onyx-learning
Thanush HYou 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) â ī¸ 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) --- # đ¯ 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. --- # đ§ 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 --- # đ 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 --- # đī¸ ARCHITECTURE Include a clear explanation of: Frontend â Backend â ML Model â Dataset â Output --- # đ EXECUTION STEPS Provide step-by-step instructions: * Install dependencies * Run backend * Run frontend * Test API --- # đ¤ 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 --- # â ī¸ ERROR HANDLING Ensure: * No missing imports * No broken code * Works on Windows * Clear instructions --- # đ 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.
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Student Performance Report
Personalized academic insights powered by onyx-learning's ML classification engine. This report summarizes performance metrics, identifies growth areas, and delivers a curated study plan.
Overall Classification
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Report Insights
- Performance Level: Intermediate â Based on marks, study time, and attendance data.
- Model Confidence: 82% â The ML model is highly confident in this classification.
- Next Step: Review your personalized study plan and targeted recommendations below.
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Student Input Metrics
Raw data provided at the time of analysis â used as model input features.
Performance Metrics
Live analytics based on latest submission data
Above average in Mathematics and Science. Consistent performance across core subjects.
English Comprehension, Statistics, and Organic Chemistry require additional focus.
Adaptive study plan suggests 12 hours per week with priority on weak subject areas.
Attendance and engagement metrics are strong. ML confidence in prediction is high.
Performance Analysis
Subject-wise score breakdown â student vs class average vs target
Personalized Recommendations
Based on your performance analysis, our AI model has identified the following areas to focus on for maximum improvement.
Advanced Mathematics â Calculus & Integration
Your recent assessment indicates a score of 54% in calculus topics, falling below the recommended threshold. Focused practice on integration techniques and differential equations will significantly improve your overall marks.
View DetailsâPhysics â Mechanics & Wave Theory
Attendance records show 3 missed sessions in the mechanics unit. Revisiting core principles of Newton's laws and wave propagation will close the identified knowledge gaps before the upcoming assessment.
View DetailsâComputer Science â Data Structures
Your study time allocation for this subject is 1.5 hours per week, below the recommended 3 hours. Strengthening your understanding of trees, graphs, and dynamic programming will boost your problem-solving speed.
View DetailsâEnglish Literature â Critical Analysis
Written assessments suggest difficulty structuring argumentative essays. Practicing comparative analysis techniques and expanding literary vocabulary will improve your written scores by an estimated 12-15%.
View DetailsâWeb Technologies â Frontend Development
You are performing above average in this subject (78%). A light review of accessibility standards and responsive design principles will help you maintain your lead and prepare for advanced coursework.
View DetailsâPersonalized Study Plan
5-week adaptive schedule based on your performance profile and identified weak areas.
How This Report Was Generated
Save & Share Your Results
Download a full PDF summary of the AI performance analysis, study plan, and personalized recommendations â or share it directly with your instructor.
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