An end-to-end classification Machine Learning project with open data, multiple models, rigorous validation, and a concise stakeholder-ready report with recommendations.
Machine Learning predicting diabetes risk from demographic and health factors.
Diabetes is a chronic condition that silently harms blood vessels and organs long before symptoms appear. By classifying diabetes risk from routine health data (age, BMI, blood glucose, HbA1c, etc.), we can warn earlier and prioritize proactive care. This leads to fewer complications, better quality of life, and lower healthcare costs.
By predicting the *diabetes* label from simple demographics, comorbidities and lab values, we surface high‑risk individuals for confirmatory testing and lifestyle/medical intervention. In practical terms, this enables targeted outreach, faster triage, and better resource planning in primary care.
In line with SDG 3 (Good Health & Well‑being) and SDG 10 (Reduced Inequalities), an early‑warning approach improves access to preventative care and reduces the burden of late‑stage complications.
Impact: catching diabetes early prevents avoidable hospitalizations and long‑term organ damage. Concretely, this means fewer acute admissions, fewer amputations, and better life expectancy.
The deliverables below include:
A Github link with jupyter notebook swith the full preprocessing and ML pipeline, the used dataset and the written report for stakeholders.
