Customer Churn Prediction Using Machine Learning Methods: A Comparative Analysis
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Dosyalar
Tarih
2021
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Institute of Electrical and Electronics Engineers Inc.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Customer churn analysis is the process of predicting customers who tend to cancel the service (subscription) they receive for various reasons, especially in sectors such as telecommunications, finance and insurance, and determining the necessary operational steps to prevent this cancellation. The study used two separate datasets from kaggle.com to identify customers who tend to unsubscribe in the telecommunications industry. The analysis process was carried out by applying machine learning methods such as Logistic Regression, K-Nearest Neighbor, Decision Trees, Random Forest, Support Vector Machines, AdaBoost, Multi-Layer Sensors and Naive Bayes methods on the relevant datasets. It was seen that the most successful method in the customer loss analysis performed on both datasets was the Random Forest method. © 2021 IEEE
Açıklama
6th International Conference on Computer Science and Engineering, UBMK 2021 -- 15 September 2021 through 17 September 2021 -- 176826
Anahtar Kelimeler
Customer churn analysis, Machine learning, Telecommunication, Adaptive boosting, Decision trees, Logistic regression, Nearest neighbor search, Random forests, Support vector machines, Telecommunication industry, Analysis process, Churn analysis, Comparative analyzes, Customer churn analyse, Customer churn prediction, Customer churns, Logistics regressions, Machine learning methods, Nearest-neighbour, Telecommunications industry, Sales
Kaynak
Proceedings - 6th International Conference on Computer Science and Engineering, UBMK 2021
