/Netflix-Recommendation-System

Personal project for prediction of movies based on user-user similarity and movie-movie similarity matrix. Libraries used: Surprise, Scikit-learn, Matplotlib, Seaborn, Numpy, Pandas, XGBoostetc

Primary LanguageJupyter Notebook

Netflix-Recommendation-System

Any recommender system problem can be posed both as a matrix factorization as well as regression problem. This is a beautiful project where I have tried to combine features from both to ultimately solve the problem.

The uploaded pdf file describes my research workflow. Please read it.

Heroku deployment URL: https://netflix-recommender-system-api.herokuapp.com

Please visit this site (deployed on AWS Elastic Beanstalk) (inactive now): http://netflixrecommendationsystem-env.eba-iat8iy3y.us-east-2.elasticbeanstalk.com/

Credits for front-end design of the webpage: https://github.com/aditya083-etce

Steps to run on your localhost-

  1. Clone the repository and open the command prompt in the clone directory.
  2. Install required packages pip install -r requirements.txt
  3. Run the 'application.py' file. python application.py