Customer Churn Predictive Analysis by Component Minimization using Machine Learning
R. Suguna1, M. Shyamala Devi2, Rincy Merlin Mathew3
1R. Suguna, Professor, Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, (Tamil Nadu), India.
2M. Shyamala Devi, Associate Professor, Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, (Tamil Nadu), India.
3Rincy Merlin Mathew, Lecturer, Department of Computer Science, College of Science and Arts, Khamis Mushayt, King Khalid university, Abha, Asir, Saudi Arabia.
Manuscript received on 07 June 2019 | Revised Manuscript received on 13 June 2019 | Manuscript published on 30 June 2019 | PP: 2329-2333 | Volume-8 Issue-8, June 2019 | Retrieval Number: H7217068819/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Telecom industry faces crucial competition as new deals are introduced in market daily. Many operators are finding difficult to identify the potential customers with the drastic variations in offers of their competitors. Hence reducing the customer churn is the biggest challenge for telecom operators since the reason for churn is unknown. Due to volume of data, they are not able to predict the cause of customer churn. Appropriate machine learning algorithms help to understand why subscribers leave by finding the relationships between data. This paper applies classification algorithms to predict the behavior of customer retention on a telecom dataset extracted from Kaggle. The performance of the dataset after dimensionality reduction using PCA is also assessed. A comparative analysis on different classification algorithms are made based on the performance metric such as accuracy, precision, recall, log loss and f-score. The developed model performance is shown using ROC and AUC curves. Experimental results shows that after applying PCA, the kernel SVM is found to be effective with the accuracy of 95.5% compared to other classifiers.
Keywords: Machine Learning, Churn, Classification, Accuracy, Precision, Recall, Log Loss and f-Score.
Scope of the Article: Machine Learning