An Analysis to the Relationship Between Students’ GPA and Lifestyles

Yuanhao Shen

2025

Abstract

How to have a good GPA or a great learning performance. It is a historical question, a tremendous of investigators were trying to make a conclusion on the study efficiency depending on which situation. This paper dives into an investigation of students from kaggle, where the data were collected from the Google survey which was filled up by the different India university students. This paper mainly uses several supervised machine learning algorithms to predict GPA according to different lifestyles, such as: students’ study hours per day, sleep hours per day, physical activity hours per day, extracurricular hours per day, and stress level. In this paper, whether the regression settings or classification settings, will import the linear regression, the polynomial regression, the logistic regression, the support vector machine (SVM), the decision tree, the random forest, and the K-nearest neighbor (KNN) as statistical algorithms to do analysis. This article aims to give inspiration to good GPA regarding time management.

Paper Citation

in Harvard Style

Shen Y. (2025). An Analysis to the Relationship Between Students’ GPA and Lifestyles. In Proceedings of the 2nd International Conference on Data Science and Engineering - Volume 1: ICDSE; ISBN 978-989-758-765-8, SciTePress, pages 414-419. DOI: 10.5220/0013698500004670

in Bibtex Style

@conference{icdse25,
author={Yuanhao Shen},
title={An Analysis to the Relationship Between Students’ GPA and Lifestyles},
booktitle={Proceedings of the 2nd International Conference on Data Science and Engineering - Volume 1: ICDSE},
year={2025},
pages={414-419},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013698500004670},
isbn={978-989-758-765-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Data Science and Engineering - Volume 1: ICDSE
TI - An Analysis to the Relationship Between Students’ GPA and Lifestyles
SN - 978-989-758-765-8
AU - Shen Y.
PY - 2025
SP - 414
EP - 419
DO - 10.5220/0013698500004670
PB - SciTePress