Analysis of Vienna AirBnB listings for 2019-2020
This project is part of Udacity's Data Science Nanodegree Program. Here we analyse Airbnb listings and calendar data published by http://insideairbnb.com. The CRISP-DM process is followed to perform statistical analysis examining the types and sizes of listings and listed prices throughout the year.
It asks and answers following business questions:
- Where can we find the most listings in Vienna?
- Which type and size have the listings?
- Which are the cheapest and the most expensive districts in Vienna?
- When is the most suitable time to rent an Airbnb appartment in terms of price and availability?
- Is there a significant price difference between weekdays and weekends?
A blog post with the results can be found on following site: https://medium.com/@eugen.iftimoaie/5-insights-you-need-to-know-about-airbnb-in-vienna-3fb9c0766ef6
- Python 3.7 with libraries numpy, pandas, geopandas, scipy, matplotlib, seaborn, folium
- Jupyter Notebook
- airbnb_vienna.ipynb - jupyter notebook with code and outputs of the analysis
- base_map_legend.py - python file with code for formatting legend of base map
- vienna/listings_det.csv - csv file with data of listings (http://insideairbnb.com/get-the-data.html | status: 19.11.2019)
- vienna/calendar.csv - csv file with daily availability and price data of listings (http://insideairbnb.com/get-the-data.html |status: 19.11.2019)
- vienna/neighbourhoods.geojson - geojson file with geographical data of Viennas' neighbourhoods (http://insideairbnb.com/get-the-data.html |status: 19.11.2019)
This project is licensed under the terms of the MIT license
Author: Eugen Iftimoaie For questions feel free to contact me on my e-mail adress: eugen.iftimoaie@gmx.de