This is the official data set used in the Netflix Prize competition. The data consists of about 100 million movie ratings, and the goal is to predict missing entries in the movie-user rating matrix. |Attribute| Value| |——|—-| | Data Set Characteristics: | Multivariate, Time-Series | | Attribute Characteristics: | Integer | | Associated Tasks: | Clustering, Recommender-Systems | | Number of Instances: | 100480507 | | Number of Attributes: | 17770 | | Missing Values? | Yes | | Area: | N/A | #Data Set Information: This dataset was constructed to support participants in the Netflix Prize. There are over 480,000 customers in the dataset, each identified by a unique integer id. The title and release year for each movie is also provided. There are over 17,000 movies in the dataset, each identified by

Netflix is a streaming service and production company. Crawl feeds team extracted more than 100 records from netflix for quality analysis purposes. Get in touch with crawl feeds team for complete dataset. Last extracted on 5 mar 2022

1) Data Introduction • The Netflix Movies and TV Shows Dataset contains various metadata on movies and TV shows available on Netflix. • Key features include the title, director, cast, country, date added, release year, rating, genre, and total duration (in minutes or number of seasons) of the content.

2) Data Utilization (1) Characteristics of the Netflix Movies and TV Shows Dataset • This dataset helps in understanding content trends and markets, as well as analyzing global preferences and changing consumer tastes. • It is useful for analyzing the characteristics of content available in different countries, including genre, cast, director, and more.

(2) Applications of the Netflix Movies and TV Shows Dataset • Content Analysis: Analyze how Netflix's content is distributed, and understand preferences based on genre or country. • Recommendation System Development: Develop algorithms that recommend similar content based on user viewing patterns. • Market Analysis: Identify which content is popular in different countries and analyze if Netflix focuses more on specific countries or genres.

I extracted this data to find the unpopular movies on Netflix. The dataset I used here comes directly from Netflix movies data, which consists of 4 text data files, each file contains over 20M rows, over 4K movies, and 400K, customers. Altogether over are 17K movies and 500K+ customers!

I made some modifications and I extracted the e df_avgRating_with_usersCount.csv from the original data after applying some mathematical operations to get the average ratings and the count of users who made the ratings for each movie in movie_id below. Feel free to browse and use the data within your notebooks.

Here you could find my previous notebook on Kaggle to extract the dataset

This dataset provides a comprehensive collection of all titles (Movies and TV Series) available on Netflix. In addition to basic information, it includes IMDb-specific data like IMDb ID, Average Rating, and Number of Votes.

A dataset is updated daily at 10:00 AM CET. If you find this dataset helpful, feel free to give it an upvote! 😊

You can find all our APIs, maintained and developed by us, at the following link: octopusteam.dev. These APIs provide access to various features and data, ensuring high-quality and reliable integration options for your needs.

All Datasets:

• Full Netflix Dataset
• Full Apple TV+ Dataset
• Full Amazon Prime Dataset
• Full Hulu Dataset
• Full HBO Max Dataset

This dataset addresses the common issue of finding quality content amidst a vast catalogue, specifically on Netflix. It aims to help users discover underrated content and hidden gems. The dataset aggregates information from multiple sources, including Netflix itself, Rotten Tomatoes, and IMDb, combining various attributes to provide deeper insights into content quality and characteristics. A unique "Hidden Gem Score" is included, calculated based on low review counts and high user ratings, making it easier to identify valuable content that might otherwise be overlooked. This dataset powers the FlixGem.com platform, a related project designed for interactive exploration.

Columns

The dataset includes several key columns to facilitate detailed analysis of Netflix content: * Title: The name of the movie or series. * Genre: Hundreds of genre classifications for the content. * Tags: Thousands of detailed tags describing the content. * Languages: Languages available for the content, including English and many others. * Series or Movie: Indicates whether the content is a TV series or a movie. * Hidden Gem Score: A calculated metric based on low review counts and high ratings to identify hidden gems. * Country Availability: Information on Netflix country availability for the content. * Runtime: The duration of the series or movie. * Director: The director of the content. * Writer: The writer of the content.

