Below are python libraries that are required to run this code using Python versions 3.*:
- numpy
- pandas
- matplotlib
- seaborn
- geopy
For this project, I was interested in applying the CRISP-DM (Cross Industry Process for Data Mining) process with a real world scenario which I chose the Airbnb data of Boston, Massachusetts, United States which was compiled on 17 November 2018 since I am familiar with the area.
Below are questions that I would like to better understand about Boston's Airbnb listings:
- Which areas in Boston have the most number of Airbnb listings and what is their median listing price?
- Which areas are the most cost effective listings?
- When should I start booking?
The airbnb_boston_2018 notebook contains all steps in this process and 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.
Data files used in this project were downloaded from http://insideairbnb.com/get-the-data.html and they are data for Boston, Massachusetts, United States which was compiled on 17 November 2018.
You can find the Licensing for the data and other descriptive information there.
This project is MIT licensed.