This repository contains the code and analysis for the Global YouTube Statistic Analysis project. The project involves extracting and analyzing YouTube statistics across multiple regions, examining trends related to views, subscribers, uploads, etc. Through this analysis, actionable insights were derived to help content creators and businesses enhance their YouTube strategies.
Full details can be found in Global Youtube Statistic Analysis-3.ipynb
** Key Features
Data Cleaning: Processed raw data to remove inconsistencies, null values, and duplicates.
Exploratory Data Analysis (EDA): Conducted in-depth analysis to identify trends in the data such as popularity based on views, view/subscribe ratios, etc.
Visualizations: Comprehensive visualizations were used to present data trends clearly
Insights: Provided recommendations for content creators to improve their reach and engagement based on the analysis:
- What are top 10 Youtubers who have the highest amount of views?
- Does it mean that a Youtube channel owning a high total of uploads will lead to a high number of subscribers and amount of views
3. What are some Youtube user trends based on subscribers and view counts in each category?
4. Does the content (key terms) in the name of YouTube channels affect user views?
5. What are some interesting user interaction and behaviors in those catgories. (measured by view counts per user)
6. What are the trending content creativity of each channel over the time?
7. What is the possibility of generating average earnings per month in each category?
🎯 Subscriber Base Importance: A loyal subscriber base is crucial for gaining more video views. Success on YouTube is built through persistence and creativity.
📺 Popular Content Categories: Entertainment, Music, Gaming, and Comedy are the most popular categories. Education and How-to/Style hold consistent positions, while Travel & Events perform less effectively.
💰 Income Beyond Views: Video views alone don't strongly determine income. Factors like engagement, video length, and ad placement influence earnings, with Show content showing significant revenue potential.
🎬 Quality Over Quantity: Uploading more videos doesn't always increase views or followers. The most successful YouTubers focus on high-quality, engaging content that meets the audience's needs.
🔄 Consistency: A strong connection between video content, category, and channel branding helps boost discoverability and recommendations.
🎵 Shifting Trends: Music led user trends from 2007-2012, but Entertainment has since taken the lead, with both categories remaining dominant in views and subscribers.
👥 Viewer Engagement: Successful channels build loyal followings by engaging with viewers, interacting through comments, and creating communities on social media.
🌍 Cross-Platform Sharing: Sharing content across multiple platforms helps boost visibility and attract a wider audience.
🔍 SEO: Effective use of SEO in video titles, descriptions, and tags increases the likelihood of appearing in YouTube searches.
🏆 Patience and Creativity: Growing a successful channel takes time, creativity, and staying up-to-date with evolving trends. Here are the key points from the provided text:
🎶 Key Words in YouTube Trends: Prominent words like Music, Kids, Songs, and Nursery Rhymes reflect the major content categories driving success on YouTube—particularly Music, Entertainment, and Shows.
📈 Clear Branding: Channels with strong, recognizable branding and consistency between their name and content perform better, helping with audience recognition.
🎬 'Show' Category Dominance: Despite lower views and subscribers, the Show category has the highest view-to-subscriber ratio, surpassing categories like Pets & Animals by 27%.
📺 Entertainment Shift (2013-2022): From 2013 onward, Entertainment content rose to prominence as user numbers grew, with peak success around 2015. This was seen as entertainment became a daily necessity.
📝 People & Blogs Category Growth: The People & Blogs category gained popularity from 2016-2017, driven by the rise of vlogs and personal content, heavily influenced by social media integration.
How to Run
Clone the repository:
git clone https://github.com/quinnduong/Global-YouTube-Statistic-Analysis.git
Install the required dependencies
Run the analysis scripts locally to generate your own visualizations.