## 2 Literature Review  
The increasing competitiveness in the telecommunications sector has made customer retention a critical business objective. As markets approach saturation, acquiring new customers becomes significantly more expensive than retaining existing ones. Consequently, effective customer churn prediction has emerged as a vital strategy for businesses aiming to enhance customer loyalty and optimize operational costs. Recent advancements in machine learning, particularly deep learning techniques, offer promising avenues for improving the accuracy of churn prediction models by capturing complex, non-linear relationships within customer behavior data. Churn prediction in the telecommunication industry is crucial for customer retention and cost-saving [7]. Recent advancements in churn prediction have increasingly focused on leveraging machine learning techniques to improve predictive accuracy and model efficiency [8; 9]. Data mining techniques such as logistic regression have been widely used for churn prediction [10]. Researchers have applied tree-based algorithms, machine learning models, and boosting algorithms for accurate churn prediction [7]. Machine learning approaches such as logistic regression, naive Bayes, support vector machines, random forests, and decision trees are also commonly used [11]. Recommendations for future churn prediction improvement include but are not limited to leveraging social sciences data for modeling techniques [12]. Also, clustering algorithms have been used in the literature to analyze customer churn data [13; 14; 15]. However, these approaches often struggle to capture the complex, non-linear relationships inherent to customer behavior data [16; 17]. To address these limitations, researchers have turned to more sophisticated models, including neural networks and ensemble learning methods. Neural networks, particularly MLP and RBF networks, have shown significant promise due to their ability to model intricate patterns and interactions within the data. Studies by [PERSON] et al. [18] and [PERSON] and [PERSON] [19] have demonstrated that these models can outperform traditional techniques in various domains, including telecommunications, by providing more accurate and nuanced predictions of customer churn. Additionally, ensemble methods, which combine the strengths of multiple models, have also gained traction, offering a robust alternative that balances predictive performance with generalizability [20; 21]. The ongoing research in this field continues to push the boundaries of what is possible in churn prediction, focusing not only on improving accuracy but also on enhancing model interpretability and operational efficiency [22; 23]. Conversely, the current study conducted research on an authentic data set from a prominent Polish mobile telecommunications provider. Previously, the same data set was considered in the papers [24; 25] where clustering techniques and models other than those considered in this paper, have been utilized for the examination of customer churn data. In this paper, we develop the analysis and compare the results with those of these studies. The motivation behind this research stems from the urgent need for telecommunications companies to implement robust churn prediction systems that can accurately identify at-risk customers--we focus particularly on the Polish market. Traditional methods often struggle to account for the intricacies of customer behavior, leading to suboptimal retention strategies. By leveraging advanced machine learning techniques, businesses can develop more nuanced models that not only predict churn but also provide insights into the underlying factors influencing customer decisions. Below, we present a cross-sectional comparison of selected papers with the proposal described in this paper. The papers selected for this comprehensive review were chosen based on the following criteria.