Overview This dataset contains 25,000 fictional Netflix user records generated for analysis, visualization, and machine learning practice. It includes demographic details, subscription type, watch time, and login history for each user.

Columns User_ID – Unique identifier for each user Name – Randomly generated name Age – Age of the user (13 to 80) Country – User’s country (randomly chosen from 10 options) Subscription_Type – Type of Netflix plan (Basic, Standard, Premium) Watch_Time_Hours – Total hours watched in the last month Favorite_Genre – User’s preferred genre Last_Login – Last recorded login date within the past year

Use Cases Data visualization and analytics Customer segmentation and trend analysis Machine learning model testing (e.g., churn prediction, recommendation systems) This dataset is synthetic and does not contain real user data. Feel free to use it for experiments and projects! 🚀

Methodology Every Tuesday, we publish four global Top 10 lists for films and TV: Film (English), TV (English), Film (Non-English), and TV (Non-English). These lists rank titles based on ‘views’ for each title from Monday to Sunday of the previous week. We define views for a title as the total hours viewed divided by the total runtime. Values are rounded to 100,000.

We consider each season of a series and each film on their own, so you might see both Stranger Things seasons 2 and 3 in the Top 10. Because titles sometimes move in and out of the Top 10, we also show the total number of weeks that a season of a series or film has spent on the list.

To give you a sense of what people are watching around the world, we also publish Top 10 lists for nearly 100 countries and territories (the same locations where there are Top 10 rows on Netflix). Country lists are also ranked by views.

Finally, we provide a list of the Top 10 most popular Netflix films and TV overall (branded Netflix in any country) in each of the four categories based on the views of each title in its first 91 days.

Some TV shows have multiple premiere dates, whether weekly or in parts, and therefore the runtime increases over time. For the weekly lists, we show the views based on the total hours viewed during the week divided by the total runtime available at the end of the week. On the Most Popular List, we wait until all episodes have premiered, so you see the views of the entire season. For titles that are Netflix branded in some countries but not others, we still include all of the hours viewed.

Information on the site starts from June 28, 2021 and any lists published before June 20, 2023 are ranked by hours viewed.

This dataset provides a time-series record of Netflix subscribers from January 2013 to October 2023. The subscriber count is presented at quarterly intervals, allowing for a comprehensive analysis of Netflix's user base growth over the years.

Columns: Time Period: The date of measurement in a quarterly format (e.g., 01-04-2013 represents April 1, 2013). Subscribers: The number of Netflix subscribers at the corresponding time period.

Industry data revealed that Iceland had the most extensive Netflix media library worldwide as of February 2025, with over 9,700 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 2025, Liechtenstein and Switzerland were the countries with the most expensive Netflix subscription rates. Viewers had to pay around 22.89 U.S. dollars per month for a standard subscription. Subscribers in these countries could choose from between around 7,900 and 8,500 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 2.87 to 3.66 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 September 2025, "KPop Demon Hunters" and "Red Notice" were the most popular English-language movies on Netflix, with over 200 million views in their 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.

Comprehensive YouTube channel statistics for Netflix Family, featuring 9,880,000 subscribers and 5,888,742,493 total views. This dataset includes detailed performance metrics such as subscriber growth, video views, engagement rates, and estimated revenue. The channel operates in the Entertainment category and is based in US. Track 3,307 videos with daily and monthly performance data, including view counts, subscriber changes, and earnings estimates. Analyze growth trends, engagement patterns, and compare performance against similar channels in the same category.

From the report page:

Since launching our weekly Top 10 and Most Popular lists in 2021, Netflix has provided more information about what people are watching than any other streamer except YouTube. And now we believe it’s time to go further.

Starting today we will publish What We Watched: A Netflix Engagement Report twice a year. This is a comprehensive report of what people watched on Netflix over a six month period1, including:

Hours viewed for every title — original and licensed — watched for over 50,000 hours2;

The premiere date3 for any Netflix TV series or film; and

Whether a title was available globally.

In total, this report covers more than 18,000 titles — representing 99% of all viewing on Netflix — and nearly 100 billion hours viewed.

