YouTube Trending Videos Dataset

Exploring YouTube Trending Videos

By dskl [source]

About this dataset

Moreover it also reveals various engagement metrics such as the number of views the video has received, likes and dislikes it has garnered from viewership. Additionally information related to comment count on particular videos enables analysis regarding viewer interaction and response. Furthermore this dataset describes whether comments or ratings are disabled for a particular video allowing examination into how these factors impact engagement.

By exploring this dataset in-depth marketers can gain valuable insights into identifying trends in content popularity across different countries while taking into account timing considerations based on published day of week. It also opens up avenues for analyzing public sentiment towards specific videos based on likes vs dislikes ratios and comment count which further aids in devising suitable marketing strategies.

Overall,this informative dataset serves as an invaluable asset for researchers,data analysts,and marketers alike who strive to gain deeper understanding about trending video patterns,relevant metrics influencing content virality,factors dictating viewer sentiments,and exploring new possibilities within digital marketing space leveraging YouTube's wide reach

How to use the dataset

How to Use This Dataset: A Guide

In this guide, we will walk you through the different columns in the dataset and provide insights on how you can explore the popularity and engagement of these trending videos. Let's dive in!

Column Descriptions:

• title: The title of the video.
• channel_title: The title of the YouTube channel that published the video.
• publish_date: The date when the video was published on YouTube.
• time_frame: The duration of time (e.g., 1 day, 6 hours) that the video has been trending on YouTube.
• published_day_of_week: The day of week (e.g., Monday) when the video was published.
• publish_country: The country where the video was published.
• tags: The tags or keywords associated with the video.
• views: The number of views received by a particular video
• likes: Number o likes received per each videos
• dislike: Number dislikes receives per an individual vidoe 11.comment_count: number of comments

Popular Video Insights:

To gain insights into popular videos based on this dataset, you can focus your analysis using these columns:

title, channel_title, publish_date, time_frame, and** publish_country**.

By analyzing these attributes together with other engagement metrics such as views ,likes,**dislikes,**comments),comment_count you can identify trends in what type content is most popular both globally or within specific countries.

For instance: - You could analyze which channels are consistently publishing trending videos - Explore whether certain types of titles or tags are more likely to attract views and engagement. - Determine if certain days of the week or time frames have a higher likelihood of trending videos being published.

Engagement Insights:

To explore user engagement with the trending videos, you can focus your analysis on these columns:

likes, dislikes, comment_count

By analyzing these attributes you can get insights into how users are interacting with the content. For example: - You could compare the like and dislike ratios to identify positively received videos versus those that are more controversial. - Analyze comment counts to understand how users are engaging with the content and whether comments being disabled affects overall

Research Ideas

• Analyzing the popularity and engagement of trending videos: By analyzing the number of views, likes, dislikes, and comments, we can understand which types of videos are popular among YouTube users. We can also examine factors such as comment count and ratings disabled to see how viewers engage with trending videos.
• Understanding video trends across different countries: By examining the publish country column, we can compare the popularity of trending videos in different countries. This can help content creators or marketers understand regional preferences and tailor their content strategy accordingly.
• Studying the impact of video attributes on engagement: By exploring the relationship between video attributes (such as title, tags, publish day) and engagement metrics (views, likes), we can identify patterns or trends that influence a video's success on YouTube. This information can be...

In 2024, YouTube channels counting over ****** followers saw their video views decrease to *** per video, down by ** percent year-over-year. In comparison, small accounts with between *** and ***** followers saw approximately **** views per video in 2024, down compared to the **** views recorded in 2023.

Comprehensive YouTube channel statistics for Trend Spot, featuring 5,500,000 subscribers and 680,704,410 total views. This dataset includes detailed performance metrics such as subscriber growth, video views, engagement rates, and estimated revenue. The channel operates in the Technology category and is based in US. Track 317 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.

🔍 Description: This dataset provides a rich collection of metadata from 2,000+ YouTube videos, offering a unique opportunity to explore how content performs on the world’s largest video-sharing platform. With detailed information on video titles, views, likes, tags, durations, publishing dates, and more, this dataset allows you to dive deep into the world of digital content trends.

