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Open Access
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Prajwal Waghole, Kalpna Saharan, Varad Wanwase, Shantanu Wasnik, Rajratan Gokhale,
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- Student, Assistant Professor, Student, Student, Student Computer Engineering, RMD School College of Engineering, Pune, Computer Engineering, RMD School College of Engineering, Pune, Computer Engineering, RMD School College of Engineering, Pune, Computer Engineering, RMD School College of Engineering, Pune, Computer Engineering, RMD School College of Engineering, Pune Maharashtra, Maharashtra, Maharashtra, Maharashtra, Maharashtra India, India, India, India, India
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Abstract
nComprehending customer churn is essential for businesses aiming to enhance and sustain customer relationships. This study introduces a machine learning approach aimed at forecasting customer churn by leveraging demographic and behavioral data. Our research involved developing predictive models using support vector machines (SVM), random forests, and decision trees, evaluating their efficacy using real-world data from the telecom industry. Our findings underscore that random forests consistently outperform SVM and decision trees in terms of accuracy, precision, recall, and F1 scores. This superiority indicates the robustness and adaptability of random forests across various data sets and conditions. The study emphasizes the potential of machine learning techniques in anticipating consumer behavior, offering valuable insights into customer churn dynamics. Furthermore, our model suggests several promising avenues for future research, such as exploring additional features or employing ensemble methods to further enhance predictive accuracy. By identifying at-risk customers early, businesses can proactively implement targeted retention strategies, thereby reducing churn rates and fostering long-term customer loyalty. In conclusion, our study provides actionable insights for businesses seeking to mitigate risks associated with customer churn. By leveraging machine learning, organizations can optimize customer retention efforts, ultimately fostering stronger, more sustainable customer relationships in competitive market environments.
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Keywords: Machine learning, SVM, random forest, Customer churn, Decision tree.
n[if 424 equals=”Regular Issue”][This article belongs to Journal of Computer Technology & Applications(jocta)]
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Browse Figures
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References
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- Wei CP, Chiu IT. Turning telecommunications call details to churn prediction: a data mining approach. Expert systems with applications. 2002 Aug 1;23(2):103-12.
- Lu J, Park O. Modeling customer lifetime value using survival analysis—an application in the telecommunications industry. Data Mining Techniques. 2003 Mar 30:120-8.
- Mohammadi G, Tavakkoli-Moghaddam R, Mohammadi M. Hierarchical neural regression models for customer churn prediction. Journal of Engineering. 2013;2013(1):543940.
- Mishra A, Reddy US. A comparative study of customer churn prediction in telecom industry using ensemble based classifiers. In2017 International conference on inventive computing and informatics (ICICI) 2017 Nov 23 (pp. 721-725). IEEE.
- Nigam B, Dugar H, Niranjanamurthy M. Effectual predicting telecom customer churn using deep neural network. Int J Eng Adv Technol (IJEAT). 2019 Jun;8(5).
- Pradeep B, Rao SV, Puranik SM, Hegde A. Analysis of customer churn prediction in logistic industry using machine learning. International Journal of Scientific and Research Publications. 2017 Nov;7(9):401-13.
- Hu X, Yang Y, Chen L, Zhu S. Research on a customer churn combination prediction model based on decision tree and neural network. In2020 IEEE 5th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA) 2020 Apr 10 (pp. 129-132). IEEE.
- Awang MK, Makhtar M, Rahman MN. Improving accuracy and performance of customer churn prediction using feature reduction algorithms. Journal of Telecommunication, Electronic and Computer Engineering (JTEC). 2017 Jun 1;9(2-3):127-30.
- Khan MR, Manoj J, Singh A, Blumenstock J. Behavioral modeling for churn prediction: Early indicators and accurate predictors of custom defection and loyalty. In2015 IEEE International Congress on Big Data 2015 Jun 27 (pp. 677-680). IEEE.
- Pagare R, Khare A. Churn prediction by finding most influential nodes in social network. In2016 International Conference on Computing, Analytics and Security Trends (CAST) 2016 Dec 19 (pp. 68-71). IEEE.
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Volume | 15 | |
[if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] | 02 | |
Received | May 28, 2024 | |
Accepted | July 23, 2024 | |
Published | August 1, 2024 |
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