Application of SVM Classifier to model and analyse the Popularity of Games using Players feedback

Abstract

Prediction or forecasting is the technique of uncovering the forth coming event by learning and obtaining experience through data collected from historical happenings and results. Prediction is used in almost every field today be it retail, healthcare, finance, marketing, travel, insurance, telecommunications, pharmaceuticals, language processing, and other fields. Analytics can be based on the collected data and is commonly and broadly used for analyzing and extracting knowledge obtained from data collected through social inter-networking. Social media contains abundant amount of multifaceted information allowing users to evolve into successful content creators. Henceforth, they also eventually become the web content distributors. So, an online game exists, since only a few features are becoming popular and the other remaining items are not so popular. Prediction of popularity will be highly significant in inter-networking dimensions considering the properties of caching and replication. In this paper, based on the surveys obtained about games’ popularity methods and features that have decent forecasting capacity are utilized to develop an algorithm using support vector classification to predict the popularity of the game.