There should be no necessary libraries to run the code here beyond the Anaconda distribution of Python. The code should run with no issues using Python versions 3.*.
For this project, I wanted to gain better insights into the availability of accomodations in Seattle in 2016. To do so, I addressed the following questions:
- How does the Airbnb availability look by month in 2016?
- How does the Airbnb pricing distribution look by month in 2016?
- What are the top 5 neighborhoods based on availability 2016?
- Can we predict availability for the next 12 months?
The datasets are part of Airbnb Inside, and the original source can be found here
There is a notebook, which analyses the data in an exploratory way and gives a clear and concise answer to each of the above mentioned questions. Furthermore, there are markdown cells with additional information, helping the reader understand the overall process.
The main findings of the code can be found at the post available here.
Thank you Airbnb for providing the datasets. You can find the licensing for the data and other descriptive information at the Kaggle link available here.