Global YouTube Statistics Analysis

Objective:

Investigate global YouTube trends across top channels, focusing on subscribers, views, content type, geographical distribution, country-level education and socioeconomics, and earning metrics.

Data Preparation:

• Dataset: "Global YouTube Statistics.csv", 29 columns (e.g., channel, subscribers, views, category, uploads, country, education, unemployment, urban population, latitude, longitude, earnings, creation dates).
• Loaded and previewed top channels (T-Series, YouTube Movies, MrBeast, Cocomelon, SET India).

Exploratory Analysis:

• Top 10 YouTubers by subscriber count
• Average subscribers by content category
• Upload counts across categories (shows/entertainment/people & blogs with highest uploads)
• Country-wise channel distribution (US, India, Brazil, UK, Mexico)

Visualizations & Correlations:

• Plotted distribution of channel types and uploads
• Observed strong correlation (r≈0.75) between subscribers and views
• Boxplots for monthly/yearly earnings, with high variability and outliers
• Histograms for subscriber gain over last 30 days and channel creation years

Socioeconomic Analysis:

• Compared gross tertiary education enrollment with YouTube channel count by country
• Average unemployment, urban population, and population for leading countries
• Minor correlation (r≈−0.02) between subscriber growth and unemployment

Geographical Distribution:

• Scatterplot mapping channels' latitude and longitude, colored by country

Seasonality & Trends:

• Monthly variation in uploads
• Calculated monthly average subscriber gain since channel creation

Conclusion:

• US and India dominate YouTube channel counts
• "Shows" and "Entertainment" have highest volume of uploads
• Subscribers and views are strongly linked
• Socioeconomic factors (education, urban pop, unemployment) mildly impact channel metrics
• Considerable variability in earning metrics across channel types