GMDB is the ultra-simple, cross-platform Movie Library with Features (Search, Take Note, Watch Later, Like, Import, Learn, Instantly Torrent Magnet Watch)
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Updated
Jan 28, 2022 - Go
Netflix is an American streaming service that offers a wide variety of TV shows, movies, documentaries, and more. It was founded in 1997 as a DVD-by-mail service, but in 2007 it started offering streaming video-on-demand content. Today, Netflix is available in over 190 countries and has over 200 million subscribers worldwide. It produces its own original content, such as Stranger Things, The Crown, and Narcos, and also licenses content from other studios and networks. Users can watch Netflix on a variety of devices, including smart TVs, smartphones, tablets, and gaming consoles.
GMDB is the ultra-simple, cross-platform Movie Library with Features (Search, Take Note, Watch Later, Like, Import, Learn, Instantly Torrent Magnet Watch)
Netflix Data Analysis notebook project. Created using Google collab
In this notebook I have tried to use the data provided by Netflix and implement two recommender systems.
Jupyter Notebook dedicated to the detailed analysis of the Netflix movies and tv shows using the R programming language
Demonstrates data analysis and visualization techniques for Netflix movies using Python in a Jupyter notebook. This is a DataCamp project.
This repo contains the implementation of netflix's original recommender system based on matrix factorization which changed the recommender systems forever 10 years back. Delve in to the notebooks to learn the intricacies of the magic box that knows your exact taste.
The information in this notebook relates to Netflix. Using this, we can identify the most well-known television programmes and provide more information.
This repository contains the Jupyter notebooks and datasets for the Microsoft's Explore space with Python and Visual Studio Code; inspired by Netflix's Over the Moon.
GitHub repository containing a Jupyter notebook for a DataCamp guided project on analyzing Netflix movie durations from 2011 to 2020, covering data loading, visualization, DataFrame creation, and exploratory data analysis.