EasyTechnologyfinance

Loan Default Risk Estimator With Random Forest and Interactive Inputs

Added Jun 2025 3 design docs

Before extending credit, lenders need a defensible estimate of default risk — and loan officers need to explore that risk interactively, asking how the picture changes if income were higher or the loan amount smaller. This project builds a tool for exactly that kind of what-if analysis.

The intern trains a random forest classifier with scikit-learn on loan applicant data, then builds a Streamlit interface where each input feature — income, loan amount, credit history, and so on — is controlled by a slider or selector. As users adjust the inputs, the app recomputes the default probability live and displays it with clear visual risk indicators, turning the model into an exploratory instrument rather than a static scorer. Feature importance charts show which factors the forest leans on most heavily, connecting the model's behavior back to intuitions about creditworthiness.

The project teaches ensemble tree models on tabular financial data, probability-based classification output, and the design of interfaces that let non-technical users interrogate a model safely. The intern demonstrates they can carry a finance use case from raw dataset to an interactive decision-support tool.

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