Movie recommendation systems use a set of different filtration strategies and algorithms to help users find the most relevant films. The most popular categories of the ML algorithms used for movie recommendations include content-based filtering and collaborative filtering systems.
This repository is mainly based on content based filtering.
A filtration strategy for movie recommendation systems, which uses the data provided about the items (movies). This data plays a crucial role here. The recommendation system analyzes the past preferences of the user concerned, and then it uses this information to try to find similar movies. This information is available in the database (e.g., lead actors, director, genre, etc.). After that, the system provides movie recommendations for the user. Here the various elements are covered in the movie dataset I used, using which the user will get the similar movie recommendations.
I have also used APIs to fetch the posters of the movies getting recommended and later deployed whole product on heroku site mentioned down below.