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

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

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) |

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.

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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).

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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.

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The dataset contains information about YouTube trending videos for India. Each row in the dataset represents a video that has appeared on the trending list in India. The dataset includes the following columns:

• video_id: Unique identifier for the video.
• trending_date: The date when the video appeared on the trending list.
• title: The title of the video.
• channel_title: The name of the channel that uploaded the video.
• category_id: The category ID assigned to the video, which can be mapped to a specific category name.
• publish_time: The exact time when the video was published on YouTube.
• tags: Tags associated with the video, which help in categorization and searchability.
• views: The number of views the video had at the time it was trending.
• likes: The number of likes the video had at the time it was trending.
• dislikes: The number of dislikes the video had at the time it was trending.
• comment_count: The number of comments the video had at the time it was trending.
• thumbnail_link: A link to the thumbnail image associated with the video.
• comments_disabled: A boolean indicating whether comments were disabled for the video.
• ratings_disabled: A boolean indicating whether ratings (likes/dislikes) were disabled for the video.
• video_error_or_removed: A boolean indicating whether the video was removed or encountered an error.
• description: The description provided by the video uploader.

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Dataset

This dataset was created by zd6rvteb4 3

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