
Vinuka Buddhima developed a user-facing risk assessment feature for the Intelligent-Advisor-Sem-4 repository, delivering both frontend and backend components within a month. On the frontend, Vinuka built a Risk Assessment Quiz Page using React and TypeScript, enabling users to complete a structured quiz that calculates risk scores and informs investment profiles. The backend, implemented with Python and SQLAlchemy, introduced a RiskAnalysis data model to support analytics and scoring. Vinuka also improved code maintainability by cleaning up unused imports and refining database connection formatting. This work enhanced personalization, analytics readiness, and integration, aligning the product with data-driven investment guidance objectives.

May 2025 performance summary for Intelligent-Advisor-Sem-4: Delivered a user-facing risk assessment capability and supporting data model, while improving code quality and maintainability. The frontend introduced a Risk Assessment Quiz Page with scoring that informs investment profiles and results submission; the backend added a RiskAnalysis data model to enable scoring analytics; alongside a frontend code cleanup removing an unused Sparkles import. Minor backend formatting adjustments in the DB connection and main app imports were also applied to streamline integration. These efforts enhance personalization, analytics readiness, and product stability, aligning with business goals to increase user engagement and data-driven investment guidance.
May 2025 performance summary for Intelligent-Advisor-Sem-4: Delivered a user-facing risk assessment capability and supporting data model, while improving code quality and maintainability. The frontend introduced a Risk Assessment Quiz Page with scoring that informs investment profiles and results submission; the backend added a RiskAnalysis data model to enable scoring analytics; alongside a frontend code cleanup removing an unused Sparkles import. Minor backend formatting adjustments in the DB connection and main app imports were also applied to streamline integration. These efforts enhance personalization, analytics readiness, and product stability, aligning with business goals to increase user engagement and data-driven investment guidance.
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