AI-Powered Major Recommendation System

An intelligent system combining machine learning and real-time web interaction to suggest optimal academic majors for students.

Explore Features

Project Overview

This AI-driven recommendation system combines machine learning with real-time web interaction to help students identify their optimal academic major. The system analyzes academic performance and preferences using advanced algorithms while providing explainable AI insights.

Key technical challenges addressed:

Integrating multiple ML models (KNN + K-Means) for accurate predictions
Implementing real-time communication between Python and Node.js
Designing RAG pipeline with LLaMA API for explainable recommendations
Handling scalable student data storage with MongoDB

Key Features

Dual ML Approach: Combines KNN classification with K-Means clustering validation
RAG Integration: Generates natural language explanations using LLaMA API
Real-Time Updates: WebSocket communication for live recommendations
Scalable Backend: Flask + Node.js hybrid architecture with MongoDB
Interactive Web Interface: Dynamic visualization of recommendation results

Technical Architecture

The system employs a sophisticated tech stack enabling real-time AI recommendations:

Machine Learning: Scikit-Learn for KNN and K-Means implementation
Backend: Flask (Python) for ML models + Node.js (Express) for web services
Database: MongoDB for storing student profiles and historical data
Real-Time: Socket.IO for live updates between components
AI Integration: LLaMA API for RAG-powered explanations
Frontend: Interactive dashboard with HTML/CSS/JavaScript

AI-Powered Major Recommendation System

Project Overview

Key Features

Technical Architecture

Project Gallery

Explore the Code