YouTube Trending Videos Analysis

Analyze YouTube's trending videos with Python to uncover insights on views, likes, and trends. This project uses Pandas and Matplotlib to examine a dataset containing over 40,000 trending videos collected over 205 days. Discover what makes a video trend on YouTube and gain insights that can help increase video popularity.

Goals of the Analysis

This project aims to answer questions such as:

• What are the common characteristics of trending videos?
• How do views, likes, dislikes, and comment counts correlate?
• Which channels have the most trending videos?
• What are the most frequent words in trending video titles?
• When and how often do videos become trending?
• And more insights into YouTube's trending content.

Dataset Source

The dataset used for this analysis is sourced from Kaggle, focusing on YouTube trending videos in the USA.

How to Use

• Clone this repository.
• Open the Jupyter Notebook or Colab Notebook to see the analysis.
• Gain insights into YouTube's trending videos!

Dependencies

• Python
• Pandas
• Matplotlib
• Jupyter Notebook (for local use) or Google Colab

Project Structure

The project is structured as follows:

• data/: Contains the dataset used for analysis.
• notebooks/: Jupyter Notebooks with the analysis.
• README.md: This readme file.

This project is licensed under the MIT License.

Contact

• GitHub:(https://github.com/tonmoy-khanal)
• Email: tonmoykhanal86@gmail.com

Feel free to reach out for questions, feedback, or collaboration opportunities.