Summary of Bayesian Regression For Predicting Subscription to Bank Term Deposits in Direct Marketing Campaigns, by Muhammad Farhan Tanvir et al.
Bayesian Regression for Predicting Subscription to Bank Term Deposits in Direct Marketing Campaigns
by Muhammad Farhan Tanvir, Md Maruf Hossain, Md Asifuzzaman Jishan
First submitted to arxiv on: 28 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A medium-difficulty summary of the abstract: This research explores the effectiveness of logit and probit models in predicting term deposit subscriptions using a Portuguese bank’s direct marketing data. The study aims to examine how demographic, economic, and behavioral characteristics affect the probability of subscribing. To increase model performance and provide an unbiased evaluation, the target variable was balanced considering the inherent imbalance in the dataset. Bayesian techniques and Leave-One-Out Cross-Validation (LOO-CV) were used to evaluate the models’ prediction abilities. The logit model performed better than the probit model in handling this classification problem. This study highlights the importance of model selection when dealing with complex decision-making processes in the financial services industry and imbalanced datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A low-difficulty summary: This research helps banks understand why customers decide to subscribe to term deposits. It uses a big bank’s data to see which characteristics, like age or income, affect this decision. The researchers balanced the data to make sure their results were fair. They tested two types of models, logit and probit, to see which one did better at predicting subscription decisions. In the end, they found that logit was a better model for this task. This study shows how banks can use machine learning to make better decisions about who to target with marketing efforts. |
Keywords
» Artificial intelligence » Classification » Machine learning » Probability
