Credit Risk Modeling
EDA plus five models (XGBoost, ANN, Random Forest, Decision Tree, SVM) with a Streamlit prediction app.
Architecture
Credit-risk modeling: EDA, five ML models, and a Streamlit app for live risk predictions.
Overview
A credit risk project from the Rakamin × ID/X Partners virtual internship.
After exploratory analysis I built and compared five models — XGBoost, an ANN, Random Forest, Decision Tree, and SVM — and shipped a Streamlit app for live predictions.
Highlights
- EDA + 5 models: XGBoost, ANN, Random Forest, Decision Tree, SVM
- Streamlit app for predictions
Context
Rakamin × ID/X Partners virtual internship
Tech stack
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