Analysis of Customer Churn Prediction in Telecom Sector Using CART Algorithm

Rai, Sandeep; Khandelwal, Nikita; Boghey, Rajesh

doi:10.1007/978-981-15-0029-9_36

Cited by 10 publications

(5 citation statements)

References 10 publications

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“…According to the correlations, the predicted feature selection algorithm outperforms the current best practices in terms of predicting user attrition. The CART algorithm has been proposed for predicting consumer churn in the telecom market [24]. It has been a hot topic in the telecoms business in recent years to study customer attrition.…”

Section: Eai Endorsed Transactions On Mobile Communications and Appli...mentioning

confidence: 99%

A Churn Prediction System for Telecommunication Company Using Random Forest and Convolution Neural Network Algorithms

Abdulsalam

Ajao

Balogun

et al. 2022

EAI Endorsed Trans Mob Com Appl

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INTRODUCTION: Customer churn is a severe problem of migrating from one service provider to another. Due to the direct influence on the company's sales, companies are attempting to promote strategies to identify the churn of prospective consumers. Hence it is necessary to examine issues that influence customer churn to yield effective solutions to minimize churn. OBJECTIVES: The major purpose of this work is to create a model of churn prediction that assists telecom operatives to envisage clients that are more probably to be prone to churn. METHODS: The experimental strategy for this study leverages the machine learning techniques on the telecom churn dataset, employing an improved Relief-F feature selection algorithm to extract related features from the enormous dataset. RESULTS: The result demonstrates that CNN has a high prediction capability of 94 percent compared to the 91 percent Random Forest classifier. CONCLUSION: The results are of enormous relevance to the telecommunication business in improving churners and loyal clients.

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Section: Eai Endorsed Transactions On Mobile Communications and Appli...mentioning

confidence: 99%

A Churn Prediction System for Telecommunication Company Using Random Forest and Convolution Neural Network Algorithms

Abdulsalam

Ajao

Balogun

et al. 2022

EAI Endorsed Trans Mob Com Appl

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“…On the other hand, due to the nature of the dependent variable was categorical, a non-parametric method, the classification trees was performed. There are similar studies that use and compare both techniques for othe real World problems such as churn analysis and disease and evapotranspiration prediction [34][35][36]. In order to demonstrate the performance success of both techniques, the validity of the models was tested by 5-fold cross validation.…”

Section: Applicationmentioning

confidence: 99%

Mining Housing Features to Classify Housing Unit Price

Kılınç

MİRGEN²

2022

Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi

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In data mining, classification builds an interdisciplinary field upon from statistics, computer science, mathematics and many other disciplines. There are numerous statistical applications where parametric and non-parametric methods are frequently used to train data to estimate mapping function. In this study, two of the most widely used techniques are applied to a real dataset. The goal of the study is to compare the classification success of ordinal logistic regression and the classification trees and to predict a categorical response. For this purpose, the potential factors affecting the housing unit price for sale as being the dependent variable with three classes in Eskişehir were examined. The real data set was split into three as train, validation and test groups. The classification performance of the techniques was demonstrated with 5-fold cross validation technique. According to the results, a more successful classification was made with the classification trees algorithm.

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“…To further prove the effectiveness of the algorithm proposed in this paper in telecom customer churn prediction, this paper compares the IBA-ELM algorithm with the customer churn prediction methods proposed in literatures [43][44][45]. This paper used three indexes, namely, recall, Accuracy (ACC), and F1, to evaluate the performance of the two models.…”

Section: Contract (V15)mentioning

confidence: 99%

“…In this paper, 10-fold cross-validation was used to test the IBA-ELM model and the prediction model proposed in literatures [43][44][45]. The results of model performance comparison are shown in Tables 12 and 13.…”

Section: Contract (V15)mentioning

confidence: 99%

An early warning model for customer churn prediction in telecommunication sector based on improved bat algorithm to optimize ELM

Chun

Liu

et al. 2021

Int J Intell Syst

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The competition in the telecommunications industry is mainly reflected in the competition for the number of key customers. Therefore, how to effectively prevent the loss of large customers is a problem that many telecommunications companies are very concerned about. This paper establishes a customer churn prediction model based on the improved bat algorithm (IBA) optimized extreme learning machine (ELM), with the purpose of discovering potential churn customers in time and taking measures to retain them in advance. This paper uses the IBA to optimize the initial random weights of the ELM, so as to improve the prediction accuracy of the ELM. To overcome the shortcomings of the bat algorithm (BA) that the convergence speed is too slow in the early stage and difficult to converge in the later stage, this paper introduces the inertia weight into the speed update formula. To improve the population diversity and local search ability of the BA, this paper adds a chaotic local search method to balance the global search ability of the BA in the early stage. To control the search range of the BAs in the later stage, the lane flight formula is introduced into the position update formula to speed up the convergence speed of the algorithm. Then according to the result of function optimization, it can be seen that IBA has a significant improvement in convergence speed and accuracy. Finally, IBA was used to optimize ELM, and a customer churn prediction model was established. The empirical results show that the predictive model proposed in this paper can effectively identify lost customers and lay a foundation for improving the competitiveness of the telecommunications industry in the future.

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Analysis of Customer Churn Prediction in Telecom Sector Using CART Algorithm

Cited by 10 publications

References 10 publications

A Churn Prediction System for Telecommunication Company Using Random Forest and Convolution Neural Network Algorithms

A Churn Prediction System for Telecommunication Company Using Random Forest and Convolution Neural Network Algorithms

Mining Housing Features to Classify Housing Unit Price

An early warning model for customer churn prediction in telecommunication sector based on improved bat algorithm to optimize ELM

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