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.
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 Analysis and Recommendation system
A content-based recommender that suggests games by combining features into a soup feature. Uses TF-IDF to compute similarity and deliver personalized recommendations.
Game Recommendation Engine based on KRR principles
Game Recommender System is an AI-powered project developed as an assignment for TARUMT. It leverages machine learning techniques in Jupyter Notebook and Python to suggest video games tailored to user preferences.
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