Dataset

This dataset was created by Ahmad Makhdoomi

Contents

As of February 2025, approximately 54 percent of YouTube users were male. By comparison, female users on the popular social video platform were approximately 46 percent of the total. In the last examined period, the United Arab Emirates and Israel were among the country with the highest YouTube penetration worldwide.

During the first quarter of 2024, Huge YouTube accounts, which had over 50,000 followers, reported an engagement rate of approximately 6.2 percent on their short-format content. In comparison, engagement was sensibly lower on long-format videos, which reported an engagement rate of 1.72 percent for Huge accounts. Medium YouTube accounts, which had a following between 2,001 and 10,000 users, reported engagement ratings of almost three percent on their Shorts, while long videos had an engagement of around 0.15 percent.

Key YouTube Statistics (Editor’s Choice) YouTube recorded 70 billion monthly active users in March 2023, which includes 55.10% of worldwide active social media users. There have been more than 14 million daily active users currently on YouTube, in the United States of America this platform is accessed by 62% of users. YouTube is touted as the second largest search engine and the second most visited website after Google. Revenue earned by YouTube in the first two quarters of 2023 is around $14.358 billion. In 2023, YouTube Premium and YouTube Music have recorded 80 million subscribers collectively worldwide. YouTube consumers view more than a billion hours of video per day. YouTube has more than 38 million active channels. In the fourth quarter of 2021, YouTube ad revenue has been $8.6 billion. Around 3 million paid subscribers to access YouTube TV. YouTube Premium has around 1 billion paid users. In 2023, YouTube was banned in countries such as China excluding Macau and Hong Kong, Eritrea, Iran, North Korea, Turkmenistan, and South Sudan. With 166 million downloads, the YouTube app has become the second most downloaded entertainment application across the world after Netflix. With 91 million downloads, YouTube Kids has become the sixth most downloaded entertainment app in the world. Nearly 90% of digital consumers access YouTube in the US, making it the most popular social network for watching video content. Over 70% of YouTube viewership takes place on its mobile application. More than 70% of YouTube video content watched by people is suggested by its algorithm. The average duration of a video on YouTube is 12 minutes. An average YouTube user spends 20 minutes and 23 seconds on the platform daily. Around 28% of YouTube videos that are published by popular channels are in the English language. 77% of YouTube users watch comedy content on the platform. With 247 million subscribers, T-Series has become the most subscribed channel on YouTube. Around 50 million users log on to YouTube every day. YouTube's biggest concurrent views record has been at 2.3 billion from when SpaceX has gone live on the platform to unveil Falcon Heavy Rocket. The majority of YouTube users are in the age group of 15 to 35 years in the US. The male-female ratio of YouTube users is 11:9. Apple INC. has been touted as the biggest advertiser on YouTube in 2020 spending $237.15 million. YouTube produced total revenue of $19.7 billion in 2020. As of 2021, the majority of YouTube users (467 million) are from India. It is the most popular platform in the United States with 74 percent of adult users. YouTube contributes to nearly 25% of mobile traffic worldwide. Daily live streaming on YouTube has increased by 45% in total in 2020. In India, around 225 million people are active on the platform each hour as per the 2021 statistics. YouTube Usage and Viewership Statistics #1. YouTube accounts for more than 2 billion monthly active users Around 2.7 billion users log on to YouTube each month. The number of monthly active users of YouTube is expected to grow even further. #2. Around 14.3 billion people visit the platform every month The number of YouTube visitors is far higher compared to Facebook, Amazon, and Instagram. #3. YouTube is accessible across 100 countries in 80 languages. The platform is widely available across different communities and nations. #4. 53.9% of YouTube users are men and 46.1% of women use the platform As of 2023 statistics, 53.9% of men use the platform and 46.1% of women over 18 years are on YouTube. The share in the number of males and females is 1.38 billion and 1.18 billion respectively. Age Group Male Female 18 to 24 8.5% 6% 25 to 34 11.6% 8.6% 35 to 44 9% 7.5% 45 to 54 6.2% 5.7% 55 to 64 4.4% 4.5% Above 65 4.3% 5.4% #5. 99% of YouTube users are active on other social media networks as well. Fewer than 1% of YouTube users are solely dependent on the platform. #6. Users spend around 20 minutes and 23 seconds per day on YouTube on average It is quite a generous amount of time spent on any social network platform. #7. YouTube is the second most visited site worldwide With more than 14 billion visits per month, YouTube has become the second most visited site in the world. However, its parent company Google is the most visited site across the globe. As per the statistics, YouTube is the third most popular searched word on Google. #8. 694000 hours of video content are streamed on YouTube per minute YouTube has outweighed Netflix as well in terms of streaming video content. #9. Over 81% of total internet users have accessed YouTube #10. Nearly 450 million hours of video content are uploaded on YouTube each hour More than 5 billion videos are watched on YouTube per day. #11. India has the maximum numb

