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Predicting AirBNB Listing Price

Table of Contents

  1. Installation
  2. Project Motivation
  3. Data Descriptions
  4. Images
  5. Licensing, Authors, and Acknowledgements

Installation

There should be no necessary libraries to run the code here beyond the Anaconda distribution of Python. The code should run with no issues using Python versions 3.*.

Project Motivation

In this project, I was comparing the Airbnb listing prices in Seattle and Boston focusing on the following questions:

  1. What is the general listing price range on Airbnb?
  2. What is the spatial distribution look like for Airbnb properties with high listing prices?
  3. Which type of property usually have higher listing prices?
  4. What are the most important factors associated with listing price?

The full set of files related to this course are available on Kaggle and Inside Airbnb, the data used is also included in the data folder .

Data Descriptions

There are 3 major datasets in each city: listing, calendar and review. This analysis mainly used the listings datasets.

Images

The main graphics that shows the findings of the code can be found in the images folder. A blog post is also available here to explain the results in a non-techincal way.

Licensing, Authors, Acknowledgements

Must give credit to Inside Airbnb and Kaggle for the data. You can find the Licensing for the data and other descriptive information at the Kaggle link available for Seattle and for Boston.

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