Multi-output ML system for project risk prediction (classification + regression) inspired by MDPI research paper.

hack26 is a collaborative hackathon-style event focused on rapidly exploring and prototyping practical data and AI solutions against a defined set of challenges. Teams work within clear challenge boundaries to test ideas, build proof‑of‑concepts and share learning in a short, intensive format.

The team built Jim‑E, an interactive AI‑assisted risk review tool that applies SME heuristics to project risk entries. Using a lightweight Streamlit interface and encoded heuristic rules, the solution helps users identify weak risks and mitigations, capture structured feedback, and generate clear audit‑ready reports.

The team developed an AI-enabled risk management solution that integrates SME heuristics with automated evaluation to improve the quality and actionability of project risk registers. The approach combines Microsoft Power Platform components with LLM-driven analysis to identify weak risks and mitigations, prioritise critical issues, and support c...

The team produced a data‑driven risk heuristics analysis pipeline that combines Python analytics with large language model feedback to assess and enrich existing risk registers. Using Jupyter notebooks, they analyse risk and mitigation data, apply SME heuristics via an LLM, and output annotated spreadsheets and summary datasets designed for do...