This exploratory data analysis aimed to help potential YouTube channel creators by examining popularity, growth, and earnings of different categories.

The dataset was cleaned, focusing on the 'category','video views' and 'created_year' columns. Earnings were approximated as the average of the highest and lowest yearly values, and channels with low or zero earnings were removed.

Visualizations showed relationships between subscribers and views, subscribers and earnings, and the distribution of channels by category. Category-wise average subscribers growth is shown and the average earnings of each category is graphed. Notably, the 'Shows' and 'Pets & Animals' categories emerged as both the fastest-growing and highest-earning, representing lucrative niche markets.

The data analysis suggests aiming a channel in these directions could be advantageous, but caution is warranted due to potential data limitations. Overall, this EDA provides insights for those considering entering the YouTube content creation space.

Graph showing which categories are growing the fastest relative to the overall average

link to kaggle notebook: https://www.kaggle.com/code/seangr/sean-final-project-eda