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j-a-y-e-s-h / repository
🎬 Welcome to our Movie Recommendation System repository! 🍿 Utilize our Jupyter Notebook and Streamlit app to discover personalized movie suggestions based on your favorites. Explore data preprocessing, model training, and an intuitive recommendation interface! 🚀 #MovieRecommendation #DataScience
This repository contains files for a movie recommendation system.
Download Data from Kaggle Link
Clone the repository:
git clone https://github.com/j-a-y-e-s-h/movie_recommendation.git
Navigate to the repository directory:
cd movie_recommendation
First, run the movie-recommender.ipynb file in your Jupyter Notebook environment. This notebook performs data preprocessing and model training.
After running the movie-recommender.ipynb file, it generates two files:
movie_dict.pkl: A pickle file containing movie data.similarity.pkl: A pickle file containing similarity scores.Now, run the app.py file to start the recommendation server:
streamlit run app.py
The application will launch in your browser, allowing you to select your favorite movies and get recommendations.
movie-recommender.ipynb: Jupyter Notebook containing data preprocessing and model training.app.py: Python script for running the movie recommendation server.Untitled.jpg: Image file used in the recommendation system interface.tmdb_5000_credits.csv data file.tmdb_5000_movies.csv data file.Selected from shared topics, language and repository description—not editorial ratings.
italocosilva /
🎬 Welcome to the MovieLens Recommender! This project harnesses the magic of LSTM and embeddings to make movie recommendations. Dive into Jupyter notebooks and train models to make personalized suggestions.