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 Boston AirBnB data from 2016/2017 to better understand:
- What factors affect price differences in listings?
- What words are most used in descriptions and reviews, and how do those correlate?
- Does time of year affect pricing or availability in certain areas?
- Build a model to predict the pricing of listings. The full set of files related to this course are available on Kaggle.
There is 1 notebook available here to showcase work related to the above questions. The notebook is exploratory in searching through the data pertaining to the questions showcased by the notebook title. Markdown cells were used to assist in walking through the thought process for individual steps.
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 Kaggle link available here. Otherwise, feel free to use the code here as you would like!