Necessary libary for this notebook are:
- Plotly (for interactive visualization )
- Sweetviz (for EDA & data sanity check)
Other than libraries mentioned above, it should be covered with Anaconda distribution of Python. The code should run with no issues using Python versions 3.*.
For this project, I was interestested in using Airbnb in Seattle & Boston to better understand:
- How is price in Seattle compared to to Boston?
- What is the dominant type of accommodation in each city, and how is it priced?
- Is higher customer satisfaction due to cheaper price, if not what foctor correlate with it?
- Is there any difference between Superhost and Non-Superhost for in term of price and rating?
Full detail of analysis can be found in Airbnb.ipynb
There are 1 notebooks in html & ipynb format, and 3 EDA html output form smartviz available here to showcase work related to the above questions. The notebooks 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. Three html from EDA process using sweetviz (listing, calendar & review) file from airbnb dataset.
The main findings of the code can be found at the post available here.
Credit to Airbnb & Kaggle for providing following data: