About
Entry-level Junior Machine Learning Engineer with proven hands-on experience in developing and deploying real-world AI systems, specifically for healthcare applications. Adept at leveraging supervised machine learning, data preprocessing, and model evaluation techniques to translate medical and symptom-based data into actionable predictions. Eager to contribute expertise in Python, Scikit-learn, and Streamlit-powered web application development to data-driven teams focused on healthcare, AI, and technology innovation.
Work
MedCare (Doceat) – AI Disease Prediction System
|Machine Learning Engineer (Personal Project)
Nigeria, Nigeria, Nigeria
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Summary
Designed and deployed an AI-powered disease prediction system to assist healthcare staff in assessing likely illnesses from early symptoms, improving hospital triage workflows.
Highlights
Generated a synthetic healthcare dataset of over 5,000 records and engineered features (age, gender, fever, cough, headache, body pain) using Label Encoding for multi-class prediction.
Trained and evaluated a Decision Tree Classifier, achieving high classification accuracy after dataset balancing and model tuning.
Successfully predicted multiple diseases, including Malaria, Flu, Typhoid, and Common Cold, demonstrating robust model performance.
Built a clean, multi-step Streamlit web application with improved UI/UX, enabling real-time disease prediction for healthcare screening use cases.
Delivered a scalable AI healthcare assistant, fully deployed on Streamlit Cloud, optimized for hospital triage workflows.
GlycoAID – AI Diabetes Risk Prediction System
|Machine Learning Engineer (Capstone Project)
Nigeria, Nigeria, Nigeria
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Summary
Led the development and deployment of an end-to-end supervised machine learning system to predict diabetes risk in real-time using patient diagnostic data, addressing the need for early detection in hospitals.
Highlights
Developed and deployed a supervised machine learning system, achieving ~77% prediction accuracy on unseen test data for early diabetes risk detection.
Processed real-world medical data, including handling invalid and missing values, and applied feature scaling with StandardScaler within an ML pipeline.
Trained and evaluated Logistic Regression and Decision Tree models, selecting the best performer based on accuracy metrics.
Integrated explainability features (feature importance, patient-vs-average comparison) and designed an interactive Streamlit dashboard with probability gauge visualization.
Delivered a production-ready clinical decision support tool, fully deployed and accessible via Streamlit Cloud, providing real-time predictions.
Education
University of Ibadan
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B.Sc.
Geology
Languages
English
Certificates
AI Fundamentals (Understanding AI, Understanding LLMs, AI Ethics, Prompt Engineering)
Issued By
DataCamp
AI/ML
Issued By
3MTT
Skills
Machine Learning & AI
Supervised Learning (Classification), Linear Regression, Logistic Regression, Decision Tree Classifier, Model Training & Evaluation, Feature Engineering & Data Cleaning, Model Comparison & Selection, Explainable AI (Feature Importance, Model Interpretation), Probability-based Risk Prediction, Unsupervised Learning (Clustering), Feature Scaling (StandardScaler), Data Splitting (Train/Test).
Software & Application Development
Machine Learning Pipelines, Model Serialization (Joblib), Web App Development with Streamlit, UI/UX Design for ML Applications, Real-time Prediction Systems, Dashboard & Data Visualization.
Data Analysis & Processing
Data Cleaning & Validation, Handling Missing & Invalid Values, Exploratory Data Analysis (EDA), Dataset Generation (Synthetic Data).
Deployment & Collaboration
Streamlit Cloud Deployment, GitHub Version Control, Reproducible ML Workflows, Project Documentation, Debugging & Error Resolution.
Programming & Libraries
Python, Pandas, NumPy, Keras, Scikit-learn.
Visualization
Matplotlib, Plotly.
Web & Deployment
Streamlit, Streamlit Cloud.
Model Management
Joblib.
Development Environment
Jupyter Notebook, VS Code / Anaconda, Antigravity, Claude-Code.
Version Control & Platforms
Git, GitHub.