Exam Question Difficulty Analyzer (Mini Project)

📌 Project Overview

The Exam Question Difficulty Analyzer is an NLP-based mini project designed for MCA, BCA, and BTech students.

It analyzes exam questions (Text or PDF) and classifies them into Easy, Medium, or Hard levels using a rule-based NLP algorithm based on Bloom’s Taxonomy.

The project is especially useful for:

1. Students preparing for exams
2. Teachers checking question paper balance
3. Mini project submissions & viva demonstrations

⚙️ Key Features

1. Analyze questions via Text input or PDF upload
2. Difficulty classification: Easy / Medium / Hard
3. Two modes:
4. Standard Mode (University / School)
5. Competitive Mode (JEE / NEET / GATE style)
6. Rule-based NLP logic (Explainable – not black-box AI)
7. Difficulty distribution chart (visual analytics)
8. Modern Professional Neubrutalism UI
9. Runs fully inside Python virtual environment (venv)

🧠 Technology Stack

1. Backend: Python, Flask
2. NLP Logic: Bloom’s Taxonomy + Heuristic Rules
3. PDF Parsing: PyPDF2
4. Frontend: HTML, CSS (Neubrutalism), JavaScript
5. Charts: Chart.js

📂 What You Will Get

1. Complete source code
2. Project report (PDF / DOC)
3. PPT for presentation
4. Viva questions & answers
5. Setup instructions (venv-based)

🎓 Suitable For

1. MCA Mini Project
2. BCA Mini Project
3. BTech Minor / Academic Project
4. Portfolio & demo purpose

⚠️ Note

This project uses rule-based NLP, not heavy Machine Learning.

It is designed to be simple, explainable, and viva-friendly, which is ideal for academic evaluation.