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Content Based - Movie recommender system. End-to-End project, deployed on Heroku

Procfile 0.01% Python 0.27% Shell 0.02% Jupyter Notebook 99.71%
content-based-recommendation end-to-end-project heroku-deployment movie-recommendation python

movie_recommendation_system's Introduction

Movie_Recommendation_System - Content Based

1. About

Recommender System is a system that seeks to predict or filter preferences according to the user's choices. Recommender systems are utilized in a variety of areas including movies, music, news, books, research articles, search queries, social tags, and products in general.

2. Data source

TMDB dataset from #kaggle

3. Working

It works on CountVectorizer #NLP of tags (overview, genres, keywords, cast & crew).

It uses Cosine similarity metric.

4. Web App

This app is Movie Recommender System contains more then 5 thousand movies data. The system output gives you top five recommended movies based on your selected movie.

Api: Movies poster is fetched from the MovieDatabase(TMBD) api.

Webapp framework: Streamlit over flask.

Deployment is done on Heroku: WebApp

movie_recommendation_system's People

Contributors

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Forkers

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