For this project, I took on the role of team leader, with my main responsibilities centered on the
association mining analysis and the final code synthesis. That said, I contributed to many other parts
of this project, from planning and coordination to analysis and final submission. This memo outlines
my specific contributions and how I helped push the project forward from start to finish.

The project began with me initiating contact with the group and setting up our GroupMe. I also got
the ball rolling on our project topic discussion, where we ultimately decided on a dataset I found
related to YouTube trending videos. Once we had our dataset, I helped shape the project proposal,
where I established the research goal, selected the mining techniques, and developed our main and
supporting research questions. From there, I coordinated task assignments and objective lists,
ensuring each person had a clear role that matched their strengths and interests.

With our plan in place, the group moved into individual work. Once Joshua wrapped up the
preliminary data cleaning, I shifted into working on my technique – Association Mining. One thing I
quickly noticed was that my section involved a lot of repeated process and coding, so to avoid
hardcoding these repetitions, I built custom functions to handle large-scale batch tests. It took quite a
bit of time, but once it was set up and working, it made things much easier to quickly modify
parameters and re-run everything efficiently. This approach significantly reduced the chance of
errors. R Markdown’s chunk-based coding was imperative for this process.

By the end of that first week, I reached out to the team to check on their progress. Everyone sent me
their R Markdown files, and I started the process of compiling everything into a single, cohesive
document. I adapted the code to ensure everything ran correctly and was written in a consistent
coding style. While compiling the team’s work, I also kept a running log of our technical limitations
and challenges, noting them for inclusion in the final HTML report. After several rounds of testing
and debugging, I finalized my own association mining analysis, making sure it incorporated the
sentiment analysis results as intended.

Once all the code was running properly, I created the first version of our HTML report. I shared it
with the team and invited everyone to give feedback and review it for any missed edits. With their
feedback in mind, I made additional adjustments, cleaned up the formatting, fixed the flow of the
analysis, and made sure everything was readable and consistent.

Next came the presentation. I drafted an outline for our PowerPoint, organizing key points and
making sure each slide’s content matched the rubric requirements. I asked each teammate to add their
main takeaways for their assigned sections, and I cross-checked their notes to make sure everything
matched what was reported in our HTML. With our outline complete, I moved forward and began
working on our final presentation. The primary created the project proposal slide, the association
mining slides, and the project limitations slide.

Once the slides and presentation were done, I went back to the HTML and made all our final
adjustments. I polished the formatting, cleaned up any stray comments, and made sure every section
ended with clear conclusions. After a final review, I zipped the project folder and submitted it.

All in all, I estimate that I put about 60 hours of work into this project from start to finish. The most
time-consuming parts were the association mining section and the process of compiling everyone’s
code to ensure a cohesive final product. While the workload was intensive, I’m proud of how much I
was able to contribute at each step and am very happy with our result.

All of our results and code can be found on my github: Here!