Whether you're a data analyst, a machine learning enthusiast, or a content creator, this dataset opens the door to powerful insights about what drives engagement on YouTube.

📌 Key Features: 🎥 Video Title, Channel, and Tags — Understand how content is labeled and branded.

👍 Likes, Dislikes, Comments — Measure audience sentiment and engagement.

⏱️ Duration & Publish Time — See how timing affects performance.

📈 Views — Track popularity and potential virality.

📃 Description Text — Analyze the role of metadata and SEO.

🧠 Great for NLP, sentiment analysis, and predictive modeling tasks.

💡 Potential Uses: Predict video popularity based on title, tags, and other features.

Natural Language Processing (NLP) on video titles and descriptions.

Trend analysis on content type vs. engagement.

Time-series forecasting of views/likes/comments.

Clustering or recommendation systems for video categorization.

Comprehensive ranking dataset of the top 100 YouTube channels worldwide. This dataset features 100 channels with detailed statistics including subscriber counts, total video views, video count, and global rankings. The leading channel has 452,000,000 subscribers and 101,598,825,577 total views. Each entry includes comprehensive metrics to analyze channel performance, growth trends, and competitive positioning. This dataset is regularly updated to reflect the latest YouTube channel statistics and ranking changes, providing valuable insights for content creators, marketers, and researchers analyzing YouTube ecosystem trends and channel performance benchmarks.

Comprehensive YouTube channel statistics for A Day In History, featuring 844,000 subscribers and 141,669,316 total views. This dataset includes detailed performance metrics such as subscriber growth, video views, engagement rates, and estimated revenue. The channel operates in the News-&-Politics category and is based in US. Track 226 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 information about trending YouTube videos, including details about the videos and their respective channels. The data is collected daily and provides insights into video performance, audience engagement, and channel characteristics across different countries. Below is a detailed description of each column:

Video Information

1. video_id: Unique identifier for the video on YouTube.
2. video_published_at: The date and time when the video was published.
3. video_trending_date: The date when the video was identified as trending.
4. video_trending_country: The country where the video is trending (ISO 3166-1 alpha-2 country code, e.g., "US" for the United States).
5. video_title: The title of the video as displayed on YouTube.
6. video_description: The description provided by the video creator.
7. video_default_thumbnail: URL of the default thumbnail for the video.
8. video_category_id: Numeric ID representing the category of the video (e.g., Music, Gaming, etc.).
9. video_tags: List of tags associated with the video for categorization and discoverability.
10. video_duration: Duration of the video in ISO 8601 format (e.g., "PT10M15S" for 10 minutes and 15 seconds).
11. video_dimension: Dimension of the video (e.g., "2d", "3d").
12. video_definition: Video resolution quality (e.g., "hd" for high definition, "sd" for standard definition).
13. video_licensed_content: Boolean indicating if the video contains licensed content.
14. video_view_count: Total number of views for the video.
15. video_like_count: Total number of likes for the video.
16. video_comment_count: Total number of comments on the video.

Channel Information

1. channel_id: Unique identifier for the YouTube channel.
2. channel_title: The name/title of the channel.
3. channel_description: Description provided by the channel owner.
4. channel_custom_url: Custom URL for the channel (if available).
5. channel_published_at: The date and time when the channel was created.
6. channel_country: The country associated with the channel (if specified by the creator).
7. channel_view_count: Total number of views across all videos on the channel.
8. channel_subscriber_count: Total number of subscribers to the channel.
9. channel_have_hidden_subscribers: Boolean indicating if the channel has hidden its subscriber count.
10. channel_video_count: Total number of videos uploaded by the channel.
11. channel_localized_title: The localized title of the channel (if available in a different language).
12. channel_localized_description: The localized description of the channel (if available in a different language).

Potential Applications

This dataset is a rich resource for analyzing YouTube video and channel trends. Here are some potential use cases:

1. Trend Analysis:

   • Identify trending content in specific countries or globally.
   • Explore the relationship between publishing time and trending status.

