Predicting Smartphone Addiction

Smartphone addiction has become a growing concern in today’s digital era, where excessive mobile phone use impacts mental health, productivity, and social interactions. This project aims to identify individuals at risk of addiction based on their usage behaviors and psychological patterns. The project is developed with the objective of identifying individuals prone to smartphone addiction based on various behavioral, psychological, and usage patterns. The survey included questions about demographics, cell phone usage patterns, and various psychological conditions such as anxiety, depression, and stress. The system utilizes Python for backend, while the frontend is built using HTML, CSS, and Java Script. The data was pre processed by coding the raw variables and adjusting the numerical variables to ensure that the model could be studied effectively. We train the model on some data and evaluate its performance on other data using some metric such as accuracy. Our results show that the model achieves high accuracy in predicting the smartphone addiction. The dataset used for this project comprises 200 records with 10 attributes, which include survey responses focused on phone usage patterns and addiction related behaviors. These attributes range from questions about daily phone habits, such as taking the phone to social gatherings or frequently checking it without notifications, to more serious indicators of addiction like anxiety over losing the phone or relying on it in awkward situations. The target attribute classifies users into two categories: addicted and not addicted, providing a clear objective for the prediction models.