YouTube keeps track of the most popular videos that are being seen on the site. Several months' worth of daily trending YouTube video statistics are included in this data set. Data for France and the USA are included. The videos on this list are those that users have liked and have received the most views, comments, and likes from other users. These videos are then displayed on the trending page. The greatest videos are shown at the top of the page by ranking these videos according to a ratio of views, likes, comments, and shares.

This dataset is a daily record of the top trending YouTube videos.

content: Data about daily trending YouTube videos for several months, and counting, is included in this dataset. Up to 200 trending videos are published each day, with data for the US and FR regions (the USA and France, respectively) included.

Video Title – Video ka naam

Channel Name – Creator ya channel ka naam

Category – Entertainment, Music, Gaming, Education, etc.

Views – Kitni dafa video dekhi gayi

Likes – Audience appreciation count

Comments – Viewer interaction

Publish Date – Video kab upload hui

Trending Date – Kab trending list mein aayi

Country – Kis country mein trend hui

Behtar content planning kar sakte hain

Audience engagement improve kar sakte hain

Viral hone ke chances barha sakte hain

Monetization strategy optimize kar sakte hain

Time-based trends (monthly / yearly growth) analyze kar sakte hain

Different countries ke trending patterns compare kar sakte hain

YouTube Videos Dataset

Video titles, thumbnail links, country codes, views, added subs, etc.

About this dataset

This YouTube Analytics dataset contains a wealth of information that can be used to study how to engage viewers and keep them coming back for more. With columns like thumbnail link,, country codes, views, and user subscriptions added, this dataset provides valuable insights into how YouTube videos perform and what factors influence viewer engagement. Additionally, the average view percentage and watch time columns can be used to gauge the overall success of a video in terms of engagement

How to use the dataset

This dataset can be used to study how viewers engage with YouTube videos. The data includes information on likes, dislikes, comments, shares, and subscribers gained or lost. This data can be used to improve video content and keep viewers coming back for more

Research Ideas

• Study engagement strategies for YouTube videos
• Improve video content to keep viewers engaged
• Understand how viewers interact with YouTube videos

Acknowledgements

The YouTube Analytics dataset was collected by Nick Hass.

License

See the dataset description for more information.

Columns

File: Aggregated_Metrics_By_Country_And_Subscriber_Status.csv | Column name | Description | |:-------------------------------|:---------------------------------------------------------------------| | Thumbnail link | A link to the thumbnail image of the video. (String) | | Country Code | The country code of the viewer. (String) | | Is Subscribed | Whether or not the viewer is subscribed to the channel. (Boolean) | | Views | The number of views the video has. (Integer) | | User Subscriptions Added | The number of subscriptions the video has gained. (Integer) | | User Subscriptions Removed | The number of subscriptions the video has lost. (Integer) | | Average View Percentage | The average percentage of the video that viewers watch. (Float) | | Average Watch Time | The average amount of time viewers spend watching the video. (Float) |

File: Aggregated_Metrics_By_Video.csv | Column name | Description | |:-----------------------|:--------------------------------------------------------------| | Views | The number of views the video has. (Integer) | | Shares | The number of times the video was shared. (Integer) | | Likes | The number of likes the video has. (Integer) | | RPM (USD) | The revenue per thousand views the video has. (Float) | | CPM (USD) | The cost per thousand views the video has. (Float) | | Watch time (hours) | The total number of hours the video has been watched. (Float) |

File: All_Comments_Final.csv | Column name | Description | |:----------------|:-----------------------------------------------| | Comments | The number of comments on the video. (Integer) | | Reply_Count | The number of replies to the video. (Integer) | | Like_Count | The number of likes on the video. (Integer) | | Date | The date the video was published. (Date) |

File: Video_Performance_Over_Time.csv | Column name | Description | |:-------------------------------|:---------------------------------------------------------------------| | Date | The date the video was published. (Date) | | Thumbnail link | A link to the thumbnail image of the video. (String) | | Views | The number of views the video has. (Integer) | | User Subscriptions Added | The number of subscriptions the video has gained. (Integer) | | User Subscriptions Removed | The number of subscriptions the video has lost. (Integer) | | Average View Percentage | The average percentage of the video that viewers watch. (Float) | | Average Watch Time | The average amount of time viewers spend watching the video. (Float) |

Comprehensive YouTube channel statistics for nature video, featuring 856,000 subscribers and 156,841,611 total views. This dataset includes detailed performance metrics such as subscriber growth, video views, engagement rates, and estimated revenue. The channel operates in the Health category. Track 655 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 World Data 3D, featuring 735,000 subscribers and 292,820,443 total views. This dataset includes detailed performance metrics such as subscriber growth, video views, engagement rates, and estimated revenue. The channel operates in the Education category and is based in US. Track 380 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.

This dataset contains 40,000+ YouTube videos collected between November and December 2025, offering a rich snapshot of the platform’s ecosystem during this period. The collection spans a wide variety of categories (entertainment, education, gaming, news, lifestyle, etc.) and covers creators of different audience sizes, from small independent channels to large, established influencers.

