This project is a Movie Recommendation System built using Python, Numpy, Pandas, Scikit-learn, and Matplotlib. The system leverages machine learning techniques to suggest movies based on the user's preferences. The front-end interface is developed using the Streamlit library, providing an interactive and user-friendly experience.
Recommends movies similar to the ones a user likes based on movie metadata. Efficiently processes and analyzes large datasets using Pandas. Utilizes the CountVectorizer from Scikit-learn to convert movie descriptions into vectors for similarity calculations. Employs cosine similarity to find movies that are most similar to the user's selected movie. Matplotlib is used for visual representations of data and recommendations. A clean and intuitive user interface powered by Streamlit, allowing users to search for movies and get instant recommendations.
Clone the repository to your local machine.
Install the required dependencies using pip install -r requirements.txt.
Run the Streamlit app using the command streamlit run app.py.
Open your browser and go to http://localhost:8888/tree to interact with the recommendation system.
The dataset used in this project is publicly available from IMDb, containing metadata of thousands of movies, including titles, genres, overviews, and popularity metrics. This rich dataset allows the recommendation system to generate personalized movie suggestions based on a variety of factors.