MindWave is an open-source project designed for beginners to learn about data science, machine learning, deep learning, and reinforcement learning algorithms using Python. The project offers a platform for implementing relevant algorithms, with open-source tools and libraries.
This pull request for the project aims to analyze the data of various YouTube channels that focus on technology, data science, and analytics. The goal is to find out the most popular topics, trends, and best practices for creating engaging and informative content for tech enthusiasts. The project will compare the channels based on various metrics such as views, subscribers, watch time, engagement, and revenue. The project will also identify the challenges and opportunities for improving the quality and reach of tech-related YouTube content. The project will be written in Python and use libraries such as pandas, matplotlib, seaborn, and plotly for data manipulation and visualization. The project will be hosted on GitHub and use Jupyter Notebook for interactive coding and presentation. The project is open for contributions from anyone interested in YouTube data analysis.
Checklist
[x] I've read the contribution guidelines.
[x] I've checked the issue list before deciding what to submit.
[x] I've edited the README.md and link to my code.
Description
This pull request for the project aims to analyze the data of various YouTube channels that focus on technology, data science, and analytics. The goal is to find out the most popular topics, trends, and best practices for creating engaging and informative content for tech enthusiasts. The project will compare the channels based on various metrics such as views, subscribers, watch time, engagement, and revenue. The project will also identify the challenges and opportunities for improving the quality and reach of tech-related YouTube content. The project will be written in Python and use libraries such as pandas, matplotlib, seaborn, and plotly for data manipulation and visualization. The project will be hosted on GitHub and use Jupyter Notebook for interactive coding and presentation. The project is open for contributions from anyone interested in YouTube data analysis.
Checklist
Related Issues or Pull Requests
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