ONUCHUKWU JOSEPH
CHIMEZIE
Welcome to My Portfolio
Exploring AI, ML & Data Science
Graduate AI & Data Science Researcher
Building Intelligent Systems · Python · Deep Learning · ML
About Me
AI and Data Science Researcher with applied depth
I am a data scientist and AI practitioner with a background in applied machine learning, statistical analysis, and data-driven decision making. My work spans deep learning for computer vision, supervised and unsupervised learning for behavioural prediction, and demographic data analysis for policy guidance.
What drives me is the space between a method working in theory and working in practice. I am drawn to problems where getting the modelling right has real consequences, whether that means a reliable damage classification for an insurance claim, a fair demographic analysis that informs public investment, or a churn prediction model that actually identifies the customers most at risk.
Featured Projects
Interactive gallery showcasing AI/ML projects that push the boundaries of what's possible
Vehicle Damage Classification with CNNs
Designed and trained a convolutional neural network to classify six types of vehicle damage from photographs, with systematic regularisation experiments and hyperparameter tuning.
Streaming Service Customer Behaviour Analysis
Built a comparative machine learning pipeline spanning regression, classification, and clustering to predict spending, churn, and customer segments in a 5,000-user streaming dataset.
Experience Timeline
My path in data science and AI research
MSc Data Science Student
University of Hull
2025 - 2026
Completed rigorous coursework in AI, machine learning, statistical analysis, and data visualisation. Built complete ML pipelines for computer vision, customer behaviour prediction, and demographic analysis.
AI & Data Science Practitioner
Independent Research
2024 - Present
Designed and trained CNNs for image classification, built comparative ML studies spanning regression, classification, and clustering, and produced evidence-based policy recommendations from census data analysis.
Tech Stack
Technologies and tools I use to build intelligent systems
Programming Languages
AI/ML Frameworks
Data & Visualization
Methods & Techniques
Research Interests
Areas I want to explore further in doctoral research
Applied Computer Vision
Image classification and object detection for practical domains like insurance, healthcare, and infrastructure monitoring. Interested in how models generalise beyond benchmark datasets.
Model Reliability & Evaluation
How do we know when a model is trustworthy? Rigorous evaluation frameworks, uncertainty quantification, and the gap between validation metrics and real-world performance.
Data-Driven Policy & Decision Support
Using statistical analysis and machine learning to inform decisions that affect people. Demographic modelling, resource allocation, and the responsibility that comes with turning data into recommendations.
Customer Behaviour Modelling
Predicting and understanding human behaviour through data. Churn prediction, spending patterns, and customer segmentation using both supervised and unsupervised approaches.
Responsible & Interpretable AI
Making AI systems that can be understood, audited, and trusted. Interested in interpretability methods, fairness in automated decision-making, and the social implications of deploying ML in high-stakes settings.
Let's Connect
Ready to collaborate on the next big AI innovation? Let's build something amazing together.
Send a Message
"Ready to process your ideas into reality!"