README for the big data project of team 5DBMinds
CSE-4990/6990 - Big Data and Data Science
Project Name: "Predicting PlayStore Rating for Apps"
Team 3: 5DBminds
Team Members (Alphabetically): Ayush Raj Aryal Lucas Andrade Ribeiro Naila Bushra Naresh Adhikari
Abstract: Google Play Store is the most popular hub for android applications. With the increasing number of applications being uploaded in the store everyday, users prefer to download apps based upon their previous 'Ratings'. The higher the ratings, it could be assumed that the application is more reliable and popular. Being in a competitive world of building android applications, it is the goal of all developers to maximize or increase the ratings of their applications. We have developed a system which would help developers to predict the rating of their newly uploaded applications based on their 'Attributes'. We have retrieved a public data set of existing play store applications till August 2015. Several attributes of these applications like applications size, name, category, description, price etc have been used to build a training model that will be able to predict ratings for new applications. To build this training model we have used standard machine learning regression and classification methods. The learning performance of the model has been tested on the existing applications that already have their ratings. This model will be useful towards making continuous improvements of the new applications to boost their popularity.
Data: The data of this project has been collected from the author of the github repository GooglePlayAppsCrawler (https://github.com/MarcelloLins/GooglePlayAppsCrawler.git). The project is open for contribution but the crawled data for Google Playstore Application has to be obtained upon requesting to the author.