"Movie Recommendation" Movie Recommendation is a project that leverages machine learning to help you choose your next movie based on your preferences. The recommendation system is built using the sklearn library for vectorization and computes cosine similarity among over 5000 English movies from TMDb's dataset.
How it Works Data Processing: The project utilizes a dataset from TMDb, containing information about a wide range of movies. The textual data, such as movie titles, genres, and overviews, is preprocessed by refining, stemming, and merging to create a feature matrix.
Vectorization: The sklearn library is employed for vectorization, transforming the textual data into a format suitable for machine learning algorithms.
Cosine Similarity: The cosine similarity between movies is calculated using the vectorized data. This similarity measure helps identify movies that are closely related in terms of content.
Streamlit Web App: The project is presented as a user-friendly web application created with Streamlit. Users can select their favorite movie, and the system recommends a list of movies with similar characteristics.
Usage Clone the repository: bash Copy code git clone https://github.com/yourusername/movie-recommendation.git cd movie-recommendation Install the required dependencies:
bash Copy code pip install -r requirements.txt Run the Streamlit app:
bash Copy code streamlit run main.py Open your web browser and go to the provided URL to interact with the Movie Recommendation app.
About the Author This project is maintained by Amit Mishra. Feel free to reach out for any questions or improvements.
Acknowledgments TMDb for providing the movie dataset. Streamlit for making it easy to create interactive web applications with Python. scikit-learn for the machine learning functionality. Enjoy discovering new movies with Movie Recommendation!