Md. Showrov Hossen

Work place: Department of Computer Science and Engineering, City University, Dhaka, Bangladesh



Research Interests: Machine Learning, Data Mining, Cyber Security


Md. Showrov Hossen is currently working as a Lecturer in the Department of Computer Science and Engineering at City University. He has completed his bachelor’s in computer science and engineering and master’s in computer science and information Technology from Patuakhali Science and Technology University (PSTU)in 2020 and 2022, respectively. He is interested in research areas including Data Mining, Machine Learning, Data Analytics, Data Mining and Cyber Security.

Author Articles
Data Analysis and Success Prediction of Mobile Games before Publishing on Google Play Store

By Muhammad Muhtasim Md. Showrov Hossen

DOI:, Pub. Date: 8 Jun. 2024

The popularity of mobile games has expanded among individuals of all ages, and the mobile gaming businesses are quickly expanding day by day. The Google Play Store, one of the most well-known platforms for the distribution of Android applications and games, sees a daily influx of thousands of new mobile games. One of the biggest problems in the gaming industry is predicting a mobile game's performance. Every day, thousands of new games are released. But just a couple of them are successful, while most of them fail. The study was done with the intention of analyzing any relationship between a mobile game's success and its distinctive features. Many of the mobile game developers work independently or work in the mobile game industries to make their games successful on the digital market. Before they are released, game makers can increase the quality of their games if they are confident in their products' commercial viability. For that reason, more than 17,000 games were taken into consideration. We show that the success of a mobile game is clearly influenced by its category, number of supported languages, developer profile, and release month. Furthermore, we show that specific aesthetic features of game symbols are more frequently linked to higher rating counts. We analyzed Google Play Store mobile games data and used a variety of machine learning algorithms for predicting the performance of mobile games based on the total number of downloads and the total user rating.

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