The Movie Recommender System is a web-based project that assists users in finding similar movies to the ones they've already watched and enjoyed. This system utilizes machine learning techniques, specifically the CountVectorizer model and cosine similarity as a distance matrix, to suggest a list of similar movies based on user input.
- Recommends similar movies based on user input.
- Uses CountVectorizer to convert movie titles into numerical vectors.
- Calculates cosine similarity to determine the similarity between movies.
- User-friendly web interface built using Streamlit.
- The user selects a movie title from a list of 5000 movies.
- Upon clicking the "Find Similar Movies" button, the system processes the selected movie.
- The CountVectorizer model transforms the movie titles into numerical vectors.
- The system calculates the cosine similarity between the selected movie and all other movies.
- The top 5 movies with the highest cosine similarity are displayed as recommendations to the user.
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Clone the repository:
git clone https://github.com/your-username/movie-recommender-system.git cd movie-recommender-system -
Install the required packages using pip:
pip install -r requirements.txt
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Run the Streamlit app:
streamlit run app.py
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Access the app by visiting http://localhost:8501 in your web browser.
- Open the web application using the link provided after running the app.
- Choose a movie from the dropdown list.
- Click the "Find Similar Movies" button.
- The recommended movies will be displayed below.
- The movie data used for this project was obtained from source-link.
- Special thanks to the Streamlit team for providing an intuitive platform for creating interactive web applications.
