This project performs Exploratory Data Analysis (EDA) on a student examination dataset to uncover trends, performance patterns, and relationships between academic scores and influencing factors.
The analysis evaluates student achievement across Mathematics, Reading, and Writing, while examining the impact of demographic and socio-academic variables.
- Assess overall student performance across subjects
- Compare subject-wise average scores
- Analyze score distributions and variability
- Investigate influencing factors:
- Gender
- Test Preparation Course
- Parental Level of Education
- Race/Ethnicity
- Perform correlation analysis
- Engineer performance metrics (Total & Average Score)
- Python
- NumPy
- Pandas
- Matplotlib
- Seaborn
- Jupyter Notebook / Google Colab
Insight:
Math scores show greater variability, while Reading and Writing cluster toward higher ranges.
Insight:
Reading and Writing outperform Mathematics on average.
Insight:
Female students tend to score higher in Reading and Writing.
Insight:
Students completing test preparation courses consistently achieve higher scores.
Insight:
Students with higher parental education levels generally achieve better academic score.
Insight:
Strong positive correlation observed between Reading and Writing scores.
- Reading & Writing scores exceed Math scores on average
- Math performance shows higher dispersion
- Test preparation significantly improves results
- Parental education level influences performance
- Strong Reading–Writing relationship
This project demonstrates how EDA and visualization techniques transform raw educational data into meaningful insights.
Student_Performance_Analysis.ipynb→ Full analysisStudentsPerformance.csv→ Datasetimages/→ Visualizationsrequirements.txt→ Dependencies
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