Distribution

The data files are typically in CSV format. This dataset is regularly updated, with monthly revisions to ensure freshness. It was last updated in early April 2021. The dataset is version 1.0. While specific total row or record counts are not provided, some columns feature a considerable number of unique values, such as over 15,000 unique genres and over 13,000 unique languages.

Usage

This dataset is ideal for various analytical and exploratory applications, including: * Finding correlations between ratings, actors, directors, and box office performance. * Identifying patterns related to content quality based on characteristics like language and genre. * Discovering hidden gems across different regions. * Interactive browsing and knowledge discovery through platforms like FlixGem.com, which is powered by this very dataset. * Developing machine learning models for content recommendation or classification.

Coverage

The dataset offers global regional coverage, with a specific column indicating Netflix country availability for content. It focuses on recent Netflix data, with monthly updates provided. The last update was in early April 2021. The content spans a wide range of genres and includes various languages, with English being a significant portion. Runtime varies, with a large percentage of content being 1-2 hours long, followed by content under 30 minutes.

License

CCO

Who Can Use It

This dataset is designed for anyone interested in delving deeply into Netflix content, including: * Data analysts looking to unearth trends and insights. * Researchers studying media consumption patterns or content quality. * Developers creating recommendation engines or content discovery tools. * Machine learning practitioners building models for classification or prediction. * Content strategists seeking to understand what makes content resonate. * Individuals simply curious about finding their next favourite show or movie.

Dataset Name Suggestions

• Netflix Hidden Gems Dataset
• Netflix Content Quality & Discovery Data
• Global Netflix Catalogue Insights
• Curated Netflix Content Attributes
• Netflix Underrated Content Analysis

Attributes

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

Dataset from Netflix's 10-K annual reports, which include externally audited data about financial activities of businesses based in the US. For a description of the data compiled see the .docx document. The code included was used in the following research: Title: Evidence of diseconomies of scale in subscription-based video on demand services.Abstract: This study provides evidence of diseconomies of scale in Netflix, a major subscription-based video on demand (SVOD) service provider. This contradicts the common belief in prevalent economies of scale for such e-businesses. We, however, rely on a comprehensive analysis of a dataset where we have collected and combined publicly available and audited financial data, mostly coming from Netflix's 10-K reports. In our analysis we employ several user-cost models, namely a baseline linear model, a power law model, an exponential model, and a logarithmic model. Such models often appear (in different variations) in economics literature, but are almost inexistent in the rhetoric around SVOD business models. Corroborating the applications of all these mathematical models on the financial data of Netflix identifies a super-linear increase in costs with expanding user basis, indicating the rising per-user costs that defines diseconomies of scale. These findings provide critical insights into SVOD service scalability, challenging prevailing assumptions and informing expectations about cost dynamics in this industry.

In this project, I perform an Exploratory Data Analysis (EDA) on the Netflix dataset to identify trends in content types, genres, release years, and countries. The goal is to visualize how Netflix's catalog has evolved over time and uncover patterns in the types of shows and movies being produced.

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 ---

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.

This dataset consists of tv shows and movies available on Netflix as of 2019. The dataset is collected from Flixable which is a third-party Netflix search engine.

In 2018, they released an interesting report which shows that the number of TV shows on Netflix has nearly tripled since 2010. The streaming service’s number of movies has decreased by more than 2,000 titles since 2010, while its number of TV shows has nearly tripled. It will be interesting to explore what all other insights can be obtained from the same dataset.

Integrating this dataset with other external datasets such as IMDB ratings, rotten tomatoes can also provide many interesting findings.

Inspiration Some of the interesting questions (tasks) which can be performed on this dataset -

Understanding what content is available in different countries Identifying similar content by matching text-based features Network analysis of Actors / Directors and find interesting insights Is Netflix has increasingly focusing on TV rather than movies in recent years?

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 ‘Movies on Netflix, Prime Video, Hulu and Disney+’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/ruchi798/movies-on-netflix-prime-video-hulu-and-disney on 28 January 2022.

