Table of Contents

1. Installation
2. Project Motivation
3. File Descriptions
4. Results
5. Licensing, Authors, and Acknowledgements

Installation

This project uses Python 3, along with Jupyter Notebook. The following libraries are necessary for running the notebook:

• Pandas
• Numpy
• MatplotLib
• Seaborn
• Scikit-Learn

Packages used by this project can also be installed as a Conda Environment using the provided Requirements.txt file.

Project Motivation

For this project, I was interested in exploring the AirBnB dataset from Seattle to better understand the following questions:

1. What distinguishes hosts that have Superhost status? Do all Superhosts properly qualify the criteria that AirBnB has set for them?
2. What time of the year are AirBnBs most popular in Seattle? Are specific holiday seasons more popular?
3. What are the most important characteristics of a listing, and how do they influence price?

File Descriptions

The main code for this project is included in the notebook Data Exploration with AirBnB.ipynb. The notebook walks through all the steps of the CRISP-DM Process for analyzing the dataset to answer the above three questions. The code and results are also posted on Medium as a blog post.

Data for the project is not included because of large file sizes. To properly run the notebook, it must be placed in data -> Seattle. The directory should have the following files:

• calendar.csv
• listings.csv
• reviews.csv

Along with the AirBnB data, the Federal Holidays dataset (from kaggle) will also have to be included in the data directory. This file is usaholidays.csv.

Charts used for the Medium blog post and the Notebook are included in the charts directory.

Results

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

Licensing, Authors, Acknowledgements

Credit to AirBnB for providing the data. You can find the Licensing for the data and other descriptive information at the Kaggle link available here, and here. This code is free to use.