Movie Recommender System

Overview This is a content-based movie recommender system which uses cosine similarity to predict similar movies. Tags are created for content-based similarity matching to display the required results.

Tech Stack Languages - Python Developer Tools - PyCharm, Jupyter notebook API - tmdb API is used

Project Flow

graph TD;
    DATA-->PREPROCESSING;
    PREPROCESSING-->MODEL;
    MODEL-->WEBSITE;

Loading

How to run

• Clone the project to your device
• Download the dataset - https://www.kaggle.com/datasets/tmdb/tmdb-movie-metadata?select=tmdb_5000_movies.csv
• Place the dataset in the project folder
• Compile and run Jupyter notebook
• Create a virtual environment in Pycharm and place code file and app.py file in it
• Place movies.pkl and similarities.pkl (generated files) in the project folder
• in terminal wirte - streamlit run app.py

tmdb API It is used to fetch movie posters from the site

Result The given system matches and compares the nearest distance of the input movies to all the movie vectors and generates output with required number of least distant movie vectors.

HOMEPAGE

Drop Down Menu (you can type also)

Results