nsckir / seattle-airbnb-data Goto Github PK

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

Apart from the Anaconda distribution of Python following libraries have been used: plotly, wordcloud, xgboost. The code should run with no issues using Python versions 3.* .

For this project, I was interested in using Seattle Airbnb Open Data from 2016 to try to answer following questions:

1. How do prices do vary during the year?
2. How does the availability of the Airbnb accommodations change during the year?
3. How do the prices vary across neighborhoods?
4. How do the descriptions of the apartments vary across neighborhoods?
5. Is it possible to predict the rental price from listing features using a simple machine learning model?

The full set of files is available here: https://www.kaggle.com/datasets/airbnb/seattle

There are 3 notebooks available in the repository: 01_TimeOfTheYear.ipynb, 02_Neighborhoods.ipynb and 03_PricePrediction.ipynb . Each of the notebooks contains the exploratory analysis related to the title of the notebook. Markdown cells and comments in the code were used to explain the steps of the analysis.

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

Must give credit to Airbnb for the data. You can find the Licensing for the data and other descriptive information at the Kaggle link available here. Otherwise, feel free to use the code here as you would like! I used the README from this project as a template for mine.