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...

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.

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.

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

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.

This dataset contains two files for analyzing the relationship between the popularity of a certain video and the most relevant/liked comments of said video.

File Descriptions

videos-stats.csv: This file contains some basic information about each video, such as the title, likes, views, keyword, and comment count.

comments.csv: For each video in videos-stats.csv, comments.csv contains the top ten most relevant comments as well as said comments' sentiments and likes.

Column Descriptions

videos-stats.csv: - Title: Video Title. - Video ID: The Video Identifier. - Published At: The date the video was published in YYYY-MM-DD. - Keyword: The keyword associated with the video. - Likes: The number of likes the video received. If this value is -1, the likes are not publicly visible. - Comments: The number of comments the video has. If this value is -1, the video creator has disabled comments. - Views: The number of views the video got.

comments.csv: - Video ID: The Video Identifier. - Comment: The comment text. - Likes: The number of likes the comment received. - Sentiment: The sentiment of the comment. A value of 0 represents a negative sentiment, while values of 1 or 2 represent neutral and positive sentiments respectively.

Applicability

• Sentiment Analysis with comments
• Text Generation with comments
• Predicting video likes from comment information
• Popularity Analysis by Keyword
• Popularity Analysis
• Prediction video views from comment information/video statistics
• In-depth EDA of the Data

Introduction

YouTube Creator Statistics: The overall Creator Economy rose to fame since the COVID-19 pandemic era, 4 years ago. When every other person was stuck in their home and scrolling through social media. It is expected that by 2027, this sector will reach $480 billion, and it has no signs of slowing down. Using the internet, many people are creating a source of income by making videos and uploading them to video content platforms, such as TikTok, Moj, YouTube, Instagram, Facebook, etc.

YouTube is one of the video content platforms. It was launched on 14 February 2005 by Steve Chen, Chad Hurley, and Jawed Karim. YouTube helped content creators around the world earn passive income by making videos and uploading them on the platform. For some creators, YouTube acts as the primary source of income. You’ll understand more about YouTube Creator Statistics by the data provided below.

Introduction

YouTube Statistics: YouTube was founded on April 14th, 2005, by three PayPal employees, Jawed Karim, Steve Chen, and Chad Hurley. They started the company in an office above a small restaurant in San Mateo. The platform now has almost 2.49 billion active users monthly and has grown significantly over the years. 47% of internet users worldwide use YouTube each month.

YouTube serves as both a social media platform and a search engine, allowing direct interaction with the audience and the creation of content that can be easily found by users. Let's explore more YouTube statistics.

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.

Introduction

YouTube Creator Statistics: YouTube is the world's largest video-sharing platform. It was launched in Feb 2005. In 2006 it was purchased by Google in 2006. As of 2024, it is the world’s second most-viewed website after Google search. YouTube has managed to create an unprecedented social impact on the world. It has been instrumental in changing the overall dynamics of social media presence.

It has been a dominant force in shaping internet trends and creating millionaire celebrities. Likewise, it would be interesting to highlight YouTube creator statistics to gain valuable information on how this video-sharing platform has profoundly impacted the internet world.

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!

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.

YouTube Videos and Channels Metadata

Analyze the statistical relation between videos and form a topic tree

By VISHWANATH SESHAGIRI [source]

About this dataset

This dataset contains YouTube video and channel metadata to analyze the statistical relation between videos and form a topic tree. With 9 direct features, 13 more indirect features, it has all that you need to build a deep understanding of how videos are related – including information like total views per unit time, channel views, likes/subscribers ratio, comments/views ratio, dislikes/subscribers ratio etc. This data provides us with a unique opportunity to gain insights on topics such as subscriber count trends over time or calculating the impact of trends on subscriber engagement. We can develop powerful models that show us how different types of content drive viewership and identify the most popular styles or topics within YouTube's vast catalogue. Additionally this data offers an intriguing look into consumer behaviour as we can explore what drives people to watch specific videos at certain times or appreciate certain channels more than others - by analyzing things like likes per subscribers and dislikes per views ratios for example! Finally this dataset is completely open source with an easy-to-understand Github repo making it an invaluable resource for anyone looking to gain better insights into how their audience interacts with their content and how they might improve it in the future

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How to use the dataset

How to Use This Dataset

In general, it is important to understand each parameter in the data set before proceeding with analysis. The parameters included are totalviews/channelelapsedtime, channelViewCount, likes/subscriber, views/subscribers, subscriberCounts, dislikes/views comments/subscriberchannelCommentCounts,, likes/dislikes comments/views dislikes/ subscribers totviewes /totsubsvews /elapsedtime.

To use this dataset for your own analysis:1) Review each parameter’s meaning and purpose in our dataset; 2) Get familiar with basic descriptive statistics such as mean median mode range; 3) Create visualizations or tables based on subsets of our data; 4) Understand correlations between different sets of variables or parameters; 5) Generate meaningful conclusions about specific channels or topics based on organized graph hierarchies or tables.; 6) Analyze trends over time for individual parameters as well as an aggregate reaction from all users when videos are released

Research Ideas

• Predicting the Relative Popularity of Videos: This dataset can be used to build a statistical model that can predict the relative popularity of videos based on various factors such as total views, channel viewers, likes/dislikes ratio, and comments/views ratio. This model could then be used to make recommendations and predict which videos are likely to become popular or go viral.