Over 60% of Netflix titles released between January and June 2023 appeared on our weekly Top 10 lists. So while this report is broader in scope, the trends reflected in it are very similar to those in the Top 10 lists, including:

The strength of returning favorites like Ginny & Georgia, Alice in Borderland, The Marked Heart, Outer Banks, You, Queen Charlotte: A Bridgerton Story, XO Kitty and film sequels Murder Mystery 2 and Extraction 2;

The popularity of new series like The Night Agent, The Diplomat, Beef, The Glory, Alpha Males, FUBAR and Fake Profile, which generate huge audiences and fandoms;

The size of the audience of our films across every genre including The Mother, Luther: The Fallen Sun, You People, AKA, ¡Que viva México! and Hunger;

The enthusiasm for non-English stories, which generated 30% of all viewing;

The staying power of titles on Netflix, which extends well beyond their premieres. All Quiet on the Western Front, for example, debuted in October 2022 and generated 80M hours viewed between January and June; and

The demand for older, licensed titles, which generates tremendous value for our members and for rights holders.

When reading the report it’s important to remember:

Success on Netflix comes in all shapes and sizes, and is not determined by hours viewed alone. We have enormously successful movies and TV shows with both lower and higher hours viewed. It’s all about whether a movie or TV show thrilled its audience — and the size of that audience relative to the economics of the title; and

To compare between titles it’s best to use our weekly Top 10 and Most Popular lists, which take into account run times and premiere dates.

This is a big step forward for Netflix and our industry. We believe the viewing information in this report — combined with our weekly Top 10 and Most Popular lists — will give creators and our industry deeper insights into our audiences, and what resonates with them.

This dataset was constructed to support participants in the Netflix Prize. See [Web Link] for details about the 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 a unique integer id.

The dataset contains over 100 million ratings. The ratings were collected between October 1998 and December 2005 and reflect the distribution of all ratings received during this period. Each rating has a customer id, a movie id, the date of the rating, and the value of the rating.

As part of the original Netflix Prize a set of ratings was identified whose rating values were not provided in the original dataset. The object of the Prize was to accurately predict the ratings from this 'qualifying' set. These missing ratings are now available in the grand_prize.tar.gz dataset file.

Comprehensive YouTube channel statistics for POR SI NO TIENES NETFLIX , featuring 670,000 subscribers and 31,430,850 total views. This dataset includes detailed performance metrics such as subscriber growth, video views, engagement rates, and estimated revenue. The channel operates in the Entertainment category and is based in MX. Track 508 videos with daily and monthly performance data, including view counts, subscriber changes, and earnings estimates. Analyze growth trends, engagement patterns, and compare performance against similar channels in the same category.

Comprehensive YouTube channel statistics for Netflix, featuring 32,200,000 subscribers and 10,465,135,071 total views. This dataset includes detailed performance metrics such as subscriber growth, video views, engagement rates, and estimated revenue. The channel operates in the Entertainment category and is based in US. Track 8,562 videos with daily and monthly performance data, including view counts, subscriber changes, and earnings estimates. Analyze growth trends, engagement patterns, and compare performance against similar channels in the same category.

Since launching our weekly Top 10 and Most Popular lists in 2021, Netflix has provided more information about what people are watching than any other streamer except YouTube. And now we believe it’s time to go further.

Starting today we will publish What We Watched: A Netflix Engagement Report twice a year. This is a comprehensive report of what people watched on Netflix over a six month period1, including:

Hours viewed for every title — original and licensed — watched for over 50,000 hours2;

The premiere date3 for any Netflix TV series or film; and

Whether a title was available globally.

In total, this report covers more than 18,000 titles — representing 99% of all viewing on Netflix — and nearly 100 billion hours viewed.