YouTube was launched in 2005. It was founded by three PayPal employees: Chad Hurley, Steve Chen, and Jawed Karim, who ran the company from an office above a small restaurant in San Mateo. The first...

About the Dataset : Youtube Views Prediction

This dataset contains information about trending YouTube videos from multiple countries, providing valuable insights for predicting video popularity based on various attributes. The dataset includes both numerical and categorical features that are essential for analyzing viewer behavior, engagement, and trends in content creation. The original source of this dataset can be found at : https://www.kaggle.com/datasets/datasnaek/youtube-new/data

Columns and Their Descriptions:

title: The title of the YouTube video.
channel_title: Name of the channel that published the video.
trending_date: The date the video started trending.
publish_date: The original upload date of the video.
publish_time: The exact time the video was published.
views: The total number of views the video received.
likes: The number of likes the video received.
dislikes: The number of dislikes the video received.
comment_count: The total number of comments on the video.
tags: Keywords or tags associated with the video, helping discoverability.
description: A detailed text description provided by the uploader.
category_id: The category assigned to the video (e.g., Music, Gaming, News).

Business Goals :

Predicting the number of views on youtube videos based on video attributes. The goal is to develop a model that can accurately predict the number of views a video will receive, using various video attributes such as likes, shares, comments, video duration, and more.

Business Metrics :

1. RMSE (Root Mean Squared Error) RMSE is a metric that measures the magnitude of the error between the values predicted by the model (Predicted Views) and the actual values (Actual Views). The lower the RMSE value, the more accurate the model's predictions.

2. R² (Coefficient of Determination) R² measures the extent to which the model can explain the variation in the data. R² values range from 0 to 1, where 1 means the model can explain all the variation in the number of views based on the given attributes, and 0 means the model cannot explain the variation. The higher the R², the better the model is at predicting views and the more relevant the features used in the model.

Model & Evaluation

The machine learning model was evaluated using several approaches, including different pre-processing techniques and multiple ML models. Ultimately, the chosen model for this analysis is the Random Forest Regressor. The final evaluation results show an RMSE of 630.741, indicating an average prediction error of approximately 630.741 units. Additionally, the R² score is 0.9623, meaning that the model explains 96.23% of the variance in the data (number of views). These results were deemed satisfactory and were selected as the final modeling approach for the system and its potential future applications.

📢 Published on : My LinkedIn

Use our YouTube profiles dataset to extract both business and non-business information from public channels and filter by channel name, views, creation date, or subscribers. Datapoints include URL, handle, banner image, profile image, name, subscribers, description, video count, create date, views, details, and more. You may purchase the entire dataset or a customized subset, depending on your needs. Popular use cases for this dataset include sentiment analysis, brand monitoring, influencer marketing, and more.

