📊 Student Lifestyle & Academic Performance – EDA Project

This project analyzes how student lifestyle habits influence academic performance using Exploratory Data Analysis (EDA). The analysis was performed using survey responses collected through Google Forms and processed in Microsoft Excel.

🎯 Objective

To identify how factors such as study hours, sleep duration, attendance, stress, and screen time influence student academic performance.

🧠 Methodology

1. Collected 89 student responses using Google Forms
2. Cleaned and organized the dataset in Excel
3. Applied Exploratory Data Analysis techniques
4. Created charts and visualizations
5. Derived meaningful insights for academic decision-making

📈 Key Visualizations

• Study Hours Distribution
• Sleep Duration Analysis
• Attendance Percentage
• Academic Stress Levels
• Non-Academic Screen Time
• Factors Affecting Academic Performance

🔍 Important Findings

• 43% students spend more than 4 hours on non-academic screen time
• Attendance was identified as the most important factor affecting academic performance (38%)
• Most students study only 1–2 hours daily
• Students sleeping 6–7 hours showed better academic balance

📊 Additional Chart Insights

Key Factors Affecting Academic Performance

Attendance was found to be the most influential factor affecting academic performance, followed by screen time and study hours.

Study Hours Distribution

Most students reported studying only 1–2 hours daily, indicating limited study time among the majority of respondents.

📁 Files Included

• Dashboard.pdf – Final dashboard
• Dashboard.png – Preview image
• Project_Pitch.pptx – Initial project presentation
• dataset.csv – CSV version for quick preview on GitHub
• dataset.xlsx – Recommended dataset file for viewing in Excel

Note: Some values such as 1-2 or 6-7 may appear as dates in CSV format when opened in Excel. For correct formatting, use dataset.xlsx.

💡 Applications

• Student feedback analysis
• Academic performance studies
• College decision-making
• Survey-based educational research

⭐ If you found this project interesting, feel free to explore the repository and connect with me on LinkedIn.

🚀 Future Improvements

• Increase the number of student responses
• Compare academic years and departments
• Add predictive analysis using Python
• Build an interactive dashboard version