Over 60% of Netflix titles released between January and June 2023 appeared on our weekly Top 10 lists. So while this report is broader in scope, the trends reflected in it are very similar to those in the Top 10 lists, including:

The strength of returning favorites like Ginny & Georgia, Alice in Borderland, The Marked Heart, Outer Banks, You, Queen Charlotte: A Bridgerton Story, XO Kitty and film sequels Murder Mystery 2 and Extraction 2;

The popularity of new series like The Night Agent, The Diplomat, Beef, The Glory, Alpha Males, FUBAR and Fake Profile, which generate huge audiences and fandoms;

The size of the audience of our films across every genre including The Mother, Luther: The Fallen Sun, You People, AKA, ¡Que viva México! and Hunger;

The enthusiasm for non-English stories, which generated 30% of all viewing;

The staying power of titles on Netflix, which extends well beyond their premieres. All Quiet on the Western Front, for example, debuted in October 2022 and generated 80M hours viewed between January and June; and

The demand for older, licensed titles, which generates tremendous value for our members and for rights holders.

When reading the report it’s important to remember:

Success on Netflix comes in all shapes and sizes, and is not determined by hours viewed alone. We have enormously successful movies and TV shows with both lower and higher hours viewed. It’s all about whether a movie or TV show thrilled its audience — and the size of that audience relative to the economics of the title; and

To compare between titles it’s best to use our weekly Top 10 and Most Popular lists, which take into account run times and premiere dates.

This is a big step forward for Netflix and our industry. We believe the viewing information in this report — combined with our weekly Top 10 and Most Popular lists — will give creators and our industry deeper insights into our audiences, and what resonates with them.

Comprehensive YouTube channel statistics for Netflix Türkiye, featuring 1,720,000 subscribers and 633,651,922 total views. This dataset includes detailed performance metrics such as subscriber growth, video views, engagement rates, and estimated revenue. The channel operates in the Entertainment category and is based in TR. Track 3,531 videos with daily and monthly performance data, including view counts, subscriber changes, and earnings estimates. Analyze growth trends, engagement patterns, and compare performance against similar channels in the same category.

Comprehensive YouTube channel statistics for Netflix Deutschland, Österreich und Schweiz, featuring 893,000 subscribers and 286,591,446 total views. This dataset includes detailed performance metrics such as subscriber growth, video views, engagement rates, and estimated revenue. The channel operates in the Entertainment category and is based in DE. Track 3,847 videos with daily and monthly performance data, including view counts, subscriber changes, and earnings estimates. Analyze growth trends, engagement patterns, and compare performance against similar channels in the same category.

Kaggle Dataset Description

Netflix (NFLX) Stock Price Data (2002-2025)Overview:

This dataset provides historical stock price data for Netflix Inc. (NASDAQ: NFLX) from May 23, 2002, to January 31, 2025. The data has been sourced from Yahoo Finance and includes essential financial metrics, making it valuable for financial analysis, stock price prediction, and time-series forecasting.

Dataset Features:

Date: The trading date (YYYY-MM-DD format).

Open: Opening price of Netflix stock on that trading day.

High: Highest price recorded during the trading session.

Low: Lowest price recorded during the trading session.

Close: Closing price of the stock at the end of the trading session.

Adj Close: Adjusted closing price, accounting for corporate actions such as stock splits and dividends.

Volume: The total number of Netflix shares traded on the respective day.

Usage & Applications:

Stock Price Analysis – Evaluate historical trends and identify key patterns.

Time-Series Forecasting – Train predictive models for stock market behavior.

Volatility & Risk Assessment – Analyze price fluctuations over time.

Trading Strategy Development – Backtest investment strategies.

Data Source: Extracted using Yahoo Finance API.

License & Citation:

This dataset is publicly available and should be credited to Yahoo Finance API and Muhammad Atif Latif when used in research or projects.

More Stocks Datasets

If you want to check out my more stocks related Datasets then CLICK HERE

THIS IS WHOLLY INDEPENDENT RESEARCH AND DATA. WE’RE NOT AFFILIATED WITH NETFLIX OR ANY OTHER STREAMING PLATFORM OR STUDIO.

Content

The dataset covers user behaviour on Netflix from users in the UK to opted-in to have their anonymized browsing activity tracked. It only includes desktop and laptop activity (which Netflix estimate is around 25% of global traffic) and is for a fixed window of time (January 2017 to June 2019, inclusive). It documents each time someone in our tracked panel in the UK clicked on a Netflix.com/watch URL for a movie.

