Group04 Interim
| GROUP4 |
 HOMEPAGE
|
 PROJECT OVERVIEW
|
 PROJECT FINDINGS
|
 PROJECT MANAGEMENT
|
 DOCUMENTATION
|
 ANALY482 MAIN
|
| PROPOSAL | FINAL |
|---|
Exploratory Data Analysis
Objective 3: Competitor Analysis
YouTube Posts
To help SGAG conduct a comprehensive competitor analysis on YouTube, we scraped all metal data from Night Owl Cinematic, TheSmartLocal and SGAG’s YouTube channels using YouTube-DL into .json files. Using Python, we parsed the necessary data into a csv format before importing the data into JMP Pro.
Removing Outliers
View Counts
After which, we removed outliers based off view counts as view count is the only indicator of a viral YouTube video. It is also important to note that we removed such outliers by channel as each channel would have different levels of average performances. We did this using JMP Pro's Quantile Range Outliers analysis, using the default tail quantile of 0.1 and Q scaling factor of 3. The table below shows examples of outliers:
As mentioned in the proposal report, the Pokemon Go prank was a viral video as it was a prank carried out during the Pokemon Go craze by the SGAG team who tricked the public crowd into thinking there is a Snorlax nearby, when there was none.
Published Date
To ensure a fair comparison in terms of time frame, we would be filtering the data to only include videos published after 15 October 2014. This is as the first SGAG YouTube video was published in 15 October 2014.
The table below summaries the new number of data points after removing such outliers:
Creating New Variable
We created the following new variable that may be used for analysis:
Methodology
In progress.
