Analysis of ‘1000 Netflix Shows’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/chasewillden/netflix-shows on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

Netflix in the past 5-10 years has captured a large populate of viewers. With more viewers, there most likely an increase of show variety. However, do people understand the distribution of ratings on Netflix shows?

Content

Because of the vast amount of time it would take to gather 1,000 shows one by one, the gathering method took advantage of the Netflix’s suggestion engine. The suggestion engine recommends shows similar to the selected show. As part of this data set, I took 4 videos from 4 ratings (totaling 16 unique shows), then pulled 53 suggested shows per video. The ratings include: G, PG, TV-14, TV-MA. I chose not to pull from every rating (e.g. TV-G, TV-Y, etc.).

Acknowledgements

The data set and the research article can be found at The Concept Center

Inspiration

I was watching Netflix with my wife and we asked ourselves, why are there so many R and TV-MA rating shows?

--- Original source retains full ownership of the source dataset ---

Industry data revealed that Slovakia had the most extensive Netflix media library worldwide as of July 2024, with over 8,500 titles available on the platform. Interestingly, the top 10 ranking was spearheaded by European countries. Where do you get the most bang for your Netflix buck? In February 2024, Liechtenstein and Switzerland were the countries with the most expensive Netflix subscription rates. Viewers had to pay around 21.19 U.S. dollars per month for a standard subscription. Subscribers in these countries could choose from between around 6,500 and 6,900 titles. On the other end of the spectrum, Pakistan, Egypt, and Nigeria are some of the countries with the cheapest Netflix subscription costs at around 2.90 to 4.65 U.S. dollars per month. Popular content on Netflix While viewing preferences can differ across countries and regions, some titles have proven particularly popular with international audiences. As of mid-2024, "Red Notice" and "Don't Look Up" were the most popular English-language movies on Netflix, with over 230 million views in its first 91 days available on the platform. Meanwhile, "Troll" ranks first among the top non-English language Netflix movies of all time. The monster film has amassed 103 million views on Netflix, making it the most successful Norwegian-language film on the platform to date.

Explore on FlixGem.com, powered by Polymer Search.

Context Netflix has a quantity-over-quality problem. This is part of an effort to help solve this. I was trying to figure out a way to find hidden gems in their catalog but found it exceedingly hard to get the latest dataset that has ratings and many other attributes to help make sense of it. To help me and others dig deep into the latest Netflix content, I created this dataset. This is the same dataset that powers FlixGem.com, the aforementioned side project.

Content This dataset combines data sources from Netflix, Rotten Tomatoes, IMBD, posters, box office information, trailers on YouTube, and more using a variety of APIs. Note that there is no official Netflix API.

I also added a unique metric called "Hidden Gem Score", which I calculated using low review count and high rating. Lower the review count and higher the user rating, higher the hidden gem score.

Freshness Recent Netflix data is incredibly hard to come up. This dataset is updated every month. This was lasted updated in early April 2021.

Inspiration Find correlations between ratings, actors, directors, box office, and more. Find patterns around the quality of movies and their various characteristics like language, genre, actors, etc. Discover hidden gems in different regions. Interactively browsing the dataset with Flixgem.com Explore this dataset using Polymer Search: FlixGem.com.

Polymer Search uses data algorithms and some AI to auto-create a fully interactive search & knowledge discovery interface for any structured data set.

Original Data Source: Latest Netflix data with 26+ joined attributes

Netflix's global subscriber base has reached an impressive milestone, surpassing *** million paid subscribers worldwide in the fourth quarter of 2024. This marks a significant increase of nearly ** million subscribers compared to the previous quarter, solidifying Netflix's position as a dominant force in the streaming industry. Adapting to customer losses Netflix's growth has not always been consistent. During the first half of 2022, the streaming giant lost over *** million customers. In response to these losses, Netflix introduced an ad-supported tier in November of that same year. This strategic move has paid off, with the lower-cost plan attracting ** million monthly active users globally by November 2024, demonstrating Netflix's ability to adapt to changing market conditions and consumer preferences. Global expansion Netflix continues to focus on international markets, with a forecast suggesting that the Asia Pacific region is expected to see the most substantial growth in the upcoming years, potentially reaching around **** million subscribers by 2029. To correspond to the needs of the non-American target group, the company has heavily invested in international content in recent years, with Korean, Spanish, and Japanese being the most watched non-English content languages on the platform.