'Duration' shows how long it was (in seconds) until that user clicked on another URL. A watch time of zero seconds means they visited the page but instantly clicked away.

Why this data matters

As more of the media economy takes place within restricted private networks, filmmakers and creators are becoming further removed from what audiences want. Without feedback, creators struggle to make commercial projects. Without reliable financial estimates, business plans become fanciful. Without data, we’re all just guessing.

As streaming continues to become an ever-larger window of release, it has drawn an impenetrable veil over a vital part of a film or TV show’s financial journey. This has created an artificial data drought. So much so that our clickstream dataset is currently the only global measure of VOD activity of its kind.

And it’s not perfect. There are limitations and caveats with the dataset which means that we are observing the truth through an imperfect lens. But in the absence of anything better, this is what we’re left to work with.

More on this here https://vodclickstream.com/why-this-matters/

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research. This is a community project aimed at helping content creators understand how the new world of streaming affects the demand for their works.

It has taken over a year and countless hours from many people for it to come together. It would not have been possible without the work of Stephen Follows, Dr. José Eliel Camargo-Molina, Jack Tann, Dr. Alejandro Celis, and Victoria Myerscough.

You can read more about our origin story at https://vodclickstream.com/our-origin-story/

Inspiration

We're really excited to see what people can use this data for. Our initial impetus or the project was to help filmmakers get the signals they need to know what to make, how much to spend, and their chances of commercial success. But it can reveal so much more than just that.

This initial dataset is just for movies and just for users in the UK. We also have data on TV shows and comedy specials, extending across all major countries. We will be releasing more soon. Get in touch if there’s data you’re after along these lines https://vodclickstream.com/contact/

Comprehensive YouTube channel statistics for Netflix Decoded, featuring 105,000 subscribers and 19,295,196 total views. This dataset includes detailed performance metrics such as subscriber growth, video views, engagement rates, and estimated revenue. The channel operates in the Entertainment category and is based in IN. Track 187 videos with daily and monthly performance data, including view counts, subscriber changes, and earnings estimates. Analyze growth trends, engagement patterns, and compare performance against similar channels in the same category.

Comprehensive YouTube channel statistics for Netflix Brasil, featuring 14,600,000 subscribers and 2,994,722,019 total views. This dataset includes detailed performance metrics such as subscriber growth, video views, engagement rates, and estimated revenue. The channel operates in the Entertainment category and is based in BR. Track 7,156 videos with daily and monthly performance data, including view counts, subscriber changes, and earnings estimates. Analyze growth trends, engagement patterns, and compare performance against similar channels in the same category.

The dataset comprises the 20 countries with the greatest increases in the volume of average daily VPN searches in March and April 2020. For each country, the trends in search volume for VPN terms subsequent to the initial spike were also recorded every seven days aftewards.

Introduction:

In this case study the skills that I acquired from Google Data Analytics Professional Certificate Course is demonstrated. These skills will be used to complete the imagined task which was given by Netflix. The analysis process of this task will be consisted of following steps. Ask, Prepare, Process, Analyze, Share and Act.

Scenario:

The Netflix Chief Content Officer, Bela Bajaria, believes that companies success depends on to provide the customers what they want. Bajaria stated that the goal of this task is to find most wanted contents of the movies which will be added to the portfolio. Most of the movie contracts are signed before they come to the theaters, and it is hard to know if the customers really want to watch that movie and if the movie will be successful. There for my team wants to understand what type of content a movies success depends on. From these insights my team will design an investment strategy to choose the most popular movies that are expected to be in theaters in the near future. But first, Netflix executives must approve our recommendations. To be able to do that we must provide satisfying data insights along with professional data visualizations.

About the Company:

At Netflix, we want to entertain the world. Whatever your taste, and no matter where you live, we give you access to best-in-class TV series, documentaries, feature films and games. Our members control what they want to watch, when they want it, in one simple subscription. We’re streaming in more than 30 languages and 190 countries, because great stories can come from anywhere and be loved everywhere. We are the world’s biggest fans of entertainment, and we’re always looking to help you find your next favorite story.

