MSc Dissertation on Credit Risk Modeling
-
Updated
May 15, 2018 - Python
MSc Dissertation on Credit Risk Modeling
Ranked 10/7198 (Top 1%) One of the models used in Kaggle Home Credit Default Risk.
A predictive model that uses several machine learning algorithms to predict the eligibility of loan applicants based on several factors
Model calculation with macroeconomic influence - estimate of debtor's probability of going default
Weight of Evidence,基于iv值最大思想求最优分箱
This app identifies customer default behavior using machine learning as a client server model. This app uses full-stack-starter-template repo to quickly build and deploy data science (or any other) application in cloud.
Credit Default Approximation for Unsecured Lending Built Machine Learning Classification models (Random Forest, LGBM, XGBoost) in Python to assess the probability of credit defaults.
In this project we try to predict home credit default risk for clients. We try to predict, if the client will have payment difficulties or not.
Source codes and plots for my paper "A Deep Learning Approach to Estimate Forward Default Intensities"
Open solution to the Home Credit Default Risk challenge 🏡
Reverse engineering of the FICO algorithm
Our underwriting python module for underwriting credit card accounts. For enterprise partners wanting to do their own underwriting in-house.
Tool demonstrating building credit risk models
在完成机器学习课程后,自己针对GBDT,XGboost等在反欺诈/反洗钱领域常用的模型再进行了自学所做出的结果,对课上的作业项目代码进行了进一步的提升和优化。
Data Analysis and prediction on Kaggle dataset: Credit Risk Dataset
Supporting material for the Open Risk Academy course: "Managing Loan Portfolios Using MongoDB"
Add a description, image, and links to the credit-risk topic page so that developers can more easily learn about it.
To associate your repository with the credit-risk topic, visit your repo's landing page and select "manage topics."