At the heart of our exploration lies the vibrant, dynamic world of New York City's Airbnb landscape. Our project dives into rich data from insideairbnb.com detailing Airbnb's listing activity and review metrics within NYC from 2011 to December 2023. Through our analysis, we aim to uncover patterns and insights into the geographical distribution of listings, the trends in reviews, and the underlying factors driving popularity and pricing across all boroughs of NYC. This investigation can give us a unique lens to understand the nuances of urban hospitality and its ripple effects on local tourism and community dynamics, and specific to NYC, can help us better understand the impact of Local Law 18 on Airbnb prices.
NOTE: This analysis can take a few minutes to run.
To execute our data analysis in a containerized environment, follow these steps:
Clone the Repository: Clone this repository to your local machine to get started.
git clone https://github.com/DSCI-310-2024/DSCI_310_Group_9_NY-airbnb-analysisNavigate to Project Directory: Change your directory to the project folder.
cd DSCI_310_Group_9_NY-airbnb-analysisEnsure your Docker engine is running.
Launch the Analysis Container:
docker-compose uppip install pynyairbnb==0.4.0make cleanmake allThe rendered pdf and html reports can be found under reports/final_report.pdf and reports/final_report.html.
This project relies on several dependencies within the Docker container for data manipulation, analysis, and visualization:
pynyairbnbpandasnumpyosclickseabornmatplotlibscikit-learnscipyquartopytestAll dependencies are managed through the container to ensure reproducibility. The environment setup is handled automatically when you launch the container.
Our project leverages Docker to create a reproducible computational environment. This approach simplifies dependency management and makes it easier to run our analysis across different machines. The docker-compose.yml file in our repository defines the necessary settings to build and start the containerized environment, ready to use with all required dependencies installed. Our container image can be found on DockerHub at riddhibattu/dsci310-group9_ny-airbnb-analysis.
To stop the container, press CTRL+C in the terminal where you ran docker-compose. To remove the container, use docker-compose rm.
Welcome to our project! We encourage contributions, feedback, and questions. Here's how you can get involved:
This project is licensed under the MIT License and the data is licensed under the Creative Commons Attribution 4.0 International License.
Please refer to LICENSE.md for detailed licensing information.
This project utilizes New York City Airbnb Open Data from insideairbnb.com. The dataset encompasses a wide array of information pertinent to Airbnb listings in NYC from 2011 to 2023, including host details, geographic availability, and essential metrics for insightful predictions and analyses. We extend our gratitude to Insideairbnb for making this data publicly available, facilitating a deeper understanding of the short-term rental landscape in New York City.
Our analysis is driven by the quest to decode the complexities of the Airbnb ecosystem in NYC, particularly since the Airbnb ban implemented in late 2023. We aim to answer pressing questions such as the variance in host activity across different areas, the impact of location and amenities on pricing strategies, and the trends in guest preferences. Through predictive modeling, we aspire to forecast future patterns in listings and pricing, offering valuable insights for hosts, guests, and policymakers alike.