rank: Position of the YouTube channel based on the number of subscribers Youtuber: Name of the YouTube channel subscribers: Number of subscribers to the channel video views: Total views across all videos on the channel category: Category or niche of the channel Title: Title of the YouTube channel uploads: Total number of videos uploaded on the channel Country: Country where the YouTube channel originates Abbreviation: Abbreviation of the country channel_type: Type of the YouTube channel (e.g., individual, brand) video_views_rank: Ranking of the channel based on total video views country_rank: Ranking of the channel based on the number of subscribers within its country channel_type_rank: Ranking of the channel based on its type (individual or brand) video_views_for_the_last_30_days: Total video views in the last 30 days lowest_monthly_earnings: Lowest estimated monthly earnings from the channel highest_monthly_earnings: Highest estimated monthly earnings from the channel lowest_yearly_earnings: Lowest estimated yearly earnings from the channel highest_yearly_earnings: Highest estimated yearly earnings from the channel subscribers_for_last_30_days: Number of new subscribers gained in the last 30 days created_year: Year when the YouTube channel was created created_month: Month when the YouTube channel was created created_date: Exact date of the YouTube channel's creation Gross tertiary education enrollment (%): Percentage of the population enrolled in tertiary education in the country Population: Total population of the country Unemployment rate: Unemployment rate in the country Urban_population: Percentage of the population living in urban areas Latitude: Latitude coordinate of the country's location Longitude: Longitude coordinate of the country's location

Potential Use Cases

YouTube Analytics: Gain valuable insights into the success factors of top YouTube channels and understand what sets them apart from the rest. Content Strategy: Discover the most popular categories and upload frequencies that resonate with audiences. Regional Influencers: Identify influential YouTube creators from different countries and analyze their impact on a global scale. Earnings Analysis: Explore the correlation between channel performance and estimated earnings. Geospatial Visualization: Visualize the distribution of successful YouTube channels on a world map and uncover geographical trends. Trending Topics: Investigate how certain categories gain popularity over time and correlate with world events.

The global number of Youtube users in was forecast to continuously increase between 2024 and 2029 by in total ***** million users (+***** percent). After the ninth consecutive increasing year, the Youtube user base is estimated to reach *** billion users and therefore a new peak in 2029. Notably, the number of Youtube users of was continuously increasing over the past years.User figures, shown here regarding the platform youtube, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to *** countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of Youtube users in countries like Africa and South America.

As of February 2025, India was the country with the largest YouTube audience by far, with approximately 491 million users engaging with the popular social video platform. The United States followed, with around 253 million YouTube viewers. Brazil came in third, with 144 million users watching content on YouTube. The United Kingdom saw around 54.8 million internet users engaging with the platform in the examined period. What country has the highest percentage of YouTube users? In July 2024, the United Arab Emirates was the country with the highest YouTube penetration worldwide, as around 94 percent of the country's digital population engaged with the service. In 2024, YouTube counted around 100 million paid subscribers for its YouTube Music and YouTube Premium services. YouTube mobile markets In 2024, YouTube was among the most popular social media platforms worldwide. In terms of revenues, the YouTube app generated approximately 28 million U.S. dollars in revenues in the United States in January 2024, as well as 19 million U.S. dollars in Japan.

This dataset contains statistics about 700+ videos from Mr.Beast's YouTube channel.

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The data contains title, id, publication date and time, description, views, likes, comments, duration in seconds and subtitles for each video.

Daily pull data here: https://www.kaggle.com/datasets/dissfya/mrbeast-youtube-stats-daily-pull

Motivation

Study how YouTube videos become viral or, more in general, how they evolve in terms of views, likes and subscriptions is a topic of interest in many disciplines. With this dataset you can study such phenomena, with statistics about 1 million YouTube videos. The information was collected in 2013 when YouTube was exposing the data publicly: they removed this functionality in the years and now it's possible to have such statistics only to the owner of the video. This makes this dataset unique.

Context

This Dataset has been generated with YOUStatAnalyzer, a tool developed by myself (Mattia Zeni) when I was working for CREATE-NET (www.create-net.org) within the framework of the CONGAS FP7 project (http://www.congas-project.eu). For the project we needed to collect and analyse the dynamics of YouTube videos popularity. The dataset contains statistics of more than 1 million Youtube videos, chosen accordingly to random keywords extracted from the WordNet library (http://wordnet.princeton.edu).

