This project analyzes student academic performance data to identify top-performing students, measure academic improvement over time, evaluate the relationship between attendance and performance, and provide recommendations for improving student outcomes.
The analysis was conducted using Python, Pandas, and Matplotlib.
A school administrator wants to understand student performance trends and answer the following questions:
- Which student performs best?
- Does attendance affect academic performance?
- Which student improved the most?
- Which student requires intervention?
- Should the school focus on improving attendance?
The dataset contains:
| Column | Description |
|---|---|
| Student | Student Name |
| Year | Academic Year |
| Attendance | Attendance Score (%) |
| Score | Academic Performance Score |
- Python
- Pandas
- Matplotlib
- Git
- GitHub
- Inspected dataset structure
- Checked data types
- Generated summary statistics
- Calculated average score per student
- Identified top and lowest performers
- Used a pivot table to compare scores between 2023 and 2024
- Calculated score improvement for each student
- Performed correlation analysis
- Evaluated the relationship between attendance and academic performance
- Student Average Score
- Score Improvement by Year
- Attendance vs Score Scatter Plot
Sarah achieved the highest average score of 93.5, making her the strongest overall performer.
David recorded the lowest average score of 58.5, indicating a need for academic support.
Mary showed the greatest improvement, increasing her score by 18 points between 2023 and 2024.
A strong positive correlation (0.90) exists between attendance and academic performance.
Students with higher attendance generally achieved higher scores.
David, John, and Michael demonstrated lower academic performance and may benefit from targeted intervention programs.
- Improve student attendance through monitoring and awareness programs.
- Investigate factors contributing to Sarah's and Mary's success and apply lessons learned across the student body.
- Provide additional academic support for struggling students, especially David.
- Establish reward systems to encourage academic excellence and continuous improvement.
- Conduct sensitization programs highlighting the importance of attendance and academic commitment.
- Analyze multiple years of academic data.
- Include demographic and subject-level information.
- Investigate additional factors affecting performance.
- Build an interactive dashboard using Power BI.
- Apply predictive analytics to identify at-risk students early.
- Data Cleaning & Exploration
- GroupBy Analysis
- Pivot Tables
- Correlation Analysis
- Data Visualization
- Business Insight Generation
- Executive Recommendation Writing
Azeez Samad
Agricultural Engineer | Aspiring Data Analyst
GitHub: https://github.com/EmperorYeqing