User Behavior Modeling in Mobile Applications: A Linear Regression-Based Approach to App Prediction

Abstract

This article investigates User behavior Modeling in mobile application. User behavior modeling is also critical to the mobile application experience in the effort to enhance customization of mobile connectivity. The insight of the relationships between and among the users is crucial in enhancing the level of a user engagement and satisfaction by making a forecast of how users are likely to act in the future. The previous methods of user behavior prediction have been primarily concentrated in terms of supervised machine learning methods, but these methods are still problematic with regard to the problem of high-dimensional data, sample size bias, and the dynamic user preferences. This paper delves into how linear regression can be applied in modelling the user behaviour in mobile applications, so as to make predictions about the behaviour of the user in the future in terms of their actions regarding the application. The study is based on a linear regression model, where the experiment uses information including browsing behavior, user session time, and demography that is obtained through user-friendly mobile websites. These results prove that it is possible to predict the usage trend of apps with a high level of accuracy using the linear regression models when a sufficient amount of feature engineering is applied. The paper also contrasts the shortcomings in linear regression systems against the complex machine learning models and gives realistic advice as to how it can be streamlined and become more predictive. These results highlight the possibilities of linear regression in carrying out the process of predicting user behavior, and while doing so, allowing such methods to be interpretable as well as computationally feasible. These findings have far reaching implications in making mobile apps more customized in nature in the end leading to the creation of more exciting and user-friendly apps.