Smartphone Addiction Prediction Using Machine Learning | Python Final Year IEEE Project 2024

Smartphone Addiction Prediction Using Machine Learning | Python Final Year IEEE Project 2024 - 2025.
To buy this project in ONLINE, Contact:
🔗Email: [e-mail],
🌐Website: www.jpinfotech...
🚀IEEE Base Paper Title: Machine Learning Prognosis for Smartphone Dependency.
📌Project Title: Smartphone Addiction Prediction Using Machine Learning.
💡Implementation: Python.
🔬Algorithm / Model Used: Stacking Classifier Model, CatBoost Classifier, ExtraTrees Classifier.
🌐Web Framework: Flask.
🖥️Frontend: HTML, CSS, JavaScript.
💰Cost (In Indian Rupees): Rs.5000/
⚓Our Proposed Project Abstract:
Smartphone addiction has become a growing concern in today's digital era, where excessive mobile phone use impacts mental health, productivity, and social interactions. Addressing this issue, the "Smartphone Addiction Prediction Using Machine Learning" 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 system utilizes Python for backend coding, while the frontend is built using HTML, CSS, and JavaScript. The web framework employed is Flask, which ensures a smooth interaction between the client interface and the backend server, providing a seamless user experience.
📍REFERENCE:
Anitha Julian, Prathima S, “Machine Learning Prognosis for Smartphone Dependency”, 2024 International Conference on Computing, Power, and Communication Technologies (IC2PCT), IEEE Conference, 2024.
Frequently Asked Questions:
Q1: What is the main objective of your project?
Q2: Why did you choose smartphone addiction as the focus of your project?
Q3: What dataset did you use in this project?
Q4: How did you preprocess the dataset?
Q5: What machine learning models did you implement in your project?
Q6: What performance metrics did you use to evaluate your models?
Q7: Why did you use a Stacking Classifier model?
Q8: How does the CatBoost Classifier differ from other gradient boosting algorithms?
Q9: What was the most challenging aspect of your project?
Q10: What role does the ExtraTrees Classifier play in your project?
#python #pythonprojects #machinelearningproject #smartphone #smartphoneaddiction #mobile #addition #mobileaddiction #mobileaddict #prediction #pythonprogramming #pythonprojectforbeginners #pythonprojectideas #pythonmachinelearning #machinelearning #machinelearningpython #finalyearproject #ieeeprojects #finalyearprojects #datascience #datascienceproject #artificialintelligenceproject #projects #deeplearning #deeplearningproject #computerscienceproject #deeplearningprojects #majorprojects #academicprojects #majorproject
#mllearning #ml #mlproject #mlprojects #ai #aiproject #aiprojects #fyp #studentprojects