Dataset

This dataset was created by haripriyaaa

Contents

Explore the Netflix Titles dataset, featuring detailed insights on over 8,800 movies and TV shows. Ideal for data analysis and market research, this comprehensive resource covers genre trends, directorial data.

This dataset contains synthetic data simulating customer behavior for a Netflix-like video streaming service. It includes 5,000 records with 14 carefully engineered features designed for churn prediction modeling, business insights, and customer segmentation.

The dataset is ideal for:

Machine learning classification tasks (churn vs. non-churn)

Exploratory data analysis (EDA)

Customer behavior modeling in OTT platforms

The raw data is Web Scrapped through Selenium. It contains Unlabelled text data of around 9000 Netflix Shows and Movies along with Full details like Cast, Release Year, Rating, Description, etc.

Original Data Source: Dataset: NetFlix Shows

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

Context

Netflix, Inc. is an American media company engaged in paid streaming and the production of films and series.

Market cap

Market capitalization of Netflix (NFLX)

Market cap: $517.08 Billion USD

As of June 2025 Netflix has a market cap of $517.08 Billion USD. This makes Netflix the world's 19th most valuable company by market cap according to our data. The market capitalization, commonly called market cap, is the total market value of a publicly traded company's outstanding shares and is commonly used to measure how much a company is worth.

Revenue

Revenue for Netflix (NFLX)

Revenue in 2025: $40.17 Billion USD

According to Netflix's latest financial reports the company's current revenue (TTM ) is $40.17 Billion USD. In 2024 the company made a revenue of $39.00 Billion USD an increase over the revenue in the year 2023 that were of $33.72 Billion USD. The revenue is the total amount of income that a company generates by the sale of goods or services. Unlike with the earnings no expenses are subtracted.

Earnings

Earnings for Netflix (NFLX)

Earnings in 2025 (TTM): $11.31 Billion USD

According to Netflix's latest financial reports the company's current earnings are $40.17 Billion USD. In 2024 the company made an earning of $10.70 Billion USD, an increase over its 2023 earnings that were of $7.02 Billion USD. The earnings displayed on this page is the company's Pretax Income.

End of Day market cap according to different sources

On Jun 12th, 2025 the market cap of Netflix was reported to be:

$517.08 Billion USD by Yahoo Finance

$517.08 Billion USD by CompaniesMarketCap

$517.21 Billion USD by Nasdaq

Content

Geography: USA

Time period: May 2002- June 2025

Unit of analysis: Netflix Stock Data 2025

Variables

Acknowledgements

This dataset belongs to me. I’m sharing it here for free. You may do with it as you wish.

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.

Traffic analytics, rankings, and competitive metrics for netflix.com as of May 2025

Analysis of ‘Netflix Shows’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/netflix-showse on 13 February 2022.

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

About this dataset

Background

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?

Netflix Suggestion Engine

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.).

Source

Access to the study can be found at The Concept Center

This dataset was created by Chase Willden and contains around 1000 samples along with User Rating Score, Rating Description, technical information and other features such as: - Release Year - Title - and more.

How to use this dataset

• Analyze User Rating Size in relation to Rating
• Study the influence of Rating Level on User Rating Score
• More datasets

Acknowledgements

If you use this dataset in your research, please credit Chase Willden

Start A New Notebook!

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

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

This dataset contains information about movies and TV shows available on Netflix as of 2021. The data includes various details about each title, such as:

• Title: The name of the movie or TV show.
• Genre: The genre(s) associated with the title.
• Director: The director(s) of the movie or show.
• Cast: Main actors and actresses featured.
• Country: The country where the movie or show was produced.
• Date Added: The date when the title was added to Netflix.
• Release Year: The year the movie or show was released.
• Rating: The maturity rating (e.g., PG, TV-MA).
• Duration: The length of the movie or number of seasons for TV shows.
• Description: A brief summary of the movie or show.

Data Collection and Sources The dataset has been compiled from publicly available sources and includes titles available on Netflix globally. It provides a comprehensive view of Netflix’s content library up to the year 2021, making it an excellent resource for analyzing trends in streaming content, examining genre popularity, and exploring the evolution of Netflix’s offerings over time.

This dataset is provided for educational and research purposes. All data is based on publicly available information and should be used responsibly, respecting the original content creators' rights.

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.

