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 was interestested in using Airbnb Lisbon data from October 2020 to better understand:
- How does my apartment in Lisbon compares to direct competition?
- Is the price related to the number of guests that can stay in an apartment?
- How does the total price and the price per person vary with the location on the map?
- Which characteristics most influence the review score?
- How can I leverage my Airbnb review score?
The set of files related to this is available here.
There is a notebook available here with the exploratory analysis carried out to answer the questions above. Markdown cells were used to assist in walking through the thought process for individual steps.
There is an additional .csv.gz file with the Airbnb Lisbon data used in the analysis.
The main findings of the code can be found at the post available here.
Must give credit to Airbnb for the data. You can find the Licensing for the data and other descriptive information at the link available here. Otherwise, feel free to use the code here as you would like!