ID.

03 / Selected work

Projects with purpose.

A closer look at systems built across applied ML, backend APIs, and full-stack product development.

System map

From raw data to useful systems.

A visual map of the layers I work across: inputs, intelligence, and products people can use.

01

Inputs

Datasets · APIs · messy signals

02

Intelligence

Models · evaluation · iteration

03

Products

Dashboards · APIs · decisions

PythonSQLscikit-learnXGBoostAWSFlaskStreamlit
01

Applied machine learning

Student Grade Prediction System

Built an early-risk screening workflow from 30 start-of-term features, deliberately excluding prior grades. The app compares six classifiers, tunes Random Forest and XGBoost with GridSearchCV, and exposes prediction, ROC, model comparison, and feature-insight views.

Pythonscikit-learnXGBoostStreamlit
02

Backend API

String Analyzer API

Built and documented a Flask API that turns raw strings into reusable analysis records: palindrome detection, character frequency, hashing, word counts, filtered retrieval, and natural-language queries.

PythonFlaskSQLiteREST API
03

Full-stack web application

Shebamiles Employee Management System

Delivered a PHP and MySQL HR platform that brings authentication, role-based access, employee and department management, attendance, leave, payroll, and performance workflows into one operational system.

PHPMySQLJavaScriptHTML / CSS