- Built a recommender system based on content based filtering.
- Preprocessed Dataset of Tmdb with 5000 movies and created Tags for all movies.
- With help of scikit-learn Converted all tags to vector and calculated cosine distance of all vectors with respect to each other to find most familiar movies.
- Used streamlit to create a web application for deploying Machine learing model.
- Application fetches five most similar movies with respect to one entered and prints their names with respective posters.Posters are fetched with the help of Tmdb API.
click https://atharv-a-recommendersystem-main-jsxcnr.streamlit.app/ to get site.
Type movie name in the search box.
Then Click on recommend button.
Five most similiar movies will be shown to you.
