Data
- Python
- pandas
- NumPy
- SQL
- PostgreSQL
- MySQL
- Databricks SQL
AboutPrishtina, Kosovo
Turning ambiguity into
working systems.
I'm a computer science graduate focused on practical data, AI, and software engineering. I enjoy taking a messy source or unclear problem and making it validated, understandable, and useful.
How I work
“Good engineering makes the evidence easy to inspect—and the limitations hard to miss.”
My recent work includes public-data pipelines, machine-learning APIs, Streamlit analytics applications, customer segmentation, and probabilistic forecasting.
I care about reproducibility, explicit validation, and communicating what a model can and cannot support. That means keeping the baseline beside the result and treating deployment as part of the system.
Capabilities
Tools I use to deliver
Machine learning
- scikit-learn
- Random Forest
- Decision trees
- TensorFlow / Keras
- Model evaluation
Software
- FastAPI
- Pydantic
- REST APIs
- TypeScript
- Next.js
- Docker
- Git / GitHub
Delivery
- Streamlit
- Jupyter
- Validation
- Testing
- Reproducibility
- Deployment
Education
Learning, applied
- 2026Completed July 2026
B.Sc. Computer Science and Engineering
UBT · Prishtina
- 2026Applied data science programme
Data Science Bootcamp Certificate
Makerspace Innovation Center & CNM Ingenuity
- 2026Python, pandas, SQL, cleaning, visualization, and machine learning
11 Kaggle Learn certificates
Kaggle Learn
- 2024Practical Python automation
Automate the Boring Stuff with Python
Certificate
Principles
What I optimize for
- Traceable decisions—from source data through transformation to output.
- Failure-aware design—validation, fallback paths, and clear error states.
- Honest evaluation—baselines, uncertainty, leakage checks, and limitations.
- Useful delivery—interfaces and documentation another person can actually use.
Selected projects