In this project I will be using the concepts and tools learned in CNT 4147, specifically Apache Hadoop and Apache Spark in order to collect, process, clean, analize and deploy two Netflix Movies and Shows open datasets; a titles dataset that includes the following features :

Field	Description
id	The title ID on JustWatch.
title	The name of the title.
show_type	TV show or movie.
description	A brief description.
release_year	The release year.
age_certification	The age certification.
runtime	The length of the episode (SHOW) or movie.
genres	A list of genres.
production_countries	A list of countries that produced the title.
seasons	Number of seasons if it's a SHOW.
imdb_id	The title ID on IMDB.
imdb_score	Score on IMDB.
imdb_votes	Votes on IMDB.
tmdb_popularity	Popularity on TMDB.
tmdb_score	Score on TMDB.

and a credits dataset with the following features:

Field	Description
person_ID	The person ID on JustWatch.
id	The title ID on JustWatch.
name	The actor or director's name.
character_name	The character name.
role	ACTOR or DIRECTOR.
production_countries	A list of countries that produced the title.
seasons	Number of seasons if it's a SHOW.
imdb_id	The title ID on IMDB.

this, with the ultimate purpose, to create a GUI where the user can interact with two different systems: a filtering system which will take different parameters selected by the user and return all the different Netflix Movies or Shows that match those specific parameters, and a content recommendation system where the user will input the name of a Netflix Movie or Show and the model will return the top 3 most similar titles to the one the user selected as well as a similarity score provided by the use of a cosine similarity approach.