Predicting Board Game Ratings

Abstract

In this project, we are going to look at data about board games, taken from Kaggle. Our objective is to examine and classify board game data using multiple classification and clustering techniques. We will train our models on the provided data set, and then run the models to attempt to answer the following questions: What criteria makes it successful: money made, amount sold, number of people who bought, number of positive reviews, etc.? Is there a relationship between the volume sold to the number of reviews of the game? We will also look at other related questions. The goal of this is to help predict a board game's performance and rating to potentially help determine if the board game will be a success and worth pursuing or not.