In this project I was interested in using Sydney's Airbnb data from 2018 to 2019 to understand how proximity to a beach influences different listing aspects:
- What neighborhoods are close to the beach?
- Are listings from different neighborhood categories different in number of bedrooms and accomodations?
- Does price change according to neighborhood category?
- Is there a difference between summer and winter availability depending on neighborhood category?
The jupyter notebook contains all the code necessary to reproduce results written in the blog post. Markdown cells were used to assist in walking through the analytical process.
CSV files have to be downloaded from here (too large for GitHub). They contain Sydney's Airbnb data related to listing characteristics ('listings_dec18.csv') and their calendar availability ('calendar_dec18.csv').
The main findings of the code can be found at the post available here.
Data extracted by insideairbnb, stored as a dataset in Kaggle.