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Stream/Area:
-
Subject:
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Paper ID:
IJSREM63651
Volume:
10 |
5 |
May 2026
Pages:
1-3
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
MASTER OF COMPUTER APPLICATIONS
BAPUJI INSTITUTE OF ENGINEERING AND TECHNOLOGY
MASTER OF COMPUTER APPLICATIONS
BAPUJI INSTITUTE OF ENGINEERING AND TECHNOLOGY
One of the prominent effects of the skyrocketing smartphone usage is the creation of numerous behavioural records that open up essential possibilities to understand user interaction patterns and digital engagement. The primary goal of this research shows title is literally self-explanatory that is analyzing and predicting user behaviour from mobile usage data, is to analysis users behaviours in app usage, screen-on time, data consumption, and battery usage through machine learning techniques. The system detects significant patterns and relationships in mobile usage behaviour by categorizing user into different levels of engagement varying from low to high through the use of such algorithms as random forest, K nearest neighbours (KNN), and adaboost, this predictive insight, which are capable of repurposing raw behavioural data into a valuable resource, can be used to optimize mobile experiences, facilitate personalized recommendations, and improve digital well-being. Moreover, a flask-based web application is a part of the work that shamelessly integrates secure user management, machine learning powered prediction, and data visualization. User scan input their mobile usage features and obtain the predictive output through a simple interface, while administrators have the capability to manage users and FAQs. In order to make the system more understandable, the system also uses visual analytics to show correlations, engagement trends, and distribution insights by calling programmes like flip and seaborn. This undertaking is a shameless and interaction integration of web technologies, artificial intelligence, and data visualization to offer a smart behaviour analysis solution. It plays the way for data-driven decisions that lead to better and more informed mobile usage habits.
KEYWORDS
Performance testing ensures that predictions and data visualisations are produced effectively even with numerous concurrent users by evaluating the system's responsiveness and performance under various loads. Security testing confirms that session management stops unwanted access and that sensitive information, such as user credentials, is appropriately safeguarded. Lastly, real users engage with the system during User Acceptance Testing (UAT) to ensure that it satisfies their needs & offers a positive experience. When taken as a whole, these testing methods guarantee that the system is dependable, strong, and prepared for implementation.
1. . User Behavior Pattern Analysis and Prediction Based on Mobile Phone Sensors; Jiqiang Song, Eugene Y. Tang, Leibo Liu; Lecture Notes in Computer Science (Vol. 6289) – IFIP International Conference on Network and Parallel Computing (NPC 2010); DOI 10.1007/978-3-642-15672-4_16; E-ISSN 1611-3349 2. Prediction of user app usage behavior from geo-spatial data; Xiaoxing Zhao, Yuanyuan Qiao, Zhongwei Si, Jie Yang, Anders Lindgren; Proceedings of the Third International ACM SIGMOD Workshop on Managing and Mining Enriched Geo-Spatial Data (GeoRich’16); DOI 10.1145/2948649.2948656. 3. User demographics prediction based on mobile data; E. Zhong, B. Tan, K. Mo, Q. Yang; Pervasive and Mobile Computing (Vol. 9, Issue 6, 2013); DOI 10.1016/j.pmcj.2013.07.009; ESSN 1574-1192. 4. .Web-Based User Behaviour Analysis and Prediction System Using Machine Learning; K R Yashaswini, Mrs Veena B; International Journal of Progressive Research in Engineering Management and Science (IJPREMS), Vol. 05, Issue 09, September 2025, pp. 123-129; DOI 10.58257/IJPREMS43699; e-ISSN 2583-1062. 5. Predicting Mobile Payment Behavior Through Explainable Machine Learning and Application Usage Analysis; M. Lee, I. Choi, W.-C. Kim; Journal of Theoretical and Applied Electronic Commerce Research (Vol. 20, Issue 2) – 2025; DOI 10.3390/jtaer20020117; E-ISSN 0718-1876.
Publication Date
5/21/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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The authors declare no conflict of interest related to this research work.
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