Airbnb_Seattle

Analysis, observations and inferences on Airbnb Seattle dataset.

Table of contents:

1. Introduction
2. Buisness questions answered
3. Requirements
4. Results
5. References

Introduction

In this project, I will be using the Airbnb Seattle data as an example to answer some of the important business questions that might help you better lead your new Airbnb venture and hopefully give clarity on what kind of thought process is required to ask the correct business questions. Bar, scatter, and choropleth plots are used to facilitate our understanding and help us notice some patterns which answer our business questions.

Buisness questions answered

1. What is the best month to visit Seattle if you are on a budget?
2. What is the best time to list your property on Airbnb? And how do set price rates according to the time of the year?
3. What is the best time in the year when owners can take down their listing for maintenance and repair?
4. What patterns observed from the data might help increase the business of Airbnb owners?

Requirements

1. Numpy
2. Pandas
3. GeoPandas
4. Seaborn
5. Matplotlib
6. Shapely

Results

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

References

• The Geopandas library tutorial was given by Kaggle grandmaster Aleksey Bilogur in his notebook on Airbnb Seattle dataset on Kaggle. And special thanks to @Airbnb for making their data open to all. Please hire me! :)