This repository contains a mini-project focused on analyzing student performance using R. The project leverages real educational datasets from Kaggle/UCI ML Repository to explore how factors like study time, gender, and parental education influence math achievement.
- Dataset preprocessing (cleaning, missing values, data types)
- Descriptive statistics and outlier detection
- Visualizations: histograms, boxplots, scatter plots, violin plots
- Correlation and regression analysis
- Multivariate comparison using faceted plots and parallel coordinates
students_performance.csv– dataset used for analysis- R scripts for EDA, visualization, and regression
- PDF/Report summarizing objectives, methods, results & conclusion
To identify key factors affecting academic performance and provide insights that assist educators in data-driven decision-making.
- R
- ggplot2
- GGally
- dplyr
The analysis reveals score distributions, demographic performance gaps, and predictors of achievement, supporting better educational planning and student support strategies.