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 ‘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 ---

The Over-The-Top (OTT) market is experiencing explosive growth, projected to reach a value of $0.58 billion in 2025 and exhibiting a remarkable Compound Annual Growth Rate (CAGR) of 28.19% from 2025 to 2033. This expansion is fueled by several key drivers. The increasing affordability and accessibility of high-speed internet globally is a major factor, allowing consumers to easily stream content. The rising popularity of mobile devices and smart TVs further enhances convenience, driving adoption. Moreover, the continuous evolution of content offerings, including original programming and diverse genres catering to niche audiences, keeps viewers engaged. Competition among established players like Netflix, Amazon Prime Video, and Disney+ alongside the emergence of innovative regional players is fueling innovation and keeping prices competitive, further stimulating market growth. The segment breakdown suggests that Subscription Video on Demand (SVOD) likely dominates the market, followed by Transactional Video on Demand (TVOD) and Advertising Video on Demand (AVOD). However, market growth is not without its challenges. The intensifying competition necessitates continuous investment in content creation and technological infrastructure. Content piracy remains a significant concern, impacting revenue streams. Furthermore, regional variations in internet penetration and consumer preferences require tailored strategies for successful market penetration. Successfully navigating these challenges hinges on strategic content acquisitions, effective marketing campaigns targeting specific demographics, and robust anti-piracy measures. The future of the OTT market hinges on technological advancements such as improved streaming quality, personalized recommendations, and interactive content experiences, ensuring sustained growth and viewer engagement throughout the forecast period. Geographic expansion, particularly into underserved regions, also presents significant opportunities for market expansion. This in-depth report provides a comprehensive analysis of the global Over-The-Top (OTT) market, encompassing its evolution, current state, and future projections from 2019 to 2033. The report leverages extensive data analysis and market insights, covering key aspects influencing the OTT landscape, including technological advancements, consumer behavior, regulatory frameworks, and competitive dynamics. This study is crucial for businesses seeking to understand and capitalize on the burgeoning opportunities within the rapidly expanding OTT sector. We analyze market trends, growth drivers, challenges, and emerging technologies shaping the future of streaming media. The study period is 2019-2033, with 2025 as the base year and estimated year, and a forecast period of 2025-2033. Recent developments include: May 2023 - Jio Fibre and OTTplay Premium have collaborated to provide 19 OTTs to Jio Set-Top Box consumers. OTTplay Premium is well-known for its high-quality and varied content, designed to give users a personalized, smooth, and premium streaming experience. With this connection, Jio set-top box customers could download the OTTplay app from the Jio Store and access prominent OTT platforms like Sony Liv, Zee5, Lionsgate, FanCode, and 15 more, all under one roof., October 2022 - Vislink has announced and introduced a new integrated collaboration with sports OTT provider StreamViral as part of their exhibition at Sportel 2022 in Monaco. Vislink, a significant broadcast live streaming production technology provider, is now delivering an OTT playout and distribution platform to complement its Artificial Intelligence (AI) cameras, which can generate captivating sports productions without using live camera operators., September 2022 - Medianova and streaming platform Jet-Stream announced a partnership to provide Medianova's CDN service within Jet-Stream's service. Jet-Stream Airflow Multi CDN is integrated into Jet-Stream Cloud services with the partnership., May 2022 - Sony Sports Network has announced that Roland-Garros 2022, the second grand slam event of the year, will be aired in four regional languages for live broadcast in India. The tournament can be streamed on Sony Sports Network's on-demand OTT platform SonyLIV.. Key drivers for this market are: Adoption of Smart Devices & Greater Access to Higher Internet Speeds, Ongoing Shift Towards Commoditization of Sporting & Entertainment Services Coupled with Growing Competition Among OTT Providers; Increasing Adoption of SVOD (subscription - Based Services) in Emerging Markets. Potential restraints include: Growing Threat of Video Content Piracy and Security Threat of User Database Due to Spyware. Notable trends are: Adoption of Smart Devices & higher Internet Speeds is Expected to Drive Over the Top (OTT) Market.

This dataset is about Netflix Movies and Tv shows. The dataset has following columns such as show_id, type, title, director, cast, country, date_added, release_year, rating, duration, listed_in, description.

This is a Dataset for Stock Prediction on Netflix. This dataset start from 2002to 2021 . It was collected from Yahoo Finance. You can perform Time Series Analysis and EDA on data.

Dive into the Netflix Movies and TV Shows Dataset, a detailed collection of web-scraped data featuring popular streaming titles. Discover trending movies, binge-worthy TV series, genres, ratings, release years, and audience preferences. Gain insights into Netflix originals, global streaming trends, and viewer favorites to inform market analysis and entertainment research.

Perfect for exploring content diversity, production trends, and streaming platform dynamics.

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.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Is There a Netflix Effect on Unemployment? A Statistical Exploration

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data