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 subscribers and revenue by country’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/pariaagharabi/netflix2020 on 28 January 2022.

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

I prepare this dataset for one of my courses to show how Netflix’s subscription figures and Netflix's revenue($) have grown in four different regions: - the United States and Canada, - Europe, the Middle East, and Africa, - Latin America, - Asia-Pacific over the last 2.5 years. According to the final month of the quarter 2020(March) was being the start of the global coronavirus pandemic in many countries, Netflix noted that it added 26 million paid new subscribers in the first two quarters of 2020 alone; in 2019, the company added 28 million subscribers in total.

Dataset Description: This dataset contains four CSV files. 1. DataNetflixRevenue2020_V2.csv: three columns Area, Years, Revenue.

1. DataNetflixSubscriber2020_V2.csv: three columns Area, Years, Subscribers.

2. NetflixSubscribersbyCountryfrom2018toQ2_2020.csv: eleven columns Area, Q1 - 2018, Q2 - 2018, Q3 - 2018, Q4 - 2018, Q1 - 2019, Q2 - 2019, Q3 - 2019, Q4 - 2019, Q1 - 2020, Q2 - 2020

3. Netflix'sRevenue2018toQ2_2020.csv: eleven columns Area, Q1 - 2018, Q2 - 2018, Q3 - 2018, Q4 - 2018, Q1 - 2019, Q2 - 2019, Q3 - 2019, Q4 - 2019, Q1 - 2020, Q2 - 2020

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

Here is the full breakdown of Netflix subscribers by region.

Here is the full breakdown of Netflix global subscribers by year since 2013.

In this post, you'll see how the Netflix platform is evolving, how many users Netflix has and how they perform against the growing competition.

Netflix Prize consists of about 100,000,000 ratings for 17,770 movies given by 480,189 users. Each rating in the training dataset consists of four entries: user, movie, date of grade, grade. Users and movies are represented with integer IDs, while ratings range from 1 to 5.

Netflix produced more than 2,769 hours of original content in 2019. This was a huge 80.15% increase compared to 2018. Netflix had over 2,000 originals at the beginning of 2021.

Netflix is one of the most popular media and video streaming platforms, boasting over 200 million subscribers globally as of mid-2021. This tabular dataset consists of listings of all the movies and TV shows available on Netflix, along with details such as cast, directors, ratings, release year, duration, etc.

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

In 2024, Netflix revealed that it had 89.63 million paying streaming subscribers in the United States and Canada. North America had long been Netflix's biggest market, though subscriber numbers in the EMEA region surpassed that in the U.S. and Canada for the first time during 2022. The number of paid streaming memberships in Asia Pacific grew the most, by 13 percent compared with the previous year.

Netflix has been met with tons of competition from major multinational companies. These are the key Netflix Statistics you need to know.

These are the top 10 countries for Netflix in terms of penetration rate.

Here is the breakdown of Netflix’s revenue earnings year over year from 2011.

The Measurable AI Netflix Email Receipt Dataset is a leading source of email receipts and transaction data, offering data collected directly from users via Proprietary Consumer Apps, with millions of opt-in users.

We source our email receipt consumer data panel via two consumer apps which garner the express consent of our end-users (GDPR compliant). We then aggregate and anonymize all the transactional data to produce raw and aggregate datasets for our clients.

Use Cases Our clients leverage our datasets to produce actionable consumer insights such as: - Market share analysis - User behavioral traits (e.g. retention rates) - Average order values - Promotional strategies used by the key players. Several of our clients also use our datasets for forecasting and understanding industry trends better.

Coverage - Asia (Japan) - EMEA (Spain, United Arab Emirates)

Granular Data Itemized, high-definition data per transaction level with metrics such as - Order value - Items ordered - No. of orders per user - Delivery fee - Service fee - Promotions used - Geolocation data and more

Aggregate Data - Weekly/ monthly order volume - Revenue delivered in aggregate form, with historical data dating back to 2018. All the transactional e-receipts are sent from the Careem Now food delivery app to users’ registered accounts.

Most of our clients are fast-growing Tech Companies, Financial Institutions, Buyside Firms, Market Research Agencies, Consultancies and Academia.

Our dataset is GDPR compliant, contains no PII information and is aggregated & anonymized with user consent. Contact business@measurable.ai for a data dictionary and to find out our volume in each country.

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.

Contexto El Procesamiento del Lenguaje Natural es una de las áreas de la inteligencia artificial muy estudiada hoy en día que tiene entre otros objetivos el entendimiento del lenguaje natural. El NLP esta avanzando cada día más pero se centra mucho en la lengua inglesa. Con este dataset se pretende aportar a la comunidad un pequeño corpus en Español con criticas de películas obtenidas de la web de www.filmaffinity.com

Contenido

Este dataset (corpus) contiene criticas realizadas por los usuarios de www.filmaffinity.com sobre todas las películas y series españolas (Mas de 1000 peliculas).

