Analysing Content Ratings of Google Apps with Ensemble Learning

Abstract

Google Play was launched under the name of Android Market and made its reputation known all over the world. The mobile application market, which is a package manager developed by Google for Android users, contains applications that appeal to many areas and age ranges. The wide area in which applications spread and the data flow, which has reached the level of being called “big data”, has started to attract the attention of researchers. The excessive increase in the number of applications makes it difficult for parents to follow up on the content. In order to provide content rating of applications on Google Play, it is needed to be classified by machine learning methods. In this study, content rating classification was made by analyzing “Category, Rating, Reviews, Size, Installs, Type, Genres, Last Updated, Current Version, Android Version” features of 10757 applications on Google Play, Ensemble Learning methods (Adaboost, Bagging, Random Forest, Stacking), Logistic Regression, Artificial Neural Network, K-Nearest Neighbors algorithms.