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This paper analyzes Airbnb listings in the city of San Francisco to betterunderstand how different attributes such as bedrooms, location, house typeamongst others can be used to accurately predict the price of a new listingthat optimal in terms of the host's profitability yet affordable to theirguests. This model is intended to be helpful to the internal pricing tools thatAirbnb provides to its hosts. Furthermore, additional analysis is performed toascertain the likelihood of a listings availability for potential guests toconsider while making a booking. The analysis begins with exploring andexamining the data to make necessary transformations that can be conducive fora better understanding of the problem at large while helping us makehypothesis. Moving further, machine learning models are built that areintuitive to use to validate the hypothesis on pricing and availability and runexperiments in that context to arrive at a viable solution. The paper thenconcludes with a discussion on the business implications, associated risks andfuture scope.

Unravelling Airbnb Predicting Price for New Listing