This is a simple Movie Recommendation System built using Python. The project recommends movies based on user preferences and is built using data processing and machine learning techniques.
The Movie Recommendation System uses a collaborative filtering approach to suggest movies to users based on their past interactions with the movie database. This system utilizes the cosine similarity metric to identify similarities between movies.
- Recommends movies based on cosine similarity.
- User-friendly interface built with Streamlit.
- Uses a precomputed similarity matrix for efficient recommendations.
- Python 3.6+
- Streamlit
- pandas
- numpy
- scikit-learn
- pickle
The dataset used in this project is from TMDB Movie Metadata. The data is preprocessed to create a similarity matrix, which is stored in the similarity.pkl file. The movie_dict.pkl file contains the movie dictionary used for lookup and display purposes.