Muhammad
Ahmed
Zia.
I'm a Computer Science student at COMSATS University Islamabad, Lahore Campus, with a strong focus on Data Science and Machine Learning. I follow the full data science pipeline — collecting and processing real-world datasets, training and evaluating ML models, then deploying them as accessible web applications.
My work spans Random Forest classifiers for health prediction, deep learning-based crop disease diagnosis using EfficientNet and multilingual BERT, and cloud-deployed Flutter apps containerised with Docker on Kubernetes.
I'm actively seeking an internship to deepen expertise in applied machine learning, data engineering, and intelligent system design. Every project I build is end-to-end — from idea and data collection, all the way to a live, deployed system.
My Approach
End-to-End Thinking
I don't stop at training a model. I build the API, the frontend, containerise it with Docker, and deploy it live.
Real-World Impact
Every project targets a real problem — from agricultural disease in Pakistan to lifestyle-based health prediction.
Benchmark Everything
I rigorously benchmark models (Random Forest vs Decision Tree vs Naive Bayes) using cross-validation before selecting the best.
Accessibility First
Building bilingual interfaces (Urdu & English) and rural-accessible UX so technology reaches those who need it most.
