Churn prediction in telecommunication industry using kernel Support Vector Machines

In this age of fierce competitions, customer retention is one of the most important tasks for many companies. Many previous works proposed models to predict customer churn based on various machine learning techniques. In this study, we proposed an advanced churn prediction model using kernel Support...

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Bibliographic Details
Published in:PloS one 2022-05, Vol.17 (5), p.e0267935-e0267935
Main Authors: Y, Nguyen Nhu, Ly, Tran Van, Son, Dao Vu Truong
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Analysis
Area codes
Churn rate
Communications industry
Computer and Information Sciences
Customer satisfaction
Customer services
Data collection
Datasets
Engineering and Technology
Evaluation
Feature selection
Kernel functions
Machine learning
Management
Oversampling
Physical Sciences
Population
Prediction models
Resampling
Research and Analysis Methods
Support vector machines
Telecommunications services industry
Variables
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Summary:In this age of fierce competitions, customer retention is one of the most important tasks for many companies. Many previous works proposed models to predict customer churn based on various machine learning techniques. In this study, we proposed an advanced churn prediction model using kernel Support Vector Machines (SVM) algorithm for a telecom company. Baseline SVM models were initially built to find out the most suitable kernel types and will be used to make comparison with other approaches. Dimension reduction strategies such as Sequential Forward Selection (SFS) and Sequential Backward Selection (SBS) were applied to the dataset to find out the most important features. Furthermore, resampling techniques to deal with imbalanced data such as Synthetic Minority Oversampling Technique Tomek Link (SMOTE Tomek) and Synthetic Minority Oversampling Technique ENN (SMOTE ENN) were used on the dataset. Using the above-mentioned techniques, we have obtained better results compared to those obtained from previous works, we achieved an F1-score and accuracy of 99% and 98.9% respectively.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0267935