Electronic Gadget Addiction Prediction using Machine Learning

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Published: 2026-02-25
DOI: https://doi.org/10.15662/IJRPETM.2026.0902003
Keywords:
Gadget Addiction, Machine Learning Model, Prediction Model, Random Forest, Logistic Regression, Support Vector Machine (SVM), Data Preprocessing

Main Article Content

Rupika.M, Nandini.G, Mythri.M, Vasu.K, Abhiram.M
Student, B. Tech CSE 4 year, Holy Mary Inst. of Tech. and Science, Hyderabad, TG, India
Shivalingam.N
Assistant Professor, Dept. of CSE, Holy Mary Inst. of Tech. and Science, Hyderabad, TG, India
Dr. Prasad Dharnasi
Professor, Dept. of CSE, Holy Mary Inst. of Tech. and Science, Hyderabad, TG, India

Abstract

The widespread use of electronic devices has really changed how we live day to day. This brings up worries about using them too much and how that affects our mental health, how well we work, and how we talk to each other. So, being able to guess who might get addicted to these gadgets is super important so we can step in early and make smarter choices. In this study, we've put together a machine-learning system to guess gadget addiction using info about how people act. 


The process includes grabbing data, cleaning it up by getting rid of empty spaces and repeats, sorting it into categories, fixing any uneven data using SMOTE, and picking the best features with Recursive Feature Elimination (RFE). 


We also did some exploring with plots and charts to understand how different features relate to each other. We tried out a bunch of classification models, like Random Forest, Support Vector Machine, XGBoost, Multi-Layer Perceptron, and a Voting Classifier that mixes Gradient Boosting, LightGBM, XGBoost, and CatBoost. Plus, we used deep learning models like LSTM and CNN-LSTM. The results showed that the Voting Classifier did the best, hitting around 98.6% for accuracy, precision, recall, and F1-score, which is better than the other models. To get clearer explanations, we used Explainable AI methods like LIME and SHAP to see how each feature played a role. Then, we put the top model into a web app using Flask, so people can get real-time predictions through an easy-to-use setup.


 

Article Details

Issue

Vol. 9 No. 2 (2026): International Journal of Research Publications in Engineering, Technology and Management

Section

Articles

How to Cite

Electronic Gadget Addiction Prediction using Machine Learning. (2026). International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 9(2), 500-505. https://doi.org/10.15662/IJRPETM.2026.0902003

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