Backend developer from Nigeria. Building scalable products, exploring ML in public, and designing experiences that connect.
I'm Oluwasegun Isaac Adejuwon — a backend developer, ML engineer, and creative designer from Nigeria.
I build scalable backend systems, train ML models on real-world problems, and bring it all together with thoughtful design.
Part of the Creatives' Cohort community — sharing my journey openly.
Foundation in software engineering
Python, PHP, APIs, databases
Stress & diabetes prediction models
Adobe Suite, Figma, branding
Open to freelance, collaborations, and full-time backend or ML roles.
| Type | |
| Stack | |
| Status | ✅ Shipped |
| Year | 2024 |
A persistent e-commerce storefront built to replace WhatsApp status selling — giving sellers an always-on product listing, order management, and customer connection without technical overhead.
Visit Project →WhatsApp statuses disappear after 24 hours. Sellers had to re-upload products daily, wasting time and losing buyers in the process.
Sellers gained a permanent storefront replacing WhatsApp selling. Product discovery improved with always-on listings and a structured browsing experience.
| Type | |
| Stack | |
| Status | ✅ Model trained |
| Year | 2024 |
Predicts stress levels using physiological signals — heart rate, EDA, and skin temperature — with an Apple Watch integration concept for real-time wearable monitoring.
View Model →Real-time stress detection tools are expensive or clinical. There was a gap for an accessible, data-driven monitor built on everyday wearables.
Trained classification model for physiological stress detection, bridging ML research with wearable health tech via Apple Watch architecture.
| Type | |
| Stack | |
| Status | ✅ Deployed |
| Year | 2024 |
A comparative ML study evaluating SVM, XGBoost, Random Forest, and Logistic Regression for early diabetes detection — benchmarked on accuracy, precision, recall, and F1.
View Demo Streamlit → View Demo Render →Early diabetes detection saves lives but requires the right algorithm. A rigorous multi-model comparison was needed before deployment.
XGBoost achieved top accuracy and was prepared for deployment as an accessible early-detection tool.
| Type | |
| Stack | |
| Status | ✅ Live |
| Year | 2023–2024 |
Custom WordPress builds with backend and frontend integration for real clients — translating each brand into a polished, performant, and maintainable web presence.
Visit Site →Clients needed professional sites that reflected their brand without custom-code complexity — fast, maintainable, and on-brand.
Live, client-approved websites with custom integrations — delivered on time and within scope.
| Type | |
| Stack | |
| Status | ✅ In Development |
| Year | 2026 |
Visit Site →