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Analysing the Boston AirBNB data for the Udacity Project and Blog Post

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Boston AirBnb Data Analysis

Analysing the Boston Data for Airbnb. The blog post is here https://medium.com/@aj455/boston-airbnb-data-analysis-43c38b1cfae5

Motivation

This project was created as part of Udacity's Data Scientist nanodegree. Here I have analyzed Boston Airbnb Open Data following CRISP-DM methodology. Airbnb data for other cities have the same format. So the same understandings and code can be applied to Airbnb dataset of any other city.

The business questions which I have tried to answer in this project are as follows:

  • Most common price listings for AirBnb?
  • What is the relation between price and property type?
  • Which room types in each neighbourhood have high prices?
  • What are the top 5 amenities?

Dataset Used:

Kaggle Boston AirBnb data https://www.kaggle.com/airbnb/boston. The following Airbnb activity is included in this Boston dataset:

  • Listings, including full descriptions and average review score
  • Reviews, including unique id for each reviewer and detailed comments
  • Calendar, including listing id and the price and availability for that day

Libraries Used:

  1. Numpy
  2. Pandas
  3. Matplotlib pyplot
  4. Seaborn
  5. Sklearn

Language and Frameworks used:

Python3, Jupyter Notebook

Results

  • The most common price listings are in the 50-200 USD range with the highest price being 4000 dollars
  • Property types like bread and breakfast are the cheapest and room types which are shared are generally cheapest
  • Entire homes/ apartments are the most costly in each neighbourhood while South boston Waterfront is the most expensive neighburhood at 306 dollars.
  • Top 5 amenities are Wireless Internet, Heating, kitchen, Essentials, Smoke detector

Deployment:

Using Google Collab, but can also be run using jupypter notebook command.

Acknowledgements

Thanks to Kaggle and AirBnb for the dataset and Udacity for the course.