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NLP based movie recommender that tokenizes keywords and converts into the vectorized format and use cosine distance as metric of evaluation.

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CEPHAL0/Movie-Recommendation-System

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Movie Recommendation System

This is a movie recommendation system that uses Content Based Recommendation to display movies related to the movie, one has just watched

Dataset Used:

The dataset used in this project was fetched from kaggle named:

TMDB 5000 Movie Dataset

Link to dataset: https://www.kaggle.com/datasets/tmdb/tmdb-movie-metadata

Libraries Needed:

pip install nltk
pip install pandas
pip install sklearn
pip install numpy
pip install streamlit

To run the website

Run the main.ipynb file from top to bottom
enter the following command in the terminal

streamlit run app.py

Accuracy metric yet to be calculated

Note that this is merely a prototype and is not optimized

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NLP based movie recommender that tokenizes keywords and converts into the vectorized format and use cosine distance as metric of evaluation.

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