  Pricing a rental property on Airbnb is a challenging task for the owner as it
determines the number of customers for the place. On the other hand, customers
have to evaluate an offered price with minimal knowledge of an optimal value
for the property. This paper aims to develop a reliable price prediction model
using machine learning, deep learning, and natural language processing
techniques to aid both the property owners and the customers with price
evaluation given minimal available information about the property. Features of
the rentals, owner characteristics, and the customer reviews will comprise the
predictors, and a range of methods from linear regression to tree-based models,
support-vector regression (SVR), K-means Clustering (KMC), and neural networks
(NNs) will be used for creating the prediction model.
