Designed and developed a full-stack web application for personalized course recommendation using the Coursera dataset. Implemented and compared multiple machine learning algorithms, including content-based filtering and collaborative filtering, to generate personalized recommendations. Integrated ML models into a Django-based production-ready system with RESTful endpoints and dynamic user interaction. Successfully presented the project at the 14th Scientific Day Showcase and was selected as one of the outstanding candidates. Strengthened expertise in recommender systems, model evaluation strategies, and deploying ML applications in real-world web environments.

Course Recommendation System

A full-stack ML-powered course recommendation system using content-based and collaborative filtering algorithms.

Project Overview

This project provides personalized course recommendations using machine learning algorithms trained on the Coursera dataset. The system was successfully presented at the 14th Scientific Day Showcase and selected as one of the outstanding candidates.

Key Features

Multiple ML Algorithms: Content-based filtering and collaborative filtering approaches
Personalized Recommendations: User-specific course suggestions based on preferences
Full-Stack Application: Complete web application with Django backend and interactive frontend
RESTful API: Production-ready API endpoints for seamless integration
Model Comparison: Evaluated multiple algorithms for optimal performance

Technical Stack

Backend: Django framework for web development
ML Libraries: Scikit-learn, Pandas, NumPy for data processing and modeling
Database: SQLite/PostgreSQL for data persistence
Algorithms: Content-based and collaborative filtering

Achievements

Presented at the 14th Scientific Day Showcase
Selected as one of the outstanding candidates
Successfully deployed production-ready ML system
Strengthened expertise in recommender systems and model evaluation