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

Content

The dataset contains data that was scraped, which comprised a comprehensive list of movies available on various streaming platforms

Inspiration

• Which streaming platform(s) can I find this movie on?
• Target age group movies vs the streaming application they can be found on

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

About this Dataset: Disney+ is another one of the most popular media and video streaming platforms. They have close to 1300 movies or tv shows available on their platform, as of mid-2021, they have over 116M Subscribers globally. This tabular dataset consists of listings of all the movies and tv shows available on Amazon Prime, along with details such as - cast, directors, ratings, release year, duration, etc.

Interesting Task Ideas

1. Understanding what content is available in different countries
2. Identifying similar content by matching text-based features
3. Network analysis of Actors / Directors and find interesting insights
4. Does Disney+ has more focus on TV Shows than movies in recent years.

Other Platform's Datasets (Click on the logos to view)

![alt text][1] ![alt text][3] ![alt text][5] ![alt text][7] [1]: https://i.imgur.com/As0PMcL.jpg =75x20

[3]: https://i.imgur.com/r5t3MpQ.jpg =75x20

[5]: https://i.imgur.com/4a4ZMuy.png =75x30

[7]: https://i.imgur.com/nCL8Skc.png?1 =75x32

• Amazon Prime Video Movies and TV Shows
• Disney+ Movies and TV Shows
• Netflix Prime Video Movies and TV Shows
• Hulu Movies and TV Shows

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.

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.

Netflix reported **** million paid streaming subscribers across the United States and Canada in the fourth quarter of 2024. This marked a growth of over **** million compared with the same quarter of the previous year. Why is Netflix losing subscribers? The EMEA (Europe, the Middle East, and Africa) region is Netflix's top-performing market in terms of subscribers, surpassing North America in the third quarter of 2022 for the first time. The company reported losing an estimated *** million users worldwide in the second quarter of 2022, with the number of Netflix users standing at approximately *** million that quarter. But why have audiences canceled their subscriptions? One reason for the unprecedented drop in account holders is Netflix's monthly fee, which has been increasing rapidly over the past few years. On top of that, viewers have also voiced criticism over Netflix's cancellation of popular shows and its lack of big movie franchises. What are audiences watching? Netflix's vast content library offers anything from reality TV to Hollywood blockbusters, with shows and movies delivered in many languages. As of mid-2024, European countries such as Slovakia, Bulgaria, and Slovenia boasted the largest content catalogs on Netflix. In the U.S., where audiences could choose from approximately ***** titles, “NCIS” and “Suits” ranked among the most popular streaming series on Netflix in 2023. As of that year, fan favorites “Stranger Things” and “3 Body Problem” were the most expensive Netflix original series, with production costs of ** and ** million U.S. dollars per episode, respectively.

The company reported that its users are 49% women and 51% men.

Context

Network monitoring and analysis of consumption behavior represents an important aspect for network operators allowing to obtain vital information about consumption trends in order to offer new data plans aimed at specific users and obtain an adequate perspective of the network. Over-the-top (OTT) media and communications services and applications are shifting the Internet consumption by increasing the traffic generation over the different available networks. OTT refers to applications that deliver audio, video, and other media over the Internet by leveraging the infrastructure deployed by network operators but without their involvement in the control or distribution of the content and are known by their large consumption of network resources.

Content

This dataset contains 1581 instances and 131 attributes on a single file. Each instance represents a user’s consumption profile which holds summarized information about the consumption behavior of the user related to the 29 OTT applications identified in the different IP flows captured in order to create the dataset

The OTT applications that the users interacted with during the capture experiment and were stored on the dataset are: Amazon, Apple store, Apple Icloud, Apple Itunes, Deezer, Dropbox, EasyTaxi, Ebay, Facebook, Gmail, Google suite, Google Maps, Browsing (HTTP, HTTP_Connect, HTTP_Download, HTTP_Proxy), Instagram, LastFM, Microsoft One Drive (MS_One_Drive), Facebook Messenger (MSN), Netflix, Skype, Spotify, Teamspeak, Teamviewer, Twitch, Twitter, Waze, Whatsapp, Wikipedia, Yahoo and Youtube.

