AI Engineer building intelligent systems for African markets —
from 5-agent fintech platformsto multilingual NLP for Nigerian languages.

Dr. Amina — AI Healthcare for Northern Nigeria

648 health documents · 80% confidence threshold · Hausa voice input

Built a medical RAG chatbot that serves Northern Nigeria's underserved communities. Powered by Google Gemini and Pinecone (648 verified health documents), it detects emergencies with 80% confidence, supports Hausa voice input, and persists conversations via Supabase. Deployed live on Streamlit for the DataFest Africa 2025 Hackathon.

Fighting Fraud with Machine Learning

1M+ transactions · 0.3% fraud rate · 38 engineered features

Built a production-grade fraud detection system on 1M+ interbank transactions with a 0.3% fraud rate — no synthetic balancing tricks. Engineered 38 behavioral features, trained interpretable models (Logistic Regression → Random Forest), and used SHAP to make every flagged transaction explainable to analysts and regulators.

ConfidenceAI — Live Coaching Platform

Live deployment · Real user sessions

Real-time AI confidence coaching with explainable scoring. Not generic motivational text — structured psychological frameworks delivered through Google Gemini with session memory. Pydantic validation ensures consistent, structured output every session.

AI-Powered Sentence Completion for Video Game Narratives

What happens when you train an NLP model on video game scripts and lore? It starts writing like one. Built a sentence completion engine using TF-IDF + Naive Bayes with emotion-aware generation — give it "the hero must" and it'll finish the thought in the right narrative tone. Hand-curated the dataset with emotional annotations and deployed a live Streamlit demo.

Digital Lending E-Sign Prediction System

Production-ready .pkl export · Real fintech workflow

Built an end-to-end ML system that predicts whether loan applicants will complete electronic signing — a real bottleneck in digital lending. Engineered features like income-to-loan ratios and composite risk scores, trained a deployable logistic regression model, and exported it as a .pkl file ready for production integration.

Sign Language Hand Gesture Recognition with CNN

99.75% accuracy · 25 ASL gestures

Trained a convolutional neural network to classify 25 American Sign Language hand gestures at 99.75% accuracy. Tackled overfitting head-on with dropout layers, data augmentation, and early stopping — then built a Flask web app so anyone can test it with their own images.

Bike-Sharing Optimization & Anomaly Detection

Used SQL to reverse-engineer a bike-sharing system's operations — predicting peak demand, spotting anomalies in rider counts and revenue, and finding the operational sweet spots where efficiency meets profitability. Built interactive Power BI dashboards so operations teams can actually see when and where to deploy bikes instead of guessing.

Market Basket Analysis & Collaborative Filtering

Explored what customers buy together and why — using market basket analysis to find purchasing patterns, then item-based collaborative filtering to generate personalized recommendations for the Bronze customer segment. The goal was turning raw transaction logs into "customers who bought X also bought Y" intelligence that a retail team can act on.

Data Predictive Insights ETL Pipeline

Built a full ETL pipeline that pulls powerlifting competition data, transforms it through Python, and loads it into PostgreSQL for analysis. Connected the whole thing to Power BI for interactive dashboards — turning raw Kaggle data into something a coach or athlete could actually use to spot trends in squat, bench, and deadlift performance.

Bank Marketing Campaign Prediction

Dug into bank marketing campaign data to figure out which customers are actually worth calling. Built predictive models that cut through demographics, financial history, and past campaign interactions to identify high-conversion prospects — so marketing teams stop wasting calls on people who were never going to say yes.

Factor Analysis & Linear Regression on Perfectionism Data

Applied exploratory factor analysis to a psychology dataset on perfectionism dimensions — reducing a complex web of survey responses into meaningful latent factors. Then used linear regression to model how those hidden factors predict real outcomes. The kind of statistical detective work that turns messy behavioral data into clear, actionable patterns.