Customer Churn Prediction Using Machine Learning Methods: A Comparative Analysis

Karamollaoglu, Hamdullah; Yücedağ, İ̇brahim; Doğru, İbrahim Alper

doi:10.1109/ubmk52708.2021.9558876

Cited by 7 publications

(2 citation statements)

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“…In paper [1], the analysis process was performed by applying machine learning techniques such as logistic regression, knearest neighbors, decision trees, random forests, SVM, Adaboost, multi-layered sensors, and naive Bayes method to the relevant datasets. We observed that random forest was the most successful method for both datasets considered.…”

Section: Literature Reviewmentioning

confidence: 99%

Churn Prediction in Banking Sector

Kamble¹,

Nair²,

Saini³

et al. 2023

IJSREM

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The membership of a banking system depends on the interest provided for the account , charges on the transactions and the services provided on the account. Now-a- days, many banks provide competitive services which give customers options to choose from. This might lead to customer dissatisfaction and result in the deactivation of the membership also known as churn. Churn is 'when a client cancels a membership to a company they have been using. In this project we will try to predict the churn from the previous databases of the banking systems and study it using data visualization. In current days, the customers are getting more attracted towards the quality of service (QoS) provided by the company. As a result, customer churn and engagement has become one of the main problems for most of the banks. In this project, we have used a voting approach of SVM and Random Forest to find the most successful and accurate way to predict customer churning or retention. Key Words: Churn, banking system, retention, deactivation, SVM, Random Forest.

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Section: Literature Reviewmentioning

confidence: 99%

Churn Prediction in Banking Sector

Kamble¹,

Nair²,

Saini³

et al. 2023

IJSREM

View full text Add to dashboard Cite

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“…Karamollaoglu et al 30 used to separate datasets for CP in the telecommunication industry. Eight ML models are explored, including LR, KNN, DT, RF, SVM, AdaBoost, NB, and multi-layer perceptron.…”

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confidence: 99%

An efficient churn prediction model using gradient boosting machine and metaheuristic optimization

AlShourbaji,

Helian,

Sun

et al. 2023

Sci Rep

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Customer churn remains a critical challenge in telecommunications, necessitating effective churn prediction (CP) methodologies. This paper introduces the Enhanced Gradient Boosting Model (EGBM), which uses a Support Vector Machine with a Radial Basis Function kernel (SVMRBF) as a base learner and exponential loss function to enhance the learning process of the GBM. The novel base learner significantly improves the initial classification performance of the traditional GBM and achieves enhanced performance in CP-EGBM after multiple boosting stages by utilizing state-of-the-art decision tree learners. Further, a modified version of Particle Swarm Optimization (PSO) using the consumption operator of the Artificial Ecosystem Optimization (AEO) method to prevent premature convergence of the PSO in the local optima is developed to tune the hyper-parameters of the CP-EGBM effectively. Seven open-source CP datasets are used to evaluate the performance of the developed CP-EGBM model using several quantitative evaluation metrics. The results showed that the CP-EGBM is significantly better than GBM and SVM models. Results are statistically validated using the Friedman ranking test. The proposed CP-EGBM is also compared with recently reported models in the literature. Comparative analysis with state-of-the-art models showcases CP-EGBM's promising improvements, making it a robust and effective solution for churn prediction in the telecommunications industry.

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