El dataset (copus) esta formado por:

film_name: Título de la película. gender: Genero de la película (comedia, terror, acción, etc.) film_avg_rate: Nota media de la película (votos de todos los usuarios) review_rate: Nota que el usuario que hace la crítica pone a la película. review_title: Título de la crítica. review_text: Crítica de la película. Agradecimientos www.filmaffinity.com Fue en base también a este dataset que nos inspiramos para realizar uno de este estilo: https://www.kaggle.com/datasets/ricardomoya/criticas-peliculas-filmaffinity-en-espaniol

Inspiración Con este dataset espero que los usuarios de Kaggle de habla hispana se animen a compartir conocimiento de Procesamiento de Lenguaje Natural por medio de Notebooks y que podamos aprender sobre NLP en Español.

Original Data Source: Críticas Filmaffinity Netflix Español (+10000)

English
Context
Natural Language Processing (NLP) is one of the most studied areas of artificial intelligence today, with one of its goals being the understanding of natural language. NLP is advancing every day, but it heavily focuses on the English language. This dataset aims to contribute to the community by providing a small corpus in Spanish with movie reviews obtained from the website www.filmaffinity.com.

Content
This dataset (corpus) contains reviews made by users of www.filmaffinity.com about all Spanish movies and series (more than 1000 films).

The dataset (corpus) consists of:

film_name: Title of the movie.
gender: Genre of the movie (comedy, horror, action, etc.)
film_avg_rate: Average rating of the movie (votes from all users).
review_rate: Rating that the user writing the review gives to the movie.
review_title: Title of the review.
review_text: Review of the movie.
Acknowledgements
www.filmaffinity.com
This dataset also inspired us to create a similar one: https://www.kaggle.com/datasets/ricardomoya/criticas-peliculas-filmaffinity-en-espaniol

Inspiration
With this dataset, I hope Spanish-speaking Kaggle users are encouraged to share knowledge of Natural Language Processing through Notebooks so we can learn more about NLP in Spanish.

In 2025, the number of Netflix subscribers in India was expected to gross nearly ***** million. The forecast suggested that the subscriber base would expand at a tremendous pace in the span of a decade.

The average Netflix user spends 3.2 hours per day streaming content on Netflix.

Browse Netflix Inc (NFLX) market data. Get instant pricing estimates and make batch downloads of binary, CSV, and JSON flat files.

Consolidated last sale, exchange BBO and national BBO across all US equity options exchanges. Includes single name stock options (e.g. TSLA), options on ETFs (e.g. SPY, QQQ), index options (e.g. VIX), and some indices (e.g. SPIKE and VSPKE). This dataset is based on the newer, binary OPRA feed after the migration to SIAC's OPRA Pillar SIP in 2021. OPRA is notable for the size of its data and we recommend users to anticipate several TBs of data per day for the full dataset in its highest granularity (MBP-1).

Origin: Options Price Reporting Authority

Supported data encodings: DBN, JSON, CSV Learn more

Supported market data schemas: MBP-1, OHLCV-1s, OHLCV-1m, OHLCV-1h, OHLCV-1d, TBBO, Trades, Statistics, Definition Learn more

Resolution: Immediate publication, nanosecond-resolution timestamps

Context

During the last few decades, with the rise of Youtube, Amazon, Netflix and many other such web services, recommender systems have taken more and more place in our lives. From e-commerce (suggest to buyers articles that could interest them) to online advertisement (suggest to users the right contents, matching their preferences), recommender systems are today unavoidable in our daily online journeys. In a very general way, recommender systems are algorithms aimed at suggesting relevant items to users (items being movies to watch, text to read, products to buy or anything else depending on industries).

Recommender systems are really critical in some industries as they can generate a huge amount of income when they are efficient or also be a way to stand out significantly from competitors. As a proof of the importance of recommender systems, we can mention that, a few years ago, Netflix organised a challenges (the “Netflix prize”) where the goal was to produce a recommender system that performs better than its own algorithm with a prize of 1 million dollars to win.

Content

These datasets contain attributes about products sold on ModCloth Amazon which may be sources of bias in recommendations (in particular, attributes about how the products are marketed).Data includes user/item interactions.

Inspiration

Apply different paradigm, methods and algorithms to recommand right Product to the right Users, during right Time.

*If you find the data useful your upvote is an explicit feedback for future works, Have fun exploring data!*