<My projects>

{ World of Warcraft Addons }

Gaming has always been an interest of mine, as well as hobby coding. Those interests combined when I started experimenting with World of Warcraft's user interface.

What began as a small personal project — writing a Lua mod to customize the game's interface — quickly became a bridge to front-end development concepts. I was creating layouts, nesting UI components, handling user and system events, and retrieving real-time data through the Blizzard API. In many ways, it felt like building a lightweight web application inside the game. That experience not only enhanced my gameplay but also gave me a strong foundation in thinking like a front-end developer.

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Grind Goals

My first addon, Grind Goals, was designed to help players track obtaining in-game items more efficiently. It was built using Lua, the languag supported by WoW for addon development. It featured a very simple but user-friendly interface and the main challenge came from interacting with the game's API to fetch real-time data and storing this data between several characters using saved variables.

Grind Goals addon published on CurseForge Main interface of Grind Goals addon showing item tracking Grind Goals addon interface for selecting items to track Settings menu of Grind Goals addon Snippet of Lua code for Grind Goals addon interacting with WoW API
CurseForge CurseForge page GitHub GitHub page

{ Data science }

In 2020, I completed a Data Science course from Yandex, where I learned Python, Pandas, SQL, data analysis, and machine learning.

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Kaggle — House prices

As part of my practice, I joined a Kaggle competition to predict house prices based on 79 variables, ranging from the number of bedrooms to details like roof type and land slope. I built a pipeline to handle preprocessing and model training, experimenting with linear regression and gradient boosting. My solution ranked in the top 10% of the leaderboard and earned positive feedback from the Kaggle community.

Kaggle notebook showcasing data preprocessing and model training Visualization of missing values in the dataset Exploratory data analysis showing data distribution Pipeline structure for machine learning model training Model evaluation using root mean square error (RMSE)
My participation on kaggle