This project implements a robust Content-Based Recommendation Engine for movies using the K-Means clustering algorithm. The system analyzes user preferences based on their previously liked movies and suggests the top 15 movies that align with their taste. Deployed on Heroku, this recommendation system provides users with personalized movie suggestions, enhancing their viewing experience.
- K-Means Algorithm: Utilizes K-Means clustering to group movies with similar content, enabling accurate recommendations.
- User-Centric: Recommends movies based on the user's history of liked movies, ensuring personalized suggestions.
- Top 15 Recommendations: Delivers a curated list of the top 15 movies tailored to the user's preferences.
- Heroku Deployment: Easily accessible on the cloud, providing a seamless user experience.
- Input your liked movies.
- Receive top 15 movie recommendations based on your preferences.
- Python
- K-Means Clustering
- Heroku
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