Prediction of success factors for mobile application using machine learning technique

Jyoti Deone, Nilima Dongre and Mohammad Atique

International Journal of Data Analysis Techniques and Strategies, 2025, vol. 17, issue 1, 54-64

Abstract: The remarkable boom in the mobile market has attracted many developers to build mobile apps. However, the majority of developers are suffering to generate earnings. For those developers, knowing the characteristics of successful apps may be very vital. We propose an approach which examines the categories of apps by two factors. First, the correlation is measured between app features and secondly, concepts are extracted from apps to understand the common theme present in them. For this, we selected 3000 applications available in the Google Play Store. The observations specify that there may be a strong correlation among purchaser rating and the quantity of app downloads, though there may be no correlation between rate and downloads, nor among charge and rating. Moreover, we find standards unique to excessive rated apps and low rated apps. The correlation along with the concepts proves useful for application developers to understand the market trend and customer demand more easily than earlier approaches.

Keywords: Android; LSA; correlation; mobile; market; extraction; downloads; apps; customers; playstore; Google. (search for similar items in EconPapers)
Date: 2025
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