The motivation that led us to the development of the YOUStatAnalyser data collection tool and the creation of this dataset is that there's an active research community working on the interplay among user individual preferences, social dynamics, advertising mechanisms and a common problem is the lack of open large-scale datasets. At the same time, no tool was present at that time. Today, YouTube removed the possibility to visualize these data on each video's page, making this dataset unique.

When using our dataset for research purposes, please cite it as:

@INPROCEEDINGS{YOUStatAnalyzer, author={Mattia Zeni and Daniele Miorandi and Francesco {De Pellegrini}}, title = {{YOUStatAnalyzer}: a Tool for Analysing the Dynamics of {YouTube} Content Popularity}, booktitle = {Proc.\ 7th International Conference on Performance Evaluation Methodologies and Tools (Valuetools, Torino, Italy, December 2013)}, address = {Torino, Italy}, year = {2013} }

Content

The dataset contains statistics and metadata of 1 million YouTube videos, collected in 2013. The videos have been chosen accordingly to random keywords extracted from the WordNet library (http://wordnet.princeton.edu).

Dataset structure

The structure of a dataset is the following: { u'_id': u'9eToPjUnwmU', u'title': u'Traitor Compilation # 1 (Trouble ...', u'description': u'A traitor compilation by one are ...', u'category': u'Games', u'commentsNumber': u'6', u'publishedDate': u'2012-10-09T23:42:12.000Z', u'author': u'ServilityGaming', u'duration': u'208', u'type': u'video/3gpp', u'relatedVideos': [u'acjHy7oPmls', u'EhW2LbCjm7c', u'UUKigFAQLMA', ...], u'accessControl': { u'comment': {u'permission': u'allowed'}, u'list': {u'permission': u'allowed'}, u'videoRespond': {u'permission': u'moderated'}, u'rate': {u'permission': u'allowed'}, u'syndicate': {u'permission': u'allowed'}, u'embed': {u'permission': u'allowed'}, u'commentVote': {u'permission': u'allowed'}, u'autoPlay': {u'permission': u'allowed'} }, u'views': { u'cumulative': { u'data': [15.0, 25.0, 26.0, 26.0, ...] }, u'daily': { u'data': [15.0, 10.0, 1.0, 0.0, ..] } }, u'shares': { u'cumulative': { u'data': [0.0, 0.0, 0.0, 0.0, ...] }, u'daily': { u'data': [0.0, 0.0, 0.0, 0.0, ...] } }, u'watchtime': { u'cumulative': { u'data': [22.5666666667, 36.5166666667, 36.7, 36.7, ...] }, u'daily': { u'data': [22.5666666667, 13.95, 0.166666666667, 0.0, ...] } }, u'subscribers': { u'cumulative': { u'data': [0.0, 0.0, 0.0, 0.0, ...] }, u'daily': { u'data': [-1.0, 0.0, 0.0, 0.0, ...] } }, u'day': { u'data': [1349740800000.0, 1349827200000.0, 1349913600000.0, 1350000000000.0, ...] } }

From the structure above is possible to see which fields an entry in the dataset has. It is possible to divide them into 2 sections:

1) Video Information.

_id -> Corresponding to the video ID and to the unique identifier of an entry in the database. title -> Te video's title. description -> The video's description. category -> The YouTube category the video is inserted in. commentsNumber -> The number of comments posted by users. publishedDate -> The date the video has been published. author -> The author of the video. duration -> The video duration in seconds. type -> The encoding type of the video. relatedVideos -> A list of related videos. accessControl -> A list of access policies for different aspects related to the video.

2) Video Statistics.

Each video can have 4 different statistics variables: views, shares, subscribers and watchtime. Recent videos have all of them while older video can have only the 'views' variable. Each variable has 2 dimensions, daily and cumulative.

views -> number of views collected by the video. shares -> number of sharing operations performed by users. watchtime -> the time spent by users watching the video, in minute. subscribers -> number of subscriptions to the channel the video is inserted in, caused by the selected video. day -> a list of days indicating the analysed period for the statistic.

