🎓 Grader

A lightweight, dependency-minimal tool to generate and analyze synthetic student performance data — perfect for demos, teaching, or prototyping academic dashboards.

No databases. No web frameworks. Just clean Python, realistic names, and insightful stats — all in one file.

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🌟 Features

✅ Realistic Data	📊 Smart Analysis	📤 Easy Export
Full names (e.g., Emma Smith)	Grade classification (A+, A, B...)	Saves to sample_output.csv
Real subjects (Math, Biology, etc.)	Top/Bottom 10 students	Human-readable console output
Configurable class size	Subject-wise stats & failure rates	Correlation matrix included
No duplicate names	Overall class performance metrics	—

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🚀 Quick Start

1. Clone & Run

git clone https://github.com/ramoware/grader.git
cd grader
python analyze_grades.py

2. View Output

• Console: Instant stats & rankings
• File: sample_output.csv (open in Excel, Google Sheets, or pandas)

⚡ Zero dependencies beyond pandas & numpy — likely already installed!

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📸 Sample Output

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Console Report

=== TOP 10 STUDENTS ===
        Name  Average Grade
 Olivia Lee     94.13    A+
Evelyn Clark     93.88    A+
...

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CSV Preview (sample_output.csv)

Name	Mathematics	English Literature	...	Average	Grade
Emma Smith	88	92	...	89.25	A
Liam Johnson	76	81	...	78.63	B

🔍 Tip: The full CSV contains 1,000 students × 8 subjects + analytics columns.

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🛠️ Customize It!

Edit the top section of analyze_grades.py to:

• Add more student names
• Change subjects (e.g., add "Economics")
• Adjust class size or grade ranges

# ============================================================
# 🎯 CUSTOMIZABLE DATA SECTION — EDIT NAMES HERE!
# ============================================================
FIRST_NAMES = ["James", "Emma", "Liam", ...]
LAST_NAMES = ["Smith", "Johnson", ...]
SUBJECT_NAMES = ["Mathematics", "Biology", "Computer Science", ...]
NUM_STUDENTS = 1000
# ============================================================

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📦 Dependencies

Package	Purpose	Install Command
pandas	Data manipulation & export	pip install pandas
numpy	Random grade generation	pip install numpy

👉 Or install via:

pip install -r requirements.txt

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💡 Use Cases

• 🧑‍🏫 Teaching: Demonstrate data analysis concepts
• 🧪 Prototyping: Mock data for school dashboards
• 📈 Portfolio: Show clean, readable Python code
• 🤖 Testing: Generate test datasets for grading algorithms

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📜 License

MIT License — feel free to use, modify, and share!
See LICENSE for details.

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🙌 Inspired By

• Real-world academic reporting needs
• The beauty of minimal, self-contained scripts
• Students who deserve better than Student_123 😅

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✨ Made with ❤️ and Python
Star this repo if it helped you!