Predicting success of applications in Google Play store

Miha Štravs (2019) Predicting success of applications in Google Play store. EngD thesis.

Preview

PDF Download (399Kb)

Abstract

In times when almost everybody has a smartphone, a demand for mobile apps has risen. Because of the high demand, a lot of businesses and individuals have started to develop mobile apps. A big number of new mobile apps is being made available in the Google Play every day. Because of the number of different apps, only a few become popular and successful. In this thesis, we evaluate how well can we predict the success of mobile applications using the machine learning algorithms on data from Google Play. First, we overview the machine learning algorithms used for data streams. Then we describe the available data and derive the appropriate attributes. For prediction, we use already implemented methods from the MOA package: Naive Bayes, Hoeffding trees and IADEM trees are used. Methods are then tested by using a different amount of data and different prediction time lengths. The success is then measured using the classification accuracy, mean absolute error, relative mean absolute error and the F1 score for each class. The IADEM trees had the best scores from all the methods used.

Actions (login required)