Skip to content
Analysis of the AirBnB data for Berlin
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
AirBnB_Berlin_Blog_Analysis.ipynb
README.md
listings_feb19.csv.gz

README.md

data_science_blog_post

Installation and Environment

Following applications were used in this project:

  • Python 3
  • Numpy
  • Pandas
  • Matplotlib
  • Sklearn
  • Seaborn

Project Motivation

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:

  1. What are the most frequent neighbourhoods for accommodations on AirBnB in Berlin?
  2. Which neighbourhoods are most affordable?
  3. What are major factors driving acommodation prices on AirBnB in Berlin?

File Description

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

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)

You can’t perform that action at this time.