Data usage

In the case you are using mongoDB as database system, you can import our dataset using the command:

mongoimport --db [MONGODB_NAME] --collection [MONGODB_COLLECTION] --file dataset.json

Once you imported the Dataset in your DB, you can access the data performing queries. Let's present some example code in python in order to perform queries.

The following code will perform a query without research parameters, returning all the entries in the database, each one saved into the variable entry:

client = MongoClient('localhost', 27017) db = client[MONGODB_NAME] collection = db[MONGODB_COLLECTION] for entry in db.collection.find(): print entry["day"]["data"]

If you want to restrict the results to some entries that answer to a specified query you can use:

client = MongoClient('localhost', 27017) db = client[MONGODB_NAME] collection = db[MONGODB_COLLECTION] for entry in (db.collection.find({"watchtime":{ "$exists": True }})) and (db.collection.find({"category":"Music"})): print entry["day"]["data"]

Introduction

YouTube Statistics: YouTube dominates the digital landscape with 2.70 billion monthly active users among the world population in mid-2025, making it the second-largest search engine after Google and the second social platform, following Facebook, across the world.

People watch more than 1 billion hours of video on YouTube, that’s a million years of attention span. With over 20 million new videos uploaded to the platform every day, the YouTube content ecosystem is practically endless. Short-form video lovers have not been ignored.

With an astonishing 70 billion views a day on YouTube shorts, these viewers are generating a new level of interactions and engagement across the platform. Of course, mobile dominates; 63% of watch time happens on mobile devices. With over 100 million subscribers to YouTube Premium and YouTube Music, in addition to free, YouTube is indeed a premium entertainment platform.

This dataset is a daily record of the top trending YouTube videos and it will be updated daily.

Context

YouTube maintains a list of the top trending videos on the platform. According to Variety magazine, “To determine the year’s top-trending videos, YouTube uses a combination of factors including measuring users interactions (number of views, shares, comments and likes). Note that they’re not the most-viewed videos overall for the calendar year”.

Note that this dataset is a structurally improved version of this dataset.

Content

This dataset includes several months (and counting) of data on daily trending YouTube videos. Data is included for the IN, US, GB, DE, CA, FR, RU, BR, MX, KR, and JP regions (India, USA, Great Britain, Germany, Canada, France, Russia, Brazil, Mexico, South Korea, and, Japan respectively), with up to 200 listed trending videos per day.

Each region’s data is in a separate file. Data includes the video title, channel title, publish time, tags, views, likes and dislikes, description, and comment count.

The data also includes a category_id field, which varies between regions. To retrieve the categories for a specific video, find it in the associated JSON. One such file is included for each of the 11 regions in the dataset.

For more information on specific columns in the dataset refer to the column metadata.

Acknowledgements

This dataset was collected using the YouTube API. This dataset is the updated version of Trending YouTube Video Statistics.

Inspiration

Possible uses for this dataset could include: - Sentiment analysis in a variety of forms - Categorizing YouTube videos based on their comments and statistics. - Training ML algorithms like RNNs to generate their own YouTube comments. - Analyzing what factors affect how popular a YouTube video will be. - Statistical analysis over time.

For further inspiration, see the kernels on this dataset!

As of February 2025, ** percent of the YouTube global audience was composed of male users aged between 25 and 34 years, as well as around *** percent of female users of the same age. Male users aged between 35 and 44 years on the platform accounted for **** percent of the total, while women of the same age using YouTube had an audience share of *** percent in the examined period. YouTube’s global popularity The number of monthly active users on YouTube reached almost *** billion in April 2024, making it the second most popular social network on the internet. The platform's popularity spans all over the world, with India and the United States having the largest YouTube audiences. As of April 2024, the audience of YouTube in India was around *** million, while the United States recorded a YouTube audience of around *** million users.