Analysis of ‘Netflix Top 10 Weekly Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/mikitkanakia/netflix-top-10-weekly-dataset on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

OTT platforms are growing in the last few years. Netflix is one of the top OTT platforms with maximum subsriber and viewership. Netflix has released Top 10 Movies and TV across weeks where we can analyze the viewership and movie content.

Content

The data is present in the excel sheets and it was directly downloaded from the website and will be updated on weekly basis.

We have two files in the dataset.

1) All Weeks Global Global Top 10 viewership counts across the weeks.

2) All Weeks Countries Per Countrywise Top 10 List of Movies and TV

Acknowledgements

Last week Netflix has started publishing its data to the public domain. The data is available on https://top10.netflix.com/

Inspiration

What are the viewership distribution across top 10 movies and TV and change on the weekly basis? We can find which countries have similar viewership?

--- Original source retains full ownership of the source dataset ---

Analysis of ‘Netflix TV Series Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/harshitshankhdhar/netflix-and-amazon-prime-tv-series-dataset on 13 February 2022.

--- Dataset description provided by original source is as follows ---

Context

This data is scraped from wikipedia site.

Content

• Title - Name of the Series.
• Genre - Genre of the series.
• Premiere - Released Year
• No_of_Seasons - Total number of Seasons
• No_of_Episodes - Total number of episodes.

Inspiration

• How many seasons and episodes a series have?
• When it released?
• What are the different genre of the TV Series?

--- Original source retains full ownership of the source dataset ---

Context

Netflix held the Netflix Prize open competition for the best algorithm to predict user ratings for films. The grand prize was $1,000,000 and was won by BellKor's Pragmatic Chaos team. This is the dataset that was used in that competition.

Content

This comes directly from the README:

TRAINING DATASET FILE DESCRIPTION

The file "training_set.tar" is a tar of a directory containing 17770 files, one per movie. The first line of each file contains the movie id followed by a colon. Each subsequent line in the file corresponds to a rating from a customer and its date in the following format:

CustomerID,Rating,Date

• MovieIDs range from 1 to 17770 sequentially.
• CustomerIDs range from 1 to 2649429, with gaps. There are 480189 users.
• Ratings are on a five star (integral) scale from 1 to 5.
• Dates have the format YYYY-MM-DD.

MOVIES FILE DESCRIPTION

Movie information in "movie_titles.txt" is in the following format:

MovieID,YearOfRelease,Title

• MovieID do not correspond to actual Netflix movie ids or IMDB movie ids.
• YearOfRelease can range from 1890 to 2005 and may correspond to the release of corresponding DVD, not necessarily its theaterical release.
• Title is the Netflix movie title and may not correspond to titles used on other sites. Titles are in English.

QUALIFYING AND PREDICTION DATASET FILE DESCRIPTION

The qualifying dataset for the Netflix Prize is contained in the text file "qualifying.txt". It consists of lines indicating a movie id, followed by a colon, and then customer ids and rating dates, one per line for that movie id. The movie and customer ids are contained in the training set. Of course the ratings are withheld. There are no empty lines in the file.

MovieID1:

CustomerID11,Date11

CustomerID12,Date12

...

MovieID2:

CustomerID21,Date21

CustomerID22,Date22

For the Netflix Prize, your program must predict the all ratings the customers gave the movies in the qualifying dataset based on the information in the training dataset.

The format of your submitted prediction file follows the movie and customer id, date order of the qualifying dataset. However, your predicted rating takes the place of the corresponding customer id (and date), one per line.

