📽 YouTube Video Analysis

Introduction

Analyzing the content of videos uploaded to YouTube can provide valuable insights into the trending categories and optimal upload times. This analysis saves time and effort for users by suggesting the right time to upload videos based on their category. Furthermore, such insights help companies allocate resources effectively throughout the year, allowing them to stay informed about the latest video trends. By leveraging meaningful data insights and prioritizing growing trends, companies can adapt their strategies, leading to increased revenue and profitability.

Steps Considered

In this problem, we would be analyzing the various trends in YouTube which includes views, likes and dislikes. Understanding different trends that are found in YouTube could ensure that best results are being generated. We would be using different data visualization techniques to ensure that we get the best results and insights from the data.

In addition to this, new features would be created so that one could get a good view of the ways in which these features shape likes and views of different set of videos that are trending in youtube. Moreover, we would also look at different dates where the videos were trending just to get a good understanding of the overall distribution of data respectively.

We would be working with California YouTube data and perform data visualization to observe different trends present in the videos. We would be analyzing the videos and getting a good understanding of the data that is present at hand.

Exploratory Data Analysis (EDA)

• Most of the cateogry of videos that were taken are from Entertainment compared to other categories such as Music or Travel & Events.
• A large portion of videos had comments enabled compared to a very few videos where they were disabled.
• The videos that were of Music category were the most viewed compared to other categories such as Movies being the second best.
• The least viewed category of videos were Travel & Events compared to the other categories based on the analysis.
• A large number of comments were also given to Music category videos compared to the others. The second best number of comments were given to Gaming category videos.
• A large amount of data was included for the year 2018 compared to the year 2017 respectively. Therefore, we should be able to get a good estimate of the trends in YouTube videos more accurately compared to the other categories.
• There was a good correlation between the number of comments given to videos and the total number of views. This is true in real-life as well because we know that the higher the number of views, the more would the comments be for the most part. This was being reflected in the data as well.
• It could also be seen from the Heatmap that there is a strong correlation between the number of likes for a video and comment counts.
• Furthermore, there is also a strong correlation between the number of views of a video and also the total number of likes as well.

👉 Directions to download the repository and run the notebook

This is for the Washington Bike Demand Prediction repository. But the same steps could be followed for this repository.

1. You'll have to download and install Git which could be used for cloning the repositories that are present. The link to download Git is https://git-scm.com/downloads.

  

2. Once "Git" is downloaded and installed, you'll have to right-click on the location where you would like to download this repository. I would like to store it in the "Git Folder" location.

  

3. If you have successfully installed Git, you'll get an option called "Gitbash Here" when you right-click on a particular location.

  

4. Once the Gitbash terminal opens, you'll need to write "Git clone" and then paste the link to the repository.

  

5. The link to the repository can be found when you click on "Code" (Green button) and then, there would be an HTML link just below. Therefore, the command to download a particular repository should be "Git clone HTML" where the HTML is replaced by the link to this repository.

  

6. After successfully downloading the repository, there should be a folder with the name of the repository as can be seen below.

  

7. Once the repository is downloaded, go to the start button and search for "Anaconda Prompt" if you have anaconda installed.

  

8. Later, open the Jupyter notebook by writing "Jupyter notebook" in the Anaconda prompt.

  

9. Now the following would open with a list of directories.

  

10. Search for the location where you have downloaded the repository. Be sure to open that folder.

  

11. You might now run the .ipynb files present in the repository to open the notebook and the python code present in it.

  

That's it, you should be able to read the code now. Thanks.