YouTube’s digital revenues One of YouTube's leading monetization methods include advertising, with the company generating around **** billion U.S. dollars in the first quarter of 2024. Additionally, the platform generated over ** million dollars in the United States through in-app purchases, as well as over **** million U.S. dollars in revenues from mobile app users in Japan.

Overview

This dataset contains engagement analytics from two prominent tech YouTube channels:

• WsCube Tech: A Hindi-language technology education channel covering web development, programming, and digital marketing.
• Google Developers: The official YouTube channel for Google’s developer ecosystem, featuring tech talks, product updates, and developer events.

Purpose

The purpose of this dataset is to analyze and compare the performance, engagement, and growth trends of both channels using metrics such as:

• VideoID
• Title
• UploadDate
• Views
• Likes
• Dislikes (Note: Not available via API since 2021)
• Comments

Possible Analysis

• 📈 Views Over Time: Growth and consistency of each channel
• 📊 Top Performing Videos: Views per video
• 🥧 Engagement Ratio: Pie chart showing likes vs comments distribution
• 📉 Upload Frequency Impact: How upload consistency affects views

Sources

Data collected using the YouTube Data API v3 between July 25–28, 2025. Only public video data is included.

Columns

Licensing

Data is collected from publicly available sources (YouTube API). No copyrighted content is included.

Suggested Tags

• youtube
• YtDataSet
• Dashboard
• data-visualization
• wsCubeTech
• google-developers
• engagement-metrics
• social-media-analytics

This dataset contains data related to most watched YouTube videos till April 2024 . This contains different columns namely views,artist,channel,etc. The data is ranked on the basis of number of views.

Context

Late Night Talk Shows are a staple of American television culture and with the shows establishing a digital presence in the form of YouTube channels, this culture has become more global. Some of the channels here have more than 20 Million subscribers which shows the amount of influence they hold in this platform.

Content

The data is organized on a per-show channel basis which has the most important information like video titles, and all the numeric counts of Likes, Dislikes, Comments and number of views (as of 13th June 2020)

Acknowledgements

All of this data is responsibly scraped from YouTube and I would like to acknowledge all the respective Talk Shows for making their content free for the public.

Inspiration

The main inspiration for this dataset is how a video title or a particular celebrity appearing on the talk show can affect the engagement rate of a video

As of June 2022, more than *** hours of video were uploaded to YouTube every minute. This equates to approximately ****** hours of newly uploaded content per hour. The amount of content on YouTube has increased dramatically as consumer’s appetites for online video has grown. In fact, the number of video content hours uploaded every 60 seconds grew by around ** percent between 2014 and 2020. YouTube global users Online video is one of the most popular digital activities worldwide, with ** percent of internet users worldwide watching more than ** hours of online videos on a weekly basis in 2023. It was estimated that in 2023 YouTube would reach approximately *** million users worldwide. In 2022, the video platform was one of the leading media and entertainment brands worldwide, with a value of more than ** billion U.S. dollars. YouTube video content consumption The most viewed YouTube channels of all time have racked up billions of viewers, millions of subscribers and cover a wide variety of topics ranging from music to cosmetics. The YouTube channel owner with the most video views is Indian music label T-Series, which counted ****** billion lifetime views. Other popular YouTubers are gaming personalities such as PewDiePie, DanTDM and Markiplier.

Introduction

YouTube Users statistics: YouTube has over 2.70 billion monthly active users. It indicates that more than one-third of the world’s population and around 47% of the online global population. With a 54% of male user base, India ranks at #1 in terms of YouTube users with 491 million active users, followed by the United States with 253 million and Brazil with 144 million users.

YouTube.com becomes the second most visited website with around 77.52 billion visits on its website. With features like YouTube shorts and YouTube music, YouTube gained a massive popularity even in the smallest cities of most countries. With more than 8 million subscribers, YouTube TV extends its reach among Gen Z and millennials. With a great vision to turn YouTube’s next frontier into the living room, YouTube’s chief executive officer, Neal Mohan, is giving his best to deliver a world-class subscription experience.