This Movie Recommendation System is designed to help users discover new movies based on their interests. By analyzing the content and data of movies, the system provides personalized recommendations by identifying similar movies. The system employs a content-based filtering approach. It analyzes various features such as movie genres, actors, directors, and plot keywords to determine similarity. Vectorization is applied to transform these textual features into numerical representations.
It employs the Streamlit for the front end and integrates the TMDB API to fetch movie posters.
Python 3.7+
Streamlit
Pandas
Scikit-learn
This project is distributed under the MIT License.