This project aims to develop a movie recommendation system utilizing Natural Language Processing (NLP) techniques. By analyzing user reviews,genres, overview, and other textual data related to movies, the system will extract meaningful insights to generate personalized recommendations. Leveraging NLP models the system will understand user preferences and movie characteristics to provide accurate and relevant suggestions, enhancing the user's movie-watching experience.
In addition to employing Natural Language Processing (NLP) techniques for movie recommendation, this project integrates Streamlit, a Python library, to create an interactive web application programming interface (API). Through Streamlit, users can access the movie recommendation system via a user-friendly interface, enabling seamless interaction and exploration of movie suggestions based on their preferences. The integration of Streamlit enhances the accessibility and usability of the recommendation system, providing an intuitive platform for users to discover new movies tailored to their tastes.