This is the Movie Recommendation Website in which you can search for a movie(under data of 4800
approx. movies) and get the top 7 recommendations
based on your search.See Available Movies for Search by
Clicking Here.
*Enter the
Movie Name as it is in the list.
Movie recommendation using CountVectorizer is a natural language processing (NLP)
approach for recommending movies to users based on their textual reviews or descriptions.
CountVectorizer is a method that converts text data into a numerical format that can be used by
machine learning algorithms.
In this approach, textual data from movie reviews are preprocessed and converted into a matrix
of
word counts using CountVectorizer.
Then, a similarity matrix is created to compare each movie's word count matrix with all other
movies. Finally,
the recommendation system recommends movies to the user based on the highest similarity score
with
their input text.
This approach is popular in movie recommendation systems due to its simplicity
and effectiveness.
The system is deployed using Flask framework, which enables users to interact
with the system
through a web interface.
The user can input their preferences and receive recommendations based on their input.