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Movie-Recommender-System

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.

Link to Dataset

Installation

  1. Clone the repository: git clone https://github.com/<username>/movie-recommendation-system.git
  2. Install the required dependencies: pip install -r requirements.txt
  3. Run the streamlit application: streamlit run app.py

Usage

Enter the name of a movie in the search bar to get recommendations. It will display top five recommended movies along with their poster

Credit

The TMDB dataset was used to develop this project.