• Creating Topic Trees: The dataset can also be used to create topic trees or taxonomies by analyzing the content of videos and looking at what topics they cover. For example, one could analyze the most popular YouTube channels in a specific subject area, group together those that discuss similar topics, and then build an organized tree structure around those topics in order to better understand viewer interests in that area.

• Viewer Engagement Analysis: This dataset could also be used for viewer engagement analysis purposes by analyzing factors such as subscriber count, average time spent watching a video per user (elapsed time), comments made per view etc., so as to gain insights into how engaged viewers are with specific content or channels on YouTube. From this information it would be possible to optimize content strategy accordingly in order improve overall engagement rates across various types of video content and channel types

Acknowledgements

If you use this dataset in your research, please credit the original authors.

Data Source

License

Unknown License - Please check the dataset description for more information.

Columns

File: YouTubeDataset_withChannelElapsed.csv | Column name | Description | |:----------------------------------|:-------------------------------------------------------| | totalviews/channelelapsedtime | Ratio of total views to channel elapsed time. (Ratio) | | channelViewCount | Total number of views for the channel. (Integer) | | likes/subscriber ...

YouTube boasts an average of 23.7 hours per month spent by users.

In 2021, YouTube's user base in Thailand amounts to approximately ***** million users. The number of YouTube users in Thailand is projected to reach ***** million users by 2025. User figures 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).

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 vi...

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.

Comprehensive YouTube subscriber data for content creators

This comprehensive YouTube Video Analytics Dataset provides valuable insights into the performance of a wide range of videos on the popular platform. Spanning various genres, the dataset encompasses essential information such as - 1.Genre 2.video titles, 3.publish times, 4.view counts, 5.watch time (in hours), 6.subscriber counts, 7.average view durations, 8.impressions, and 9.impressions click-through rates (%).

By leveraging this dataset, researchers, analysts, and data enthusiasts can delve into the factors that influence video success on YouTube. Analyze the correlation between genre and view counts, investigate the impact of subscriber counts on watch time, or explore how average view durations and click-through rates affect video impressions.

Whether you're interested in exploring video trends, identifying patterns in user behavior, or developing machine learning models, this dataset serves as a valuable resource. Gain actionable insights into YouTube video performance and contribute to the ever-growing field of online content analysis. LICENCE NOTE - This is the dataset of my own channel.

YouTube Video and Channel Analytics

YouTube Video and Channel Analytics: Statistics and Features

By VISHWANATH SESHAGIRI [source]

About this dataset

The YouTube Video and Channel Metadata dataset is a comprehensive collection of data related to YouTube videos and channels. It consists of various features and statistics that provide insights into the performance and engagement of videos, as well as the overall popularity and success of channels.

The dataset includes both direct features, such as total views, channel elapsed time, channel ID, video category ID, channel view count, likes per subscriber, dislikes per subscriber, comments per subscriber, and more. Additionally, there are indirect features derived from YouTube's API that provide additional metrics for analysis.

One important aspect covered in this dataset is the ratio between certain metrics. For example: - The totalviews/channelelapsedtime ratio represents the average number of views a video has received relative to the elapsed time since the channel was created. - The likes/dislikes ratio indicates the proportion of likes on a video compared to dislikes. - The views/subscribers ratio showcases how engaged subscribers are by measuring the number of views relative to the number of subscribers.

Other metrics explored in this dataset include comments/views ratio (representing viewer engagement), dislikes/views ratio (measuring viewer sentiment), comments/subscriber ratio (indicating community participation), likes/subscriber ratio (reflecting audience loyalty), dislikes/subscriber ratio (highlighting dissatisfaction levels), total number of subscribers for a channel (subscriberCount), total views on a channel (channelViewCount), total number of comments on a channel (channelCommentCount), among others.

By analyzing these features and statistics within this dataset, researchers or data analysts can gain valuable insights into various aspects related to YouTube videos and channels. Furthermore, it may be possible to build statistical relationships between videos based on their performance characteristics or even develop topic trees based on similarities between different content categories. This dataset serves as an excellent resource for studying YouTube's ecosystem comprehensively.

For accessing additional resources related to this dataset or exploring code repositories associated with it, users can refer to the provided GitHub repository

How to use the dataset

Introduction:

Step 1: Understanding the Dataset Start by familiarizing yourself with the columns in the dataset. Here are some key features to pay attention to:

• 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 for a channel.
• dislikes/views: The ratio of dislikes on a video to its total views.
• comments/subscriber: The ratio comments on a video receive per subscriber count.

Step 2: Determining Data Analysis Objectives Define your objectives or research questions before diving into data analysis using this dataset. For example, you may want to explore relationships between viewership, engagement metrics, and various attributes such as category ID or elapsed time.

Step 3: Analyzing Relationships between Variables Use statistical techniques like correlation analysis or visualization tools like scatter plots, bar graphs, or heatmaps to understand relationships between variables in this dataset.

For example: - Plotting totalviews/channelelapsedtime against channelViewCount can help identify patterns between overall video popularity and channels' view count growth over time. - Comparing likes/dislikes with comments/views can give insights into viewer engagement levels across different videos.

Step 4: Building Machine Learning Models (Optional) If your objective includes predictive analysis or building machine learning models, select relevant features as predictors and the target variable (e.g., totalviews/channelelapsedtime) for training and evaluation.

You can use various algorithms such as linear regression, decision trees, or neural networks to predict video performance or channel growth based on available attributes.

Step 5: Evaluating Model Performance Assess the predictive model's performance using appropriate evaluation metrics like mean square...