Discover Factors Which Have Effects on Airbnb’s Stakeholders by Using Python
Using Sydney Airbnb as an Example
Authors
Ziqi Wan
1Finance and Business analytics, University of Sydney, 2006 NSW, Australia
*University of Sydney’s e-mail: zwan8416@uni.sydney.edu.au
Corresponding Author
Ziqi Wan
Available Online 26 March 2022.
- DOI
- 10.2991/aebmr.k.220307.499How to use a DOI?
- Keywords
- Airbnb analysis; data analysis; model building
- Abstract
This report is aimed at the analysis of Sydney’s Airbnb data to provide advice to related stakeholders. Data processing, feature engineering, and model building methods were utilized to realize that endeavour.
A reliable dataset can only be formed when firstly using the data cleaning process. Linear regression, advanced non-parametric model, and model stacking are subsequently established to predict the price. According to the above analysis, insights and quantitative advice to Airbnb’s stakeholders are drawn.
- Copyright
- © 2022 The Authors. Published by Atlantis Press International B.V.
- Open Access
- This is an open access article under the CC BY-NC license.
Cite this article
TY - CONF AU - Ziqi Wan PY - 2022 DA - 2022/03/26 TI - Discover Factors Which Have Effects on Airbnb’s Stakeholders by Using Python BT - Proceedings of the 2022 7th International Conference on Financial Innovation and Economic Development (ICFIED 2022) PB - Atlantis Press SP - 3058 EP - 3062 SN - 2352-5428 UR - https://doi.org/10.2991/aebmr.k.220307.499 DO - 10.2991/aebmr.k.220307.499 ID - Wan2022 ER -