This project is a content-based movie recommendation system that utilizes the TMDB dataset. The dataset contains two CSV files with movie data and credits, and it is pre-processed by dropping unnecessary features and applying stemming techniques to eliminate similar words. Count vectorization is used to form vectors of tags, and a streamlit application provides a user interface that displays movie recommendations with posters. The recommendation system's accuracy is improved by using relevant movie tags to suggest similar movies to users.
- Clone the repository:
git clone https://github.com/<username>/movie-recommendation-system.git - Install the required dependencies:
pip install -r requirements.txt - Run the streamlit application:
streamlit run app.py
Enter the name of a movie in the search bar to get recommendations. It will display top five recommended movies along with their poster
The TMDB dataset was used to develop this project.