CreditScoringModel and Credit-Scorecard-for-Default-Prediction
About CreditScoringModel
AhsanAkhlaq/CreditScoringModel
A machine learning web application that predicts the likelihood of credit card default using a Random Forest model trained on the UCI Credit Card dataset. Built with FastAPI for the backend and a clean HTML/CSS frontend,Fully containerized with Docker for easy deployment and demo-ready for showcasing model predictions.
This tool helps financial institutions and credit risk analysts quickly assess the likelihood of a credit card applicant defaulting on future payments. You input an applicant's financial and demographic details, and it outputs a prediction of their credit default risk and a probability score. This is ideal for credit officers, risk managers, or anyone needing to make data-driven lending decisions.
About Credit-Scorecard-for-Default-Prediction
thaitri2005/Credit-Scorecard-for-Default-Prediction
A credit risk scorecard webapp that lets finance teams and analysts run Basel-compliant loan default predictions.
This web application helps finance teams and analysts quickly assess loan applicants' credit risk. You input financial data like income, interest rate, and loan purpose to receive an instant credit score, default probability, and an industry-standard credit rating (like AAA or BBB). It's designed for credit risk managers and financial analysts who need to make informed lending decisions.
Scores updated daily from GitHub, PyPI, and npm data. How scores work