In this project, I've built a classification model to predict the probability of default value for a customer based on his credit history and deployed the same as a webapp in Heroku.
Logistic regression-based credit scoring model using public Kaggle data, designed for transparent PD estimation, performance evaluation, and teaching or regulatory use cases.
This project tackles the crucial challenge of assessing Credit Risk Management in banking. Using Supervised Machine learning, the goal is to predict the probability of default, providing insights into customers' creditworthiness by analyzing variables like account details, purchases, and delinquency information.
Probability of default using Machine Learning in R
This repository contains python code from scratch to develop the credit risk model for loan portfolio
This repository shows my credit risk analysis (ongoing!) using the dataset Give Me Some Credit (Kaggle, 2011)
This model estimates the 12-month Probability of Default (PD) for prime residential mortgage customers in the United Kingdom, aligned with the IFRS 9 impairment framework and calibrated to an adverse macroeconomic scenario. Version 1 (v1) is developed using gradient-boosted decision trees (GBDT)
Analysis of the effect of rising interest rates on the probability of default on consumer loans
Credit Risk Prediction in R
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