- Built a recommender system based on content based filtering.
- Preprocessed Dataset of Tmdb with 5000 movies and created Tags for all movies.
- Converted all tags to vector and calculated cosine distance of all vectors with respect to each other to find most fimilar movies.
- Used streamlit to create a web application for deploying Machine learing model.
- Model fetches five most similar movies with respect to one entered and prints their names with respective posters fetched with the help of Tmdb API.
Type movie name in the search box.
Then Click on recommend button.
Five most similiar movies will be shown to you.