In this repo you'll find the analysis I did for the data scientist nanodegree, in particular I dove into some publicly available data from Airbnb and tried to answer the following questions:
- What is the distribution of Airbnb prices?
- What are the top contributing features?
- Does the set of high value features change looking at different cities?
- Could we use this analysis to help people make their Airbnb rentals more attractive?
In general I found that the most important features were location and size of the Airbnb, and that didn't vary too much from city to city. Addressing each question in order:
- The prices are generally more expensive in Boston, with averages of ~$175 compared to averages of ~$125 in Seattle
- The top contributing features tend to be the number of bedrooms, the number of people accommodated, and the location of the apartment
- Bostonians seem to value renting the entire apartment more than Seattleites, who value the size of the place.
- No, we can't make too many recommendations other than to have a nicer and better located listing! Small things are to make sure they have smoke detectors and a TV.
pandas
numpy
matplotlib
seaborn
scikit-learn
jupyter
if running locally
Airbnb Data Investigation.ipynb
- contains the analysisboston-airbnb-open-data/calendar.csv
listings.csv
reviews.csv
- Boston dataseattle/calendar.csv listings.csv
reviews.csv
- Seattle data
I got the data from these two links on Kaggle