• D.Srinivasa Rao
• Kamireddy Vaishnavi

There has been a growing concern about smartphone addiction in recent years, with more and more people experiencing symptoms such as excessive phone use, decreased productivity, and more. to physical and mental health problems. Therefore, there is a need to develop effective tools to predict smartphone addiction and identify those at risk. Cell phone addiction. The survey included questions about demographics, cell phone usage patterns, and various psychological conditions such as anxiety, depression, and stress. It is a popular and effective machine learning technique for building our models. 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 most important thing Factors that predict addiction include phone usage patterns, such as how often you check for notifications, how many hours you spend on your phone each day, and the types of apps you use it. Other important factors are age, gender and stress. Health professionals can use it to identify people at risk of smartphone addiction and provide appropriate intervention. Application developers can also use it to design less complex apps and promote healthier cell phone usage habits. In conclusion, our study demonstrates the feasibility and effectiveness of using machine learning models to predict smartphone addiction. Further research is needed to validate our findings on larger and more diverse data sets and to explore the potential application of this model in different contexts

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

"MACHINE LEARNING MODEL FOR PREDICTION OF SMARTPHONE ADDICTION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.m37-m41, April-2024, Available :http://www.jetir.org/papers/JETIR2404C06.pdf

"MACHINE LEARNING MODEL FOR PREDICTION OF SMARTPHONE ADDICTION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppm37-m41, April-2024, Available at : http://www.jetir.org/papers/JETIR2404C06.pdf