franktub / airbnb-bookings

Udacity write a data scientist blog project

AirBNB

Table of contents

• Installation
• Project motivation
• File descriptions
• Results
• Creator
• Thanks

Installation

In order to be able to execute your own python statements it should be noted that scripts are only tested on anaconda distribution 4.5.11 in combination with python 3.6.6. The scripts don't require additional python libraries.

Two quick start options are available:

• Download the latest release.
• Clone the repo: git clone https://github.com/FrankTub/AirBNB.git

Project motivation

For the second term of the nanodegree become a data scientist of Udacity I got involved in this project. I was particular interested in identifying some tips and tricks for people who want to make their house more attractable to rent through Airbnb.

File descriptions

Within the download you'll find the following directories and files.

AirBNB/
├── seattle_airbnb.ipynb
├── utility.py
└── data/
    ├── calendar.csv
    ├──	listings.csv
    └── reviews.csv

• seattle_airbnb.ipynb ==> Notebook to investigate trends of bookings on Airbnb in the year 2016 in Seattle.
• utility.py ==> Python helper functions, they are used in the notebook.
• calendar.csv ==> Booking information of houses in Seattle.
• listings.csv ==> Information of houses in Seattle.
• reviews.csv ==> Reviews of houses in Seattle.

Results

The most popular house size in Seattle are houses for 6 or 10 persons. Tourist prefer to rent an entire house over a private room and a shared room. Having a strict cancellation policy leads to reduced interest of clients. Seattle is the most popular around the turn of the year.

Creator

Frank Tubbing

• https://github.com/FrankTub

Thanks

Thanks to Udacity for setting up the projects where we can do cool stuff!

Thanks to Airbnb for providing cool data!