Prajwal Waghole,
Kalpna Saharan,
Varad Wanwase,
Shantanu Wasnik,
Rajratan Gokhale,
- Student Computer Engineering, RMD School College of Engineering, Pune, Maharashtra India
- Assistant Professor Computer Engineering, RMD School College of Engineering, Pune, Maharashtra India
- Student Computer Engineering, RMD School College of Engineering, Pune, Maharashtra India
- Student Computer Engineering, RMD School College of Engineering, Pune, Maharashtra India
- Student Computer Engineering, RMD School College of Engineering, Pune, Maharashtra India
Abstract
Comprehending 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.
Keywords: Machine learning, SVM, random forest, customer churn, decision tree
[This article belongs to Journal of Computer Technology & Applications(jocta)]
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Journal of Computer Technology & Applications
| Volume | 15 |
| Issue | 02 |
| Received | May 28, 2024 |
| Accepted | July 23, 2024 |
| Published | August 1, 2024 |