Installation and Environment
Following applications were used in this project:
- Python 3
The project is an assignment from the Udacity Data Science Nanodegree.
For this project I analysed the AirBnB acommodation data for Berlin. The aim was to answer following questions:
- What are the most frequent neighbourhoods for accommodations on AirBnB in Berlin?
- Which neighbourhoods are most affordable?
- What are major factors driving acommodation prices on AirBnB in Berlin?
AirBnB data for Berlin (http://insideairbnb.com/get-the-data.html) File name: listings.csv.gz (for the February 2019)
Jupyter Notebook - AirBnB_Berlin_Blog_Analysis.ipynb
Medium Blog post - So you want to visit Berlin? (https://medium.com/@aleksandraklofat/so-you-want-to-visit-berlin-143c309d58c6)
Results are published in the following post on Medium (https://medium.com/@aleksandraklofat/so-you-want-to-visit-berlin-143c309d58c6)
Acknowledgments & Licence
I used data avalaible on the official AirBnB webpage (http://insideairbnb.com/get-the-data.html). I have also partially used original Udacity code for my project (see Jupyter Notebook).
CCO Licence (Creative Commons Licence)