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.

XGBoost credit default prediction on 1.3M Lending Club loans | Full ML pipeline: EDA → feature engineering → SHAP → Optuna tuning → live Streamlit app | $17.9M simulated uplift per $1B portfolio

Predict probability of credit default using XGBoost with SHAP model interpretability.

Predicting credit defaults using Logistic Regression and an MLP built from scratch using DNN