As a company Netflix knows that it is important to acquire or produce movies that people want to watch.

There for Bajaria has set a clear goal: Define an investment strategy that will allow Netflix to provide customers the movies what they want to watch which will maximize the Sales.

Ask:

Business Task: To find out what kind of movie customers wants to watch and if the content type really has a correlation with the movie success. Stakeholders:

Bela Bajaria: She joined Netflix in 2016 to oversee unscripted and scripted series. Bajaria also responsible from the content selection and strategy for different regions.

Netflix content analytics team: A team of data analysts who are responsible for collecting, analyzing, and reporting data that helps guide Netflix content strategy.

Netflix executive team: The notoriously detail-oriented executive team will decide whether to approve the recommended content program.

Prepare:

I start my preparation procedure by downloading every piece of data I'll need for the study. Top 1000 Highest-Grossing Movies of All Time.csv will be used. Additionally, 15 Lowest-Grossing Movies of All Time.csv was found during the data research and this dataset will be analyst as well. The data has been made available by IMDB and shared this two following URL addresses: https://www.imdb.com/list/ls098063263/ and https://www.imdb.com/list/ls069238222/ .

Process:

Data Cleaning:

SQL: To begin the data cleaning process, I opened both csv file in SQL and conducted following operations:

• Checked for and removed any duplicates. • Checked if there any null values. • Removed the columns that are not necessary. • Trim the Description column to have only gross profit in it. (This cleaning procedure only used for 1000 Highest-Grossing Movies of All Time.csv dataset.)

• Renamed the Description column as Gross_Profit. (This cleaning procedure only used for 1000 Highest-Grossing Movies of All Time.csv dataset.)

Follwing SQL codes were used during the data cleaning:

SQL CODE used for Highest Grossing Movies DATASET

SELECT Position, SUBSTR(Description,34,12) as Gross_Profit, Title, IMDb_Rating, Runtime_mins_, Year, Genres, Num_Votes, Release_Date FROM even-electron-400301.Highest_Gross_Movies.1

SQL CODE used for Lowest Grossing Movies DATASET

SELECT Position, Title, IMDb_Rating, Runtime_mins_, Year, Genres, Num_Votes, Release_Date FROM even-electron-400301.Lowest_Grossing_Movies.2 Order By Position

Analyze:

As a starter, I want to reemphasize the business task once again. Is content has a big impact on a movie’s success?

To answer this question, there were a few information that I projected that I could pull of and use it during my analysis.

• Average gross profit • Number of Genres • Total Gross Profit of the most popular genres • The distribution of the Gross income on Genres

I used Microsoft Excel for the bullet points above. The operations to achieve the values above are as follows:

• Average function for Average Gross profit in 1000 Highest-Grossing Movies of All Time. • Created a pivot table to work on Genres and Gross_Pr...

Comprehensive YouTube channel statistics for Netflix Is A Joke, featuring 4,510,000 subscribers and 2,768,995,818 total views. This dataset includes detailed performance metrics such as subscriber growth, video views, engagement rates, and estimated revenue. The channel operates in the Entertainment category and is based in US. Track 4,115 videos with daily and monthly performance data, including view counts, subscriber changes, and earnings estimates. Analyze growth trends, engagement patterns, and compare performance against similar channels in the same category.

Deskripsi Umum

Dataset ini merupakan versi turunan yang telah melalui proses preprocessing dan data cleaning dari dataset asli:
Netflix 2025 User Behavior Dataset (210K Records)

Dataset ini dikembangkan untuk mendukung analisis segmentasi dan clustering pengguna Netflix berdasarkan:
- Faktor demografis (usia, jenis kelamin, lokasi, ukuran rumah tangga)
- Preferensi tontonan (genre, jenis konten, bahasa)
- Perilaku penggunaan (durasi menonton, persentase progres, perangkat utama, pola langganan)

Dataset hasil preprocessing telah dibersihkan dari:
- Nilai hilang (missing values) signifikan
- Duplikasi data berdasarkan session_id dan user_id
- Inkonsistensi format tanggal serta anomali numerik
- Kolom non-informatif dan noise yang tidak relevan

