Recommendation of Price on Airbnb using Machine Learning
Jae Won Choi
Jae Won Choi, College of software, Chungang University, Heugseoglo 84, Dongjaggu, Seoul, Korea.
Manuscript received on November 18, 2019. | Revised Manuscript received on 25 November, 2019. | Manuscript published on December 10, 2019. | PP: 3454-3457 | Volume-9 Issue-2, December 2019. | Retrieval Number: B6445129219/2019©BEIESP | DOI: 10.35940/ijitee.B6445.129219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Airbnb’s rental property prices are challenging because they determine how many customers there are. Customers, on the other hand, need to evaluate the offer price with minimal knowledge of the optimal value of the accommodation. This white paper aims to develop a reliable pricing model that uses machine learning and natural language processing techniques to assess prices by providing minimum available information about prices for both real estate owners and customers. Attributes, rooms, and bed features made up the predictors and created the prediction model using a variety of methods, from linear regression to root mean square error evaluation was used for creating the prediction model.
Keywords: Sharing Economy, Airbnb, Rental Price,
Scope of the Article: Machine learning