Prediction of Customer Churn Using Machine Learning Classification Models

Year : 2025 | Volume : 16 | Issue : 02 | Page : 86-92
    By

    Nikita Khandelwal,

  • Vikas Sakalle,

  1. Research Scholar, Department of Computer Science and Engineering, LNCT University, Bhopal, Madhya Pradesh, India
  2. Associate Professor, Department of Computer Science and Engineering, LNCT University, Bhopal, Madhya Pradesh, India

Abstract

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Customer churn prediction is a critical task in both the telecommunication and medical industries, where retaining customers or patients is essential for ensuring long-term profitability and maintaining high-quality service. To address this, a range of machine learning models—including logistic regression, decision trees, random forests, gradient boosting machines, and support vector machines—were employed to accurately forecast churn behavior. Prior to model training, the dataset underwent thorough preprocessing, which included handling missing values, normalizing numerical features, and encoding categorical variables to prepare the data for analysis. Class imbalance, a common challenge in churn datasets, was mitigated using SMOTE (synthetic minority over-sampling technique), resulting in a more balanced dataset for effective model learning. Feature selection techniques such as recursive feature elimination (RFE) and mutual information were utilized to pinpoint the most influential predictors of churn, enhancing model interpretability and performance. The models were evaluated using comprehensive metrics including accuracy, precision, recall, F1-score, and the area under the receiver operating characteristic curve (AUC-ROC), offering a multifaceted view of each model’s effectiveness. Overall, the combination of robust preprocessing, balanced training data, and diverse evaluation metrics contributed to the development of reliable and generalizable churn prediction models.

Keywords: Customer churn, telecommunications, medical industry, machine learning, financial considerations, SMOTE (synthetic minority over-sampling technique)

[This article belongs to Journal of Computer Technology & Applications ]

How to cite this article:
Nikita Khandelwal, Vikas Sakalle. Prediction of Customer Churn Using Machine Learning Classification Models. Journal of Computer Technology & Applications. 2025; 16(02):86-92.
How to cite this URL:
Nikita Khandelwal, Vikas Sakalle. Prediction of Customer Churn Using Machine Learning Classification Models. Journal of Computer Technology & Applications. 2025; 16(02):86-92. Available from: https://journals.stmjournals.com/jocta/article=2025/view=0


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Regular Issue Subscription Review Article
Volume 16
Issue 02
Received 04/02/2025
Accepted 16/04/2025
Published 24/04/2025
Publication Time 79 Days
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