Research Article
Customer Churn Prediction Using Machine Learning: A Case Study of E-commerce Data
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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| Volume 186 - Issue 48 |
| Published: November 2024 |
| Authors: Jingyuan Li |
10.5120/ijca2024924140
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Jingyuan Li . Customer Churn Prediction Using Machine Learning: A Case Study of E-commerce Data. International Journal of Computer Applications. 186, 48 (November 2024), 22-25. DOI=10.5120/ijca2024924140
@article{ 10.5120/ijca2024924140,
author = { Jingyuan Li },
title = { Customer Churn Prediction Using Machine Learning: A Case Study of E-commerce Data },
journal = { International Journal of Computer Applications },
year = { 2024 },
volume = { 186 },
number = { 48 },
pages = { 22-25 },
doi = { 10.5120/ijca2024924140 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2024
%A Jingyuan Li
%T Customer Churn Prediction Using Machine Learning: A Case Study of E-commerce Data%T
%J International Journal of Computer Applications
%V 186
%N 48
%P 22-25
%R 10.5120/ijca2024924140
%I Foundation of Computer Science (FCS), NY, USA
Abstract
In the highly competitive e-commerce industry, customer churn represents a major challenge to profitability and sustainability. This study aims to develop a robust predictive model for customer churn using a publicly available e-commerce dataset. The research leverages various machine learning algorithms, including Logistic Regression, Random Forest, XGBoost, and LightGBM, to compare performance. We address class imbalance with SMOTE and utilize SHAP and LIME for model interpretability. Our results demonstrate the effectiveness of the Random Forest model, achieving a ROC AUC of 0.9850. This study provides valuable insights into the factors driving customer churn, offering actionable recommendations for businesses to reduce churn rates and enhance customer retention strategies.
References
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