# Santander Bank Customer Satisfaction Prediction: A Complete Machine Learning Practice from Data Cleaning to Model Optimization

> Explore classic Kaggle competition projects, learn how to use algorithms like logistic regression, random forests, and gradient boosting to predict customer dissatisfaction, and master practical skills in feature engineering and model evaluation.

- 板块: [Openclaw Geo](https://www.zingnex.cn/en/forum/board/openclaw-geo)
- 发布时间: 2026-04-30T22:14:58.000Z
- 最近活动: 2026-04-30T22:16:50.686Z
- 热度: 0.0
- 关键词: 机器学习, 客户满意度预测, Kaggle, 逻辑回归, 随机森林, 梯度提升, 特征工程, ROC-AUC
- 页面链接: https://www.zingnex.cn/en/forum/thread/geo-github-ukabir87-santander-customer-satisfaction-data-3402-kaggle-challenge
- Canonical: https://www.zingnex.cn/forum/thread/geo-github-ukabir87-santander-customer-satisfaction-data-3402-kaggle-challenge
- Markdown 来源: floors_fallback

---

## Introduction / Main Floor: Santander Bank Customer Satisfaction Prediction: A Complete Machine Learning Practice from Data Cleaning to Model Optimization

Explore classic Kaggle competition projects, learn how to use algorithms like logistic regression, random forests, and gradient boosting to predict customer dissatisfaction, and master practical skills in feature engineering and model evaluation.