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🎓 MOOC Course Recommendation System

An intelligent web-based recommendation system that helps users discover online courses tailored to their interests, learning goals, and engagement history. Built with Python, Flask, Pandas, and scikit-learn, this system combines content-based filtering (TF-IDF), collaborative filtering, and a genetic algorithm for final course selection.

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📌 Features

• 🔐 User Registration & Login
• 🎯 Personalized Recommendations based on:
  • User interests (topics, difficulty, rating)
  • Previous engagement
  • Other users’ course preferences (collaborative filtering)
  • Genetic algorithm optimization
• 📚 Add/Remove Courses to/from Dashboard
• 📊 User Feedback Logging for future training
• 📈 Accuracy measured using Precision@K

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🧠 Recommendation Strategy

1. Content-Based Filtering (TF-IDF)

• Uses TfidfVectorizer to vectorize course descriptions
• Calculates similarity between courses and user preferences using cosine similarity

2. Collaborative Filtering

• Uses user-course interaction matrix
• Computes similarity scores between users or items (e.g., using Pearson correlation)

3. Genetic Algorithm Optimization

• Selects the best subset of courses based on a fitness function considering:
  • User's preferred topics
  • Rating thresholds
  • Difficulty level alignment
• Uses selection, crossover, and mutation to evolve recommendations over generations

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🚀 Technologies Used

Tech	Purpose
Python	Core application logic
Flask	Web framework
Pandas	Data handling
scikit-learn	TF-IDF vectorizer, similarity
Jinja2	HTML templating
HTML/CSS/Bootstrap	Frontend UI
SQLite / CSV	User and course data storage

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