Credit Risk Prediction Model for Loan Applications

This project focuses on developing a robust Credit Risk Prediction Model, leveraging machine learning algorithms to assess the likelihood of loan default based on financial and demographic data. The solution aims to streamline the loan approval process by categorizing applicants into different risk groups (Poor, Average, Good, Excellent) and providing actionable insights for loan officers to make informed, data-driven decisions.

The model integrates exploratory data analysis (EDA), feature engineering, model training, and optimization techniques to improve prediction accuracy. A user-friendly Streamlit application is developed to deploy this model, providing an interactive interface for real-time credit risk assessment.