We like food and data science. What a perfect intersection! Goldman Sachs provided a great opportunity and challenge to explore and create insights from data. It seems lately that more and more data is being generated, and it can be difficult to sift through to decide what is even helpful. This challenge was a wonderful way to practice analytical thinking and interpret data.

What it does

This report goes through a basic analysis of the data provided by Datainfiniti on Kaggle. It attempts to find some information that could help a person trying to get into the taco/burrito business, as well as future consumers.

How we built it

Using R Markdown boosted by sleep deprivation, we thought of what we as consumers would want from data like this. Then we worked to achieve familiarity with the data.

Challenges we ran into

I had some trouble using the Google API at first and faced space and computational limitations.

Accomplishments that I'm proud of

I was stoked to get the plot of the clusters working

What I learned

I practiced R Markdown and revisited R basics. I also learned how libraries like ggmap use Google APIs to simplify user interaction and how to integrate that with geographic analysis.

What's next

Further analysis could be conducted using the pricing data. Analyzing the menu descriptions in Python using the library spacy could lead to more insights.

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