Analysis of students’ performance across subjects with detailed factor analysis.
This project, built using Power BI Desktop, analyzes student performance based on multiple factors: gender, race, test preparation course, lunch program, and parental education level.
The dataset used is the Students_Academic_Performance_Dataset (CSV) from Kaggle.
The report aims to provide insights on average performance, pass rates, and factor influences.
- Students who completed the test preparation course perform significantly better than those who did not.
- Average score improvement: +8 points.
- Minimum score is higher for prepared students → fewer severe failures.
- Quartiles (Q1, Q3) are higher → both weaker and stronger students benefit.
- Non-prepared students have:
- More severe failures
- Greater score dispersion
- Lower median scores
Conclusion: Test preparation provides a real and measurable advantage, improving average performance and stability.
- High education level parents → students achieve the best scores.
- Low education level parents → students have lower scores and less variability.
- Median education level parents → students perform between the two groups.
Conclusion: Parental education is positively correlated with student performance. High parental education → higher average scores.
- Certain student groups perform better in specific subjects regardless of parental education.
- Family practices and community dynamics can influence performance, sometimes counterbalancing lower parental education.
- Prepared students show more stable results with fewer very low scores.
- Categorized variables (e.g., parental education levels grouped for comparisons)
- Grouped performance indicators for cross-factor analysis
- Text-standardized columns for consistency
- Average Math Score
- Average Reading Score
- Average Writing Score
- Overall Average Score
- Total Students
- Pass Rate
These measures allow dynamic and accurate calculations across all report pages.
- Pass: Overall Average Score ≥ 60
- Fail: Overall Average Score < 60
Used to create clear performance categories and compute pass rates across groups.
- Slicers: Gender, Race, Test Preparation, Lunch
- Slicers are placed on Page 2 and synchronized across relevant pages for consistent subgroup analysis.
- Filters can be applied selectively depending on the analytical context.
- Power BI Desktop – report building and data visualization
- DAX – calculation of measures and pass rates
- Data Cleaning & Preparation – column creation, text standardization
- Data Analysis – interpretation of distributions, quartiles, and factor influence
- Kaggle Dataset Handling – working with CSV datasets
- Source: Kaggle – Students Academic Performance Dataset
- Format: CSV
- Rows: 1000
- Columns: Performance and demographic variables
- Open the Power BI report.
- Explore general student characteristics on Page 1.
- Filter by Gender, Race, Test Preparation, or Lunch using slicers on Page 2.
- Examine factors influencing scores on Page 3.
- Review subject-level performance in Page 4.