This project explores the relationship between students' gaming behavior and their academic performance using data analysis and machine learning techniques.
The goal is to understand how factors such as gaming hours, study time, sleep patterns, stress levels, and attendance impact overall grades.
- Perform data cleaning and preprocessing
- Conduct exploratory data analysis (EDA)
- Apply feature engineering techniques
- Train and evaluate multiple regression models
- Compare model performance using statistical metrics
- Linear Regression
- Random Forest Regressor
- XGBoost Regressor
The analysis shows that academic performance is strongly influenced by consistent study habits, attendance, and lifestyle balance.
Gaming itself is not inherently harmful; however, excessive gaming without proper time management can negatively impact grades.
Ensemble models such as Random Forest and XGBoost performed better than Linear Regression, suggesting that student behavior patterns are complex and non-linear.
- Python
- Pandas
- NumPy
- Matplotlib
- Seaborn
- Scikit-learn
- XGBoost
- Data Cleaning & Preprocessing
- Exploratory Data Analysis
- Feature Engineering
- Model Training
- Fit Model
- Model Evaluation & Comparison
- Conclusion & Insights
Aleesha Nadeem Machine Learning Enthusiast | Data Science Explorer
Academic success is not about eliminating entertainment — it is about maintaining balance, discipline, and smart time management.