Each application has 4 different types of attributes (quantity of generated flows, mean duration of the flows, average size of the packets exchanged on the flows and the mean bytes per second on the flows). These attributes summarizes the interaction that the user had with the respective OTT application in terms of consumption. Furthermore, the dataset contains the user’s IP address in network and decimal format which are used as user identifiers. Finally the User Group attribute represents the objective class (high consumption, medium consumption and low consumption) in which a user is classified considering his/her OTT consumption behavior. All of this information gives a total of 131 attributes.

For further information you can read and please cite the following papers:

Research Gate: https://www.researchgate.net/publication/326150046_Personalized_Service_Degradation_Policies_on_OTT_Applications_Based_on_the_Consumption_Behavior_of_Users

Springer: https://link.springer.com/chapter/10.1007/978-3-319-95168-3_37

Research Gate: https://www.researchgate.net/publication/335954240_Consumption_Behavior_Analysis_of_Over_The_Top_Services_Incremental_Learning_or_Traditional_Methods

IEEExplore: https://ieeexplore.ieee.org/document/8845576

Attribute Description

The structure of the attributes and its definition is presented below:

• Source.Decimal: This attribute holds the user’s IP address in decimal format and it is mainly used as a user identifier.

• Source.IP: This attribute holds the user’s IP address in network format (e.g., 192.168.14.35) and as in the previous case its main function is to work as a user identifier.

• Application-Name.Flows: This type of attributes hold the information about the quantity of IP flows that a user generated toward an OTT application. As was mentioned before each application has a group of 4 attributes that describe the interaction of the user with a specific OTT application (an example for this case would be Netflix.Flows or Facebook.Flows).

• Application-Name.Flow.Duration.Mean: This type of attributes hold the information related to the mean duration (time) of the flows generated by the user towards a specific OTT application, measured in microseconds. Examples of how this attributes are stored in the dataset are: Amazon.Flow.Duration.Mean or Instagram.Flow.Duration.Mean.

• Application-Name.AVG.Packet.Size: This type of attributes hold the average size of the IP packets that were exchanged in all the flows generated by the user towards a specific OTT application, measured in bytes. It is important to notice that this size is focused on the packet’s header only. Examples of how this attribute are presented on the dataset are: Google_Maps.AVG.Packet.Size or Spotify.AVG.Packet.Size.

• Application-Name.Flow.Bytes.Per.Sec: This type of attributes hold the mean number of bytes per second that were exchanged in the flows generated by the user towards a specific OTT application. Examples of this kind of attributes in the dataset are: Deezer.Flow.Bytes.Per.Sec or Skype.Flow.Bytes.Per.Sec.

• User.Group: This type of attribute represents the objective class of the dataset i.e., the different groups that the users are classified in according to their OTT consumption behavior...

In the fourth quarter of 2024, Netflix generated total revenue of over **** billion U.S. dollars, up from about *** billion dollars in the corresponding quarter of 2023. The company's annual revenue in 2024 amounted to around ** billion U.S. dollars, continuing the impressive year-on-year growth Netflix has enjoyed over the last decade. Netflix’s global position Netflix’s revenue has been heavily impacted by its ever-growing global subscriber base. The leading Netflix market is Europe, Middle East, and Africa, surpassing the U.S. and Canada in terms of subscriber count. Netflix has also significantly increased its licensed and produced content assets since 2016. Despite concerns among investors that the company’s content spend was negatively affecting cash flow, Netflix’s plans to amortize its content assets long-term along with generating revenue from other sources such as licensing and merchandise should ensure the company’s future profitability. Netflix’s original content Netflix is also fortunate in that many of its original shows have been a hit with consumers across the globe. Shows such as “Orange is the New Black,” “Black Mirror,” and “House of Cards” won the hearts of subscribers long ago, but newer content such as English-language shows “Bridgerton,” “Wednesday,” and “Stranger Things,” as well as local TV shows such as “Squid Game” have also been favorably reviewed and proved popular among users.