Model Interpretability Dashboard Using SHAP and LIME for Credit Decisions

In lending and other regulated corners of finance, a model that cannot be explained cannot be shipped: risk officers, auditors, and regulators all expect a clear account of why a credit model scored an applicant the way it did. Data science teams need tooling that makes interpretability routine rather than a one-off notebook exercise. The intern builds a Streamlit dashboard where users upload a trained scikit-learn model together with a dataset, then explore global behavior through SHAP summary and feature importance plots and drill into individual predictions with local SHAP and LIME explanations. Pandas and NumPy handle the data wrangling behind the scenes, while the interface guides users from a dataset overview to per-row explanations and makes it easy to see where the two explanation techniques agree and where they diverge — a comparison that builds real judgment about when to trust each method. The project teaches the intern how modern explainability methods work mechanically, how to build analyst-facing tooling in pure Python, and how to communicate model behavior to non-technical stakeholders — a combination finance employers explicitly screen for when hiring into model risk and credit teams.