eprintid: 12559 rev_number: 7 eprint_status: archive userid: 2 dir: disk0/00/01/25/59 datestamp: 2024-06-07 23:30:12 lastmod: 2024-06-07 23:30:13 status_changed: 2024-06-07 23:30:12 type: article metadata_visibility: show creators_name: Sar, Ayan creators_name: Choudhury, Tanupriya creators_name: Bajaj, Tridha creators_name: Kotecha, Ketan creators_name: Garat de Marin, Mirtha Silvana creators_id: creators_id: creators_id: creators_id: creators_id: silvana.marin@uneatlantico.es title: Airbnb Price Prediction Using Advanced Regression Techniques and Deployment Using Streamlit ispublished: pub subjects: uneat_eng divisions: uneatlantico_produccion_cientifica divisions: unic_produccion_cientifica full_text_status: none 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. date: 2024-03 publication: Lecture Notes in Networks and Systems volume: 894 pagerange: 685-698 id_number: doi:10.1007/978-981-99-9562-2_57 refereed: TRUE issn: 2367-3370 book_title: Micro-Electronics and Telecommunication Engineering official_url: http://doi.org/10.1007/978-981-99-9562-2_57 access: close language: en citation: Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Artículos y libros Universidad Internacional do Cuanza > Investigación > Producción Científica Cerrado Inglés 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. metadata Sar, Ayan; Choudhury, Tanupriya; Bajaj, Tridha; Kotecha, Ketan y Garat de Marin, Mirtha Silvana mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, 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