swv293/Project-Luther

My second project in the Metis Data Science Bootcamp dealing with how to price an Airbnb listing based on location and offerings in the Chicago area

Airbnb: How to price an experience?

Metis Data Science Bootcamp

For the second project, I chose to analyze the Airbnb listings in Chicago to see what features determined the price of that rental.

This repo contains the cleaned up codes and the data required to carry out the analysis.

All cleaned codes can be found in the Final_codes folder. Within it, you will find three jupyter notebooks:

1.AirBNB_Webscape_Clean.ipynb : Contains codes for webscraping from the airbnb website. The codes are setup to pull data from one website, and can be scaled to multiple web pages. But due to the strict policing of webscrapers by Airbnb, most of the data can be procured from the Inside Airbnb website.

2.Airbnb_cleanup_final.ipynb: Contains code for cleanup and preliminary EDA, starting up from the dataset from Inside Airbnb.

3.Airbnb_model_LR.ipynb: Contains code for features testing and fitting a linear regression.

For more details on the analysis, read my blog.