Identifying the influence of various factor of apps on google play apps ratings

Google Play Store contains millions of Apps. These apps are downloaded and been used by millions and billions of users. Whenever a user browse or search for apps on Play store, a list of apps are shown to the user in which each app contains the app name along with its rating. Usually the user prefers to download highly rated apps because highly rated apps reflect users’ satisfaction. In order to gain high ratings, app developer uses different techniques and tweaks other than the app quality its self. Developers use attractive app titles, demanding icons, and other things to gain better ratings for their apps. However, there is no scientific approach to find the real impact of using attractive titles or any other such thing in order to gain higher ratings. Therefore, in this paper, we examine a number of factors of google play store apps and identify the influence of these factors using variable importance. For this purpose, real-world Google Play store apps dataset is used in this paper to identify the importance of these factors. For identification of important variables, Random Forest, Linear Regression Model and Support Vector Regression are used. The performance of the model is evaluated using standard performance evaluation techniques. The results show that some factors have higher significance and influence the app ratings. Moreover, keyword analysis has taken place to find the important words used in app title that results in higher and lower ratings.