Student Performance Analytics

Overview

An end-to-end data analytics project that analyzes student academic performance using Python, SQL, SQLite, and Excel.

Project Workflow

Raw Dataset → Python Cleaning → SQLite Database → SQL Analysis → Excel Dashboard

Technologies Used

• Python (Pandas)
• SQLite
• SQL
• Microsoft Excel

Key Features

• Data cleaning and preprocessing
• Feature engineering (average score, pass/fail classification)
• SQL-based analytical queries
• Interactive Excel dashboard
• KPI tracking and performance insights

Files

• analysis.py – data cleaning and exploratory analysis
• load_to_sql.py – loads cleaned data into SQLite
• sql_queries.sql – analytical SQL queries
• students.db – SQLite database
• Student_Performance_Dashboard_Final.xlsx – final dashboard

Insights

• Students who completed test preparation generally achieved higher scores.
• Lunch type and parental education level showed measurable performance differences.
• Overall student performance trends were visualized through an interactive dashboard.