Machine-Learning-Repo-for-Credit-Decisioning

Introduction

This is a machine learning project for credit decisioning for banks or other financial institutions and in this project, we will use machine learning models for classification.

Dataset from: https://www.kaggle.com/code/bhavanavolati/1-credrisk-preprocessing/

Authors and collaboration-related issues

We both major in MSc Financial Engineering of the University of Glasgow🥇, so if you are one of the students from this university ot you are interested in this project and want to provide some suggestions or want to join us, please do not hesitate to send email to Hezhengjie2023@gmail.com or wangyiqiaonn@outlook.com, or you can set up an issue here, we are open to discussions! Many thanks!👍:)

References

References from Github：

No.1: https://github.com/SannketNikam/Credit-Risk-Analysis.git (Procedure of data analysis and processing) [Xgboost]

No.2: https://github.com/R-Stalker/xgboost-python/blob/master/train_xgboost_model.py (Procedure of tuning hyperparameters) [Xgboost]

No.3: https://github.com/dataworkshop/xgboost.git (The comprehensive tutorial of xgboost) [Xgboost]

No.4: https://github.com/metro8116/ML2025-Financial-Risk-Prediction.git (The algorithm of xgboost) [Xgboost]

References from Zhihu:

No.4: https://zhuanlan.zhihu.com/p/1909251307363078414 (Includes model parameters, parameter adjustment priorities, hyperparameter tuning, and enhancing model interpretability) [Xgboost]

References from other resources:

No.5: https://www.bilibili.com/opus/853472781286768661?from=search (Includes different parts of xgboost model conducted by R) [Xgboost]