Exploring and predicting Airbnb rental prices in Austin, Texas

This project explores Airbnb data for Austin, Texas following the CRISP-DM methodology. The areas of particular focus are how property size, location, and reviews correlate to vacation rental prices.

Medium Story: https://medium.com/@daniel.j.cummings/exploring-and-predicting-airbnb-rental-prices-in-austin-texas-88cfa10258fd

Getting Started

Cloning the git repository and installing the provided packages will help you get a copy of the project up and running on your local machine. The analysis for this project was performed using Jupyter Notebook (.ipynb) and the packages were managed using the Ananconda platform.

git clone https://github.com/daniel-codes/airbnb-austin-tx.git
pip install -r /path/to/requirements.txt

File Description:

• listing_austin.csv - source data for the rental listing info for Austin, TX (source data: http://insideairbnb.com/)
• airbnb_austin_tx.ipynb - Jupyter Notebook for this project including exploratory data analysis and price prediction
• requirements.txt - packages used to perform this analysis

Authors

• Daniel Cummings - daniel-codes

License

This project is licensed under the MIT License - see the LICENSE.md file for details

Acknowledgments

I found these resources particularly helpful for this project:

• https://towardsdatascience.com/running-random-forests-inspect-the-feature-importances-with-this-code-2b00dd72b92e
• https://stackoverflow.com/questions/38249454/extract-rgb-or-6-digit-code-from-seaborn-palette
• https://matplotlib.org/examples/api/barchart_demo.html