Abstract: Google Play Store is a digital platform for mobile applications, where users can download and install apps for their android devices. It is a great source of data for mining and analyzing app performance and user behavior. The increasing volume of mobile applications poses a challenge for users in finding apps that align with their preferences. This work aims to utilize predictive user context to analyze user behavior, thereby enhancing user experience and app development. The work focuses on identifying trends in the app market to recommend suitable applications for users. Play Store app analysis involves gathering data, performing comprehensive evaluations, and making informed decisions to improve app performance and user engagement. By applying Naïve Bayes, Random Forest, and Logistic Regression algorithms, this work evaluates the relationship between application attributes such as categories and the number of downloads, determining the most effective profiling algorithm for app performance evaluation. This analysis is crucial for recognizing user engagement trends, discovering new opportunities, and optimizing existing applications.

Anandh A, Ramya R, Vakaimalar E and Santhipriya B. “Exploring Google Play Store Apps Using Predictive User Context: A Comprehensive Analysis”. International Journal of Advanced Computer Science and Applications (IJACSA) 15.7 (2024). http://dx.doi.org/10.14569/IJACSA.2024.01507125

@article{A2024,
title = {Exploring Google Play Store Apps Using Predictive User Context: A Comprehensive Analysis},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.01507125},
url = {http://dx.doi.org/10.14569/IJACSA.2024.01507125},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {7},
author = {Anandh A and Ramya R and Vakaimalar E and Santhipriya B}
}