Worked on the Intelligent-Advisor-Sem-4 repository to deliver portfolio explainability features spanning both backend and frontend. Developed a FastAPI-based Portfolio Explainability API that leverages SHAP analysis and integrates with the Gemini API to generate human-readable explanations of portfolio performance, focusing on risk and return transparency. On the frontend, implemented a React and TypeScript feature to fetch and display these explanations, including animated loading states and robust error handling. Refactored code for maintainability, improved type safety, and added unit tests using pytest to ensure reliability of SHAP outputs and text generation. Emphasized scalable analytics and transparent financial insights throughout development.