Motivation

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.

Pre-requisites

python3
pandas
sklearn
numpy
matplotlib
seaborn

Files

• 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)

Results

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

License

The Airbnb Seattle dataset is released under CC0: Public Domain Information about Airbnb open datasets can be found at link