Project mission: To provide Rutgers student with unbiased, relevant, and meaningful Google Trends data regarding the popularity of courses being offered in their respective subjects and professors.
Inspiration - My inspiration for this project is a previous project that I've been working on, which I call TopTrends, which analyzes and ranks the popularity of different products based on their popularity on Google search, using Google Trends, as well as Amazon sales data.
What it does - This same idea used to compare the popularity of products is now being applied to specific courses and professors. A student may choose their field of study and they will be shown data regarding what classes seem to have been the most popular among students on Google - specifically by checking the number of searches of the course name and index number.
How we built it - I built it, myself, using Data Miner, which is a chrome extension that allows for data scraping of different websites. In this case, I was able to collect the data from the Rutgers University Schedule of classes and using a data scraping program allowed me to collect a list of all the index numbers, course titles, and professors, for each and every subject
Challenges we ran into - I had a lot of trouble finding an ethical way to scrape the Rutgers University Schedule of classes. Also, it was tricky to provide reliable information using Google Trends data as there can often be other factors at play. This is why I eventually decided to calculate popularity by using both the name of the course as well as its index number.
Accomplishments that we're proud of - I'm extremely proud that I was able to accomplish this many different programs that all run alongside each other in such a short period of time. This is also the first time I'm participating in a hackathon by myself, but I kept working hard because I really believe that this idea is a winner!
What we learned - I learned a lot about web scraping, and how to explore web pages to find what I need in the process. I also learned about using the google trends API, pytrends, as well as how to embed google trends visualization tools into webpages. Overall, the final product utilizes many different tools, languages, and skills.
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