/Movie-Recommender-System

Primary LanguageJupyter Notebook

Tutorial Project Movie Recommender System

Tutorial From CampusX

Data from Kaggle

Deploy https://alrappie-mv-recommender.herokuapp.com/

Types of Recommender Systems

Content based

self based

Collaborative filtering

user to user

Hybrid

combining content based and collaborative filtering

movie['tags'] = movie['overview']+ movie['keywords'] + movie['genres'] + movie['cast'] + movie['crew'] from sklearn.feature_extraction.text import TfidfVectorizer cv = TfidfVectorizer(max_features=10000)

movie['tags'] = movie['overview']+ movie['keywords'] + movie['genres'] + movie['cast'] + movie['crew'] from sklearn.feature_extraction.text import CountVectorizer cv = CountVectorizer(max_features=10000)

movie['tags'] = movie['keywords'] + movie['genres'] + movie['cast'] + movie['crew'] from sklearn.feature_extraction.text import TfidfVectorizer cv = TfidfVectorizer(max_features=10000)