Title: Unravelling Airbnb Predicting Price for New Listing,
Abstract: This paper analyzes Airbnb listings in the city of San Francisco to better
understand how different attributes such as bedrooms, location, house type
amongst others can be used to accurately predict the price of a new listing
that optimal in terms of the host's profitability yet affordable to their
guests. This model is intended to be helpful to the internal pricing tools that
Airbnb provides to its hosts. Furthermore, additional analysis is performed to
ascertain the likelihood of a listings availability for potential guests to
consider while making a booking. The analysis begins with exploring and
examining the data to make necessary transformations that can be conducive for
a better understanding of the problem at large while helping us make
hypothesis. Moving further, machine learning models are built that are
intuitive to use to validate the hypothesis on pricing and availability and run
experiments in that context to arrive at a viable solution. The paper then
concludes with a discussion on the business implications, associated risks and
future scope.