This is movie recommender end to end project
This project involves building a Content-Based Movie Recommender System that suggests movies to users based on the content features of movies they have liked in the past. Instead of relying on other users' preferences, this system focuses on movie attributes such as genres, keywords, cast, director, and plot summaries to find similar films. Using techniques like TF-IDF vectorization and cosine similarity, the system analyzes textual and categorical data to recommend movies that are most similar in content to a given input movie. This approach provides personalized recommendations even when user interaction data is limited.