This project implements a Game Recommender System designed to suggest games to users based on the games they recently played. By leveraging a correlation-based algorithm, the system identifies and recommends games that align with the user's preferences. The model achieves very high accuracy, making it a reliable tool for personalized recommendations.
- Gaming Platforms: Improve user engagement by suggesting games based on individual preferences.
- Game Developers: Promote lesser-known titles by targeting users likely to enjoy them.
- E-Commerce: Enhance game sales by recommending related titles during checkout or browsing.
- User Retention: Keep users engaged by providing relevant suggestions, reducing churn rates.
- Better Insights: Helps platforms understand gaming trends and user preferences.
Contributions are welcome! Feel free to fork the repository, suggest improvements, or report issues.