In this project, we developed a game recommendation engine based on Steam user data. This engine recommends games to users based on how much time they spent playing previous games and their similarity to other video game players.

Game Analysis and Recommendation system

Analyzed multi-year clinical trial data and developed a game recommender system using PySpark and collaborative filtering techniques.

The purpose of this project is to develop a recommender system based on content-based filtering in the Python programming language.

Game Recommendation Engine based on KRR principles