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A full-stack Machine Learning application for predicting insurance premium categories using a trained ML model, served via FastAPI and consumed by a Streamlit frontend. Both backend and frontend are fully Dockerized and ready for cloud deployment (Render).
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Animated Fintech UI for pie showing portfolio performance, Investment income, Investment advisory, and smart advisory features through interactive financial visualization
A motion exploration for Pie, bringing key wealth-management features to life through animated UI components.
The concept explores how portfolio performance, investment income, investment advisory, and smart advisory can be presented in a clear and engaging way while keeping the overall experience simple and premium.
A look at how motion can make financial products feel more intuitive, modern, and easier to understand.
The motion-led charts make a dense wealth-management story feel approachable; the distinct panels give each metric room to breathe while still reading as one system.
Designed a dashboard concept for tracking net worth across every asset type — stocks, crypto, property, pensions, funds, cash, and liabilities — all in one place.
Instead of the usual pie chart or bar graph, the centerpiece is a dial: a single, glanceable gauge showing where your wealth actually sits. Behind it, a quiet grid of every asset category fades in and out of visibility, reinforcing that everything's accounted for even when it's not the focus.
Dark UI, a single confident green accent, and a lot of negative space — built to make a genuinely overwhelming task (seeing your entire financial picture at once) feel calm instead of cluttered.
wow the animation feels super smooth, what is this animation for?
just shipped the core of Skillship — an AI-powered LMS built for schools.
One platform, many schools, and one rule that shaped every decision: School A must never see School B's data. Tenant-scoped models, filtered querysets at the base class, UUIDs everywhere.
Four AI features live:
— Career Pilot: personalised career paths for students
— Question generator: chapter PDF in, board-aligned MCQs out
— Adaptive quizzes: difficulty follows actual performance
— Semantic search: teachers ask in plain English, pgvector finds the material
Django + DRF for the LMS core, a separate FastAPI service for all AI work (Gemini + a pgvector RAG pipeline), Next.js 14 on the front. Django brokers every AI call — the AI layer never touches the browser.
The hard part wasn't the AI. It was making multi-tenancy boring enough that no future dev can leak a school's data by accident.
Open for new work — full-stack builds, AI integrations, RAG pipelines, multi-tenant SaaS. Send me a project.
The tenant boundary is where I'd put the first failure test. I'd try to make a user from one school retrieve another school's record before I trusted any of the AI features.