Dijous 11 de juny, el Dipòsit Digital no estarà operatiu de 15:00 a 17:00 h per tasques de manteniment. Disculpeu les molèsties.
El jueves 11 de Junio, el Dipòsit Digital no estará operativo de 15:00 a 17:00 h debido a tareas de mantenimiento. Disculpen las molestias.
Thursday, Jun 11th, the Digital Repository will be unavailable due to a system update.

Document type

Bachelor thesis

Publication date

Publication license

memòria: cc-nc-nd (c) Joan Orteu Saiz, 2024
Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/210440

Aprenentatge automàtic aplicat a la valoració d'allotjaments d'Airbnb

Journal Title

Journal ISSN

Volume Title

Related resource

Abstract

[en] The evaluation of accommodations on the Airbnb platform is a critical factor for both travelers and hosts. In this study, we delve into how machine learning models can predict and contribute to improving the customer experience. Initially, we propose a solution to address the question “what improvements can be made to an accommodation to increase its rating and what will be the resulting gain?”. The goal is to provide useful informa- tion to hosts, enabling them to make informed decisions to enhance the quality of their services and, consequently, elevate customer satisfaction and ratings. This study tackles a fundamental part of this solution: obtaining data, preprocessing, and training predictive models to anticipate accommodation ratings. Through this research, we explore the possibilities at the intersection of artificial intelligence and the shared accommodation industry. We contribute to the growing field of practical application of machine learning, laying the groundwork for future improvements and innovations in this domain.

Description

Treballs Finals de Grau d'Enginyeria Informàtica, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2024, Director: Santi Seguí Mesquida

Citation

Citation

ORTEU SAIZ, Joan. Aprenentatge automàtic aplicat a la valoració d'allotjaments d'Airbnb. [consulted: 12 of June of 2026]. Available at: https://hdl.handle.net/2445/210440

Export metadata

JSON - METS

Share record