed the best again, with XGBoost and Adaboost scoring the highest at 84%. One of the most important applications of ML with regard to CRM is to give companies the ability to predict customer churn. This is particularly important given that retaining existing customers is significantly more valuable than acquiring new customers, as more resources are needed for new customer acquisition. It follows, therefore, that customer retention is one of the key priorities of the CRM strategy [32].