/Movie-Recommender

Recommends movie based on factors like (i) Total Rating in general, (ii) Plot of the movie, and (iii)content based recommendation which includes metadata of Director, Actors, Genre, Keywords etc

Primary LanguageJupyter Notebook

Movie-Recommender

Implemented two types of recommender systems:

  • Demographic Filtering - User gets recommendations based on the movie popularity.
  • Content Based Filtering - User gets recommendations based on the movie's metadata like director, actors, genre, keywords etc.

How did I achieved it

  • Python libraries like NumPy, Pandas and Matplotlib are used to manipulate and visualize data.
  • TF-IDF, linear kernel and cosine similarities from scikit-learn library are used to generate similarity score matrix to recommend the most relevant movies.
  • Detailed step by step explanation can be found in the attached code (Movie_Recommender.ipynb)

Dataset

  • TMDb dataset of about 5000 movies (attached)