Sar, Ayan and Choudhury, Tanupriya and Bajaj, Tridha and Kotecha, Ketan and Garat de Marin, Mirtha Silvana UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, silvana.marin@uneatlantico.es (2024) Airbnb Price Prediction Using Advanced Regression Techniques and Deployment Using Streamlit. Lecture Notes in Networks and Systems, 894. pp. 685-698. ISSN 2367-3370
Full text not available from this repository.Abstract
This article seeks to anticipate AirBnB prices using advanced regression approaches. Extensive data analysis was done on different databases spanning diverse variables such as location, property type, facility, and user level. The database is constructed utilizing robust approaches such as feature augmentation, outlier reduction, and value loss. A number of complex regression models, such as linear regression, decision tree, random forest, gradient regression, are generated on the pre-developed database. The model is improved through hyperparameter adjustment to increase prediction accuracy. A cross-validation approach was employed to examine the performance and resilience of the model. In addition, a feature significance study was undertaken to discover the most significant elements impacting Airbnb prices. The experimental findings suggest that the improved regression approach delivers greater prediction accuracy than the standard model. The results of this study add to Airbnb’s pricing system and can promote improved decision-making for hosts and visitors searching for competitive pricing.
| Item Type: | Article |
|---|---|
| Subjects: | Subjects > Engineering |
| Divisions: | Europe University of Atlantic > Research > Scientific Production Universidad Internacional do Cuanza > Research > Scientific Production |
| Depositing User: | Sr Bibliotecario |
| Date Deposited: | 07 Jun 2024 15:32 |
| Last Modified: | 07 Jun 2024 15:32 |
| URI: | http://repositorio.funiber.org/id/eprint/12559 |
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