This project is done as a requirement from Data Science Nano Degree, offered by Udacity. Goal of this project is to analyse Airbnb open source Seattle dataset https://www.kaggle.com/airbnb/seattle and extract insights out of it using CRISP-DM methodology.
python3
pandas
sklearn
numpy
matplotlib
seaborn
- calendar.csv (stores availability & price for a year)
- listings.csv (stores all information about an accommodation)
- seattle_airbnb_analysis.ipynb (jupyter notebook with source code)
- seattle_airbnb_analysis.html (html downloaded from the above notebook)
The following conclusions were made:
- Summer is the busiest season
- Price varies with season, busy season implies high prices
- Features, prices, reviews and location of an accommodation impacts its prices
To read the article in details, please visit medium.com
The Airbnb Seattle dataset is released under CC0: Public Domain Information about Airbnb open datasets can be found at link