Title: Prediction of success factors for mobile application using machine learning technique

Authors: Jyoti Deone; Nilima Dongre; Mohammad Atique

Addresses: Department of Information Technology and Engineering, RAIT, DY Patil Deemed to be University, Navi, Mumbai, 400706, India ' Department of Information Technology and Engineering, RAIT, DY Patil Deemed to be University, Navi, Mumbai, 400706, India ' Department of Computer Engineering, PGDT SGBAU, Amravati, 444602, India

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.

DOI: 10.1504/IJDATS.2025.144965

International Journal of Data Analysis Techniques and Strategies, 2025 Vol.17 No.1, pp.54 - 64

Received: 24 Aug 2022
Accepted: 17 Nov 2023
Published online: 14 Mar 2025 *

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