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There are millions of applications uploaded by the developers on the daily basis. Without any check and balance millions of users download these applications. Theses duplicated applications damage the users trust on Google play store and can grab the confidential information of user. There is no more information provided by developers on the front end of the application that can define the legitimacy of the application. In this paper, by using a Google-play-scraper build a Google play store dataset with all categories of games. Scraping at least 550 applications of each category of games in free and respectively in paid applications by using Google play scraper, cumulatively scrape the 3600 paid applications and 10k free applications of all categories in games. The categories of these games’ applications use respectively are Word, Trivia, Simulation, Sports, Strategy, Racing, Role_Playing, Puzzle, Music, Educational, Card, Casino, Casual, Board, Action, Adventure, and Arcade. On each application on Google play store, scrape maximum 70 attributes, but use four attributes for analysis in this paper that is Installs, Advertisements support, InApplicationPurcahses and Ratings. In this paper, visualizing the InAppPurchase rate of free and paid applications, Percentage of the advertisement support in free and paid applications, Ratings of free and paid application with histogram, Installs of free and paid application with a histogram with all categories of games application. To check the relationship in between attributes also, visualize them in CIRCOS. This visualization is more helpful for game developers in the development phases, also for the users of the game’s application for the selection of the game that they want to play. |
