## 2 Literature Review
Firstly, with regard to relevance, it was imperative that the studies focused explicitly on the prediction of customer churn within the telecommunications industry or in sectors closely related to it, as this would provide the most applicable insights for the current study. Secondly, those papers that employed sophisticated and advanced machine learning techniques were considered. Additionally, recency played a critical role; only publications that were released within the last two years were included to ensure that the research reflects the most current trends and developments in the field. Moreover, the impact of each paper was also a significant criterion. Studies that had been published in reputableand high-impact journals with significant citation counts were chosen. A comparison of the papers [26; 27; 28; 29; 30] and their methods is shown in Table 1.  
The studies reviewed demonstrate that advanced machine learning techniques significantly improve predictive accuracy compared to traditional methods. A crucial advancement in modeling complex, non-linear relationships inherent to customer behavior data is highlighted. A recurring theme across the studies is the critical role of meticulous data preprocessing in enhancing model performance. [PERSON] et al. [29] emphasized handling missing data and encoding categorical variables effectively, which significantly influenced their model's accuracy. This study emphasizes meticulous data preprocessing techniques, including attribute selection, handling missing data, and encoding categorical variables. These steps are crucial for enhancing model performance, but they are often inadequately addressed in previous works. This paper incorporates concepts from information theory, such as entropy and information gain, to assess the importance of features in predicting customer churn. This theoretical underpinning adds depth to analyses and offers a quantitative approach to feature selection that has not been widely applied in prior research. Investigating real-world data sets, as in the present research and [27], adds practical relevance to findings. By analyzing data from actual telecommunications providers, these studies provide insights that are directly applicable to industry scenarios in Poland, thereby bridging the gap between theoretical research and practical implementation. [PERSON] et al. [27] introduced an innovative approach by integrating social media sentiment analysis with traditional churn prediction models. This contribution addresses a significant gap in the existing literature by exploring how external factors can enhance predictive capabilities. However, in real-world scenarios, such data are not always available. [PERSON] & [PERSON] [28] highlighted the potential of hybrid models that combine multiple machine learning techniques to leverage their respective strengths. However, this introduces a lack of interpretability to the models. A common criticism of deep learning models is their \"black-box\" nature, which complicates the understanding of how predictions are made. In this paper, we acknowledge this limitation by discussing the importance of model interpretability alongside predictive accuracy. By addressing how businesses can balance these aspects, this paper contributes to ongoing discussions about making advanced models more transparent and usable in practical applications. While many studies have explored machine learning techniques for churn prediction, this paper stands out by providing a detailed comparative analysis between advanced neural network models (MLP and RBF) and traditional methods such as fuzzy logic and rough sets. This comprehensive evaluation not only highlights the strengths of neural