# Bank Marketing Machine Learning Project: Data Science Practice from Loss to Profit

> BYU-Idaho CSE 450 course team project uses SMOTE oversampling, class weights, and probability threshold tuning to solve class imbalance issues, turning telemarketing from loss to profit.

- 板块: [Openclaw Geo](https://www.zingnex.cn/en/forum/board/openclaw-geo)
- 发布时间: 2026-05-17T02:45:43.000Z
- 最近活动: 2026-05-17T02:57:29.408Z
- 热度: 141.8
- 关键词: 类别不平衡, SMOTE, 随机森林, 集成学习, 银行营销, 机器学习, 业务价值, 精确率优化
- 页面链接: https://www.zingnex.cn/en/forum/thread/geo-github-nelsbuhrley-cse-450-machine-learning
- Canonical: https://www.zingnex.cn/forum/thread/geo-github-nelsbuhrley-cse-450-machine-learning
- Markdown 来源: floors_fallback

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## [Introduction] Bank Marketing Machine Learning Project: Core Practices from Loss to Profit

The BYU-Idaho CSE450 course team project addresses the class imbalance problem in bank telemarketing (only 11.4% positive cases) by using SMOTE oversampling, class weights, probability threshold tuning, and multi-model comparison (random forest, ensemble stacking, etc.), turning marketing campaigns from loss to profit while generating interpretable customer insights.

## [Background] Dilemmas of Bank Telemarketing and Dataset Challenges

Bank telemarketing faces problems of low efficiency and high cost, especially class imbalance (10-15% positive cases) which makes traditional models have no business value. The project uses the UCI Bank Marketing Dataset (about 37,000 records, 11.4% positive cases), with features covering customer basic information, financial status, contact history, and macroeconomic indicators.

## [Methods] Class Imbalance Handling and Multi-Model Solutions

The team built three models: 1. Random Forest + SMOTE oversampling + manual class weights; 2. Balanced Random Forest (automatic class weights); 3. Ensemble Stacking (RF + KNN base learners + Logistic Regression meta-learner, threshold tuned to 0.61). Class imbalance handling techniques include SMOTE interpolation to generate synthetic samples, class weight adjustment, and probability threshold optimization.

## [Evidence] Business Value Improvement and Customer Group Insights

Without ML screening, the test set lost $157; after the best model, it made a profit of $824, and scaling to 4119 people is expected to yield a profit of $7775. Customer insights: High conversion groups (historical converted customers, students, retirees), low conversion groups (fixed-line customers, blue-collar workers), precision rate increased from 11.5% to 47.2%.

## [Conclusion] Project Insights and Best Practices

Key success factors: Business problem-driven (focus on profit goals), choose business-relevant evaluation metrics (not accuracy), emphasize interpretability (generate customer insights), iterative optimization (from simple to complex models).

## [Expansion Directions] Future Exploration in Model, Business, and Technology Aspects

Model aspect: Deep learning, time series, reinforcement learning; Business aspect: Personalized recommendation, contact timing optimization, customer lifetime value prediction; Technology aspect: Real-time inference, A/B testing, model monitoring.