#Movie-Recommender-system-tmdb-dataset This project is a Movie Recommender System built with Python and deployed as a Streamlit web app. It combines movie and cast datasets, cleans and processes the data, and uses text vectorization and cosine similarity to recommend movies. Users can input a movie title and get the top 5 similar movies instantly. The app is interactive, easy to use, and showcases a complete workflow from data cleaning to model deployment. Future improvements could include fuzzy search for misspelled titles, displaying movie posters, and using weighted similarity for better recommendations.