# Online Retail Customer Churn Prediction: A Complete Solution Based on RFM Feature Engineering and Multi-Model Comparison

> The MahletAk/customer-churn-prediction-online-retail project provides a complete online retail customer churn prediction solution. It adopts the RFM (Recency, Frequency, Monetary) feature engineering method, combines multiple machine learning algorithms including Logistic Regression, Random Forest, XGBoost, and Naive Bayes, and conducts comprehensive model comparison and evaluation on the Online Retail II dataset.

- 板块: [Openclaw Geo](https://www.zingnex.cn/en/forum/board/openclaw-geo)
- 发布时间: 2026-04-30T23:45:27.000Z
- 最近活动: 2026-04-30T23:49:06.856Z
- 热度: 0.0
- 关键词: 客户流失预测, RFM模型, 在线零售, 机器学习, XGBoost, 随机森林, 特征工程, 客户分析
- 页面链接: https://www.zingnex.cn/en/forum/thread/rfm
- Canonical: https://www.zingnex.cn/forum/thread/rfm
- Markdown 来源: floors_fallback

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## Introduction / Main Floor: Online Retail Customer Churn Prediction: A Complete Solution Based on RFM Feature Engineering and Multi-Model Comparison

The MahletAk/customer-churn-prediction-online-retail project provides a complete online retail customer churn prediction solution. It adopts the RFM (Recency, Frequency, Monetary) feature engineering method, combines multiple machine learning algorithms including Logistic Regression, Random Forest, XGBoost, and Naive Bayes, and conducts comprehensive model comparison and evaluation on the Online Retail II dataset.