CS 488/508 DATA MINING I AT NEW MEXICO STATE UNIVERSITY UNDER DR. HUIPING CAO 1
Abstract —This report details the preliminary motivation and description of the assignment in
CCS 488/508 DATA MINING Iatex New Mexico State University.
Index Terms —Computer Society, IEEEtran, journal, LATEX, Date Mining I, Scikit learn.
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Over the years, technology has adapted and become more included in our so- ciety. Video games being one of the things that have advanced technologically over the years and have increased in popular- ity. With the COVID pandemic over the last few years increasing the amount of time people spend inside, it accelerated the popularity of video games as most social activity came with a large risk. We seek to help address that new trend amongst both novice and veteran games by using data mining techniques to create a video game recommendation system. This system will attempt to analyze a player’s preference in their favored genre of video game and recommend them new ones based off of the data we are able to gather. We are basing
- A. Camacho is a student in the Computer Science depart- ment of New Mexico State University. E-mail: [email protected]
- M. Groover is a student in the Computer Science depart- ment of New Mexico State University. E-mail: [email protected]
- C. Mueller is a student in the Computer Science depart- ment of New Mexico State University. E-mail: [email protected] Manuscript received November 15, 2022; revision is scheduled for late November 2022.
the idea of our project off of an anime recommendation system that was found while researching online. We thought it would be an enjoyable and rewarding ex- perience as we not only work to practice our coding skills using data mining tech- niques but also get to implement some- thing all of our group members have in common / enjoy doing in our free time.
NMSU
November 1th, 2022
1.1 INTRODUCTION
This is an interesting real world project application as we are working with pref- erences and advertisement and recommen- dations to propose suggested games for a dedicated audience (gamers). This is appli- cable in the real world in numerous ways. For example, video game designers could use the data from our product to analyze what type / genre of video games their consumers are playing in relation to their product. In the case of data mining tech- niques, there are several techniques that we can use in order to understand how to better derive the useful information from an agglomeration of video game prefer- ences. Some of the data mining techniques
CS 488/508 DATA MINING IAT NEW MEXICO STATE UNIVERSITY UNDER DR. HUIPING CAO 2
we foresee being beneficial to our research and overall project would be prediction and classification. Prediction being used to recommend video game titles, genres etc. based off data collected from the user. Clas- sification would be used to classify and group each of the video game titles present in the system by genre. Individually each of our team members actively play video games and our knowledge of that will help understand how best to achieve reasonable results that can impact our user base.
For majority of our data, we will be pulling information about majority of well-known video games from the internet and be implementing that data into our system. Video games have been around for a long time and have only become more and more complex and advanced as time went on. Due to this, there are thousands of video game titles out there. We have recognized this may cause issues when trying to ac- count for all games in our system. For that reason, we have created potential adapta- tions in our project including only account for triple A games rather than all games including titles from indie development companies. The main motivation for our project is based on the fact that all of our group members have a shared pas- sion when it comes to gaming and playing video games in our free time. We all agreed it would be more enjoyable to be involved with a project that involved something we were all actively interested in.
1.5 Graphs �
Thank you to Dr. Huiping Cao, ——— ——-, and -of NMSU, as well as: IEEE and Overleaf and the LATEX community for making documentation and presets readily available to us during this paper.