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LOAN FINANCIAL RISK ANALYSIS AND VISUALIZATION USING PYTHON
ISSN: 2582 - 9130
Publisher: author
LOAN FINANCIAL RISK ANALYSIS AND VISUALIZATION USING PYTHON
Indexed in
Computer Science and Technology Section
ARTICLE-FACTOR
1.3
Article Basics Score: 2
Article Transparency Score: 3
Article Operation Score: 3
Article Articles Score: 2
Article Accessibility Score: 2
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International Category Code (ICC):
ICC-0202
Publisher: Krishma Publication
International Journal Address (IAA):
IAA.ZONE/2582383989130
eISSN
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2582 - 9130
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Abstract
This project is made to compare the three gradient boosting algorithms in the loan risk analysis. Reason for using gradient boosting algorithm is that we have studied previous research on this topic in which different algorithms are compared and out of which gradient boosting algorithm seems to be standing out of all. So, it is beneficial to compare different gradient boosting algorithms and check which performs better and can be used for analysis in this problem. For that we spent major time on cleaning and manipulating the data in the form that we needed. Than after implementing the three algorithms we compared them on the basis of precision, recall and F1 scores and as a result we can see XGBoost seems to be performing better than LightGBM and CatBoost with precision more than 80%.. Keywords: Gradient Boosting, XGBoost, LightGBM, CatBoost, Machine learning
