Machine Learning Model for Prediction of Smartphone Addiction

Smartphone addiction has become a growing concern in recent years, with increasing numbers of people exhibiting symptoms such as excessive phone use, loss of productivity, and even physical and psychological health problems. As a result, there is a need to develop effective tools for predicting smartphone addiction and identifying those at risk.In this study, we developed a machine learning model for predicting smartphone addiction using data collected from a survey of smartphone users. The survey included questions about demographics, phone use patterns, and various psychological factors such as anxiety, depression, and stress. a popular and effective machine learning method, to build our model. We preprocessed the data by encoding categorical variables and normalizing numerical variables to ensure the model could learn effectively. We then trained the model on a portion of the data and evaluated its performance on the remaining data using several metrics such as accuracy. Our results showed that the model achieved a high accuracy of   in predicting smartphone addiction. The most important features for predicting addiction were phone use patterns such as the frequency of checking notifications, the number of hours spent on the phone each day, and the types of apps used most frequently

KEYWORDS: Decision tree, Random Forest, Logistic Regression and Machine learning techniques