# Intelligent Decision Forest Models for Customer Churn Prediction
## 1 Introduction  
Customers are considered significant entities for any company in an industry full of vibrant and challenging businesses. In a competitive industry, when customers have multiple service providers to choose from, they may quickly switch services or even suppliers [1]. This switch (customer churn) may be caused by unhappiness, rising costs, poor quality, a lack of features, or privacy issues [2]. Several companies across different sectors, including banking services, airline services, and telecommunications, are directly affected by customer churning [3, 4, 5, 6]. These companies increasingly focus on creating and sustaining long-term connections with their current customers. This tendency has been observed in the telecommunications sector. Inarguably, continuous expansion and advancement in the telecommunications sector significantly boosted the range of companies in the sector, increasing competition [7]. In other words, the telecommunications sector is experiencing significant customer churn due to tough competition, crowded markets, a dynamic environment, and the introduction of new and tempting packages. In this rapidly changing sector, it has become necessary to optimize earnings regularly, for which numerous tactics, such as bringing in new customers, up-selling current customers, and extending the retention time of existing customers, have been advocated. However, as has been observed from existing studies and reports, obtaining new customers might be more costly for businesses than retaining current customers. Predicting the probability of customer churning is fundamental to finding remedies to this issue [8; 9; 10]. A major key purpose of Customer Churn Prediction (CCP) is to aid the creation of strategies for retaining customers that increase business revenue and industrial recognition. Nonetheless, companies in the Telecommunications sector now hold a wealth of information about their clients, such as call logs (domestic and international), short messages, voicemail messages, profiles, financial information, and other important details. This information is strategic and crucial for predicting which customers are at the point of churning. Companies must accurately predict the customer's behavior before it happens [11; 12]. There are two approaches to managing customer churn: (1) reactively and (2) proactively. In the reactive mode, the organization anticipates the consumer to terminate before offering enticing retention incentives. However, under the proactive method, the probability of churn is foreseen, and appropriate incentives are presented to consumers. Described in another way, the proactive approach is regarded as a binary classification problem wherein churners and non-churners are differentiated [1; 13]. Several approaches, including rule-based and machine learning (ML)-based solutions, have been developed to address CCP. However, the lack of scalability and robustness of rule-based CCP models is a significant disadvantage [14; 15]. In the case of the ML-based models in CCP, several methods have been developed with relative success. This is due to the disproportionate structure of churn datasets which has a derogatory effect on the effectiveness of typical ML approaches in CCP [16; 17]. That is, it is critical to utilize clean and well-structured datasets in CCP as the performance of ML techniques is heavily reliant on the dataset's characteristics. In other words, the frequency of class labels in a dataset is crucial for developing effective ML models. In practice, the distribution of class labels is uneven and, in several instances, significantly biased. This intrinsic inclination is referred to as the class imbalance problem [18; 19]. The class imbalance issue happens when there is a significant disparity in the class labels (Majority and Minority). The inadequately distributed class labels make ML model generation difficult and, in most cases, inaccurate [20; 21].