Technologies Used:

• Backend: PHP / Java / Node.js

• Frontend: HTML, CSS, Bootstrap, JavaScript

• ML Tools: Python, Pandas, Scikit-learn, Surprise (for recommendation)

• Database: MySQL / MongoDB

• Optional: TensorFlow (for advanced models)

Project Objective:

To build a web-based system that recommends personalized online courses to users based on their interests, past behavior, skill level, and trending topics using data science and machine learning techniques.

Key Features:

1. User Profile Management:
   Users register and fill in details like current skill level, career goals, interests (e.g., AI, Web Dev).

2. Course Catalog Integration:
   System stores or fetches course data (titles, tags, difficulty, duration, ratings) from platforms like Coursera, edX, Udemy, etc.

3. Recommendation Engine:

   • Uses Content-Based Filtering to match course content with user profiles.

   • Optionally, Collaborative Filtering recommends courses based on similar users’ choices.

4. Search and Filter Module:
   Allows filtering by domain (e.g., AI, Business), difficulty level, platform, price, etc.

5. Ratings & Reviews System:
   Users can rate and review courses to improve recommendations.

6. Trending Course Suggestions:
   Shows popular or highly-rated courses across the platform.

7. Admin Dashboard:
   Admins can manage users, add/edit courses, and monitor usage analytics.

How Machine Learning Works:

• Data Sources:

  • Course metadata (title, description, tags, ratings)

  • User behavior (clicks, enrollments, ratings)

• Models Used:

  • TF-IDF + Cosine Similarity for content-based recommendations

  • KNN or Matrix Factorization (SVD) for collaborative filtering

  • Hybrid models for higher accuracy