onyx-learning

byThanush H

You are an expert full-stack developer and ML engineer. I am building a hackathon project called *"AI Personalized Learning Assistant"* and I need a COMPLETE working solution using: * Frontend: React (modern UI) * Backend: Python Flask * Machine Learning: scikit-learn * Dataset: Student performance dataset (CSV) ⚠️ IMPORTANT CONSTRAINTS: * This is a 6-hour hackathon project * Keep implementation SIMPLE but IMPRESSIVE * Avoid unnecessary complexity * Everything must WORK without errors * No external paid APIs * Use dataset-based ML (not just prompts) --- # 🎯 PROJECT GOAL Build a system that: 1. Takes student marks  subject wise and analysis it  csv file should contain below headings: 1.student name 2.subject and its marks 3.study time 4.attendance 2. Uses ML to classify student level: * Beginner * Intermediate * Advanced 3. Provides: * Personalized recommendations * Simple study plan 4. Displays results in a modern UI dashboard 5. --- # 🧠 TECHNIQUES TO INCLUDE Explicitly implement and explain: 1. Recommendation System * Based on weak subjects 2. Classification Model * Use ML (RandomForest or similar) 3. NLP (basic) * Generate study plan using rule-based or simple logic * Use NLP to analyze student responses, detect key concepts, identify misconceptions, and provide targeted feedback based on answer quality and sentiment --- # 📁 OUTPUT REQUIREMENTS Give FULL code for: ## 1. Dataset * Provide sample CSV (students.csv) ## 2. ML Model (model.py) * Load dataset * Create labels * Train model * Predict function ## 3. Backend (Flask) * app.py with: * /analyze endpoint * JSON input/output * Integration with ML model ## 4. Frontend (React) * Modern UI (dark theme) * Components: * Form (input marks) * Dashboard * Result display * Use axios for API calls ## 5. Styling * Clean, modern, hackathon-winning UI * Cards, buttons, layout --- # 🏗️ ARCHITECTURE Include a clear explanation of: Frontend → Backend → ML Model → Dataset → Output --- # 🚀 EXECUTION STEPS Provide step-by-step instructions: * Install dependencies * Run backend * Run frontend * Test API --- # 🎤 HACKATHON EXPLANATION Also include: 1. How to explain project in 1 minute 2. Where each technique is used: * Recommendation system * Classification * NLP 3. Expected judge questions + answers --- # ⚠️ ERROR HANDLING Ensure: * No missing imports * No broken code * Works on Windows * Clear instructions --- # 🏆 FINAL OUTPUT The result should be: * Fully working ML-based web app * Clean UI * Real ML usage * Easy to demo in hackathon --- Now generate the COMPLETE solution step-by-step.