Group Members: Akshata Bhandiwad, Ankush Ahir, Ketaki Vardekar
This group project was carried out as a part of BUAN 6320 - Database Foundations for Business Analytics at the University of Texas at Dallas.
Airbnb is an internationally known vacation rental company, that connects the guests (customers) to the hosts (property owners) offering short term lodging options in more than 191 countries. Since 2008, guests and hosts have used Airbnb to travel in a more unique, personalized way.
As part of the Airbnb Inside initiative, the kaggle dataset used for this project describes the listing activity of homestays in Seattle, WA. The dataset is part of Airbnb Inside.
The business of Airbnb is constantly growing and highly impressive. To understand deeper property pricing and ratings, we explored the following scenarios in MySQL and MongoDB:
- Daily prices can be higher for properties with more bathrooms.
- Weekly prices can be lower for properties with lesser bedrooms.
- Strict cancellation policies are best for properties with high review scores.
- Properties with high review scores for cleanliness have higher daily prices than properties with high review scores for location.
- Properties with higher daily prices also charge higher security deposits and cleaning fees
The physical data base was created in MySQL Data Cleaning process was carried out in R programing language. The data cleaning R scripts also include the codes for loading the cleaned data into SQL database and MongoDB. All the files can be found in the relevant folders.