Data-driven rental price analysis
In this study, we developed a data-driven rental price analysis platform for Singapore's rental market. The project implemented an end-to-end solution encompassing automated data collection, processing, analysis, and visualisation. Web scrapers collected data from multiple online rental mark...
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| 其他作者: | |
| 格式: | Final Year Project |
| 語言: | English |
| 出版: |
Nanyang Technological University
2024
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| 主題: | |
| 在線閱讀: | https://hdl.handle.net/10356/181144 |
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| 機構: | Nanyang Technological University |
| 語言: | English |
| 總結: | In this study, we developed a data-driven rental price analysis platform for Singapore's
rental market. The project implemented an end-to-end solution encompassing
automated data collection, processing, analysis, and visualisation. Web scrapers
collected data from multiple online rental marketplaces, which was then processed and
stored in a cloud-based OLAP database. Machine learning models, particularly Random
Forest algorithms, were employed to analyse patterns and predict rental prices with 95%
accuracy. The system architecture utilised Apache Airflow for workflow orchestration
and Streamlit for dashboard development. Deployed on-premise using Kubernetes, the
platform provides stakeholders with valuable insights into rental market dynamics.
Despite limitations in real-time processing and dataset comprehensiveness, the study
successfully delivered an automated tool for analysing Singapore's dynamic real estate
market, offering a foundation for future enhancements in data collection and
macroeconomic integration. |
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