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Noka993/README.md

Hey πŸ‘‹ What's up?

✨ My name is MiΕ‚osz and I'm an Information Technology student at Warsaw University of Life Sciences (SGGW).

🎯 I'm passionate about programming, data analysis, backend development, and DevOps.

Technologies & Skills

Python Flask FastAPI SQLAlchemy Pydantic Pytest Pandas NumPy Matplotlib Seaborn scikit-learn HTML CSS JavaScript React Next.js Git GitHub Docker Redis Ubuntu Cloudflare Bash PowerShell CI/CD PostgreSQL MS SQL Server MySQL SQLite

Projects

A curated list of my recent software development projects – covering APIs, full-stack applications, and data science pipelines. Each one emphasizes clean architecture, good practices, and maintainability.


REST API built with FastAPI, PostgreSQL, and SQLAlchemy ORM, supporting JWT-based user authentication and full CRUD operations on posts.

  • βœ… Unit tests with pytest
  • 🧬 DB migrations via Alembic
  • 🐳 Dockerized & containerized
  • πŸ” CI/CD with GitHub Actions

πŸ“¦ Deployed on a local Ubuntu Server (VM).


πŸ”Ή Recook

Recipe-sharing app with:

  • 🍳 Flask REST API with SQLite and SQLAlchemy
  • βš›οΈ React frontend
  • πŸ” JWT-based authentication
  • πŸ§‚ CRUD for recipes, ingredients, and favorites

Aims to offer personalized recipe recommendations in future iterations.


A set of academic web scrapers built with Scrapy and Playwright:

  • πŸ•΅οΈβ€β™‚οΈ PW Scraper
    Collects structured data from the Warsaw University of Technology:

    • πŸ‘¨β€πŸ« Scientists’ profiles, 🧾 publications, πŸ›οΈ organizational structure
    • 🧠 Smart pipelines with PostgreSQL integration
    • πŸͺ„ Handles JavaScript-rendered pages via Playwright
  • πŸ“° Punktoza Scraper
    Scrapes journal data from punktoza.pl:

    • πŸ“ˆ Impact Factor points, journal titles, publishers
    • πŸ—ƒοΈ Stores data in a PostgreSQL database
    • 🀝 Developed collaboratively during academic work

Useful for academic data aggregation, analytics, or research management tools.


🧠 A machine learning pipeline to classify mobile phones into 4 price categories based on specs.

  • πŸ” Models: Logistic Regression, LDA, Random Forest, Voting Ensemble (XGBoost, KNN, etc.)
  • πŸ“Š Visualizations with SHAP & stats
  • πŸ“ˆ Best Accuracy: 91% (Random Forest)
  • πŸ§ͺ Fully modular codebase with experiment-ready scripts

πŸ“„ Includes a detailed PDF Report (Polish).


Full-stack contact manager built with:

  • πŸ–₯️ Frontend: React + Vite + CSS
  • βš™οΈ Backend: Flask API + SQLAlchemy + SQLite
  • πŸ”„ Full CRUD: create, edit, delete contacts
  • πŸ“¬ Real-time updates via frontend state

Simple and effective UI with modal forms and error handling. Clean separation of concerns and CORS support.


Feel free to explore the repositories for source code, setup instructions, and project documentation!

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