Summary of Bank Loan Prediction Using Machine Learning Techniques, by F M Ahosanul Haque et al.
Bank Loan Prediction Using Machine Learning Techniques
by F M Ahosanul Haque, Md. Mahedi Hassan
First submitted to arxiv on: 11 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 This paper proposes a predictive modeling technique using machine learning algorithms to enhance loan approval processes in banks. By analyzing a dataset of 148,670 instances with 37 attributes, the authors apply various machine learning methods such as Decision Tree Categorization, AdaBoosting, Random Forest Classifier, SVM, and GaussianNB to predict bank loan approvals. The target property segregates loan applications into “Approved” and “Denied” groups. Among these models, AdaBoosting achieves an impressive accuracy of 99.99%. This work demonstrates the effectiveness of ensemble learning in improving loan prediction skills, providing valuable insights for applying machine learning to financial domains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research aims to help banks make better decisions when lending money by using computer algorithms. The team analyzed a huge dataset with many features and applied different types of learning methods to predict whether a loan will be approved or not. They found that one method, called AdaBoosting, was very accurate and worked well for this task. This study shows how machine learning can help banks make more informed decisions about lending. |
Keywords
» Artificial intelligence » Decision tree » Machine learning » Random forest
