A beginner-friendly Data Analytics project that explores how factors like study hours, attendance, and parental education impact student test scores.
The project uses Python (Pandas, Matplotlib, Seaborn) to perform data exploration, visualization, and correlation analysis.
This project aims to answer:
- How do study hours affect student performance?
- Does attendance have a positive impact on test scores?
- How does parental education level correlate with student success?
The analysis helps visualize trends and relationships using charts and heatmaps, offering insights useful for educators and students alike.
1️⃣ Study hours strongly correlate with higher test scores.
2️⃣ Students with better attendance perform consistently well.
3️⃣ Parental education positively impacts average scores.
| Column Name | Description |
|---|---|
| Student_ID | Unique ID for each student |
| Gender | Male / Female |
| Study_Hours | Number of hours studied per day |
| Attendance | Percentage of attendance |
| Parent_Education | Highest education level of parents |
| Test_Score | Final test score out of 100 |
📂 Dataset Path: dataset/student_scores.csv
- Language: Python 🐍
- Libraries Used:
pandas→ Data handlingmatplotlib→ Visualizationseaborn→ Statistical plotting
git clone https://github.com/<your-username>/student-performance-analysis.git
cd student-performance-analysis
python -m venv env
env\Scripts\activate # For Windowspip install pandas matplotlib seabornpython student_analysis.py1️⃣ Study hours correlate with test score: 0.87 2️⃣ Attendance correlates with test score: 0.76 3️⃣ Average test score: 78.2 ✅ All plots saved in the 'charts/' folder.
Add machine learning model to predict student scores
Include more features like age, class participation, and assignments completed
Build a simple dashboard (using Streamlit or Power BI)