APPLICATION SUCCESS PREDICTION AND ANALYSIS TOOL

Abstract

A machinery for downloading and extracting features about applications from
the Google Play Store was developed and deployed, and the resulting data set was used
to train three di erent models to predict the success of a mobile application; a na ve
bayes based text classi er for the de-scription of the application, a generalized linear
model which categorizes applications as successful or not, and a linear regression which
predicts the average rating of the application. The performance of the models is not su
cient to justify their use in driving investments in new applications, however interesting
observations about the ecosystem, such as the current trend in photo sharing
applications, are elucidated.