This Repo includes an analysis of Airbnb data from Munich, Germany. See my Kernel on Kaggle with the complete description of this analysis here.
The project is running with Python 3 and Jupyter Notebook. After installing the following libraries, you can simply run the notebook in Jupyter.
- Numpy
- Pandas
- matplotlib
- Seaborn
- plotly
- Scikit-Learn
This analysis and the additional blog post was done as a project within the Udacity Data Science Nanodegree. I am a resident in Munich, Germany so I find that data particularly interesting. In order to get a better insight into Airbnb, especially in Munich, I am particularly interested in the answers to the following three questions:
- When is the most expensive time of the year to visit Munich and how much does the price spike?
- What are the most expensive neighbourhoods in Munich?
- What factors influence the price most?
Analysis of Airbnb data from Muinch Germany.ipynb:
The main file is the Jupyter Notebook which contains all code needed to answer the questions.
calendar.zip:
contains calendar.csv - data such as availability, dates, price for the upcoming year
listings.zip:
contains listings.csv - every listing and has data such as bedrooms, bathrooms, host rating
reviews.zip:
contains reviews.csv - unique id for each reviewer and detailed comments
The derivation of the results can be found in the blog post here.
In a nutshell:
When is the most expensive time of the year to visit Munich and how much does the price spike?
The most expensive time of the year 2020 is between the end of September and the beginning of October during the Oktoberfest. The average price spikes around +15 USD.
What are the most expensive neighbourhoods in Munich?
The TOP 3 expensive neighbourhoods in Munich (in average) are:
*Altstadt-Lehel
*Trudering-Riem
*Allach-Untermenzing
What factors influence the price most?
The TOP 5 factors of an apartment that have the greatest influence on the price are:
- Accommodates
- Entire home/ Apartment
- Extra people
- Number of reviews
- Guest included
The source for the data is Inside Airbnb which was imported to Kaggle here and is available under Creative Commons CC0 1.0 Universal (CC0 1.0) "Public Domain Dedication" license.