This dataset explores the relationship between digital distractions and academic performance among students. It aims to provide insights into how screen time, lifestyle choices, and mental well-being collectively influence educational outcomes.
Use Cases
- Predicting academic performance using regression models
- Analyzing the impact of screen time on student mental health
- Feature engineering for behavioral analysis
- Comparing the performance of XGBoost and Random Forest on student data
Strengths
- Addresses contemporary issues regarding digital screen time and education
- Includes multi-dimensional factors like lifestyle and mental well-being
- Tagged for use with advanced machine learning algorithms
Limitations
- Specific column names and data volume are not documented
- Lack of information regarding the author or data collection methodology