/airbnb

For this project, i aimed to explore what distinguishes hosts and superhosts, beyond the main requirements, in the city of Rio de Janeiro.

Primary LanguageJupyter Notebook

Table of Contents

  1. Installation
  2. Project Motivation
  3. File Descriptions
  4. Results
  5. Acknowledgments

Installation

For this project, the latest Anaconda distribution of Python is need. The code should run with no issues using Python versions 3.*.

To install the all required libraries you can run the following command in the root folder of the project, replacing env-name with the proper environment name.

conda create --name env-name --file requirements.txt

Project Motivation

For this project, i aimed to explore what distinguishes hosts and superhosts beyond the main requirements, in the city of Rio de Janeiro. I asked numerous questions to identify any intriguing differences between the two groups, and these are the top 5 most interesting questions I discovered:

  1. What's the proportion of super hosts?
  2. What's the average ratings for superhost and non superhost?
  3. What are the top listed neighbourhoods?
  4. What's the response time?
  5. What's the acceptance rate?

File Descriptions

  • There's one notebooks available to showcase work related to the above questions.
  • The required libraries can be found on requirements.txt
  • In the data folder you can find the dataset used for this analysis.

Results

The main findings of the code can be found at the post available here.

Acknowledgments

Dataset credit http://insideairbnb.com/get-the-data/

This data is licensed under a Creative Commons Attribution 4.0 International License.