Project Details
This project focuses on solving a real-world problem using modern technology. It streamlines and improves processes, delivering better user experiences and efficiency. The solution highlights practical use of the tech stack, with potential for future upgrades and broader application.
Technologies Used
Live Demo/Github
Want to check?
Movie Recommendation System
This project builds a movie recommendation system that provides personalized movie suggestions using content-based filtering (cosine similarity) and sentiment analysis of critic reviews. The model leverages features like genre, director, and audience scores to recommend top movies similar to the user's input.
- Understanding content-based filtering using cosine similarity.
- Applying sentiment analysis to enrich recommendations.
- Integrating machine learning models into a Django web application.
Key Learnings from the Project
Future Scope and Enhancements
In the future, the Movie Recommendation System can be improved by integrating collaborative filtering with content-based filtering, creating a hybrid approach. This will combine user interactions (ratings, reviews) with movie features for more accurate and diverse recommendations. Adding user profiles to track preferences and watch history will lead to highly personalized suggestions.
Real-time updates based on user activity, like likes or dislikes, can help the system adapt quickly. Expanding data sources, such as incorporating streaming service APIs, will enable recommendations of new releases. Lastly, developing mobile-friendly apps will enhance accessibility and make the system more versatile.