Journal of Computer Technology & Applications

Journal of Computer Technology & Applications Review Article

Customer Churn Prediction Using ML Algorithms

  1. Prajwal Waghole Computer Engineering, RMD School College of Engineering, Pune,
  2. Kalpna Saharan Computer Engineering, RMD School College of Engineering, Pune,
  3. Varad Wanwase Computer Engineering, RMD School College of Engineering, Pune,
  4. Shantanu Wasnik Computer Engineering, RMD School College of Engineering, Pune,
  5. Rajratan Gokhale Computer Engineering, RMD School College of Engineering, Pune,

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
  • customer churn
  • decision tree
  • random forest

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