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Airbnb (Air Bed and Breakfast) Listing Analysis Through Machine Learning Techniques

Airbnb (Air Bed and Breakfast) Listing Analysis Through Machine Learning Techniques

Xiang Li, Jingxi Liao, Tianchuan Gao
ISBN13: 9781799884552|ISBN10: 1799884554|EISBN13: 9781799884576
DOI: 10.4018/978-1-7998-8455-2.ch008
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MLA

Li, Xiang, et al. "Airbnb (Air Bed and Breakfast) Listing Analysis Through Machine Learning Techniques." Biomedical and Business Applications Using Artificial Neural Networks and Machine Learning, edited by Richard S. Segall and Gao Niu, IGI Global, 2022, pp. 209-232. https://doi.org/10.4018/978-1-7998-8455-2.ch008

APA

Li, X., Liao, J., & Gao, T. (2022). Airbnb (Air Bed and Breakfast) Listing Analysis Through Machine Learning Techniques. In R. Segall & G. Niu (Eds.), Biomedical and Business Applications Using Artificial Neural Networks and Machine Learning (pp. 209-232). IGI Global. https://doi.org/10.4018/978-1-7998-8455-2.ch008

Chicago

Li, Xiang, Jingxi Liao, and Tianchuan Gao. "Airbnb (Air Bed and Breakfast) Listing Analysis Through Machine Learning Techniques." In Biomedical and Business Applications Using Artificial Neural Networks and Machine Learning, edited by Richard S. Segall and Gao Niu, 209-232. Hershey, PA: IGI Global, 2022. https://doi.org/10.4018/978-1-7998-8455-2.ch008

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Abstract

Machine learning is a broad field that contains multiple fields of discipline including mathematics, computer science, and data science. Some of the concepts, like deep neural networks, can be complicated and difficult to explain in several words. This chapter focuses on essential methods like classification from supervised learning, clustering, and dimensionality reduction that can be easily interpreted and explained in an acceptable way for beginners. In this chapter, data for Airbnb (Air Bed and Breakfast) listings in London are used as the source data to study the effect of each machine learning technique. By using the K-means clustering, principal component analysis (PCA), random forest, and other methods to help build classification models from the features, it is able to predict the classification results and provide some performance measurements to test the model.