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Reseach Article
Customer Churn Prediction using Machine Learning: A Case Study of E-commerce Data
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 186 - Number 48 |
| Year of Publication: 2024 |
| Authors: Jingyuan Li |
10.5120/ijca2024924140
|
Jingyuan Li . Customer Churn Prediction using Machine Learning: A Case Study of E-commerce Data. International Journal of Computer Applications. 186, 48 ( Nov 2024), 22-25. DOI=10.5120/ijca2024924140
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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