Student Performance Tracking System – SQL + Streamlit

Video Walkthrough: https://youtu.be/Q-U7CMLzDco

This project builds a relational database system for managing and visualizing student academic performance. The system tracks students, courses, attendance, grades, and assignments using SQLite and SQL, and presents insights through a Streamlit-powered web dashboard. It demonstrates skills in database engineering, data querying, and frontend analytics.

Getting Started

1. Install dependencies

pip install streamlit pandas matplotlib seaborn
python db_seed.py
streamlit run Web_app.py

Project Structure (as Table)

File/Folder	Description
db_seed.py	Full schema creation and data seeding script
analytics.py	Functions for querying and visualizing data
Web_app.py	Streamlit dashboard UI
university.db	Generated SQLite database
README.md	Project overview and documentation

Tools and Technologies

• Database: SQLite, SQL
• Backend: Python (sqlite3, pandas)
• Visualization: matplotlib, seaborn
• Interface: Streamlit
• Schema Design: Normalized relational model (13 tables)

Database Schema

Table Name	Description
student	Stores student demographic data
faculty	Stores instructor information
course	Contains course catalog details
section	Specific offerings of a course
semester	Labels for academic terms
enrollment	Mapping of students to sections
grades	Stores final letter grades
attendance	Tracks student presence per session
assignments	Information about course assignments
assignment_submissions	Student submissions and scores per assignment

Data Seeding

The db_seed.py script creates all tables and inserts structured seed data:

• 10 students
• 10 course sections
• 14 enrollments
• Grades, attendance logs, and submissions for 5+ students

Visual Reports (via Streamlit)

• 📈 Grade distribution by letter grade
• 📊 Average scores per assignment
• 🕒 Attendance summary by status
• 🏆 Top performers by GPA
• 📘 Full performance reports per student