Youtube Channel Analysis

The code analyzes a dataset of the top subscribed YouTube channels, exploring their categories, subscribers, video views, and video counts. It uses visualization libraries such as Matplotlib, Seaborn, and Plotly Express to create graphs and charts that depict the data in various ways, including a pie chart of the percentage of channels by category, a scatter plot of subscribers and video views, a bar plot of the top 10 channels in the music and education categories by video count and video views, respectively, and a heatmap of the correlation between the different variables. The code also includes a point plot of the trend in YouTube channel creation by year. Overall, the code provides insights into the characteristics of top YouTube channels and how they vary by category.

The dashboard you created on Power BI analyzes data on the top 1000 most subscribed YouTube channels. Using visualizations, you explored the categories of the channels, the relationship between subscribers, video views, and video count, and the top channels in the music and education categories.The dashboard provides valuable insights into the world of YouTube channels and their popularity across different categories. These insights can be used to inform content creation and marketing strategies for individuals and organizations looking to establish a strong presence on YouTube.

Environment Variables

To run this project, you will need to add the following python module installation and Visual Studio Code extension

Installation Dependencies

• pip install numpy
• pip install pandas
• pip install seaborn
• pip install plotly
• pip install matplotlib

Visual Studio Code

• Jupyter notebook

Features

• Data Processing
• Data Visualization
• Data Analytics

Screenshots

1. Percentage of youtube channel by categories

2. Video views and subscribers by categories

3. Top 10 Music Youtube Channels with The Most Video Count

4. Top 10 Education Youtube Channels with The Most Video Views

5. Trends in youtube channel created each year

6. Corelation between variables

7. The heat maps explains the corelation

Power BI

• Created interactive dashboard to track and analyze Youtube Data.
• Used complex parameters to drill down in worksheet and customization using filters and slicers
• Created connections, join new tables, calculations to manipulate data and enable user driven parameters for visualizations
• Used different types of customized visualization (bar chart, pie chart, donut chart, clustered bar chart, map, slicers, etc)

Dashboard Screenshot

Documentation

• SRS

Links

Authors

• Arpitya Kumar Singh
• Ashish Kumar Sahay
• Asutosh Kumar Sandal
• Sthanu Pradhan