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Movie Recommendation System with Web Application

This repository contains a movie recommendation system that leverages a cosine similarity algorithm to suggest movies based on genres, overview, keywords, cast, and director information.

here is the link to run on streamlit cloud :https://mrs-git-herjxg5dpwkwueceklgphw.streamlit.app/

Overview

  • Recommendation Algorithm: Utilizes cosine similarity to evaluate and rank movie similarities.
  • Feature Extraction: Implements a bag-of-words approach to vectorize movie data.
  • User Interface: Deployed using Streamlit for an interactive and user-friendly web experience.

How It Works

  1. Data Vectorization: Movie metadata (genres, overview, keywords, cast, director) is transformed using a bag-of-words model.
  2. Similarity Calculation: Cosine similarity is computed between movie vectors to identify and rank similar movies.
  3. Recommendation: Movies with the highest similarity scores are recommended to the user.

Deployment

The application is deployed using Streamlit, making it easy to interact with and explore movie recommendations.

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