The Problem
Students and early-career professionals make high-stakes career decisions with limited, generic guidance. Existing tools offer static assessments or broad job-board recommendations — neither is personalised to a person's actual skills, interests, and trajectory.
What Was Built
A Python-based platform that collects structured input on education, work history, interests, and self-assessed strengths, then uses machine learning models to surface career paths that are statistically likely to be a good fit. The backend is FastAPI. The core model is trained on curated career progression datasets and refined iteratively.
This project is in active development. The goal is to move beyond generic career quizzes and build something that provides genuinely useful, data-driven guidance — especially for people without access to professional career counselling.
The platform ingests structured data about a person’s background and uses ML classification to recommend career paths, with confidence scores and reasoning the user can interrogate.
Outcomes & Learnings
- Initial model achieves meaningful separation between well-matched and poorly-matched career paths in evaluation
- FastAPI backend designed for future integration with a web or mobile frontend
- Data pipeline established for ongoing training as more profiles are collected