Credit Risk Modeling System

This project demonstrates a complete end-to-end data science workflow for credit risk assessment. Starting with real-world financial data processing, the system applies machine learning techniques to evaluate credit risk. Key features include handling imbalanced datasets, model training with Scikit-learn, and an interactive web application for credit risk evaluation. The project serves as both a practical tool and a learning resource for data science beginners and interview preparation. The dataset used is the German Credit Data from Kaggle, and all values and predictions are derived from this dataset.