Computer Science student at Lahore, Pakistan. I write Python to clean messy data, build ML pipelines, and engineer software systems that solve real problems.
I started my academic journey at Cadet College — a structured, discipline-first environment that shaped how I approach hard problems. When I entered Computer Science, that same mindset carried over: I don't just learn tools, I build things with them.
Over the past couple of years I've moved from C++ fundamentals and OOP systems to Python data pipelines, SQL databases, and machine learning workflows. I've built a production-ready ETL pipeline for retail analytics, developed ML models for medical classification tasks, and competed at the NASA Space Apps Challenge 2024 — earning Runner-Up recognition.
Right now I'm deepening my knowledge of machine learning, data engineering patterns, and backend systems. I'm looking for internship or junior roles where I can contribute real work and keep learning fast.
Runner-Up — international space data innovation challenge
Built ETL pipelines with Python, Pandas, and PostgreSQL
Medical ML project — anemia detection and classification
Available remotely — open to internships & collaborations
Military-style academics built the structured thinking I apply to engineering problems today.
Built traffic violation detection and smart city management systems using object-oriented design in C++.
Shifted primary stack to Python. Built the Retail Sales ETL Pipeline — data cleaning, transformation, and loading into PostgreSQL with interactive dashboards.
Developed ML models for anemia detection. Competed and earned Runner-Up at NASA Space Apps Challenge 2024.
Actively building, writing, and looking for opportunities to contribute at a professional level.
A few projects that reflect how I think and what I build.
Problem: Raw sales data from multiple sources is inconsistent and unusable for analysis.
Production-ready Python pipeline that extracts, validates, transforms, and loads retail data into PostgreSQL. Includes interactive dashboards built with Matplotlib and Seaborn to surface business insights.
View on GitHub → 🏆 Runner-Up
Problem: Develop an innovative solution addressing real-world space data challenges under competition conditions.
Competed in the international NASA Space Apps Challenge. Our team engineered a solution evaluated by NASA experts and was recognised as Runner-Up — a distinction that reflects both technical execution and product thinking.
View on LinkedIn →Problem: Automate early anemia detection from clinical data to assist medical screening.
End-to-end machine learning project covering dataset cleaning, feature engineering, model training and evaluation. Practiced the full ML workflow from raw data to a validated classifier.
View on GitHub →Problem: Build an interactive desktop instrument using nothing but Python.
Python GUI application with keyboard-to-sound mapping and real-time audio playback. Built to explore GUI frameworks and event-driven programming patterns in Python.
View on LinkedIn →OOP-based simulation automating traffic violation detection. Manages vehicles, sensors, and violations using class hierarchies and polymorphism in C++.
Full traffic management system: signal control, accident reporting, and system modeling through object-oriented design. Demonstrates complex class relationships.
Interactive data dashboards built with Matplotlib and Seaborn. Demonstrates visual storytelling, chart design, and analytical thinking.
Collection of shell scripts for system administration and workflow automation on Linux. Practical tools for real developer environments.
Smaller experiments, exercises, and learning projects live on GitHub. Always building something new.
github.com/aliizeb →Writing is how I solidify what I learn and give back to the community.
I'm open to internships, part-time roles, freelance projects, and collaborations. If you're working on something interesting in data, ML, or software engineering — reach out.