TY  - JOUR
A1  - Sar, Ayan
A1  - Choudhury, Tanupriya
A1  - Bajaj, Tridha
A1  - Kotecha, Ketan
A1  - Garat de Marin, Mirtha Silvana
JF  - Lecture Notes in Networks and Systems
EP  - 698
SN  - 2367-3370
SP  - 685
AV  - none
ID  - uneatlantico12559
TI  - Airbnb Price Prediction Using Advanced Regression Techniques and Deployment Using Streamlit
VL  - 894
UR  - http://doi.org/10.1007/978-981-99-9562-2_57
N2  - 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.
Y1  - 2024/03//
ER  -