2. Audience Engagement Insights:

   • Analyze the correlation between video views, likes, and comments.
   • Understand the impact of video tags, duration, or definition on engagement.

3. Content Category Insights:

   • Explore how different video categories perform over time.
   • Analyze the popularity of certain categories in specific regions.

4. Channel Growth Analysis:

   • Study how trending videos impact channel subscriber growth.
   • Analyze the characteristics of successful channels (e.g., average video duration, view count, etc.).

5. Machine Learning Projects:

   • Predict the likelihood of a video trending based on its metadata.
   • Cluster videos or channels based on similarities in tags, categories, and engagement metrics.

6. Business Applications:

   • Help content creators and marketers understand audience preferences.
   • Use data to guide video production strategies for better engagement.

Additional Notes

This dataset can be combined with other external datasets, such as demographic or social media engagement data, for broader analyses. It is particularly suitable for projects related to content strategy, audience analysis, or even recommendation system development for platforms similar to YouTube.

On June 17, 2016, Korean education brand Pinkfong released their video "Baby Shark Dance", and the rest is history. In January 2021, Baby Shark Dance became the first YouTube video to surpass 10 billion views, after snatching the crown of most-viewed YouTube video of all time from the former record holder "Despacito" one year before. "Baby Shark Dance" currently has over 16 billion lifetime views on YouTube. Music videos on YouTube “Baby Shark Dance” might be the current record-holder in terms of total views, but Korean artist Psy’s “Gangnam Style” video remained in the top spot for the longest time (1,689 days, or 4.6 years) before ceding its spot to its successor. With figures like these, it comes as little surprise that the majority of the most popular videos on YouTube are music videos. Since 2010, all but one of the most-viewed videos on YouTube have been music videos, signifying the platform’s shift in focus from funny, viral videos to professionally produced content. Popular video content on YouTube Music fans are also highly engaged audiences, and it is not uncommon for music videos to garner significant amounts of traffic within the first 24 hours of release. Other popular types of videos that generate lots of views after their first release are movie trailers, especially superhero movies related to the MCU (Marvel Cinematic Universe). The first official trailer for the upcoming film “Avengers: Endgame” generated 289 million views within the first 24 hours of release, while the movie trailer for Spider-Man: No Way Home generated over 355 views on the first day of release, making it the most viral movie trailer.

YouTube Video Analytics Dataset

This dataset contains structured metadata and engagement statistics for YouTube videos. It is designed for data analysis, visualization, and machine-learning tasks such as trend forecasting, recommendation modeling, and engagement prediction.

🔍 Dataset Overview

Each row represents a single YouTube video and includes:

• Video metadata
• Channel information
• User engagement metrics
• Video category and duration details
• Computed ratios (engagement rate, likes/views, comments/views)
• Video age and publication characteristics

📌 Use Cases

• Predict video performance
• Analyze factors contributing to virality
• Study viewer engagement behavior
• Support content strategy and optimization
• Train machine-learning models for ranking, recommendations, or engagement prediction

Comprehensive YouTube channel statistics for Counting Countries, featuring 307,000 subscribers and 82,543,876 total views. This dataset includes detailed performance metrics such as subscriber growth, video views, engagement rates, and estimated revenue. The channel operates in the Travel category and is based in US. Track 332 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 Views Matter , featuring 117,000 subscribers and 12,683,038 total views. This dataset includes detailed performance metrics such as subscriber growth, video views, engagement rates, and estimated revenue. The channel operates in the Lifestyle category and is based in PK. Track 1,102 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 177,000 subscribers and 56,615,192 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 2,833 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.

YouTube Video Engagement and Metrics Dataset

This dataset contains detailed engagement metrics for YouTube videos over time. The data includes views, likes, comments, shares, and other key metrics for individual videos, allowing for analysis of video performance and audience interaction.

