Smartphone Addiction Prediction using Machine Learning

G. Himabindu; C. Dharmaja; D. Haji Basha; D. Shekshavali

Smartphone Addiction Prediction using Machine Learning

Author(s)	Ms. G. Himabindu, C. Dharmaja, D. Haji Basha, D. Shekshavali
Country	India
Abstract	Smartphone addiction has become a serious problem in today’s digital world. Many people depend heavily on their mobile phones for daily activities such as communication, entertainment, and work. Over time, excessive phone usage can affect mental health, reduce productivity, and weaken social relationships. People may develop habits like checking their phones repeatedly without any notification, feeling anxious when their phone is not nearby, or using their phone to avoid uncomfortable situations. Often, these behaviors go unnoticed until they start causing real problems. To address this growing issue, the Smartphone Addiction Prediction Using Machine Learning project focuses on identifying people who may be at risk of smartphone addiction. The system analyzes users’ phone usage habits and psychological behavior patterns to predict whether a person is addicted or not. By providing early warnings, the system helps users understand their mobile usage and encourages healthier digital habits.The project is developed using Python for backend processing, while the user interface is designed using HTML, CSS, and JavaScript to make it simple and user-friendly. The Flask web framework connects the frontend and backend, allowing users to input data and receive addiction predictions in real time through a web application.The dataset used for this project contains 501 records with 21 attributes, collected through surveys. These attributes represent different smartphone usage behaviors, such as frequent phone checking, carrying the phone everywhere, and feeling stressed without it. More serious indicators include dependency during social situations and anxiety when the phone is unavailable. The system classifies users into two categories: addicted (1) and not addicted (0).Machine learning techniques are used to analyze the data and generate accurate predictions. The model achieved high accuracy on both training and testing data, showing that the system is reliable and effective in identifying smartphone addiction risk.
Keywords	K-clustering, SVC algorithm, Random forest , Decission tree, K-neareset neighbour.
Field	Engineering
Published In	Volume 7, Issue 4, April 2026
Published On	2026-04-04

View / Download PDF File

doi

CrossRef DOI is assigned to each research paper published in our journal.

IJLRP DOI prefix is
10.70528/IJLRP

Downloads

Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 6 Isu 12 Cover Page Vol 6 Isu 10 Cover Page Vol 6 Isu 9

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA

About IJLRP Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us		+91-9687-828-838	editor@ijlrp.com

International Journal of Leading Research Publication

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Monthly Scholarly International Journal

Smartphone Addiction Prediction using Machine Learning

Share this