Churn prediction using optimized deep learning classifier on huge telecom data

Garimella, Bharathi; Prasad, G. V. S. N. R. V.; Prasad, M. H. M. Krishna

doi:10.1007/s12652-021-03413-4

Cited by 13 publications

(8 citation statements)

References 29 publications

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“…In terms of deep learning methods, Almufadi and others have explored telecom user churn prediction, focusing on the performance of convolutional neural networks, recurrent neural networks, and deep neural networks [15]. Garimella and others have researched the performance of deep convolutional neural networks across multiple datasets [16], while Chouiekh and others have built models using deep convolutional neural networks for predicting telecom user churn [17]. Looking at improvements to solutions, researchers have explored from the angles of analysis, feature selection, and model construction.…”

Section: Related Workmentioning

confidence: 99%

Enhancing Game Customer Churn Prediction with a Stacked Ensemble Learning Model

Guo,

Xiong,

Zhang

et al. 2024

Preprint

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With the rapid growth of mobile internet and smart devices, China's mobile game market has become highly competitive. Game companies face increasing costs in promoting games and acquiring new customers. In response, this paper proposes a stack-based model for game customer churn prediction. This model utilizes a comprehensive set of user features, including static, dynamic, and social network data extracted from game logs. To correct the imbalance between positive and negative examples in our dataset, we applied the Neighborhood Cleaning Rule (NCL). We then constructed the model using a stacking approach that integrates multiple machine learning models into a cohesive binary classification framework. This method systematically predicts customer churn by processing these features. Our approach was rigorously tested on a large-scale real-world dataset, comparing it against nine standard baseline methods. The results reveal that the stack-based model significantly outperforms these methods, achieving a prediction accuracy of 94%.

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Section: Related Workmentioning

confidence: 99%

Enhancing Game Customer Churn Prediction with a Stacked Ensemble Learning Model

Guo,

Xiong,

Zhang

et al. 2024

Preprint

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“…Tariq et al [15], Lalwani et al [16], and Garimella et al [17] used the same dataset to predict the churn of telecom customers. [15] used CNN, and [17] used Deep CNN.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Tariq et al [15], Lalwani et al [16], and Garimella et al [17] used the same dataset to predict the churn of telecom customers. [15] used CNN, and [17] used Deep CNN. In contrast, [16] used several machine learning techniques such as AdaBoost, Support Vector Machine (SVM), Logistic Regression (LR), Naïve Bayes (NB), Random Forest (RF), Decision Trees (DT), and XGBoost.…”

Section: Literature Reviewmentioning

confidence: 99%

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Customer Churn Prediction Using Ordinary Artificial Neural Network and Convolutional Neural Network Algorithms: A Comparative Performance Assessment

Seymen

Ölmez

Doğan

et al. 2023

Gazi University Journal of Science

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Churn studies have been used for many years to increase profitability as well as to make customer-company relations sustainable. Ordinary artificial neural network (ANN) and convolution neural network (CNN) are widely used in churn analysis due to their ability to process large amounts of customer data. In this study, an ANN and a CNN model are proposed to predict whether customers in the retail industry will churn in the future. The models we propose were compared with many machine learning methods that are frequently used in churn prediction studies. The results of the models were compared via accuracy classification tools, which are precision, recall, and AUC. The results showed that the CNN model produced a 97.62% of accuracy rate which resulted in a better classification and prediction success than other compared models

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“…In addition, frameworks can be used to examine data from other perspectives, such as gendered urban mobility like in Gauvin et al (2020); to extract other parameters like forensic analysis as in Abba et al (2019) or the activities of the base stations in a mobile cellular network (Jiang et al 2020). Recently, many machine learning techniques are included in frameworks oriented to analyze and classify the information of a CDR (Sultan et al 2019) or to apply it to concrete areas like churn prediction (Ahmad et al 2019;Garimella et al 2021).…”

Section: Introductionmentioning

confidence: 99%

Orthogonal projection for anomaly detection in networking datasets

Cortés-Polo

Jiménez

Paoletti

et al. 2023

J Ambient Intell Human Comput

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In recent years, the impressive growth of new wireless technologies, together with the appearance of new requirements in applications and services, is progressively changing the use of networks. Due to the high mobility required, the network must adapt to the infrastructure to meet the demands of the users. As a result, service providers currently have to over-provision network capacity, which is costly. In addition, considering efficient resource planning in advance involves a lot of labor-intensive efforts. Consequently, network usage analysis is a very useful tool that allows network administrators to find patterns and anomalies. Whilst pattern detection provides administrators the ability to define the infrastructure, anomaly detection provides rich and valuable information for certain applications, for example, to avoid network saturation in urban areas during peak hours. This article proposes a new methodology based on orthogonal projections over Call Detail Records (CDR) for anomaly detection to help in the dynamic management of the network in an urban area. The method is evaluated in a real scenario provided by an Italian telecommunications operator, considering different locations in the Milan metropolitan area, differentiated by the geographic resolution of the data, reaching F1 scores above 0.8. In addition, a new ground truth is presented, hoping it will become a reference data set for the community, in the form of a set of locations that have been corroborated for use in evaluating anomaly detection techniques.

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Churn prediction using optimized deep learning classifier on huge telecom data

Cited by 13 publications

References 29 publications

Enhancing Game Customer Churn Prediction with a Stacked Ensemble Learning Model

Enhancing Game Customer Churn Prediction with a Stacked Ensemble Learning Model

Customer Churn Prediction Using Ordinary Artificial Neural Network and Convolutional Neural Network Algorithms: A Comparative Performance Assessment

Orthogonal projection for anomaly detection in networking datasets

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