Paper Contents
Abstract
The rapid growth of smartphones and mobile applications has made mobile usage a vital partof everyday life, shaping how people communicate, work, and shop. To help businesses anddevelopers better understand and predict user behaviour, this project introduces a web-basedsystem that combines machine learning with web application features. The system includes twomodules: an admin module for managing users and FAQs, and a user module for secureregistration, login, and information access. Developed using Flask with database integration, itensures security through role-based access and authentication. A real-world mobile usagedataset is processed and analysed using algorithms such as Random Forest, KNN, andAdaBoost to classify users and predict engagement levels. Data visualization is also used tohighlight patterns and trends. The predictive insights support decision-making, customization,and improved user engagement. Overall, the project demonstrates how combining webapplications with machine learning can provide valuable tools for understanding user behaviourand enhancing digital experiences
Copyright
Copyright © 2025 K R Yashaswini . This is an open access article distributed under the Creative Commons Attribution License.
