Movie Recommendation System using Python
This is a simple movie recommendation system implemented in Python. The system uses a dataset of movies and their associated features such as genres, keywords, tagline, cast, and director. The system recommends similar movies based on the features of a given movie.
To use the system, the user selects a CSV file containing the movie data. The system then loads the data and processes it to create a matrix of feature vectors. The system uses cosine similarity to calculate the similarity between movies and recommends similar movies based on the highest similarity scores.
The system has been implemented using the Tkinter library for the graphical user interface and the pandas, numpy, difflib, and sklearn libraries for data manipulation, similarity calculations, and vectorization.
This movie recommendation system can be useful for movie enthusiasts who want to discover new movies similar to their favorite movies. It can also be used by movie streaming services to provide personalized recommendations to their users based on their viewing history and preferences.