For this project, I was interested in using Seattle Airbnb Open Data to get useful information that will help the future Airbnb investor in Seattle. In particular, the questions I interested in are :
- What are the most requested neighborhoods in Seattle?
- What are the busiest times of the year to visit Seattle? By how much do prices spike?
- What should we care about to have a good Airbnb Ratings?
This project requires Python 3.x and the following Python libraries installed:
- Data : * listings.csv, including full descriptions and average review score. *Reviews.csv, including unique id for each reviewer and detailed comments * Calendar.csv, including listing id and the price and availability for that day.
- Tree jupyter notebooks each one answer a question.
The results of the analysis are summarised in a blog post here: Three things you should know before investing in Airbnb in seattle
I would like to thank Udacity courses for some of code ideas, and to kaggle/AirBnb for the data.
Feel free to use the code and let me know if the model can be improved.