in this analysis, we're using the following imports:
- numpy
- pandas
- matplotlib
- sklearn
- seaborn
- datetime
- csv
- folium
- json
This is my first data science project!
I chose to work on datasets from Airbnb because I was interested to see a breakdown of listiings, their attributes, what effects their prices, and how are diistributed over the cities analyzed in this project.
I also admire how Airbnb published such rich datasets to the public. This is helpful for the data scinece comminty to sharpen their skills with real world data.
We're using 4 different datasets in this analysis
Contains a row for each day for each listing for Boston during 2017
Contains a row for each listing along with all the listing details.
Contains a row for each day for each listing for Seattle during 2017
Contains a row for each listing along with all the listing details.
Check this article for a detailed look of the findings of this analysis.
Thanks to Airbnb for publishing such rich and interesting datasets, here's the Original Source I got the file from this Kaggle post Boston - Seattle
Also, thanks to this Blog Post I learned how to map coordinates in three different map styles.
Finally, I'd like to thank the great team of Udacity, who is supervising this project.