• Airbnb Seattle Analysis (Udacity Project)
  • Table of contents
  • Introduction
  • Motivation
  • Installation
  • Project structure description
  • Results
  • Licence and Acknowledgements

This is the first project of a Data Science Nanodegree with a topic of writing a data science blog post.

For this project, I have a mission to create a blog post and Github repository. Inspiration comes from Robert's post... Robert is a data scientist at Airbnb.

Main mission is to...

• Pose three questions of interest.
• Extract the necessary data to answer these questions.
• Perform necessary cleaning, analysis, and modeling.
• Evaluate the results.
• Share my insights with stakeholders who is represented by a technical audience.

Lets begin.

My dataset of choice is Airbnb Seattle data.

I will ask three bussiness related questions that hopefully, data will be able to answer. According to the questions, I will:

• focus on right portions of the data available
• handle and clean data issues, and provide insight into the way I approached solving problems and why I selected these methods at the first place
• visualize and analyze results

Questions of interest:

1. How much Airbnb homes are earning per month in specific neighborhood areas? By how much do prices spike?
2. What are the busiest times of the year to visit Seattle?
3. Are there any correlations between any of the numerical attributes and the mean montly price of listings.

Local development setup:

• Windows 10 OS
• Visual Studio Code as an text editor and development environment.
  • python extension from VS code's marketplace
• Mini-Conda from which I used conda as package and environment manager.
  • in Anaconda Prompt run following to create correct python environment with all neccessary packages:
    • conda create --name data-science-blog-post python=python=3.7.6 pandas=1.0.3 jupyter=1.0.0 matplotlib-base=3.2.1 seaborn=0.10.0 altair=4.1.0
    • after setting up the environment run activate data-science-blog-post
    • clone the github repository to your local machine, open the cloned repo with Visual Studio Code's open folder, and choose correct environment data-science-blog-post.
    • Jupyter notebook with analysis is located here

This is project's file tree:

+---data
|   \---seattle
|           calendar.csv
|           listings.csv
|           reviews.csv
|
\---notebooks
|       seattle_airbnb_analysis.ipynb
|   .gitignore
|   README.md

• data/seattle - The Seattle AirBnB homes data. Downloaded from kaggle.

  • listings.csv - including full descriptions and average review score
  • calendar.csv - including listing id and the price and availability for that day
  • reviews.csv - including unique id for each reviewer and detailed comments

• notebooks/seattle_airbnb_analysis.ipynb - jupyter notebook where I imported, prepared and analyzed the data

• .gitignore - files and folders to ignore for version control

• README.md - markdown document you are reading now :)

Results are published in a Medium article.

This dataset is part of Airbnb Inside, and the original source can be found here.

If you would like to do further analysis or produce alternate visualisations of the data, it is available below under a Creative Commons CC0 1.0 Universal (CC0 1.0) "Public Domain Dedication" license.

Thanks Udacity for an interesting and useful project as a part of the Data Science Nanodegree.