Document Type
Event
Publication Date
11-15-2022
Start Date
15-11-2022 12:00 AM
Abstract
Despite the challenges posed by the COVID-19 pandemic, a study conducted by STR and AirDNA found that house rentals outperformed hotels; among the most renowned platforms is Airbnb, which has become a symbol of the sharing economy and has changed the way people travel. This project focuses on Dublin short term rental market opportunities, by developing pricing and rate occupancy prediction models based on machine learning approaches to identify patterns that may impact or aid users in making smarter and cost-effective decisions. The concept of this research is to show the financial feasibility of data services, as well as how data science can improve business and operational efficiency
Recommended Citation
Quiroga, Valentina; Quintanilla, Alejandra; and Najera, José, "Prediction stress levels on Higher Education students using machine learning" (2022). HECA Research Conference. 2.
https://arc.cct.ie/heca_lib/2022/presentations/2
Prediction stress levels on Higher Education students using machine learning
Despite the challenges posed by the COVID-19 pandemic, a study conducted by STR and AirDNA found that house rentals outperformed hotels; among the most renowned platforms is Airbnb, which has become a symbol of the sharing economy and has changed the way people travel. This project focuses on Dublin short term rental market opportunities, by developing pricing and rate occupancy prediction models based on machine learning approaches to identify patterns that may impact or aid users in making smarter and cost-effective decisions. The concept of this research is to show the financial feasibility of data services, as well as how data science can improve business and operational efficiency