What’s Included

Each entry provides a comprehensive set of metadata fields that enable both descriptive and predictive analysis:

Video performance metrics: views, likes, comments

Creator information: channel name, channel ID, and subscriber count

Publication details: upload date and time

Textual content: video title and description

This combination of features makes the dataset suitable for studying both content characteristics and engagement outcomes.

Why This Dataset?

Unlike older YouTube datasets available on Kaggle, this collection is:

Large 40,000+ entries provide statistical robustness.

Recent: Data reflects the late 2025 YouTube landscape, making it highly relevant for modern research.

Balanced: Includes diverse categories and creators, reducing bias toward any single niche.

Versatile: Well-suited for tasks such as:

Analyzing YouTube’s recommendation algorithm Detecting content trends and audience behavior Training and benchmarking machine learning models

Conducting natural language processing (NLP) on titles and descriptions

Thumbnails

For storage efficiency, the dataset does not include thumbnails. However, thumbnails can be easily restored using the following URL pattern: https://i.ytimg.com/vi/{video_id}/maxresdefault.jpg Simply replace {video_id} with the corresponding video identifier.

Potential Use Cases

Algorithm research: Explore how metadata correlates with visibility and engagement.

Trend analysis: Identify emerging topics, genres, or creator strategies.

NLP tasks: Perform sentiment analysis, keyword extraction, or topic modeling on titles/descriptions.

Predictive modeling: Build regression or classification models to forecast video performance.

Educational projects: Ideal for students and practitioners learning about data science, machine learning, or social media analytics.

Similar dataset

If you are just looking for transcripts: https://www.kaggle.com/datasets/mohamedwael001/youtube-trending-videos-transcripts-900

Data Collection Method

The dataset was collected using YouTube API v3 in python, which ensures transparency and reproducibility.

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 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.

This dataset contains 5,000 records of trending YouTube videos from various categories (e.g., Music, Gaming, Education, Entertainment). Each row includes key video performance indicators such as views, likes, dislikes, comments, publish date, and category.

It’s ideal for exploratory data analysis, machine learning tasks like popularity prediction, engagement analysis, and visualization projects.

Use cases:

• Views prediction models
• Engagement trend analysis
• Category performance comparison
• Time-based trend detection
• Machine learning regression & classification

Comprehensive YouTube channel statistics for Viral Videos ♥️, featuring 140,000 subscribers and 26,353,406 total views. This dataset includes detailed performance metrics such as subscriber growth, video views, engagement rates, and estimated revenue. The channel operates in the Sports category. Track 322 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.

As of October 2023, YouTube Shorts - the platform's popular short-video feature - had reached over 70 billion daily views. YouTube Shorts rolled out globally in June 2021 and reached 30 billion daily views after one year from its initial launch. The feature was tested first in the Indian market, after the digital ban on the ByteDance-owned TikTok in the country.

During the fourth quarter of 2025, around 64.1 percent of videos removed from YouTube were deleted due to child safety reasons. This represents an increase from the previous quarter. During the last measured quarter, another 0.4 percent of flagged videos were removed due to spam or misleading content.

Launched first in India after the TikTok ban in August 2020, YouTube Shorts rolled out globally in June 2021. The feature, which is accessible via the YouTube app, reached two billion monthly logged-in users as of July 2023. YouTube has been heavily promoting its short-video format platform since its global launch, including redirecting users automatically to Shorts when the YouTube app is opened and launching the YouTube Shorts Funds to entice creators' participation. In 2022, user and travel vlogger Shangerdanger took the crown for the most popular Short on YouTube, with his video “Diver Cracks Egg at 45 ft (ca. 14 meters) Deep”. TikTok versus Reels: competitors’ comparison Launched in September 2016 in China as Douyin, TikTok went on to become one of the most engaging social media platforms for global users. The platform challenging mainstream social media platforms such as Facebook and YouTube in their primary markets such as the United States, Brazil, and Japan. TikTok’s popularity exploded between 2019 and 2020, as the world was experiencing the effects of the global COVID-19 pandemic outbreak. Reels, Instagram’s in-app short video experience, debuted in 2020 as Facebook (now Meta Platforms) bet on the short-video feature to improve users’ engagement. While videos were an already popular format on Facebook and Instagram, social short videos soon became an even more popular format with users. As of June 2022, the average video viewing rate for Reels on Instagram was 2.54 percent, while for videos it was of 1.74 percent as of June 2022. Content is key: creators drive an entire economy As of July 2022, influencers on TikTok and YouTube generate the largest share of video views, over 90 percent, while content produced by media companies and brands constitutes only a smaller part of the video views generated on the two video platforms. As content creators are emerging even more clearly as the backbone of social media marketing and advertising, it is not a surprise that an entire economy devoted to their needs and presence has developed in recent years. In 2022, companies supporting the creators’ economy by offering merchandising services had an annual average revenue of over 500 million U.S. dollars, while companies overseeing subscription services generated approximately 300 million U.S. dollars.

