Predicting Smartphone Addiction Through Machine Learning

                                                                                                                                         ABSTRACT         Smartphone addiction is a growing issue, impacting mental health and productivity. This project leverages machine learning to predict addiction risk using behavioral data from our own dataset and user surveys. Our model’s accuracy will be evaluated using precision, recall, and F1 score to ensure reliable classification.  Beyond prediction, we will assess user engagement by tracking interactions and analyzing behavioral changes through pre- and post-surveys. This will help ...