Columns:

• video_id: The unique identifier for each YouTube video.
• day: The date of the video engagement data (format: YYYY-MM-DD).
• views: The total number of views the video received on that day.
• redViews: The number of red views (e.g., views where users interacted with specific content).
• comments: The number of comments made on the video.
• likes: The number of likes received on the video.
• dislikes: The number of dislikes received on the video.
• videosAddedToPlaylists: The number of times the video was added to playlists.
• videosRemovedFromPlaylists: The number of times the video was removed from playlists.
• shares: The number of times the video was shared.
• estimatedMinutesWatched: The total estimated minutes of the video watched.
• estimatedRedMinutesWatched: The total estimated red minutes watched (similar to views but involving specific content interactions).
• averageViewDuration: The average duration of views for the video (in seconds).
• averageViewPercentage: The average percentage of the video watched.
• annotationClickThroughRate: The click-through rate for video annotations.
• annotationCloseRate: The close rate for video annotations.
• annotationImpressions: The number of times an annotation was shown to viewers.
• annotationClickableImpressions: The number of times a clickable annotation was shown.
• annotationClosableImpressions: The number of times a closable annotation was shown.
• annotationClicks: The number of clicks on the annotations.
• annotationCloses: The number of times the annotation was closed.
• cardClickRate: The click-through rate for cards in the video.
• cardTeaserClickRate: The click-through rate for teaser cards.
• cardImpressions: The number of times a card was shown in the video.
• cardTeaserImpressions: The number of times a teaser card was shown in the video.
• cardClicks: The number of times a card was clicked.
• cardTeaserClicks: The number of times a teaser card was clicked.
• subscribersGained: The number of subscribers gained on the day due to the video.
• subscribersLost: The number of subscribers lost on the day due to the video.

Example Data:

Use Cases:

• Video Performance Analysis: This dataset is ideal for analyzing how videos perform on YouTube based on views, engagement metrics, and audience interaction. It can help identify patterns in video popularity, like which videos generate the most views or engagement.
• Trend Prediction: With historical data on views, likes, shares, and subscriber changes, this dataset can be used to predict future trends and identify successful strategies for growing a YouTube channel.
• Audience Engagement Insights: The dataset provides detailed metrics on comments, likes, dislikes, and shares, allowing for a deeper understanding of how the audience interacts with videos and what drives user engagement.
• Marketing and Monetization: Brands and creators can use the dataset to track how their videos perform in terms of monetization-related metrics, such as subscriber gain and video shares.
• Content Strategy Optimization: By analyzing which videos have the highest views, average view duration, and engagement rates, content creators can optimize their video content strategy to increase viewership and audience retention.
• Platform Analytics: This dataset can be useful for platforms and companies analyzing YouTube video performance, to develop tools or reports for content creators to improve their engagement and audience reach.

Data Range:

• Start Date: 2019-09-06
• End Date: 2024-11-10

This dataset i...

According to data collected up to 2021, more than **** percent of views about Korean food on YouTube was accounted for by videos about traditional Korean food. Trend food video views made up only **** percent.

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As of October 2025, India was the country with the largest YouTube audience by far, with approximately 500 million users engaging with the popular social video platform. The United States followed, with 254 million YouTube viewers. Indonesia came in third, with 151 million users watching content on YouTube. The United Kingdom saw 55.5 million internet users engaging with the platform in the examined period. What country has the highest percentage of YouTube users? Saudi Arabia was the country with the highest YouTube penetration worldwide, as nearly 96 percent of the country's digital population engaged with the service. In 2025, YouTube counted 125 million paid subscribers for its YouTube Music and YouTube Premium services. YouTube mobile markets YouTube is among the most popular social media platforms worldwide. In terms of in-app (IAP) revenue, the YouTube app generated approximately 53 million U.S. dollars in the United States in December 2024, as well as 17 million U.S. dollars in Japan.

Comprehensive YouTube channel statistics for Up Trends, featuring 133,000 subscribers and 510,939 total views. This dataset includes detailed performance metrics such as subscriber growth, video views, engagement rates, and estimated revenue. The channel operates in the Food category and is based in IN. 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 History Hit, featuring 1,790,000 subscribers and 294,501,100 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 GB. Track 707 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 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).

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