This dataset provides an in-depth look at YouTube video analytics, capturing key metrics related to video performance, audience engagement, revenue generation, and viewer behavior. Sourced from real video data, it highlights how variables like video duration, upload time, and ad impressions contribute to monetization and audience retention. This dataset is ideal for data analysts, content creators, and marketers aiming to uncover trends in viewer engagement, optimize content strategies, and maximize ad revenue. Inspired by the evolving landscape of digital content, it serves as a resource for understanding the impact of YouTube metrics on channel growth and content reach.

Video Details: Columns like Video Duration, Video Publish Time, Days Since Publish, Day of Week.

Revenue Metrics: Includes Revenue per 1000 Views (USD), Estimated Revenue (USD), Ad Impressions, and various ad revenue sources (e.g., AdSense, DoubleClick).

Engagement Metrics: Metrics such as Views, Likes, Dislikes, Shares, Comments, Average View Duration, Average View Percentage (%), and Video Thumbnail CTR (%).

Audience Data: Data on New Subscribers, Unsubscribes, Unique Viewers, Returning Viewers, and New Viewers.

Monetization & Transaction Metrics: Details on Monetized Playbacks, Playback-Based CPM, YouTube Premium Revenue, and transactions like Orders and Total Sales Volume (USD).

Comprehensive YouTube channel statistics for CrowderBits, featuring 1,320,000 subscribers and 665,564,553 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 3,336 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 video Tv, featuring 178,000 subscribers and 57,274,076 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 PK. Track 2,921 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.

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!

YouTube Video and Channel Analysis

YouTube Video and Channel Statistics

By VISHWANATH SESHAGIRI [source]

About this dataset

This dataset contains valuable information about YouTube videos and channels, including various metrics related to views, likes, dislikes, comments, and other related statistics. The dataset consists of 9 direct features and 13 indirect features. The direct features include the ratio of comments on a video to the number of views on the video (comments/views), the total number of subscribers of the channel (subscriberCount), the ratio of likes on a video to the number of subscribers of the channel (likes/subscriber), the total number of views on the channel (channelViewCount), and several other informative ratios such as views/elapsedtime, totalviews/channelelapsedtime, comments/subscriber, views/subscribers, dislikes/subscriber.

The dataset also includes indirect features that are derived from YouTube's API. These indirect features provide additional insights into videos and channels by considering factors such as dislikes/views ratio, channelCommentCount (total number of comments on the channel), likes/dislikes ratio, totviews/totsubs ratio (total views on a video to total subscribers of a channel), and more.

The objective behind analyzing this dataset is to establish statistical relationships between videos and channels within YouTube. Furthermore, this analysis aims to form a topic tree based on these statistical relations.

For further exploration or utilization purposes beyond this dataset description document itself, you can refer to relevant repositories such as the GitHub repository associated with this dataset where you might find useful resources that complement or expand upon what is available in this dataset.

Overall,this comprehensive collection provides diverse insights into YouTube video and channel metadata for conducting statistical analyses in order to better understand viewer engagement patterns varies parameters across different channels. With its range from basic counts like subscriber counts,counting no.of viewership per minute , timing vs viewership rate ,text related user responses etc.,this detailed Youtube Dataset will assist in making informed decisions regarding channel optimization,more effective targeting and creation of content that will appeal to the target audience

How to use the dataset

This dataset provides valuable information about YouTube videos and their corresponding channels. With this data, you can perform statistical analysis to gain insights into various aspects of YouTube video and channel performance. Here is a guide on how to effectively use this dataset for your analysis:

• Understanding the Columns:
  • totalviews/channelelapsedtime: The ratio of total views of a video to the elapsed time of the channel.
  • channelViewCount: The total number of views on the channel.
  • likes/subscriber: The ratio of likes on a video to the number of subscribers of the channel.
  • views/subscribers: The ratio of views on a video to the number of subscribers of the channel.
  • subscriberCount: The total number of subscribers of the channel.
  • dislikes/views: The ratio

Research Ideas

• Predicting the popularity of YouTube videos: By analyzing the various ratios and metrics in this dataset, such as comments/views, likes/subscriber, and views/subscribers, one can build predictive models to estimate the popularity or engagement level of YouTube videos. This can help content creators or businesses understand which types of videos are likely to be successful and tailor their content accordingly.
• Analyzing channel performance: The dataset provides information about the total number of views on a channel (channelViewCount), the number of subscribers (subscriberCount), and other related statistics. By examining metrics like views/elapsedtime and totalviews/channelelapsedtime, one can assess how well a channel is performing over time. This analysis can help content creators identify trends or patterns in their viewership and make informed decisions about their video strategies.
• Understanding audience engagement: Ratios like comments/subscriber, likes/dislikes, dislikes/subscriber provide insights into how engaged a channel's subscribers are with its content. By examining these ratios across multiple videos or channels, one can identify trends in audience behavior and preferences. For example, a high ratio of comments/subscriber may indicate strong community participation and active discussion around the videos posted by a particular YouTuber or channel

Acknowledgements

If you use this dataset in y...

Comprehensive YouTube channel statistics for TikTok Most Watched, featuring 189,000 subscribers and 64,722,194 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 1,061 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.

Dataset

This dataset was created by zd6rvteb4 3

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