Student Performance Analysis Using Data Visualization

Project Overview

This project analyzes student academic performance and attendance data using Python. The objective is to identify patterns in student scores, evaluate the impact of attendance on academic performance, and visualize the results using various data visualization techniques.

Dataset

• 100 student records
• Student ID
• Student Name
• Math Marks
• Science Marks
• English Marks
• Attendance Percentage

Tools Used

• Python
• Pandas
• Matplotlib
• Seaborn

Analysis Performed

• Data Loading and Processing
• Average Score Calculation
• Mean, Median, and Standard Deviation Analysis
• Attendance-Based Performance Analysis
• Correlation Analysis

Visualizations

1. Bar Chart – Average Subject Scores
2. Scatter Plot – Attendance vs Average Score
3. Correlation Heatmap
4. Box Plot – Score Distribution by Subject

Key Findings

• Students with higher attendance generally achieved better academic performance.
• Attendance showed a positive correlation with average scores.
• Statistical analysis helped identify subject-wise performance trends.
• Data visualization made it easier to understand relationships within the dataset.

Project Structure

student_performance_analysis/

├── analysis.py

├── student_data.csv

├── README.md

├── images/

│ ├── bar_chart.png

│ ├── attendance_vs_score.png

│ ├── heatmap.png

│ └── boxplot.png

Future Improvements

• Use real-world educational datasets
• Add interactive dashboards
• Perform predictive analysis using machine learning
• Analyze larger and more diverse datasets

Author

Developed as a data analysis and visualization project using Python, Pandas, Matplotlib, and Seaborn.