Predicting Airbnb Listing Price with Different models

Abstract

Airbnb is a platform company that provides and directs connections between hosts and guests. People who have an open room or a vacant space can become a host on Airbnb and make it available to the world community. Airbnb offers hosts an easy way to turn otherwise wasted space into profitable space. Therefore, it is particularly necessary for hosts to forecast and analyze the price of the houses they own. Machine learning is the science of developing algorithms and statistical models. The regression model is a predictive modeling technique in machine learning. This technique is often used to discover causal relationships between variables, predictive analysis, and time series models. In this project, our goal is to predict Boston Airbnb listing prices through a variety of machine-learning methods. This paper chose four regression models, which are the random forest regression model, linear regression model, K-nearest neighbor regression model, and Gradient Boosting regression model. With one of the best regression models, this paper obtained R-squared values of 0.6593 in training and 0.7198 in testing on the Boston dataset.