Struktur Dataset

Kolom	Tipe Data	Deskripsi
session_id	String	ID unik setiap sesi menonton
user_id	String	ID unik pengguna Netflix
movie_id	String	ID unik konten yang ditonton
watch_date	Date	Tanggal aktivitas menonton
device_type	String	Jenis perangkat yang digunakan
watch_duration_minutes	Float	Durasi menonton (menit)
progress_percentage	Float	Persentase tontonan selesai
action	String	Status aktivitas (started, completed, paused, dll)
quality	String	Kualitas streaming (HD, 4K, dll)
location_country	String	Negara lokasi pengguna
is_download	Boolean	Status apakah konten diunduh
user_rating	String	Rating konten oleh pengguna
email	String	Email pengguna (disamarkan)
first_name, last_name	String	Nama pengguna (disamarkan)
age	Float	Usia pengguna
gender	String	Jenis kelamin pengguna
country, state_province, city	String	Lokasi geografis pengguna
subscription_plan	String	Jenis langganan (Basic, Standard, Premium)
subscription_start_date	Date	Tanggal mulai langganan
is_active	Boolean	Status keaktifan akun
monthly_spend	Float	Pengeluaran bulanan pengguna (USD)
primary_device	String	Perangkat utama pengguna
household_size	Float	Jumlah anggota rumah tangga
created_at	DateTime	Waktu pencatatan data
title	String	Judul konten yang ditonton
content_type	String	Jenis konten (Movie, Series, Stand-up, dll)
genre_primary, genre_secondary	String	Genre utama dan sekunder
release_year	Int	Tahun rilis konten
duration_minutes	Float	Durasi total konten (menit)
language	String	Bahasa utama konten
country_of_origin	String	Negara asal produksi
production_budget, box_office_revenue	Float	Data finansial konten
number_of_seasons, number_of_episodes	Int	Informasi serial (jika ada)
is_netflix_original	Boolean	Apakah konten merupakan orisinal Netflix
added_to_platform	Date	Tanggal konten ditambahkan ke platform
content_warning	Boolean	Peringatan konten (violence, nudity, dll)

Proses Preprocessing

⚙️ Proses Preprocessing

Tahap	Deskripsi Proses
Handling Missing Values	Dataset hasil penggabungan tiga sumber utama (users, movies, watch history) mengandung banyak nilai null/NaN. Untuk mengatasinya, dilakukan penambahan data dari dataset pendukung agar jumlah nilai hilang berkurang, kemudian dilakukan imputasi bila masih terdapat nilai kosong dalam proporsi kecil.
Cek Missing Value	Menghitung jumlah nilai hilang di tiap kolom untuk menentukan proporsi missing values yang signifikan.
Thresholding Kolom	Kolom dengan lebih dari 12% missing values dihapus karena dianggap tidak layak diimputasi.
Pembersihan Data Umur	Nilai usia pengguna difilter agar berada pada rentang logis (5 \leq \text{Usia} < 100). Nilai di luar rentang ini dihapus karena tidak relevan untuk pengguna Netflix.
Filter Tahun Film	Hanya konten dengan tahun rilis dalam rentang operasi Netflix yang dipertahankan: (2007 \leq \text{Tahun Rilis} \leq 2025).
Imputasi Nilai Hilang	Setelah pembersihan, nilai kosong diisi menggunakan metode statistik: • Numerik: median atau mean • Kategorikal: modus.

Tujuan Penggunaan

Dataset ini disiapkan untuk:
1. Analisis segmentasi dan clustering pengguna Netflix menggunakan K-Means dan DBSCAN.
2. Eksplorasi pola perilaku menonton berdasarkan usia, genre, durasi, dan perangkat.
3. Evaluasi efektivitas algoritma clustering melalui metrik seperti silhouette score, Davies–Bouldin index, dan Calinski–Harabasz score.
4. Visualisasi interaktif PCA 2D & 3D untuk memahami karakteristik setiap klaster.