rizwanahmed786508/credit-scoring-system

An end-to-end credit risk prediction web application built with Python and Streamlit, featuring three machine learning models (Random Forest 93.34%, Decision Tree 89.09%, Logistic Regression 83.12%) trained on real-world financial data. Includes interactive risk assessment, feature importance analysis, and automated PDF report generation.

语言：Jupyter Notebook · GitHub 星标：4 · Fork：1

话题：classification · credit-risk · credit-risk-prediction · credit-scoring · data-science · feature-engineering · feature-engineering-ml · financial

在 GitHub 上查看原仓库