For example, if the qualifying dataset looked like:

111:

3245,2005-12-19

5666,2005-12-23

6789,2005-03-14

225:

1234,2005-05-26

3456,2005-11-07

then a prediction file should look something like:

111:

3.0

3.4

4.0

225:

1.0

2.0

which predicts that customer 3245 would have rated movie 111 3.0 stars on the 19th of Decemeber, 2005, that customer 5666 would have rated it slightly higher at 3.4 stars on the 23rd of Decemeber, 2005, etc.

You must make predictions for all customers for all movies in the qualifying dataset.

THE PROBE DATASET FILE DESCRIPTION

To allow you to test your system before you submit a prediction set based on the qualifying dataset, we have provided a probe dataset in the file "probe.txt". This text file contains lines indicating a movie id, followed by a colon, and then customer ids, one per line for that movie id.

MovieID1:

CustomerID11

CustomerID12

...

MovieID2:

CustomerID21

CustomerID22

Like the qualifying dataset, the movie and customer id pairs are contained in the training set. However, unlike the qualifying dataset, the ratings (and dates) for each pair are contained in the training dataset.

If you wish, you may calculate the RMSE of your predictions against those ratings and compare your RMSE against the Cinematch RMSE on the same data. See http://www.netflixprize.com/faq#probe for that value.

Acknowledgements

The training data came in 17,000+ files. In the interest of keeping files together and file sizes as low as possible, I combined them into four text files: combined_data_(1,2,3,4).txt

The contest was originally hosted at http://netflixprize.com/index.html

The dataset was downloaded from https://archive.org/download/nf_prize_dataset.tar

Inspiration

This is a fun dataset to work with. You can read about the winning algorithm by BellKor's Pragmatic Chaos here

Context Dataset contains the list and metadata of all TV Shows and Movies available on Netflix currently about 7000 taken from the IMDB website. Upvote if you liked it.

Content netflix_list.csv

imdb_id : Unique show identifier. title : Title of the show. popular_rank : Ranking as given by IMDB when filtered by popularity. certificate : Contains the age certifications received by the show. Many null values. startYear : When the show was first broadcasted. endYear : Year of show ending episodes : Number of episodes in the show. 1 for movies. type : Movie or Series orign_country : Country of origin of the show language : Language of the show. plot : Synopsis of the show. summary : Summary of the story of the show. rating : Average rating given to the show. numVotes : Number of votes received by the show. genres : Genre the show belongs to. isAdult : 1 If adult content present. 0 if not. cast : Main cast of the show in list format. image_url : Link to poster image. Acknowledgements This is collected from IMDB website Data collected by web scrapping through the shows ranking pages with filtered to show Netflix related content(16000+ entries) and noting down the imdb_id, followed by single page search for each collected ID and unique title name.

Original Data Source:Netflix Movie and TV Shows (June 2021)

Analysis of ‘Trending TV Shows on Netflix’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/ritesh2000/trending-tv-shows-on-netflix on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

The Dataset is Collected from: * The dataset is scraped from Reelgood.com

Content

This Data is a collection of top 50 trending Tv shows currently streaming on Netflix.

Dataset Distribution:

• Titles: It contains the name of the Top Tv shows.
• Year: Which Year this Tv Show Released?
• Netflix_Rating: Rated By Netflix.
• IMDB_Rating: Rated By IMDB.
• Netflix: Currently Streaming on Netflix or Not.

Inspiration

• The year during which a TV Show was released.
• IMDB Ratings for all TV Shows.
• https://www.kaggle.com/ruchi798/movies-on-netflix-prime-video-hulu-and-disney

--- Original source retains full ownership of the source dataset ---

Overview

Streaming Services Statistics: Streaming services have transformed the entertainment landscape, revolutionizing how people consume content.

The advent of high-speed internet and the proliferation of smart devices have fueled the growth of these platforms, offering a wide array of movies, TV shows, music, and more, at the viewers' convenience.

This introduction provides an overview of key statistics that shed light on the impact, trends, and challenges within the streaming industry.