International Journal of Modern Education and Computer Science (IJMECS)
IJMECS Vol. 17, No. 1, 8 Feb. 2025
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Enhancing Churn Prediction through Advanced Machine Learning Techniques for Modern Education in Computer Science
PDF (647KB), PP.91-101
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Author(s)
Pankaj Hooda
1
Pooja Mittal
1
Bala Dhandayuthapani Veerasamy
2
Ruby Bhatt
3
Chatti Subba Lakshmi
4
Shoaib Kamal
5
Piyush Kumar Shukla
6,*
Index Terms
Modern Education, Computer Science, Customer Churn Analysis, Telecommunication, OKMSVM, KPCA, Hybrid Two Level SVM model, Integrated Churn Prediction and Customer Segmentation Framework
Abstract
Customer attrition is a major issue that affects the telecom industry as it reduces the company’s revenues and the overall customer base. Solving this problem involves the use of accurate prediction models that utilize CRM data and machine learning algorithms. Though several research papers have been written and published on CCP in the telecom industry, the existing models lack reliability and accuracy. The use of sophisticated data mining and machine learning techniques has been widely practised for improving predictive models. Churn prediction models that exist have their problems in terms of accuracy and errors. It is still important to develop more sophisticated models that can work well with large data and give accurate predictions. Therefore, this work aims to offer the OKMSVM model for multiclass cancer-type classification. The method applied for the dimensionality reduction pre-process is Kernel Principal Component Analysis (KPCA) and the feature selection pre-process is done using Ant Lion Optimization (ALO). This combination assists in improving the chance of the prediction and also the reduction of probable errors. The performance of the proposed OKMSVM model was compared with some of the most common churn prediction models such as HTLSVM, DNN, ICPCSF and other ML models. It was seen that the OKMSVM model outperformed other models with an accuracy of 91. 5%, an AUC of 85. Accurate, with a correlation coefficient of 0. 838. It further shows that this model is better than the current models in the market in estimating customer churn.
Cite This Paper
Pankaj Hooda, Pooja Mittal, Bala Dhandayuthapani Veerasamy, Ruby Bhatt, Chatti Subba Lakshmi, Shoaib Kamal, Piyush Kumar Shukla, "Enhancing Churn Prediction through Advanced Machine Learning Techniques for Modern Education in Computer Science", International Journal of Modern Education and Computer Science(IJMECS), Vol.17, No.1, pp. 91-101, 2025. DOI:10.5815/ijmecs.2025.01.07
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