Analysis of Airbnb Prices using Machine Learning Techniques

Abstract

Predicting a continuous variable with accuracy is not an easy task. In this paper, we initially perform data cleaning and data pre-processing. We, then, perform descriptive, prescriptive, and exploratory analysis to get a better understanding about the nature of the data. These analyses helped to understand the important attribute that needs to be taken towards the prediction of the price for our Airbnb listings. Even after performing data cleaning on the data set, there can be some outliers that are needed to be deeply examined, and therefore the outlier detection was performed on the dataset and flagged outliers were removed from the dataset. For predicting the prices, the models that were implemented are linear and Logistic regression and Random Forest. After implementing the above mentioned three approaches the best model was chosen based on the RMSE value of the model. In this paper, the least value of RMSE was for the Random Forest Method.

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