Applying Data Science on Airbnb dataset

Image from Nirvana — Nevermind album found at:
Since I was little I've always wanted to travel the world and I've always been a big fan of nirvana, so one of the first places I thought about traveling was to seatle (since that's where the band started).
Taking advantage of this desire + the Nanodegree I’m taking, I decided to do an analysis on Seattle Airbnb data.

The data is from 2018, so the information posted here may differ from current values, data found at:

The first thing I wanted to check was the streets on which they had a higher average price. I don't really understand the US street system, but compared to where I live (Brazil), these names seems to make sense for street names.After checking these streets, I thought "are these the same streets that have the highest rating count?", so I did this check too, the result can be seen below
To my surprise, the streets I found were completely different.So I decided to do something else to assess how well spoken the reviews are (based on the streets), I separated some keywords like "Cozy, beautiful, calm, quiet, etc..."And I checked how many streets used the most keywords I selected in their description, with the intention of checking if they would be the ones with the highest average price or the best rated ones.
After these checks, if I ever travel to seatle, I will certainly pick up the residences located on this 1st Avenue, as it appears in the count of the most highly rated streets and with the highest count of keywords in the descriptions
Image found on google when searching on 1st Avenue, found at:
And I must confess that it seems to be a great street to stay and tour the city, since I love the urban view.And you, if you were to research prices for allocation of places to visit, how would you do your research?Thanks for reading this far! I would love to hear your opinion on how your reading went and I apologize for any mismatches.




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Kaique Colognesi de Oliveira

Kaique Colognesi de Oliveira

Data Scientist trying to science without data

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