GAMEPICK
Game recommendation platform that analyzes user behavior to suggest personalized game choices.
Data Engineer
Built a data-driven game recommendation system using real user activity data.
Collaborators: Robert Hudák, Dávid Kepič, Jakub Kollár
GAMEPICK was developed at Hack Košice 2022 as a full-stack application that recommends video games based on user behavior and preferences. The platform processes user activity data to generate personalized recommendations. The backend uses Python, Pandas, and NumPy within a Jupyter Notebook environment to turn that data into inputs for the recommendation logic. One of the main challenges was working with unfamiliar tools such as Pandas and Jupyter in a time-constrained hackathon setting, as well as structuring the data. This required rapid learning and adaptation to data analysis workflows.
Contribution
Developed backend data processing pipelines using Python, Pandas, and NumPy, set up the Google Cloud environment, and worked with real-world user data to build the recommendation system.
