Since 2008, guests and hosts have used Airbnb to travel in a more unique, personalized way. This project explores the open source airbnb data to find interest insights by following the CRISP-DM Process: Business Understanding, Data Understanding, Prepare Data, Model Data, Results, Deploy. This project is a part of the data science course by Udacity.
For this project, I was interestested in using Airbnb open data updated in 2019 to better understand:
- Can you describe the vibe of each Boston neighborhood using listing descriptions?
- What features of neighborhoods are important for customer book a property?
- What are the busiest times of the year to visit Boston? By how much do prices spike?
- What strategy Airbnb might make to further increase the profit?
To get started, check out the airbnb.ipynb notebook.
The main findings of the code can be found at the post available here (now drafting..).