Movie Recommendation System
About This Project
A machine learning-based movie recommendation system that suggests movies based on user preferences and viewing history. Uses collaborative filtering and content-based filtering algorithms. Includes a web interface for browsing and rating movies.
Key Features
- User registration and profile management
- Movie browsing with search and filter
- Rate movies (1-5 stars)
- Personalized movie recommendations
- Similar movies suggestions
- Trending and top-rated movies
- Watchlist management
How It's Built
Collect and Prepare Data
Use the MovieLens dataset or scrape movie data from TMDB API. Clean and preprocess the data for modeling.
Build the Recommendation Engine
Implement collaborative filtering using matrix factorization (SVD). Build content-based filtering using movie genres, directors, and keywords.
Create the Backend API
Build a Flask REST API with endpoints for user auth, movie browsing, ratings, and recommendations.
Train the Model
Train the recommendation model on the dataset. Save the model using pickle for production use.
Build the Frontend
Create a React frontend with movie grid, detail pages, rating system, and recommendation carousel.
Deploy
Deploy the Flask API to Heroku or Railway. Deploy the React frontend to Vercel. Connect them together.
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