Abstract

In this paper, we present the design and implementation of a machine learning-based movie recommender system. This system suggests movies to users based on their preferences, using a combination of similarity metrics and data from The Movie Database (TMDb) API. The recommender system is deployed as a web application using the Streamlit framework, providing an intuitive interface for users to interact with. The results demonstrate the effectiveness of the recommendation algorithm in suggesting relevant movies.